diff --git a/.gitignore b/.gitignore index 48bf043..03c027e 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,13 @@ omrEnv +venv/ +yolo/ +output/ +orch_dataset/ .vscode/ __pycache__/ string_dataset/ !string_dataset/pdf_data/beethoven1/beethoven1.json -!string_dataset/pdf_data/beethoven1/beethoven1.pdf \ No newline at end of file +!string_dataset/pdf_data/beethoven1/beethoven1.pdf +test* +processedData.pkl +data_dataset/*/imgs \ No newline at end of file diff --git a/README.md b/README.md index 1ff0501..373955f 100644 --- a/README.md +++ b/README.md @@ -71,4 +71,19 @@ ``` -This project utilized the segmentation models from [oemer](https://github.com/BreezeWhite/oemer) \ No newline at end of file +This project utilized the segmentation models from [oemer](https://github.com/BreezeWhite/oemer) + + + +## Code Explaination + Workflow (for `pdf2musicXMLsingleLine.py`) +### General: +Input/output structure: +- +Utils functions +- `imwrite`: if `debugImg` set to True, will write debug image to +### Part 1: Code adaptation from [oemer](https://github.com/BreezeWhite/oemer) +- `runModel1`: run the SegNet model +- `runModel2`: run the staffLine detection model + +### Part 2: Post processing for oemer's output + diff --git a/best_time_signature.pt b/best_time_signature.pt new file mode 100644 index 0000000..4be05ae Binary files /dev/null and b/best_time_signature.pt differ diff --git a/calculate_psda.py b/calculate_psda.py new file mode 100644 index 0000000..668f4d6 --- /dev/null +++ b/calculate_psda.py @@ -0,0 +1,420 @@ +import csv +from music21 import converter, note, chord, stream +import os +from typing import List + +from music21.musicxml.m21ToXml import ScoreExporter +from xml.etree.ElementTree import tostring + +dataSepList = dict() +dataSepList['bee5'] = [1, 12, 33, 53, 77, 98, 119, 141, 160, 170, 181, 194, 223, 248, 267, 287, 309, 333, 356, 376,386, 397, 408, 418, 429, 441, 455, 469, 480, 491, + 1, 8, 25, 38, 57, 73, 82, 92, 102, 110, 118, 133, 148, 164, 172, 183, 187, 191, 204, 215, 230, + 1, 12, 38, 61, 82, 83, 104, 123, 131,150, 166, 182, 190, 205, 231, 263, 293, 323, 349, + 1, 6, 12, 17, 21, 26, 33, 39, 44, 49, 54, 59, 62, 69, 74, 81, 85, 90, 94, 104, 112, 117, 122, 127, 132, 136, 140, 145, 150, 161, 186, 207, 214, 219, 224, 228, 234, 240, 247, 252, 257, 262, 268, 272, 279, 285, 294, 300, 305, 309, 316, 324, 330, 336, 343, 349, 354, 364, 374, 383, 391, 397, 405, 415, 422, 432] +dataSepList['bee6'] = [1,17,31,43,57,83,95,111,124,139,154,166,177,196,209,221,241,260,276,298,317,329,354,368,381,395,407,426,440,452,464,479,493, + 1,4,8,10,12,16,21,25,29,31,35,39,42,44,46,49,51,53,57,61,65,70,74,79,83,87,89,91,93,95,97,99,101,103,107,111,116,118,121,123,125,127,133, + 1,19,39,59,80,111,141,161,176,191,205,224,244,125, + 1,14,22,25,28,31,34,40,17,51,56,65,72,78,82,87,93,103,107,110,114,125,137,145, + 1,14,23,26,32,36,40,44,48,52,57,28,77,85,93,101,105,109,117,131,134,137,141,149,153,157,161,170,179,191,196,204,212,219,225,231,245,260] +dataSepList['bee7'] = [1,8,16,19,22,30,36,39,46,53,63,75,87,91,95,99,103,108,118,127,138,148,157,162,167,171,184,196,205,209,213,225,235,245,255,260,270,279,283,287,291,295,300,311,323,331,335,346,358,366,370,376,380,384,397,409,416,422,428,433,439,444, + 1,32,63,77,84,91,106,120,134,146,158,170,182,192,212,217,230,244,261, + 1,16,34,56,76,97,118,135,143,160,181,198,214,233,250,259,277,293,317,337,357,377,396,404,424,441,459,477,496,513,531,554,574,593,613,629,637,645, + 1,7,12,17,21,28,34,39,52,60,73,81,90,104,113,119,122,129,146,151,154,159,163,177,191,203,218,226,231,238,245,251,257,263,271,278,290,302,314,320,328,335,340,347,358,370,382,394,400,406,414,421,429,437,445,453,459] +dataSepList['bee8'] = [1,8,13,20,26,34,48,64,80,92,98,112,124,138,151,163,169,175,182,187,193,200,213,219,226,241,255,263,277,289,295,307,317,328,334,345,349,353,357,364, + 1,8,16,24,32,41,48,55,63,71,78, + 1,6,11,16,21,31,38,52,66, + 1,8,22,31,42,54,65,75,85,99,112,123,135,148,161,173,183,191,201,211,225,238,244,250,260,274,291,302,312,324,336,348,361,373,383,391,394,401,407,412,417,428,439,449,454,461,474,487] + +def lcs(seq1, seq2): + n, m = len(seq1), len(seq2) + + # DP table + dp = [[0] * (m + 1) for _ in range(n + 1)] + + # Fill table + for i in range(n): + for j in range(m): + if seq1[i] == seq2[j]: + dp[i + 1][j + 1] = dp[i][j] + 1 + else: + dp[i + 1][j + 1] = max(dp[i][j + 1], dp[i + 1][j]) + print("finish creating table of size") + # Reconstruct LCS + i, j = n, m + lcs_seq = [] + + while i > 0 and j > 0: + if seq1[i - 1] == seq2[j - 1]: + lcs_seq.append(seq1[i - 1]) + i -= 1 + j -= 1 + elif dp[i - 1][j] > dp[i][j - 1]: + i -= 1 + else: + j -= 1 + + return lcs_seq[::-1] # reverse + +def musicxml_to_csv(xml_path, csv_path, trackLength): + score = converter.parse(xml_path) + + rows = [] + + # Iterate over parts (tracks) + for track_idx, part in enumerate(score.parts): + if track_idx >= trackLength: + continue + # Go measure by measure + for m_idx, measure in enumerate(part.getElementsByClass('Measure')): + bar_number = measure.number + + # Iterate over notes/rests/chords in the measure + for elem in measure.notesAndRests: + onset = elem.offset # relative to measure start + duration = elem.quarterLength + + if isinstance(elem, note.Note): + pitch = elem.pitch.nameWithOctave + + rows.append([ + track_idx, + bar_number, + onset, + duration, + pitch + ]) + + elif isinstance(elem, note.Rest): + rows.append([ + track_idx, + bar_number, + onset, + duration, + "Rest" + ]) + + elif isinstance(elem, chord.Chord): + # If chord, output one row per note + for p in elem.pitches: + rows.append([ + track_idx, + bar_number, + onset, + duration, + p.nameWithOctave + ]) + + # Write CSV + with open(csv_path, "w", newline="") as f: + writer = csv.writer(f) + writer.writerow(["track", "bar", "onset", "duration", "pitch"]) + writer.writerows(rows) + + return len(score.parts) + +# psda, psd, pda, pd, pa, p +def csv_to_string_list(csv_path): + def convertPitch(pitch:str): + if len(pitch) == 3: + return pitch[0]+pitch[2] + else: + return pitch + psda = [] + psd = [] + pda = [] + pd = [] + pa = [] + p = [] + + with open(csv_path, newline='') as f: + reader = csv.DictReader(f) + + for row in reader: + track = 0 # row["track"] + onset = row["onset"] + duration = row["duration"] + pitch = row["pitch"] + + s = f"{track}_{pitch}_{onset}_{duration}" + psda.append(s) + s = f"{track}_{convertPitch(pitch)}_{onset}_{duration}" + psd.append(s) + s = f"{track}_{pitch}_{duration}" + pda.append(s) + s = f"{track}_{convertPitch(pitch)}_{duration}" + pd.append(s) + s = f"{track}_{pitch}" + pa.append(s) + s = f"{track}_{convertPitch(pitch)}" + p.append(s) + ret = dict() + ret['psda'] = psda + ret['psd'] = psd + ret['pda'] = pda + ret['pd'] = pd + ret['pa'] = pa + ret['p'] = p + return ret +def get_idx_of_ones(lst): + return [i for i, x in enumerate(lst) if x == 1] + +def get_val_name(startOfMvt, currPgNum): + mvtNum = 0 + pageNum = 0 + for i in range(len(startOfMvt)): + if startOfMvt[i] < currPgNum: + mvtNum = i+1 + pageNum = currPgNum-startOfMvt[i] + return f"{mvtNum}_{pageNum}" + + + +def combineXMLs(xmlList: List[str]): + scores = [converter.parse(xml) for xml in xmlList] + combined = stream.Score() + + for parts in zip(*[score.parts for score in scores]): + combined_part = stream.Part() + combined_part.id = parts[0].id + combined_part.partName = parts[0].partName + + for part in parts: + for measure in part.getElementsByClass(stream.Measure): + combined_part.append(measure) + + combined.append(combined_part) + return combined + +def lcsByPage(symphonyNum: int): + rootValFolder = rf"C:\Ellie\APIs\25-omr\xml_gt\val{symphonyNum}" + rootXmlFolder = rf"C:\Ellie\APIs\25-omr\gt_pred\bee{symphonyNum}_xmls" + outputFolder = rf"C:\Ellie\APIs\25-omr\xml_gt\csv{symphonyNum}" + if not os.path.exists(outputFolder): + os.mkdir(outputFolder) + outputOutFolder = os.path.join(outputFolder, 'out') + outputValFolder = os.path.join(outputFolder, 'val') + if not os.path.exists(outputOutFolder): + os.mkdir(outputOutFolder) + if not os.path.exists(outputValFolder): + os.mkdir(outputValFolder) + dataSeperate = dataSepList.get(f'bee{symphonyNum}') + avgCsvPath = os.path.join(rf"C:\Ellie\APIs\25-omr\xml_gt\bee{symphonyNum}_psda.csv") + rootAvgCsPath = avgCsvPath + cnt = 0 + while os.path.exists(avgCsvPath): + avgCsvPath = rootAvgCsPath.replace('.csv',f'_{cnt}.csv') + cnt += 1 + for i in range(1, len(dataSeperate)+1): + mvtPg = get_val_name(get_idx_of_ones(dataSeperate), i) + valXmlPath = os.path.join(rootValFolder, rf'{symphonyNum}_{mvtPg}.musicxml') + xmlXmlPath = os.path.join(rootXmlFolder, rf'Bee_{symphonyNum}_challenge_{i}.musicxml') + + xmlScore = converter.parse(xmlXmlPath) + valScore = converter.parse(valXmlPath) + + minNumTracks = min(len(xmlScore.parts), len(valScore.parts)) + # minNumMeasures = min(len(xmlScore.parts[0].getElementsByClass('Measure')),len(valScore.parts[0].getElementsByClass('Measure'))) + + xmlCsvPath = os.path.join(outputOutFolder, rf'out{i}.csv') + valCsvPath = os.path.join(outputValFolder, rf'truth{i}.csv') + musicxml_to_csv(xmlXmlPath, xmlCsvPath, minNumTracks) + musicxml_to_csv(valXmlPath, valCsvPath, minNumTracks) + + xmlRet = csv_to_string_list(xmlCsvPath) + valRet = csv_to_string_list(valCsvPath) + print() + metricsName = ['psda','psd','pda','pd','pa','p'] + rowNames = ["lenPred","lenTruth"] + for mt in metricsName: + # /ground truth 命中多少：precision + # /our length 正確多少：recall + rowNames += [f"{mt}_length", f"{mt}/pred", f"{mt}/gt"] + numCorrect = dict() # numCorrect['psda'] = [len(lcs), len(xml), len(val), correctOfXml, correctOfVal] + lenXml = len(xmlRet[metricsName[0]]) + lenVal = len(valRet[metricsName[0]]) + rowResToWrite = [lenXml, lenVal] + + for ky in metricsName: + lcsRes = len(lcs(xmlRet[ky], valRet[ky])) + numCorrect[ky] = [lcsRes, lenXml, lenVal, lcsRes/lenXml, lcsRes/lenVal] + rowResToWrite += [lcsRes, lcsRes/lenXml, lcsRes/lenVal] + print(f"{ky}: {numCorrect[ky]}") + with open(avgCsvPath, "a", newline="") as f: + writer = csv.writer(f) + if not os.path.isfile(avgCsvPath): + writer.writerow(rowNames) + writer.writerow(rowResToWrite) + print(f"page{i}: {rowResToWrite}") + print() + +def lcsByMvt(syphonyNum: int): + rootValFolder = rf"C:\Ellie\APIs\25-omr\xml_gt\val{symphonyNum}" + rootXmlFolder = rf"C:\Ellie\APIs\25-omr\gt_pred\bee{symphonyNum}_xmls" + rootGtFolder = rf"C:\Ellie\APIs\25-omr\md_gt" + rootRootCsvFolder = rf"C:\Ellie\APIs\25-omr\xml_gt\csv{symphonyNum}" + rootCsvFolder = os.path.join(rootRootCsvFolder, "mvt") + checkAndCreateFolder([rootValFolder, rootXmlFolder, rootRootCsvFolder, rootCsvFolder]) + dataSeperate = dataSepList.get(f'bee{symphonyNum}') + outputFolder = 'xml_gt' + beginningOfMvts = [i+1 for i in range(len(dataSeperate)) if dataSeperate[i] == 1] + numMvts = len(beginningOfMvts) + beginningOfMvts.append(len(dataSeperate)+1) + for i in range(numMvts): + mvtNum = i+1 + start = beginningOfMvts[i] + end = beginningOfMvts[i+1] # should not be included + xmlPaths = [os.path.join(rootXmlFolder,f"Bee_{symphonyNum}_challenge_{f}.musicxml") for f in range(start,end)] + score = combineXMLs(xmlPaths) + outputPath = os.path.join(outputFolder, f"bee{symphonyNum}_{mvtNum}.musicxml") + avgCsvPath = os.path.join(rootCsvFolder, f"bee_{symphonyNum}_{mvtNum}") + if not os.path.exists(outputPath): + exporter = ScoreExporter(score) + xmlStr = exporter.parse() + xmlBytes = tostring(xmlStr, encoding='unicode') + with open(outputPath, 'w', encoding='utf-8') as f: + f.write(xmlBytes) + print(f"finish parsing {outputPath}") + valXmlPath = os.path.join(rootGtFolder, rf'{symphonyNum}_{mvtNum}.musicxml') + + xmlScore = score + valScore = converter.parse(valXmlPath) + + minNumTracks = min(len(xmlScore.parts), len(valScore.parts)) + # minNumMeasures = min(len(xmlScore.parts[0].getElementsByClass('Measure')),len(valScore.parts[0].getElementsByClass('Measure'))) + + xmlCsvPath = os.path.join(rootCsvFolder, rf'out_{symphonyNum}_{mvtNum}.csv') + valCsvPath = os.path.join(rootCsvFolder, rf'truth_{symphonyNum}_{mvtNum}.csv') + musicxml_to_csv(outputPath, xmlCsvPath, minNumTracks) + musicxml_to_csv(valXmlPath, valCsvPath, minNumTracks) + + xmlRet = csv_to_string_list(xmlCsvPath) + valRet = csv_to_string_list(valCsvPath) + metricsName = ['psda','psd','pda','pd','pa','p'] + rowNames = ["lenPred","lenTruth"] + for mt in metricsName: + # /ground truth 命中多少：precision + # /our length 正確多少：recall + rowNames += [f"{mt}_length", f"{mt}/pred", f"{mt}/gt"] + numCorrect = dict() # numCorrect['psda'] = [len(lcs), len(xml), len(val), correctOfXml, correctOfVal] + lenXml = len(xmlRet[metricsName[0]]) + lenVal = len(valRet[metricsName[0]]) + rowResToWrite = [lenXml, lenVal] + + for ky in metricsName: + lcsRes = len(lcs(xmlRet[ky], valRet[ky])) + numCorrect[ky] = [lcsRes, lenXml, lenVal, lcsRes/lenXml, lcsRes/lenVal] + rowResToWrite += [lcsRes, lcsRes/lenXml, lcsRes/lenVal] + print(f"{ky}: {numCorrect[ky]}") + with open(avgCsvPath, "a", newline="") as f: + writer = csv.writer(f) + if not os.path.isfile(avgCsvPath): + writer.writerow(rowNames) + writer.writerow(rowResToWrite) + print(f"page{i}: {rowResToWrite}") + +def checkAndCreateFolder(folderPaths): + for folderPath in folderPaths: + if not os.path.exists(folderPath): + os.mkdir(folderPath) +def musicxml_to_csv_single_track(xml_path, csv_path, track_idx): + score = converter.parse(xml_path) + rows = [] + part = score.parts[track_idx] + + for measure in part.getElementsByClass('Measure'): + bar_number = measure.number + for elem in measure.notesAndRests: + onset = elem.offset + duration = elem.quarterLength + + if isinstance(elem, note.Note): + rows.append([0, bar_number, onset, duration, elem.pitch.nameWithOctave]) + elif isinstance(elem, note.Rest): + rows.append([0, bar_number, onset, duration, "Rest"]) + elif isinstance(elem, chord.Chord): + for p in elem.pitches: + rows.append([0, bar_number, onset, duration, p.nameWithOctave]) + + with open(csv_path, "w", newline="") as f: + writer = csv.writer(f) + writer.writerow(["track", "bar", "onset", "duration", "pitch"]) + writer.writerows(rows) + +def lcsByMvtPart(symphonyNum: int): + rootValFolder = rf"C:\Ellie\APIs\25-omr\xml_gt\val{symphonyNum}" + rootXmlFolder = rf"C:\Ellie\APIs\25-omr\gt_pred\bee{symphonyNum}_xmls" + rootGtFolder = rf"C:\Ellie\APIs\25-omr\md_gt" + rootRootCsvFolder = rf"C:\Ellie\APIs\25-omr\xml_gt\csv{symphonyNum}" + rootCsvFolder = os.path.join(rootRootCsvFolder, "mvt") + checkAndCreateFolder([rootValFolder, rootXmlFolder, rootRootCsvFolder, rootCsvFolder]) + dataSeperate = dataSepList.get(f'bee{symphonyNum}') + outputFolder = 'xml_gt' + beginningOfMvts = [i+1 for i in range(len(dataSeperate)) if dataSeperate[i] == 1] + numMvts = len(beginningOfMvts) + beginningOfMvts.append(len(dataSeperate)+1) + + for i in range(numMvts): + mvtNum = i+1 + start = beginningOfMvts[i] + end = beginningOfMvts[i+1] + xmlPaths = [os.path.join(rootXmlFolder, f"Bee_{symphonyNum}_challenge_{f}.musicxml") for f in range(start, end)] + score = combineXMLs(xmlPaths) + outputPath = os.path.join(outputFolder, f"bee{symphonyNum}_{mvtNum}.musicxml") + + if not os.path.exists(outputPath): + exporter = ScoreExporter(score) + xmlStr = exporter.parse() + xmlBytes = tostring(xmlStr, encoding='unicode') + with open(outputPath, 'w', encoding='utf-8') as f: + f.write(xmlBytes) + print(f"finish parsing {outputPath}") + + valXmlPath = os.path.join(rootGtFolder, rf'{symphonyNum}_{mvtNum}.musicxml') + xmlScore = score + valScore = converter.parse(valXmlPath) + minNumTracks = min(len(xmlScore.parts), len(valScore.parts)) + + metricsName = ['psda', 'psd', 'pda', 'pd', 'pa', 'p'] + rowNames = ["part", "lenPred", "lenTruth"] + for mt in metricsName: + rowNames += [f"{mt}_length", f"{mt}/pred", f"{mt}/gt"] + + avgCsvPath = os.path.join(rootCsvFolder, f"bee_{symphonyNum}_{mvtNum}.csv") + + with open(avgCsvPath, "w", newline="") as f: + writer = csv.writer(f) + writer.writerow(rowNames) + + # --- Run LCS per part instead of on the whole movement --- + for trackIdx in range(minNumTracks): + xmlCsvPath = os.path.join(rootCsvFolder, rf'out_{symphonyNum}_{mvtNum}_part{trackIdx}.csv') + valCsvPath = os.path.join(rootCsvFolder, rf'truth_{symphonyNum}_{mvtNum}_part{trackIdx}.csv') + + # Pass trackLength as a single-track window: only export track trackIdx + musicxml_to_csv_single_track(outputPath, xmlCsvPath, trackIdx) + musicxml_to_csv_single_track(valXmlPath, valCsvPath, trackIdx) + + xmlRet = csv_to_string_list(xmlCsvPath) + valRet = csv_to_string_list(valCsvPath) + + lenXml = len(xmlRet[metricsName[0]]) + lenVal = len(valRet[metricsName[0]]) + rowResToWrite = [trackIdx, lenXml, lenVal] + + for ky in metricsName: + lcsRes = len(lcs(xmlRet[ky], valRet[ky])) + rowResToWrite += [lcsRes, lcsRes/lenXml, lcsRes/lenVal] + print(f" part{trackIdx} {ky}: lcs={lcsRes}, pred={lenXml}, gt={lenVal}") + + with open(avgCsvPath, "a", newline="") as f: + writer = csv.writer(f) + writer.writerow(rowResToWrite) + + print(f"mvt{mvtNum} part{trackIdx}: {rowResToWrite}") +if __name__ == '__main__': + symphonyNum = 7 + # lcsByPage() + # lcsByMvt(symphonyNum) + lcsByMvtPart(symphonyNum) + + diff --git a/calib_one.py b/calib_one.py index 6aa62e1..f03e17b 100644 --- a/calib_one.py +++ b/calib_one.py @@ -37,7 +37,8 @@ def calib_onebar(inputLst, changeWeight, desiredLength): if noDotLen <= desiredLength: singleCostTb[i,noDotLen] = 1 for times in range(int(np.ceil(np.log2(desiredLength/noDotLen)))): - singleCostTb[i,int(noDotLen*(2**times)*1.5)] = times*currWeight + if int(noDotLen*(2**times)*1.5) < len(singleCostTb[i]): + singleCostTb[i,int(noDotLen*(2**times)*1.5)] = times*currWeight for div in range(int(np.log2(noDotLen))): # noDotLen/(2**div) will be integer for sure? if int(noDotLen/(2**div)) <= desiredLength: diff --git a/cnt_staff_omr.py b/cnt_staff_omr.py new file mode 100644 index 0000000..f50f7f3 --- /dev/null +++ b/cnt_staff_omr.py @@ -0,0 +1,157 @@ +""" +cnt_staff_omr.py - Count the number of staves in a music score image. + +Usage: + python cnt_staff_omr.py [--batch-size N] + +Speed tips: + - Install onnxruntime-gpu for CUDA acceleration (replaces onnxruntime). + - Increase --batch-size (e.g. 32 or 64) when running on GPU. +""" + +import argparse +import os +import pickle +import statistics +from typing import List, Tuple + +import cv2 +import numpy as np +from PIL import Image +import onnxruntime as rt + +from omr import MODULE_PATH +from omr.staffline_extraction import staff_extract_staffobj + +MIN_BAR_HEIGHT = 8 +RESIZE_RATIO = 2 + + +# ---------- minimal Staff class (mirrors pdf2musicXML_yolo.Staff) ---------- + +class Staff: + def __init__(self, left: int, right: int, ys: Tuple[int, int, int, int, int], minMaxDiff: int = 0): + self.left = left + self.right = right + self.ys = ys + self.minDiff = minMaxDiff + + def get_yOne_float(self) -> float: + x = [self.ys[i + 1] - self.ys[i] for i in range(len(self.ys) - 1)] + return sum(x) / len(x) + + +# ---------- staff list helpers ------------------------------------------------ + +def _staffs_to_omrStaffList(staffs) -> Tuple[List[Staff], list]: + left = min([sf.x_left for sf in staffs[0, :]]) + right = max([sf.x_right for sf in staffs[-1, :]]) + omrstaff_list: List[Staff] = [] + staffRange = [] + for i in range(staffs.shape[1]): + yuppers = [sf.y_upper for sf in staffs[:, i]] + ybottoms = [sf.y_lower for sf in staffs[:, i]] + top = statistics.median(yuppers) + bottom = statistics.median(ybottoms) + unit_size = statistics.median([sf.unit_size for sf in staffs[:, i]]) + minMaxDiff = min(max(yuppers) - min(yuppers), max(ybottoms) - min(ybottoms)) + ys = [int(top), int(top + unit_size), int((top + bottom) / 2), int(bottom - unit_size), int(bottom)] + omrstaff_list.append(Staff(int(left), int(right), ys, minMaxDiff)) + staffRange.append(range(int(top), int(bottom))) + return omrstaff_list, staffRange + + +# ---------- single-model inference (first model only) ------------------------ + +def _resize_image(image: Image.Image, lower: int = 3_000_000, upper: int = 4_350_000) -> Image.Image: + w, h = image.size + pix = w * h + if lower <= pix <= upper: + return image + ratio = ((lower / pix + upper / pix) / 2) ** 0.5 + return image.resize((round(ratio * w), round(ratio * h))) + + +def _run_first_model(img_path: str, batch_size: int = 16) -> np.ndarray: + """Run only the unet_big model and return the staff prediction map.""" + model_path = os.path.join(MODULE_PATH, "checkpoints/unet_big") + onnx_path = os.path.join(model_path, "model.onnx") + metadata = pickle.load(open(os.path.join(model_path, "metadata.pkl"), "rb")) + + providers = ["CUDAExecutionProvider", "CoreMLExecutionProvider", "CPUExecutionProvider"] + try: + rt.set_default_logger_severity(3) + except AttributeError: + pass # removed in onnxruntime-gpu >= 1.18 + sess = rt.InferenceSession(onnx_path, providers=providers) + print("Providers in use:", sess.get_providers()) + + output_names = metadata['output_names'] + input_shape = metadata['input_shape'] + output_shape = metadata['output_shape'] + + image_cv = cv2.imread(img_path) + image_pil = Image.fromarray(image_cv).convert("RGB") + image_pil = _resize_image(image_pil) + image = np.array(image_pil) + + win_size = input_shape[1] + step_size = 128 + patches = [] + coords = [] + for y in range(0, image.shape[0], step_size): + y = min(y, image.shape[0] - win_size) + for x in range(0, image.shape[1], step_size): + x = min(x, image.shape[1] - win_size) + patches.append(image[y:y + win_size, x:x + win_size]) + coords.append((y, x)) + + pred_patches = [] + total = len(patches) + for i in range(0, total, batch_size): + print(f"{i+1}/{total} (batch {batch_size})", end="\r") + batch = np.array(patches[i:i + batch_size]) + out = sess.run(output_names, {'input': batch})[0] + pred_patches.append(out) + print() + + full_out = np.zeros(image.shape[:2] + (output_shape[-1],), dtype=np.float32) + full_mask = np.zeros(image.shape[:2] + (output_shape[-1],), dtype=np.float32) + for idx, (y, x) in enumerate(coords): + bi, ri = divmod(idx, batch_size) + hop = pred_patches[bi][ri] + full_out [y:y + win_size, x:x + win_size] += hop + full_mask[y:y + win_size, x:x + win_size] += 1 + + full_out /= full_mask + class_map = np.argmax(full_out, axis=-1) + staff = np.where(class_map == 1, 1, 0) + + # Resize 2x to match downstream processing + staff = cv2.resize(staff.astype(np.uint8), None, fx=RESIZE_RATIO, fy=RESIZE_RATIO, + interpolation=cv2.INTER_NEAREST) + return staff + + +# ---------- main -------------------------------------------------------------- + +def count_staff(img_path: str, batch_size: int = 16) -> int: + staff_map = _run_first_model(img_path, batch_size=batch_size) + staffs, _ = staff_extract_staffobj(staff_map, MIN_BAR_HEIGHT) + staffList, _ = _staffs_to_omrStaffList(staffs) + return len(staffList) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Count staves in a music score image.") + parser.add_argument("image_path", help="Path to the input image") + parser.add_argument("--batch-size", type=int, default=16, + help="Inference batch size (increase for GPU, e.g. 32 or 64)") + args = parser.parse_args() + + if not os.path.exists(args.image_path): + print(f"Error: file not found: {args.image_path}") + raise SystemExit(1) + + count = count_staff(args.image_path, batch_size=args.batch_size) + print("the length of staff is:", count) \ No newline at end of file diff --git a/data_dataset/dvo/debug/debug_1.jpg b/data_dataset/dvo/debug/debug_1.jpg new file mode 100644 index 0000000..180cb18 Binary files /dev/null and b/data_dataset/dvo/debug/debug_1.jpg differ diff --git a/data_dataset/dvo/debug/debug_1.txt 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0.019977 +4 0.067528 0.828377 0.009982 0.019977 +4 0.080153 0.940522 0.009982 0.019977 +4 0.070317 0.930306 0.009102 0.019523 +4 0.077951 0.747560 0.009689 0.019977 +4 0.067528 0.737684 0.009395 0.020658 +4 0.077217 0.656527 0.009982 0.019523 +4 0.067528 0.646311 0.009395 0.019977 +4 0.079565 0.160499 0.009395 0.019977 +4 0.069877 0.150284 0.009982 0.020431 +4 0.078391 0.350057 0.009982 0.020431 +4 0.068996 0.339841 0.009982 0.019977 +4 0.078978 0.270148 0.009982 0.020431 +4 0.069289 0.259932 0.009395 0.019977 \ No newline at end of file diff --git a/data_dataset/tch/tch.pdf b/data_dataset/tch/tch.pdf new file mode 100644 index 0000000..e3964d2 Binary files /dev/null and b/data_dataset/tch/tch.pdf differ diff --git a/dd_classes.py b/dd_classes.py index 92b1589..30a9a81 100644 --- a/dd_classes.py +++ b/dd_classes.py @@ -2,12 +2,13 @@ from typing import Tuple class RestNg: - def __init__(self, length, groupId, isRest, numNotes, beamLength, beamEnd): + def __init__(self, length, groupId, isRest, numNotes, beamLength, beamEnd, acrossLineNGID=None): self.length:Fraction = length self.groupId:int = groupId # =1: rest, 0: singleNote, other: groupId self.isRest:bool = isRest self.numNotes:int = numNotes # number of notes in the Ng, 0 for rest self.beamLength:Tuple[int,int] = beamLength # single note in Ng: (left, right) else (-1, -1) for rest, others self.beamEnd: Tuple[int,int]= beamEnd # single note in Ng: the x,y for the end of beam or (-1,-1) + self.acrossLineNGID: int|None = acrossLineNGID self.newGroupId: int|None = None self.newLength:Fraction|None = None # the tuned length diff --git a/exportXml.py b/exportXml.py new file mode 100644 index 0000000..95ff2ec --- /dev/null +++ b/exportXml.py @@ -0,0 +1,526 @@ +import statistics +import cv2 +import numpy as np + +from typing import List,Tuple, Union, Set +from music21 import stream, note, metadata, chord, meter, clef, key +from scipy import stats + +from utils import * + +CLEF_OPTIONS = [[1],[1],[0,1],[-1,-2,1]] +TRACK_SHIFT = [0,0,0,0] + +def exportXML(barList:List[List[Bar]], numTrack:int, image:np.ndarray|None = None, + beamMapImg:np.ndarray|None = None, + barsBreakPoints: List[int] = [0], + beamMapList:List[np.ndarray]|None = None, + beamMapRefList:List[int]|None = None, + lineNoList:List[int] | None = None): + # Stem's label -> rhythm meaning + # class_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40), (245, 220, 255),(230,130,175),(165,170, 70)] + if image is None: + debugXMLImg = None + else: + debugXMLImg = image.copy() + if beamMapImg is not None: + bb,gg,rr = cv2.split(beamMapImg) + score = stream.Score() + score.metadata = metadata.Metadata() + score.metadata.title = "Example MusicXML" + score2 = stream.Score() # for those with shift + score2.metadata = metadata.Metadata() + score2.metadata.title = "Example MusicXML" + + part = [stream.Part() for n in range(numTrack)] + part2 = [stream.Part() for n in range(numTrack)] + keyName = ['C','D','E','F','G','A','B'] + keyCharFlat = ['C','D-','D','E-','E','F','G-','G','A-','A','B-','B'] + keyCharSharp = ['C','C#','D','D#','E','F','F#','G','G#','A','A#','B'] + class_names = ['1/4','1/8','1/16','1/32', '1/64', '1/128', '1/2', '1/1'] + restClassNames = ['1/4','1/8','1/16','1/32'] # only for >=0 + currentClef = [None]*numTrack + sharps = [3,0,4,1,5,2,6] # F C G D A E B + flats = [6,2,5,1,4,0,3] # B E A D G C F + ksSharp = [set() for _ in range(numTrack)] + ksFlat = [set() for _ in range(numTrack)] + currentKs = [0 for n in range(numTrack)] + trksOriginal = [[] for _ in range (numTrack)] # for tracking all original notes we see + trksShifted = [[] for _ in range (numTrack)] # for tracking all shifted notes + def setKS(ks:float, trkNo): + ks = int(ks) + nonlocal ksSharp + nonlocal ksFlat + if ks>0: + ksSharp[trkNo] = [keyName[i] for i in [sharps[q] for q in range(ks)]] + ksFlat[trkNo] = set() + else: + ksFlat[trkNo] = [keyName[i] for i in [flats[q] for q in range(-ks)]] + ksSharp[trkNo] = set() + def setKSNeutral(Oriks:float, trkNo, trackShift): + Oriks = int(Oriks) + nonlocal ksSharp + nonlocal ksFlat + ks = Oriks+trackShift + if ks>0: + ksSharp[trkNo] = [keyName[i] for i in [sharps[q] for q in range(ks)]] + ksFlat[trkNo] = set() + else: + ksFlat[trkNo] = [keyName[i] for i in [flats[q] for q in range(-ks)]] + ksSharp[trkNo] = set() + def parseClefOne(elem:Clef, lineNo:int): + nonlocal currentClef + currentClef[lineNo%numTrack] = elem.type + return elem.type + def getClefFromType(clefType:int): + clf = clef.TrebleClef() + if clefType == 1: + clf = clef.TrebleClef() + elif clefType == 0: + clf = clef.AltoClef() + elif clefType == -1: #-1 + clf = clef.BassClef() + elif clefType == -2: + clf = clef.TenorClef() + return clf + + def parseClef(elem:Clef, lineNo:int): + nonlocal currentClef + if elem.type == currentClef[lineNo%numTrack]: + return None + clf = None + if elem.type == 1: + clf = clef.TrebleClef() + elif elem.type == 0: + clf = clef.AltoClef() + elif elem.type == -1: #-1 + clf = clef.BassClef() + elif elem.type == -2: + clf = clef.TenorClef() + currentClef[lineNo%numTrack] = elem.type + return clf + def parseAccidentals(elem:Accidentals, lineNo:int): + if not elem.isKeySignature: # it's accidentals + return None + ys = elem.shrinkYs + if elem.ksKeySop is None: + keyNum = np.sum(rr[ys[0]:ys[1],(lineNo+1)%4])/((ys[1]-ys[0]))*2-25 + elem.ksKeySop= keyNum + return elem + def calculatePitch(inputPitch:str): + outputNum = keyCharFlat.index(inputPitch[0])+12*(int(inputPitch[-1])+1) + if len(inputPitch)==3: + if inputPitch[1] == '#': + outputNum+=1 + elif inputPitch[1] == '-' : + outputNum-=1 + return outputNum + def pitchListShift(inputPitchList:List[str], shift: int, sharp = True): # shift: how many half notes + returnList = [] + for pitch in inputPitchList: + pitchNum = calculatePitch(pitch) + pitchNum = pitchNum + shift + returnList.append(pitchNum) + referenceList = keyCharFlat + if sharp: + referenceList = keyCharSharp + for idx,pitchNo in enumerate(returnList): + clefNum = pitchNo//12-1 + pitchName = referenceList[pitchNo%12] + returnList[idx] = pitchName + str(clefNum) + return returnList + def parseNoteGroupShift(elem:NoteGroup, flatSet, sharpSet, naturalSet, currClef, trkShift, flatSharp, linNo): + # trkShift: 0 if original: -2, new: -2 (two flats), 1 if original: -2, new: -1 + # flatSharp: how many flats/sharp, -2: two flats + # flatSharp = 2, trkShift = 0: (-2), flatSharp = 2, trkShift = 1: (-1) + keyLst = [] + currLength = 1 + regNoteCount = 0 + def setRemove(currSet, currElm): + if currElm in currSet: + currSet.remove(currElm) + return currSet + for stem in elem.noteStemList: + if not stem.isOrnament: + regNoteCount+=1 + else: + continue + actualPitch = stem.pitchSoprano + if currClef == 0: #viola + actualPitch -= 6 + elif currClef == -1: #cello + actualPitch -= 12 + elif currClef == -2: #tenor + actualPitch -= 8 + currKeyName = keyName[(actualPitch-1)%7] + currKey = currKeyName+str((actualPitch-1)//7+5) + if stem.accidentals is not None: # it has keySignature + sharpSet = setRemove(sharpSet, currKey) + naturalSet = setRemove(naturalSet, currKey) + flatSet = setRemove(flatSet, currKey) + if stem.accidentals == -1 or currKey in flatSet: + flatSet.add(currKey) + currKey = currKey[0]+'-'+currKey[1] + elif stem.accidentals == 1 or currKey in sharpSet: + sharpSet.add(currKey) + currKey = currKey[0]+'#'+currKey[1] + else: # stem.accidentals == 0: + naturalSet.add(currKey) + else: + if currKey in naturalList: + currKey = currKey + elif currKey in flatSet or currKeyName in ksFlat[linNo]: + currKey = currKey[0]+'-'+currKey[1] + elif currKey in sharpSet or currKeyName in ksSharp[linNo]: + currKey = currKey[0]+'#'+currKey[1] + # llen = float(Fraction(class_names[stem.rhythm])) + # if stem.hasdot: + # llen = llen*1.5 + # if llen0) + trksShifted[linNo].append(keyShiftedList) + if len(keyShiftedList) > 1: + return flatSet, sharpSet, chord.Chord(keyShiftedList, quarterLength = currLength*4) + elif len(keyShiftedList) == 1: + return flatSet, sharpSet, note.Note(keyShiftedList[0], quarterLength=currLength*4) + else: + return flatSet, sharpSet, None + # actualPitch, 0: B4, 1: C5, 2: D5 + + def parseNoteGroup(elem:NoteGroup, flatSet, sharpSet, naturalSet, currClef, linNo, trkShift = 0): + keyLst = [] + currLength = 1 + regNoteCount = 0 + def setRemove(currSet, currElm): + if currElm in currSet: + currSet.remove(currElm) + return currSet + for stem in elem.noteStemList: + if not stem.isOrnament: + regNoteCount+=1 + else: + continue + actualPitch = stem.pitchSoprano + if currClef == 0: #viola + actualPitch -= 6 + elif currClef == -1: #cello + actualPitch -= 12 + elif currClef == -2: #tenor + actualPitch -= 8 + currKeyName = keyName[(actualPitch-1)%7] + currKey = currKeyName+str((actualPitch-1)//7+5) + if stem.accidentals is not None: # it has keySignature + sharpSet = setRemove(sharpSet, currKey) + naturalSet = setRemove(naturalSet, currKey) + flatSet = setRemove(flatSet, currKey) + if stem.accidentals == -1: + flatSet.add(currKey) + currKey = currKey[0]+'-'+currKey[1] + elif stem.accidentals == 1 or currKey in sharpSet: + sharpSet.add(currKey) + currKey = currKey[0]+'#'+currKey[1] + else: # stem.accidentals == 0: + naturalSet.add(currKey) + else: + if currKey in naturalList: + currKey = currKey + elif currKey in flatSet or currKeyName in ksFlat[linNo]: + currKey = currKey[0]+'-'+currKey[1] + elif currKey in sharpSet or currKeyName in ksSharp[linNo]: + currKey = currKey[0]+'#'+currKey[1] + # llen = float(Fraction(class_names[stem.rhythm])) + # if stem.hasdot: + # llen = llen*1.5 + # if llen 1: + return flatSet, sharpSet, chord.Chord(keyLst, quarterLength = currLength*4) + elif len(keyLst) == 1: + return flatSet, sharpSet, note.Note(keyLst[0], quarterLength=currLength*4) + else: + return flatSet, sharpSet, None # ornament + # actualPitch, 0: B4, 1: C5, 2: D5 + def parseRest(elem:Rest): + restLength = float(elem.tunedLength) + if restLength>0: + # restLength = float(Fraction(restClassNames[elem.rhythm])) + # if elem.hasdot: + # restLength = restLength*1.5 + return note.Rest(quarterLength=restLength*4) + else: + pass + # print("setting rest to full") + # return note.Rest(quarterLength=4) + def assignKsCurrClef(accList:List[Accidentals], currentClef:int): + pitchShift = 0 + if currentClef == 0: + pitchShift -= 6 + elif currentClef== -1: + pitchShift -= 12 + elif currentClef== -2: + pitchShift -= 8 + i = 0 + while i= len(accList): + return None + totalLength = len(accList)-i + pitchList = [(accList[r].ksKeySop+pitchShift-1)%7 for r in range(i, len(accList))] + if not False in [abs(pitchList[k] - sharps[k%7])<1.2 or pitchList[k]-sharps[k%7]>5.8 for k in range(totalLength)]: + return totalLength + elif not False in [abs(pitchList[k] - flats[k%7])<1.2 or pitchList[k]-flats[k%7]>5.8 for k in range(totalLength)]: + return -totalLength + else: + return None + def assignKS(accList:List[Accidentals]): + pitchShift = 0 + if currentClef[lineNo%numTrack] == 0: + pitchShift -= 6 + elif currentClef[lineNo%numTrack] == -1: + pitchShift -= 12 + elif currentClef[lineNo%numTrack] == -2: + pitchShift -= 8 + i = 0 + while i= len(accList): + return None + totalLength = len(accList)-i + pitchList = [(accList[r].ksKeySop+pitchShift-1)%7 for r in range(i, len(accList))] + if not False in [abs(pitchList[k] - sharps[k%7])<1.2 or pitchList[k]-sharps[k%7]>5.8 for k in range(totalLength)]: + return totalLength + elif not False in [abs(pitchList[k] - flats[k%7])<1.2 or pitchList[k]-flats[k%7]>5.8 for k in range(totalLength)]: + return -totalLength + else: + return None + numBars = len(barList[0]) + ksBarMat = np.ones((numTrack, numBars))*np.inf + clefMat = np.ones((numTrack, numBars))*np.inf + currentClef = [None]*numTrack + for currBarNumber in range(numBars): + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): + if len(CLEF_OPTIONS[lineNo]) == 1: + clefMat[lineNo, currBarNumber] = CLEF_OPTIONS[lineNo][0] + else: + currBar = barList[lineNo][currBarNumber] + for elem in currBar.elementList: + if type(elem) == Clef: + newClef = parseClefOne(elem, lineNo) + if newClef in CLEF_OPTIONS[lineNo]: + clefMat[lineNo, currBarNumber] = newClef + if clefMat[lineNo, currBarNumber] == np.inf: + if currBarNumber == 0: + clefMat[lineNo, currBarNumber] = CLEF_OPTIONS[lineNo][0] + else: + clefMat[lineNo, currBarNumber] = clefMat[lineNo, currBarNumber-1] + global DEBUGIMG + beamMapRefIndex = -1 + for currBarNumber in range(numBars): + if beamMapRefList is not None: + if beamMapRefList[currBarNumber] != beamMapRefIndex: + beamMapRefIndex = beamMapRefList[currBarNumber] + beamMapImg = beamMapList[beamMapRefIndex] + bb,gg,rr = cv2.split(beamMapImg) + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): + currBar = barList[lineNo][currBarNumber] + accumAcc: List[Accidentals] = [] # will have the list of accidentals to process + for elem in currBar.elementList: + if type(elem) == Accidentals: + if lineNoList is not None: + currAcc = parseAccidentals(elem,lineNoList[currBarNumber]) # return none if it's accidentals + else: + currAcc = parseAccidentals(elem, lineNo) + if currAcc is None: + if debugXMLImg is not None: + x0,y0,x1,y1 = elem.boundingBox + debugXMLImg = cv2.rectangle(debugXMLImg, (x0,y0),(x1,y1),elem.getColor(), 1, cv2.LINE_AA) + DEBUGIMG = cv2.rectangle(DEBUGIMG, (x0,y0),(x1,y1),elem.getColor(), 1, cv2.LINE_AA) + if len(accumAcc)>0: + ksCurr = assignKsCurrClef(accumAcc, clefMat[lineNo, currBarNumber]) + if ksCurr is None: + for ac in accumAcc: + x0,y0,x1,y1 = ac.boundingBox + debugXMLImg = cv2.circle(debugXMLImg, ((x0+x1)//2, (y0+y1)//2), (x1-x0)//2, ac.getColor(), 3, cv2.LINE_AA) + DEBUGIMG = cv2.circle(DEBUGIMG, ((x0+x1)//2, (y0+y1)//2), (x1-x0)//2, ac.getColor(), 3, cv2.LINE_AA) + if barNumber == 1: + modShift = statistics.mode([a.shift for a in accumAcc]) + currentKs[lineNo%numTrack] = modShift*len(accumAcc) + ksBarMat[lineNo, currBarNumber] = modShift*len(accumAcc) + debugXMLImg = cv2.putText(debugXMLImg,str(modShift*len(accumAcc)), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + DEBUGIMG = cv2.putText(DEBUGIMG,str(modShift*len(accumAcc)), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + else: + for ac in accumAcc: + x0,y0,x1,y1 = ac.boundingBox + debugXMLImg = cv2.rectangle(debugXMLImg, (x0,y0),(x1,y1),ac.getColor(), 3, cv2.LINE_AA) + DEBUGIMG = cv2.rectangle(DEBUGIMG, (x0,y0),(x1,y1),ac.getColor(), 3, cv2.LINE_AA) + debugXMLImg = cv2.putText(debugXMLImg,str(ksCurr), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + DEBUGIMG = cv2.putText(DEBUGIMG,str(ksCurr), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + if ksCurr is not None and ksCurr != currentKs[lineNo%numTrack]: + currentKs[lineNo%numTrack] = ksCurr + ksBarMat[lineNo, currBarNumber] = ksCurr + elif currAcc.endKeySignature: + accumAcc.append(currAcc) + ksCurr = assignKsCurrClef(accumAcc, clefMat[lineNo, currBarNumber]) + if ksCurr is None: + for ac in accumAcc: + x0,y0,x1,y1 = ac.boundingBox + debugXMLImg = cv2.circle(debugXMLImg, ((x0+x1)//2, (y0+y1)//2), (x1-x0)//2, ac.getColor(), 3, cv2.LINE_AA) + DEBUGIMG = cv2.circle(DEBUGIMG, ((x0+x1)//2, (y0+y1)//2), (x1-x0)//2, ac.getColor(), 3, cv2.LINE_AA) + if barNumber == 1: + modShift = statistics.mode([a.shift for a in accumAcc]) + currentKs[lineNo%numTrack] = modShift*len(accumAcc) + ksBarMat[lineNo, currBarNumber] = modShift*len(accumAcc) + debugXMLImg = cv2.putText(debugXMLImg,str(modShift*len(accumAcc)), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + DEBUGIMG = cv2.putText(DEBUGIMG,str(modShift*len(accumAcc)), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + else: + for ac in accumAcc: + x0,y0,x1,y1 = ac.boundingBox + debugXMLImg = cv2.rectangle(debugXMLImg, (x0,y0),(x1,y1),ac.getColor(), 3, cv2.LINE_AA) + DEBUGIMG = cv2.rectangle(DEBUGIMG, (x0,y0),(x1,y1),ac.getColor(), 3, cv2.LINE_AA) + debugXMLImg = cv2.putText(debugXMLImg,str(ksCurr), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + DEBUGIMG = cv2.putText(DEBUGIMG,str(ksCurr), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + if ksCurr is not None and ksCurr != currentKs[lineNo%numTrack]: + currentKs[lineNo%numTrack] = ksCurr + ksBarMat[lineNo, currBarNumber] = ksCurr + else: + accumAcc.append(currAcc) + if len(accumAcc)>0: + ksCurr = assignKsCurrClef(accumAcc, clefMat[lineNo, currBarNumber]) + if ksCurr is not None: + currentKs[lineNo%numTrack] = ksCurr + ksBarMat[lineNo, currBarNumber] = ksCurr + if ksBarMat[lineNo, currBarNumber] == np.inf: + if currBarNumber in barsBreakPoints: + ksBarMat[lineNo, currBarNumber] = 0 + else: + ksBarMat[lineNo, currBarNumber] = ksBarMat[lineNo, currBarNumber-1] + kk = ksBarMat.copy() + for idx, trShift in enumerate(TRACK_SHIFT): + kk[idx,:] = kk[idx,:]-trShift + ksBarMatNeutral = np.zeros_like(ksBarMat) # the "standard" orchestra clef we want + ksBarMatNeutral[:,:] = stats.mode(kk, axis=0)[0] + ksBarMatNew = np.zeros_like(ksBarMat) + for idx, trShift in enumerate(TRACK_SHIFT): + ksBarMatNew[idx,:] = ksBarMatNeutral[idx,:]+trShift + ksBarMatNeutral = ksBarMatNeutral.astype(int) + ksBarMatNew = ksBarMatNew.astype(int) + clefMat = clefMat.astype(int) + tssList = [b.ts for b in barList[0]] + for currBarNumber in range(numBars): + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): + currBar = barList[lineNo][currBarNumber] + measure = stream.Measure(number=barNumber) + flatList = set() #put here because reset in measures + sharpList = set() + naturalList = set() + accumAcc = [] # will have the list of accidentals to process + clefType = clefMat[lineNo, currBarNumber] + ks = ksBarMatNew[lineNo, currBarNumber] + if currBarNumber == 0: + measure.append(meter.TimeSignature(f'{currBar.ts[0]}/{currBar.ts[1]}')) + elif tssList[currBarNumber]!=tssList[currBarNumber-1]: + measure.append(meter.TimeSignature(f'{currBar.ts[0]}/{currBar.ts[1]}')) + if currBarNumber == 0: + measure.append(getClefFromType(clefType)) + setKS(ks, lineNo) + measure.append(key.KeySignature(ks)) + elif clefType != clefMat[lineNo, currBarNumber-1]: + measure.append(getClefFromType(clefType)) + elif ks != ksBarMatNew[lineNo, currBarNumber-1]: + setKS(ks, lineNo) + measure.append(key.KeySignature(ks)) + if currBar.getTunedTotalBeat() == 0: + measure.append(note.Rest(quarterLength = currBar.ts[0]/currBar.ts[1]*4)) + else: + for elem in currBar.elementList: + if type(elem) == NoteGroup: + flatList, sharpList, currNote = parseNoteGroup(elem, + flatList, + sharpList, + naturalList, + clefMat[lineNo, currBarNumber], + lineNo, + trkShift=0) + if currNote is not None: + measure.append(currNote) + else: + print() + elif type(elem) == Rest: + currRest = parseRest(elem) + if currRest is not None: + measure.append(currRest) + part[lineNo%numTrack].append(measure) + for p in part: + score.append(p) + # Part 2: with shift + ksSharp = [set() for _ in range(numTrack)] + ksFlat = [set() for _ in range(numTrack)] + for currBarNumber in range(numBars): # for the new one shifted + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): + currBar = barList[lineNo][currBarNumber] + measure = stream.Measure(number=barNumber) + # barNumber = barNumber+1 + flatList = set() #put here because reset in measures + sharpList = set() + naturalList = set() + accumAcc = [] # will have the list of accidentals to process + clefType = clefMat[lineNo, currBarNumber] + ks = ksBarMatNeutral[lineNo, currBarNumber] + if currBarNumber == 0: + measure.append(getClefFromType(clefType)) + setKSNeutral(ks, lineNo, TRACK_SHIFT[lineNo]) + measure.append(key.KeySignature(ks)) + elif clefType != clefMat[lineNo, currBarNumber-1]: + measure.append(getClefFromType(clefType)) + elif ks != ksBarMatNeutral[lineNo, currBarNumber-1]: + setKSNeutral(ks, lineNo, TRACK_SHIFT[lineNo]) + measure.append(key.KeySignature(ks)) + if currBar.getTunedTotalBeat() == 0: + measure.append(note.Rest(quarterLength = currBar.ts[0]/currBar.ts[1]*4)) + for elem in currBar.elementList: + if type(elem) == NoteGroup: + flatList, sharpList, currNote = parseNoteGroupShift(elem, + flatList, + sharpList, + naturalList, + clefMat[lineNo, currBarNumber], + TRACK_SHIFT[lineNo], + ksBarMatNeutral[0,currBarNumber], + lineNo) + if currNote is not None: + measure.append(currNote) + else: + print() + elif type(elem) == Rest: + currRest = parseRest(elem) + if currRest is not None: + measure.append(currRest) + part2[lineNo%numTrack].append(measure) + for p in part2: + score2.append(p) + return score, score2, {'ksAssigned':debugXMLImg} diff --git a/instrumentMapping.json b/instrumentMapping.json new file mode 100644 index 0000000..30ba952 --- /dev/null +++ b/instrumentMapping.json @@ -0,0 +1,29 @@ +{ + "violin": ["treble"], + "viola": ["alto", "treble"], + "cello": ["bass", "tenor", "treble"], + "double_bass": ["bass"], + "bass": ["bass"], + "piccolo": ["treble"], + "flute": ["treble"], + "oboe": ["treble"], + "english_horn": ["treble"], + "clarinet": ["treble"], + "bass_clarinet": ["treble", "bass"], + "bassoon": ["bass", "tenor", "treble"], + "contrabassoon": ["bass"], + "french_horn": ["treble", "bass"], + "trumpet": ["treble"], + "trombone": ["bass", "tenor", "alto"], + "bass_trombone": ["bass"], + "tuba": ["bass"], + "harp": ["treble", "bass"], + "piano": ["treble", "bass"], + "celesta": ["treble", "bass"], + "timpani": ["bass"], + "xylophone": ["treble"], + "marimba": ["treble", "bass"], + "glockenspiel": ["treble"], + "vibraphone": ["treble"], + "horn": ["treble","bass"] +} \ No newline at end of file diff --git a/keySignatureMapping.json b/keySignatureMapping.json new file mode 100644 index 0000000..8e5ddb9 --- /dev/null +++ b/keySignatureMapping.json @@ -0,0 +1,15 @@ +{ + "C": [0,0], + "D flat": [-5, -11], + "D": [2, -10], + "E flat": [-3, -9], + "E": [4, -8], + "F": [-1, -7], + "F sharp": [6, -6], + "G flat": [-6, -6], + "G": [1, -5], + "A flat": [-4, -4], + "A": [3, -3], + "B flat": [-2, -2], + "B": [5, -1] +} diff --git a/md22musicxml.py b/md22musicxml.py new file mode 100644 index 0000000..a15be2e --- /dev/null +++ b/md22musicxml.py @@ -0,0 +1,308 @@ +from music21 import stream, note, metadata, chord, meter, clef, key, instrument +import re +from typing import Tuple, List +from fractions import Fraction +from music21.musicxml.m21ToXml import ScoreExporter +from xml.etree.ElementTree import tostring +def processLine(lineStr: str): + match lineStr[0]: + case " ": + pass +def printt(st): + print(st) +def printtt(st): + print(f" --- {st}") + +def updatePartName(part, name): + part.partName = name + +class ParserState: + def __init__(self): + self.isBeginningOfTrack: bool = True + self.isDirty: bool = True + self.currentInstrument: str = '' + self.currClef: clef = clef.TrebleClef() + self.currPart: stream.Part = stream.Part() + # quarterNum = 3: length 3 is a quarter note, length 1 is a triplet + self.quarterNum: int = 0 + # 3: ###, -4: bbbb + self.keySigature: int = 0 + self.timeSignature: Tuple[int,int] = [4,4] + + def reset(self): + self.isBeginningOfTrack: bool = True + self.isDirty: bool = True + self.currentInstrument: str = '' + self.currClef: clef = clef.TrebleClef() + self.currPart: stream.Part = stream.Part() + # quarterNum = 3: length 3 is a quarter note, length 1 is a triplet + self.quarterNum: int = 0 + # 3: ###, -4: bbbb + self.keySigature: int = 0 + self.timeSignature: Tuple[int,int] = [4,4] + + + def setCurrentInstrument(self, insName: str): + self.currentInstrument = insName + self.currPart.partName = insName + + def getClef(self): + return self.currClef + def setClef(self, clefNum: int): + match clefNum: + case 4: self.currClef = clef.TrebleClef() + case 13: self.currClef = clef.AltoClef() + case 12: self.currClef = clef.TenorClef() + case 22: self.currClef = clef.BassClef() + case _: self.currClef = clef.TrebleClef() + + def getCurrPart(self): + return self.currPart + + def getKeySignature(self): + return self.keySigature + def setKeySignature(self, ks:int): + self.keySigature = ks + + def setQuarterNum(self, qn:int): + self.quarterNum = qn + def getQuarterNum(self): + return self.quarterNum + + def setTimeSignature(self, ts: Tuple[int,int]): + if ts[1] == 1 and ts[0] == 1: + self.timeSignature = [4,4] + elif ts[1] == 0 and ts[0] == 0: + self.timeSignature = [2,2] + elif ts[1]*ts[0] != 0: + self.timeSignature = ts + def getTimeSignatureString(self): + ts = self.timeSignature + return f'{ts[0]}/{ts[1]}' + def updateParserState(self, line: str): + # line is something like '$ K:0 Q:12 T:1/1 C:4 D:Allegro (\\0& = 84)' + # parse keySignature + ksMatch = re.search(r"K:(\d+)", line) + if ksMatch: + ks = int(ksMatch.group(1)) + self.setKeySignature(ks) + printtt(f"setting key signature to {ks}") + + # parse quarter Num + qnMatch = re.search(r"Q:(\d+)", line) + if qnMatch: + qn = int(qnMatch.group(1)) + self.setQuarterNum(qn) + printtt(f"setting quarter number to {qn}") + + tsMatch = re.search(r"T:(\d+)/(\d+)", line) + if tsMatch: + num = int(tsMatch.group(1)) + den = int(tsMatch.group(2)) + self.setTimeSignature([num, den]) + printtt(f"setting time signature to {num}/{den}") + + clfMatch = re.search(r"C:(\d+)", line) + if clfMatch: + clef_num = int(clfMatch.group(1)) + self.setClef(clef_num) + printtt(f"setting clef to {clef_num}") + + self.isDirty = True + + + def addMeasureToPart(self, measure: stream.Measure): + self.currPart.append(measure) + + def getIsDirty(self): + return self.isDirty + def setDirtyFalse(self): + self.isDirty = False + + +class RestNGObj: + def __init__(self, pitchs: List[str], dur: int): + self.pitchs = pitchs + self.dur = dur + def decode(self, div: int): + onlyNote = [p.replace('f','-') for p in self.pitchs if p != 'rest'] + ql = self.dur/div + if ql == 0: + print() + if len(onlyNote) == 0: + return note.Rest(quarterLength=ql) + elif len(onlyNote) == 0: + return note.Note(onlyNote[0], quarterLength=ql) + else: + return chord.Chord(onlyNote, quarterLength=ql) + +def parseMeasure(msStringLst: List[str], parserState: ParserState, currentMeasureNumber: int): + m: stream.Measure = stream.Measure(number=currentMeasureNumber) + msDict = dict() + currOnset = 0 + prevOnset = 0 + prevLen = 0 + for msString in msStringLst: + ky = msString[0] + match ky: + case 'A'|'B'|'C'|'D'|'E'|'F'|'G'|'r': + noteStr = msString[0:4].strip() + dur = int(msString[5:8].strip()) + dictKy = f'{currOnset},{dur}' + if msDict.get(dictKy): + msDict[dictKy].append(noteStr) + else: + msDict[dictKy] = [noteStr] + prevOnset = currOnset + prevLen = dur + currOnset += dur + case ' ': + # chord + dur = int(msString[5:8].strip()) + dictKy = f'{prevOnset},{prevLen}' + noteStr = msString[1:5].strip() + msDict[dictKy].append(noteStr) + case 'b': + # going back + dur = int(msString[5:8]) + currOnset -= dur + case 'i': + dur = int(msString[5:8]) + currOnset += dur + # invisible rest, ignore + # rest + resLst = [] + while len(msDict) > 0: + currTrk = [] + prevEnd = 0 + keys_to_delete = [] + consumed_any = False + for dictKy in list(msDict): # iterate over a snapshot of keys + match = re.search(r"(\d+),(\d+)", dictKy) + if not match: + keys_to_delete.append(dictKy) + continue + onset = int(match.group(1)) + dur = int(match.group(2)) + if onset == prevEnd: + currTrk.append(RestNGObj(msDict[dictKy], dur)) + keys_to_delete.append(dictKy) + prevEnd += dur + consumed_any = True + elif onset > prevEnd: + currTrk.append(RestNGObj(['rest'], onset - prevEnd)) + currTrk.append(RestNGObj(msDict[dictKy], dur)) + keys_to_delete.append(dictKy) + prevEnd = onset + dur + consumed_any = True + for k in keys_to_delete: + del msDict[k] + resLst.append(currTrk) + if not consumed_any: # nothing was consumed → infinite loop guard + printtt(f"nothing updated in voice, left: {msDict}") + break + if parserState.getIsDirty(): + m.append(parserState.getClef()) + m.append(key.KeySignature(parserState.getKeySignature())) + m.append(meter.TimeSignature(parserState.getTimeSignatureString())) + parserState.setDirtyFalse() + # Ef3: flat F#3: sharp + if len(resLst) == 1: + # only one track + for r in resLst[0]: + m.append(r.decode(parserState.getQuarterNum())) + return m + elif len(resLst) > 1: + for i in range(len(resLst)): + v = stream.Voice() + for r in resLst[i]: + v.append(r.decode(parserState.getQuarterNum())) + m.insert(0, v) + return m + else: + printtt("issue processing measure") + return m + + + +def numberToString(num: int, strLen: int = 6): + stringNum = str(num) + strAppend = strLen-len(stringNum) + return ' '*strAppend + stringNum + + + +if __name__ == '__main__': + md2Paths = [rf"C:\Ellie\APIs\25-omr\md_gt\{j}_{i}.md2" for i in range(1,5) for j in [1,2,3,4,6,7,8]] + md2Paths.append(rf"C:\Ellie\APIs\25-omr\md_gt\6_5.md2") + for md2Path in md2Paths: + score = stream.Score() + allTrackStringsDict = dict() + allStrings = [] + with open(md2Path, "r", encoding="utf-8") as f: + for line_num, lineOrig in enumerate(f, start=1): + line = lineOrig.rstrip("\n") + allStrings.append(line) + parserState = ParserState() + skipUntilLine = -1 + isComment = False + measureAccumList = [] + currentMeasureNumber = 1 + inMeasureTitle = ['A','B','C','D','E','F','G',' ','b','i','r'] + ignoreList = ['S','*','g','f','c'] + for (line_num, line) in enumerate(allStrings): + fmtLineNum = numberToString(line_num+1) + if (line_num < skipUntilLine + or len(line) < 1 + or line[0] == '@' # single line comments + or line[0] == 'P' # print suggestion record + or (isComment and line[0] != '&') + ): + printt(f"{fmtLineNum}:") + continue + line = line.rstrip("\n") + if line.startswith("/END"): + score.append(parserState.getCurrPart()) + parserState.reset() + continue + elif line.startswith("&&&&&&"): + skipUntilLine = line_num + 16 + currentMeasureNumber = 1 + parserState.setCurrentInstrument(allStrings[line_num+11]) + printt(f"{fmtLineNum}:&&&&& start of score") + continue + elif line[0] == '&': + isComment = not isComment + printt(f"{fmtLineNum}: toggle comment to {isComment}") + continue + elif line[0] == '$': + printt(f"{fmtLineNum}: setting state {line}") + parserState.updateParserState(line) + continue + elif line[0] in inMeasureTitle: + printt(f"{fmtLineNum}: adding {line[0]} to measure") + measureAccumList.append(line) + continue + elif line[0] == 'm': + # it's the end of the current measure + newMeasure = parseMeasure(measureAccumList, parserState, currentMeasureNumber) + parserState.addMeasureToPart(newMeasure) + measureAccumList = [] + printt(f"{fmtLineNum}: m {currentMeasureNumber}") + currentMeasureNumber += 1 + continue + elif line[0] in ignoreList: + printt(f"{fmtLineNum}: {line[0]}") + continue + elif line.startswith('/'): + printt(f"{fmtLineNum}: {line}") + continue + exporter = ScoreExporter(score) + xmlStr = exporter.parse() + xmlBytes = tostring(xmlStr, encoding='unicode') + with open(md2Path.replace('.md2','.musicxml'), 'w', encoding='utf-8') as f: + f.write(xmlBytes) + print(f"finish parsing {md2Path}") + print() + + diff --git a/omr/inference.py b/omr/inference.py index 4f43e2c..eb1e169 100644 --- a/omr/inference.py +++ b/omr/inference.py @@ -35,6 +35,8 @@ def inference( manual_th: Optional[Any] = None, use_tf: bool = False ) -> Tuple[ndarray, ndarray]: + import gc + gc.collect() if use_tf: import tensorflow as tf @@ -83,7 +85,9 @@ def inference( batch = np.array(data[idx:idx+batch_size]) out = model.predict(batch) if use_tf else sess.run(output_names, {'input': batch})[0] pred.append(out) - + del sess + import gc + gc.collect() # Merge prediction patches output_shape = image.shape[:2] + (output_shape[-1],) out = np.zeros(output_shape, dtype=np.float32) diff --git a/omr/part2.py b/omr/part2.py index 5fc6c78..8f5e09d 100644 --- a/omr/part2.py +++ b/omr/part2.py @@ -360,10 +360,14 @@ def extract(image, output_dir): return staffList -def runModel2(npy_path: str, img_path: str, output_dir: str): +def runModel2(npy_path: str, img_path: str, output_dir: str, packedImg: bool = False): # output_dir will be images/tch/tch dataDict = np.load(npy_path,allow_pickle=True) dataDict = dataDict.tolist() + if packedImg: + for idx, ky in enumerate(list(dataDict.keys())): + img = dataDict[ky] + dataDict[ky] = cv2.resize(img, None, fx=2, fy=2, interpolation=cv2.INTER_NEAREST) image = loadModel1(img_path, dataDict) staffList = extract(image, output_dir) return staffList diff --git a/omr/staffline_extraction.py b/omr/staffline_extraction.py index 3082206..2c6282e 100644 --- a/omr/staffline_extraction.py +++ b/omr/staffline_extraction.py @@ -294,6 +294,10 @@ def init_zones(staff_pred: ndarray, splits: int) -> Tuple[ndarray, int, int, int accum_x = np.sum(staff_pred, axis=0) accum_x = accum_x / np.mean(accum_x) half = round(len(accum_x) / 2) + if xs.size == 0: + xs = np.append(xs, 0) + if ys.size == 0: + ys = np.append(ys, 0) right_bound = min(max(xs) + 50, staff_pred.shape[1]) left_bound = max(min(xs) - 50, 0) for i in range(half+10, len(accum_x)): diff --git a/orch_dataset/Tchai_4.pdf b/orch_dataset/Tchai_4.pdf new file mode 100644 index 0000000..628034d Binary files /dev/null and b/orch_dataset/Tchai_4.pdf differ diff --git a/pdf2Dataset.py b/pdf2Dataset.py new file mode 100644 index 0000000..14cb022 --- /dev/null +++ b/pdf2Dataset.py @@ -0,0 +1,103 @@ + + +import os +os.environ["OMP_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" + +from utils import * +from png2decode import png2decode +from pdf2png import savePdf2Png +import glob + +if __name__ == '__main__': + + pieceName = 'dvo' + if not os.path.exists(f'data_dataset/{pieceName}/imgs/{pieceName}_1/{pieceName}_1.png'): + savePdf2Png(f'data_dataset/{pieceName}', 1, False, dpi=200) + allImgPath = glob.glob(f'data_dataset/{pieceName}/imgs/{pieceName}*/{pieceName}*.png') + + for idx in range(len(allImgPath)): + if os.path.exists(f'data_dataset/{pieceName}/debug/debug_{idx+1}.txt'): + print(f"skipping page {idx+1}") + continue + imgPath = f'data_dataset/{pieceName}/imgs/{pieceName}_{idx+1}/{pieceName}_{idx+1}.png' + noteGroupMap: np.ndarray + stemIdxMap: np.ndarray + noteGroupVerticallyMerged: List[NoteGroup | None] + restMap: np.ndarray + restList: List[Rest | None] + sfnClefMap: np.ndarray + sfnClefList: List[Union[Accidentals, Clef, None]] + beamMapImg: np.ndarray + staffList: List[Staff] + imgName = os.path.basename(imgPath).split('.')[0] + + try: + ( + noteGroupMap, + stemIdxMap, + noteGroupVerticallyMerged, + restMap, + restList, + sfnClefMap, + sfnClefList, + beamMapImg, + staffList, + dataDict + ) = png2decode(imgName, imgPath) + + + # sfn_colors = [(255,255,0),(255,0,125),(255,0,255),(255,0,0),(0,255,0),(0,0,255)] + # sfn_names = ['flat', 'natural', 'sharp','BassF', 'ViolaC', 'trebleG'] + clef_colors = [(0,255,0),(0,0,255),(255,0,0)] + clef_yolo = [0,1,2] # 0: alto, 1: treble, 2: bass + sfn_colors = [(255,0,125),(255,0,255),(255,255,0)] + sfn_yolo = [3,4,5] # 3: natural, 4: sharp, 5: flat + rest_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40)] + rest_yolo = [6,7,8,9,10] # 6: 1/4, 7: 1/8, 8: 1/16, 9:1/32, 10: half or whole + knn_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40), (245, 220, 255),(230,130,175),(165,170, 70)] + knn_yolo = [11,12,13,14,15,16,17,18] # 11: 1/4, 12: 1/8, 13: 1/16, 14: 1/32, 15: 1/64, 16:1/128, 17: 1/2, 18: whole + knn_stem = [19,20,21,22,23,24,25,26] # 19: 1/4's stem, 20: 1/8 ... etc. + img = dataDict['image'].copy() + labelList = [] + h, w = img.shape[:2] + def add_yolo_label(class_id, x0, y0, x1, y1): + x_center = ((x0 + x1) / 2) / w + y_center = ((y0 + y1) / 2) / h + bw = (x1 - x0) / w + bh = (y1 - y0) / h + labelList.append( + f"{class_id} {x_center:.6f} {y_center:.6f} {bw:.6f} {bh:.6f}" + ) + + for rest in restList: + if rest is not None: + x0, y0, x1, y1 = rest.boundingBox + img = cv2.rectangle(img, (x0,y0), (x1,y1), rest_colors[rest.rhythm], 3, cv2.LINE_AA) + add_yolo_label(rest_yolo[rest.rhythm], x0, y0, x1, y1) + for ng in noteGroupVerticallyMerged: + if ng is not None: + for stem in ng.noteStemList: + x0,y0,x1,y1 = stem.noteBox + add_yolo_label(knn_yolo[stem.rhythm], x0, y0, x1, y1) + img = cv2.rectangle(img, (x0,y0), (x1,y1), knn_colors[stem.rhythm], 2, cv2.LINE_AA) + add_yolo_label(knn_stem[stem.rhythm], stem.getX()-3, stem.getTopCoord()[1], stem.getX()+3, stem.getBottomCoord()[1]) + img = cv2.rectangle(img, (stem.getX()-3, stem.getTopCoord()[1]), (stem.getX()+3, stem.getBottomCoord()[1]), knn_colors[stem.rhythm], 2, cv2.LINE_AA) + for sfn in sfnClefList: + if type(sfn) == Accidentals: + x0,y0,x1,y1 = sfn.getBbox() + add_yolo_label(sfn_yolo[sfn.getValue()], x0, y0, x1, y1) + img = cv2.rectangle(img, (x0,y0), (x1,y1), sfn_colors[sfn.getValue()], 1, cv2.LINE_AA) + elif type(sfn) == Clef: + x0,y0,x1,y1 = sfn.getBbox() + add_yolo_label(clef_yolo[sfn.getValue()], x0, y0, x1, y1) + img = cv2.rectangle(img, (x0,y0), (x1,y1), clef_colors[sfn.getValue()], 1, cv2.LINE_AA) + + if not os.path.exists(f'data_dataset/{pieceName}/debug'): + os.mkdir(f'data_dataset/{pieceName}/debug') + cv2.imwrite(f'data_dataset/{pieceName}/debug/debug_{idx+1}.jpg',img) + with open(f'data_dataset/{pieceName}/debug/debug_{idx+1}.txt', "w") as f: + f.write("\n".join(labelList)) + print() + except: + print(f"error processing, skipping {idx+1}") \ No newline at end of file diff --git a/pdf2musicXML.py b/pdf2musicXML.py index feab82b..728a86e 100644 --- a/pdf2musicXML.py +++ b/pdf2musicXML.py @@ -2086,6 +2086,7 @@ def assignRestDots(restList:List[Rest], beamMapImg:np.ndarray, noteGroupStemMap: rs.hasdot = True imwrite('dotRestBox.jpg',imgbgr) return restList,{'dotRestBox':imgbgr} + def getNoteChunks(image: np.ndarray, noteGroupMap:np.ndarray, beamMapImg:np.ndarray, noteGroupVerticallyMerged:List[NoteGroup]): imgrgb = image.copy() beamBinary = np.zeros((beamMapImg.shape[0],beamMapImg.shape[1])) @@ -3676,8 +3677,8 @@ def exportYolo(sfnClefList:List[Accidentals|Clef], image:np.ndarray,img_name:str beamMapImg,noteGroupMap = extendNotegroupsToStaff(noteGroupMap, noteGroupVerticallyMerged,staffObjList,beamMapImg) barList, barRanges, numBarsPerLine = constructBar(noteGroupMap, stemIdxMap, noteGroupVerticallyMerged, restMap,restList,sfnClefMap,sfnClefList,beamMapImg,staffObjList) # maskImg, tsBoxes, tsBoxesFiltered, debugImages = createMask(barList, barRanges, staffObjList, image, beamMapImg) - for si in debugImages.keys(): - outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + # for si in debugImages.keys(): + # outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) # saveBarToCsv(img_name, barList, desiredLengtdh=1) # trackNo = 1 # if img_wholename in is2: diff --git a/pdf2musicXMLorch.py b/pdf2musicXMLorch.py new file mode 100644 index 0000000..76be1d6 --- /dev/null +++ b/pdf2musicXMLorch.py @@ -0,0 +1,1566 @@ +from typing import List +import numpy as np +import pandas as pd +import csv +import os +import json +import pickle +import itertools +import statistics + +from collections import defaultdict, Counter +from music21 import stream, note, metadata, chord, meter, clef, key, instrument +from scipy import stats + +from png2decode import png2decode +from utils import * +from tune_bar import tuneBar + + +BAR_MAX_GAP = 5 # if two "lines" are within this pixel then they are considered the same barline +DEBUGIMG = None +class ScoreMetaData: + # static + instruments: List[str] = [] + + def __init__(self, pd_frame: pd.DataFrame, json_path: str, initInstruments = False): + self.jsonPath: str = json_path + self.pdFrame: pd.DataFrame = pd_frame + self.trackRange: List[Tuple[int,int]] = [] + self.groupingRange: List[List[Tuple[int,int]]] = [] + for i in range(1,4): + df[f"combined{i}"] = ( + df[f"ins{i}"] + + df[f"part{i}"].apply(lambda x: "" if pd.isna(x) else '__'+str(int(x))) + + df[f"tone{i}"].apply(lambda x: "" if pd.isna(x) else ':'+x) + ) # 'clarinet_1:B flat' + # special case for timpani + mask = df["ins1"] == "timpani" + df.loc[mask, "combined1"] = "timpani" + df.loc[mask, ["combined2", "combined3"]] = np.nan + if initInstruments: + df_filtered = df[['combined1', 'combined2','combined3']][df['system'] == 1] + instrument_list = [str(x) for x in df_filtered.to_numpy().flatten() if pd.notna(x)] + self.set_instruments(instrument_list) + with open(self.jsonPath, "w", encoding="utf-8") as f: + json.dump(instrument_list, f, ensure_ascii=False, indent=2) + filteredInstrument = input("filter instrument and enter") + with open(self.jsonPath, "r", encoding="utf-8") as f: + instrument_list = json.load(f) + self.set_instruments(instrument_list) + elif os.path.exists(self.jsonPath) and self.get_instruments() == []: + with open(self.jsonPath, "r", encoding="utf-8") as f: + instrument_list = json.load(f) + self.set_instruments(instrument_list) + + + def getTrackRange(self) -> List[Tuple[int,int]]: + if len(self.trackRange)!= 0: + return self.trackRange + col = self.pdFrame["system"] + groups = col.ne(col.shift()).cumsum() + ranges = ( + self.pdFrame + .groupby(groups) + .apply(lambda g: (g.index[0], g.index[-1])) + .tolist() + ) + self.trackRange = ranges + return ranges + + def getTrackXYRange(self, staffList:List[Staff]): + ranges = self.getTrackRange() + retRangeList = [] + for r in ranges: + start = r[0] + end = r[1] # included + retRangeList.append((staffList[start].left, staffList[start].right, staffList[start].ys[0], staffList[end].ys[-1])) + return retRangeList + + def getGroupingRange(self)->List[List[Tuple[int,int]]]: + if len(self.groupingRange)!= 0: + return self.groupingRange + range1 = self.getTrackRange() + + col2 = self.pdFrame["staffgroup"] + groups2 = col2.ne(col2.shift()).cumsum() + ranges2 = ( + self.pdFrame + .groupby(groups2) + .apply(lambda g: (g.index[0], g.index[-1])) + .tolist() + ) + rangeList = [] + currentIdx = 0 + for r in range1: + rangeInLine = [] + while currentIdx < len(ranges2) and ranges2[currentIdx][1] <= r[1]: + rangeInLine.append(ranges2[currentIdx]) + currentIdx += 1 + rangeList.append(rangeInLine) + self.groupingRange = rangeList + return rangeList + def getInstrumentEachTrack(self) -> List[List[str]]: + return self.pdFrame[['combined1', 'combined2', 'combined3']].apply( + lambda row: row.dropna().tolist(), + axis=1 + ).tolist() + def getInstrumentIndexEachTrack(self) -> List[List[int]]: + strList = self.getInstrumentEachTrack() + insList = self.get_instruments() + return [[insList.index(r) for r in row] for row in strList] + + # get the number of track that line belongs to + def getTrackForLineIdx(self, lineIdx: int) -> int: + for i, (start, end) in enumerate(self.trackRange): + if start <= lineIdx <= end: + return i + return -1 + + # get the line number of that line inside that track. + # let's say [3,6] are the 2nd group (inclusive), then + # fn(3) -> (1,0), 2nd group(count starts at 0), index 0 + # fn(6) -> (1,3), 2nd group, index 3 + def getTrackIdxForLineIdx(self, lineIdx: int) -> Tuple[int,int]: + for i, (start, end) in enumerate(self.trackRange): + if start <= lineIdx <= end: + return (i, lineIdx - start) + return (-1,-1) + + + # static + @classmethod + def set_instruments(cls, instrumentList: List[str]): + cls.instruments = instrumentList + + + @classmethod + def add_instrument(cls, instrument: str): + cls.instruments.append(instrument) + + @classmethod + def get_instruments(cls) -> List[str]: + return cls.instruments.copy() + +def parseCsvData(csv_path: str): + with open(csv_path) as f: + csvData = list(csv.reader(f)) + return csvData + +def findBarPosition(staffImgBinary: np.ndarray, tolerance, row_ratio:float = 0.7): + kernel = np.ones((1, 2*tolerance+1), dtype=np.uint8) + expanded = cv2.dilate(staffImgBinary.astype(np.uint8), kernel) + acc = np.sum(expanded, axis=0) + threshold = staffImgBinary.shape[0] * row_ratio + barPos = np.where(acc > threshold)[0] + return barPos + +def getBarsEachTrack(image:np.ndarray, beamMapImg:np.ndarray, staffList:List[Staff], tolerance:int = 20): + img = image.copy() + staffCenters = [sf.ys[2] for sf in staffList] + _,itemMap,_ = cv2.split(beamMapImg) + staffImgBinary = (itemMap == 4)[staffCenters,:] + barEachTrack = [] + for trkRange in pageMetadata.getTrackRange(): + staffStart = staffList[trkRange[0]].ys[0] + staffEnd = staffList[trkRange[1]].ys[-1] + barPos = findBarPosition(staffImgBinary[trkRange[0]:trkRange[1],:], tolerance) + img[staffStart:staffEnd, barPos] = (0,0,255) + barEachTrack += [barPos.tolist()] + imwrite(f"barForTracks{tolerance}.jpg", img) + return barEachTrack + + +def getAllObjectInEachLine( + noteGroupMap: np.ndarray, + noteGroupVerticallyMerged: List[NoteGroup | None], + restMap: np.ndarray, + restList: List[Rest | None], + sfnClefMap: np.ndarray, + sfnClefList: List[Union[Accidentals, Clef, None]], + beamMapImg: np.ndarray, + staffList: List[Staff] + ): + _, objMap, _ = cv2.split(beamMapImg) + mapForMatching:List[np.ndarray|None] = [None,noteGroupMap,sfnClefMap, sfnClefMap, None, restMap] + listForMatching:List[List] = [None, noteGroupVerticallyMerged,sfnClefList, sfnClefList, None, restList] + allItems = [] + for sf in staffList: + allItemInLine = [] + currX = sf.left + while currX < sf.right: + lineLst = np.unique(objMap[sf.ys[0]-sf.get_yOne():sf.ys[-1]+sf.get_yOne(), currX]).tolist() + if 0 in lineLst: + lineLst.remove(0) + if len(lineLst)>1: + for i in [1,5,3,2]: + if i in lineLst: + lineLst = [i] + break + if len(lineLst) == 0 or 4 in lineLst: + currX += 1 + else: + typeId = lineLst[0] + currMap = mapForMatching[typeId] + currLst = listForMatching[typeId] + inMapId = np.unique(currMap[sf.ys[0]-sf.get_yOne():sf.ys[-1]+sf.get_yOne(), currX]).tolist() + if 0 in inMapId: + inMapId.remove(0) + if (len(inMapId) == 1): + currentElementId = inMapId[0] + allItemInLine.append(currLst[currentElementId]) + currX = max(currX, currLst[currentElementId].boundingBox[2])+1 + else: + currX += 1 + allItems.append(allItemInLine) + return allItems + +def constructBar(noteGroupMap:np.ndarray, + noteGroupVerticallyMerged: List[NoteGroup|None], + restMap: np.ndarray, + restList:List[Rest|None], + sfnClefMap:np.ndarray, + sfnClefList:List[Union[Accidentals,Clef, None]], + beamMapImg:np.ndarray, + staffList:List[Staff], + pageMetadata:ScoreMetaData, + barEachTrack:List[List[int]], + barChangeList:dict[str, tuple[int, int]] #(track, num),(TStop, TSbottom) + ): + numLines = len(staffList) + bb,gg,rr = cv2.split(beamMapImg) + barList:List[List[Bar]] = [[] for _ in range(numLines)] + numBarsEachLine:List[int] = [0 for _ in range(numLines)] + mapForMatching:List[np.ndarray|None] = [None,noteGroupMap,sfnClefMap, sfnClefMap, None, restMap] + listForMatching:List[List] = [None, noteGroupVerticallyMerged,sfnClefList, sfnClefList, None, restList] + notFinishedBar = None + allRanges = [] + for barThisTrack in barEachTrack: + prevIdx = 0 + rangeThisTrack = [] + if len(barThisTrack) > 1: + while prevIdx < len(barThisTrack)-1: + if barThisTrack[prevIdx+1] - barThisTrack[prevIdx] > BAR_MAX_GAP: + rangeThisTrack.append((barThisTrack[prevIdx], barThisTrack[prevIdx+1])) + prevIdx += 1 + allRanges.append(rangeThisTrack) + + currTS = barChangeList.get('0,0') + for t in range(numLines): # trackNo + printedLineNo = t # the current line number + currBarList:List[Bar] = [] + sf0 = staffList[printedLineNo] + trackForThatLine = pageMetadata.getTrackForLineIdx(t) + ranges = allRanges[trackForThatLine] + isBar = barEachTrack[trackForThatLine] + + for ridx, rng in enumerate(ranges): + if barChangeList.get(f'{trackForThatLine},{ridx}'): + currTS = barChangeList.get(f'{trackForThatLine},{ridx}') + if currTS != [9,8]: + print(f'ridx: {ridx}, rng: {rng}') + if notFinishedBar is not None: + bar = notFinishedBar + notFinishedBar = None + else: + bar = Bar(currTS) + currX = rng[0] + while currX1: + for i in [1,5,3,2]: # noteGroup(1), rest(5), clef(3), accidentals(2) + if i in lineLst: + lineLst = [i] + break + if len(lineLst)==0 or 4 in lineLst: + currX+=1 + else: + typeId = lineLst[0] + currMap = mapForMatching[typeId] + currLst = listForMatching[typeId] + inMapId = np.unique(currMap[sf0.ys[0]-sf0.get_yOne():sf0.ys[-1]+sf0.get_yOne(), currX]).tolist() + if 0 in inMapId: + inMapId.remove(0) + if len(inMapId) > 1 and typeId == 1: # if it's multiple notegroups in that line + # get the noteGroup that's closer to the center of the staff + yCenter = sf0.ys[2] + newInMapIdIdx = -1 + currSmallestDist = np.inf + for idid in range(0,len(inMapId)): + x0,y0,x1,y1 = currLst[inMapId[idid]].boundingBox + if abs((y1+y0)/2-yCenter) < currSmallestDist: + currSmallestDist = abs((y1+y0)/2-yCenter) + newInMapIdIdx = idid + inMapId = [inMapId[newInMapIdIdx]] + if len(inMapId)==1: + sanityCheck = True + if typeId == 3: # clef + currClef:Clef = sfnClefList[inMapId[0]] + _,yy0,_,yy1 = currClef.boundingBox + if yy1-yy0 < sf0.get_yOne_float()*3: + sanityCheck = False + if currClef.type ==0: + if yy00)[1] + # nextIdx = 0 + # if len(notfinishedBarStuff) > 0: + # nextIdx = np.max(np.unique(np.where(gg[sf0.ys[0]-sf0.get_yOne():sf0.ys[-1]+sf0.get_yOne(), rng[1]:]>0)[1])) + # if nextIdx>5: + # rng = (isBar[-1]+sf0.left+1, rng[1]+nextIdx) + # bar = Bar(currTS) + # currX = rng[0] + # while currX1: + # for i in [1,5,3,2]: + # if i in lineLst: + # lineLst = [i] + # break + # if len(lineLst)==0 or 4 in lineLst: + # currX+=1 + # else: + # typeId = lineLst[0] + # currMap = mapForMatching[typeId] + # currLst = listForMatching[typeId] + # inMapId = np.unique(currMap[sf0.ys[0]-sf0.get_yOne():sf0.ys[-1]+sf0.get_yOne(), currX]).tolist() + # if 0 in inMapId: + # inMapId.remove(0) + # if len(inMapId)==1: + # bar.addElement(currLst[inMapId[0]]) + # currX = currLst[inMapId[0]].boundingBox[2]+1 + # else: + # currX+=1 + # print("has more than 1 id") + # notFinishedBar = bar + barList[t] = currBarList + numBarsEachLine[t] = len(currBarList) + # in order of vln1's 1st line, 2nd line ... | vln2's 1st line, 2nd line ... + return barList,allRanges, numBarsEachLine + +# TODO: rotate the image at here? as long as all maps are rotated it should be fine +def stackItemVertically(image:np.ndarray, + barList: List[List[Bar]], + pageMetadata: ScoreMetaData, + staffList:List[Staff], + noteGroupVerticallyMerged: List[NoteGroup], + restList: List[Rest]): + noteRestMap = np.zeros((image.shape[0], image.shape[1]), dtype=np.int16) + for oneLine in barList: + for oneBar in oneLine: + for elem in oneBar.elementList: + if type(elem) == NoteGroup: + x0,y0,x1,y1 = elem.boundingBox + noteRestMap = cv2.rectangle(noteRestMap, (x0,y0),(x1,y1),elem.indexNumber, -1) + elif type(elem) == Rest: + x0,y0,x1,y1 = elem.boundingBox + if elem.rhythm != -1: + noteRestMap = cv2.rectangle(noteRestMap, (x0,y0),(x1,y1),-elem.indexNumber, -1) + img2 = image.copy() + img2[noteRestMap>0] = (0,0,255) + img2[noteRestMap<0] = (255,255,0) + imwrite("finalNoteRest.jpg", img2) + retImg = image.copy() + oneLineDist = staffList[0].get_yOne() + acrossLineNGID = 1 + for (xStart, xEnd, yStart, yEnd) in pageMetadata.getTrackXYRange(staffList): + currX = xStart + while currX < xEnd: + inLineIds = np.unique(noteRestMap[yStart:yEnd, currX]).tolist() + if inLineIds == [0]: + currX += 1 + continue + allElementLists:List[NoteGroup|Rest] = [] + allXCenters = [currX] + allXEnds = [currX] + for inLineId in inLineIds: + if inLineId == 0: + continue + if inLineId > 0: # note + currElem = noteGroupVerticallyMerged[inLineId] + elif inLineId < 0: # rest + currElem = restList[-inLineId] + allElementLists.append(currElem) + if currElem.boundingBox[2]-currElem.boundingBox[1] <= oneLineDist*2: + allXCenters.append((currElem.boundingBox[0]+currElem.boundingBox[2])//2) + allXEnds.append(currElem.boundingBox[2]) + xCenterAvg = sum(allXCenters)//len(allXCenters) + newInLineIdx = np.unique(noteRestMap[yStart:yEnd, xCenterAvg]).tolist() + if set(inLineIds) == set(newInLineIdx): + # current Center is the center of this whole noteGroup thingy + # !!! I'm gonna draw on the image for now but other things have to be done here + # cv2.rectangle(sfnClefImg, (x0,y0),(x1,y1), class_colors2[predict_idx],2,cv2.LINE_AA) + retImg = cv2.rectangle(retImg, (xCenterAvg, yStart), (xCenterAvg, yEnd), (255,100,0), 3, cv2.LINE_AA) + for elem in allElementLists: + x0,y0,x1,y1 = elem.boundingBox + retImg = cv2.rectangle(retImg, (x0,y0), (x1,y1), (255,255,0), -1, cv2.LINE_AA) + noteRestMap = cv2.rectangle(noteRestMap, (x0,y0), (x1,y1), 0, -1, cv2.LINE_AA) + elem.setAcrossLineNGID(acrossLineNGID) + acrossLineNGID += 1 + currX = max(max(allXEnds), currX)+1 + else: + currX = max(xCenterAvg+1, currX+1) + imwrite("stackingNotes.jpg", retImg) + return noteRestMap, retImg + +# originally we pass in a list of each track [vln1's bars: [bar1, bar2 ...], vln2's bars: [bar1, bar2 ...]] +# now we have each line differently +# return the breakPoint (aka which bar is the starting of next line) +def tuneBarList(barList:List[List[Bar]], numBarsPerTrackLine:List[int], ): + trackRange = pageMetadata.getTrackRange() + + for lineRangeForTrack in trackRange: + startIdx = lineRangeForTrack[0] + endIdx = lineRangeForTrack[1]+1 + emptyBarsIndexs = np.where(np.sum(np.array([[len(b.elementList) for b in oneTrack] for oneTrack in barList[startIdx:endIdx]]),axis=0)==0)[0].tolist() + emptyBarsIndexs.reverse() + for emptyBarsIndex in emptyBarsIndexs: + for i in range(startIdx, endIdx): + del barList[i][emptyBarsIndex] + qq = 0 + while emptyBarsIndex+1>sum(numBarsPerTrackLine[0:qq]): + qq+=1 + numBarsPerTrackLine[qq-1]-=1 + + # delete all elements that's ornament + for trackId, oneTrack in enumerate(barList): + for barId, bar in enumerate(oneTrack): + bar.elementList = [ + elem for elem in bar.elementList + if not (isinstance(elem, NoteGroup) and all(s.isOrnament for s in elem.noteStemList)) + ] + # now all bars are suppose to be valid + for trackId, oneTrack in enumerate(barList): + for barId, bar in enumerate(oneTrack): + origLengths = bar.getRhythmList() + newLengths = [None]*len(origLengths) + if bar.getTotalBeat() == Fraction(bar.ts[0],bar.ts[1]): + newLengths = [None]*len(origLengths) + elif bar.getTotalBeat() == 0: + continue # will be later added a rest + else: + newLengths = tuneBar(bar.getRestNg(), bar.ts) + reassignedLengths = [origLengths[i] if newLengths[i] is None else newLengths[i] for i in range(len(newLengths))] + bar.reassignLength(origLengths, newLengths) + if bar.getTunedTotalBeat() != Fraction(bar.ts[0], bar.ts[1]): + print("bar doesn't have right length") + + for lineii, oneTrack in enumerate(barList): + for barii, bb in enumerate(oneTrack): + if bb.getTunedTotalBeat() != Fraction(bb.ts[0],bb.ts[1]): + print(f"bar at {lineii},{barii} has total beat {bb.getTotalBeat()}") + barBreakPoints = [0] + list(itertools.accumulate(numBarsPerTrackLine)) + return barBreakPoints + +def assignBarlistInstrument(barList:List[List[Bar]], pageMetadata:ScoreMetaData): + def getEmptyBarDefaultList(barTrack: List[Bar]): + return [Bar(b.ts) for b in barTrack] + def build_grouped_lookup(instrument_list): + grouped = defaultdict(list) + for item in instrument_list: + if ":" in item: + base, suffix = item.split(":", 1) + key_suffix = ":" + suffix + else: + base = item + key_suffix = "" + base_name = base.split("__")[0] + key = base_name + key_suffix + grouped[key].append(item) + return dict(grouped) + + def build_instrument_lookup_no_section(instrument_list): + grouped = defaultdict(list) + for item in instrument_list: + base = item.split(":", 1)[0] + base_name = base.split("__")[0] + grouped[base_name].append(item) + return dict(grouped) + def getInstruments(instrumentLookup: dict, instrumentLookupNoSec: dict, ins: str): + if instrumentLookup.get(ins): + return instrumentLookup.get(ins) + if instrumentLookupNoSec.get(ins): + return instrumentLookupNoSec.get(ins) + return [ins] + instrumentTrack = pageMetadata.getInstrumentEachTrack() + trackRange = pageMetadata.getTrackRange() + retBarList = dict() + numBarsTotal = 0 + for trackLineIdx, currTrackRange in enumerate(trackRange): + startIdx = currTrackRange[0] + endIdx = currTrackRange[1]+1 + instrumentList = ScoreMetaData.get_instruments() + if trackLineIdx == 0: + for ins in instrumentList: + retBarList[ins] = [] + instrumentLookup = build_grouped_lookup(instrumentList) + instrumentLookupNoSec = build_instrument_lookup_no_section(instrumentList) + numBarsTotal += len(barList[startIdx]) + for i in range(startIdx, endIdx): + instrumentInThisLineList = [getInstruments(instrumentLookup,instrumentLookupNoSec,ins) for ins in instrumentTrack[i]] + instrumentInThisLine = [item for sublist in instrumentInThisLineList for item in sublist] + print(f"instrument in this line: {instrumentInThisLine}") + for ins in instrumentInThisLine: + if not ins in instrumentList: + print(f'WARNING: instrument {ins} not in list') + continue + if trackLineIdx == 0: + retBarList[ins] = list(barList[i]) + else: + if len(retBarList[ins]) == numBarsTotal: + print(f"double instrument: {ins}") + N = len(barList[i]) + retBarList[ins][-N:] = barList[i] + else: + retBarList[ins] += barList[i] + if ins in instrumentList: + instrumentList.remove(ins) + else: + print(f"instrument {ins} not in list") + for resIns in instrumentList: + if trackLineIdx == 0: + retBarList[resIns] = getEmptyBarDefaultList(barList[startIdx]) + else: + retBarList[resIns] += getEmptyBarDefaultList(barList[startIdx]) + assert len(np.unique([len(retBarList[b]) for b in retBarList])) == 1 + barListPerInstrument = [retBarList[k] for k in list(retBarList.keys())] + instrumentEachLine = list(retBarList.keys()) + return retBarList, barListPerInstrument, instrumentEachLine + +# return (1) ksMat for each track (exactly as how it's presented visually), metric of [number of lines, bars] +# (2) the keySignature of each track (concert pitch) as an array +def getKSMatAndKSList(pageMetadata: ScoreMetaData, barList: List[List[Bar]], toneHelper: ToneHelper): + def checkAndAssignKS( + prevIsAcc: bool, + new_elements: List[Accidentals|Clef|Rest|NoteGroup|KeySignature], + accumulatedAccidentals: List[KeySignature], + trackGroupNo: int, + lineOfThatTrackNo: int, + barNo: int, + ksBarMatForEachTrack: List[np.ndarray]): + if prevIsAcc: + newKS = KeySignature(accumulatedAccidentals) + new_elements.append(newKS) + ksBarMatForEachTrack[trackGroupNo][lineOfThatTrackNo][barNo] = newKS.getSharpFlatInt() + accumulatedAccidentals = [] + return new_elements, accumulatedAccidentals, ksBarMatForEachTrack + # return the list of concert pitch keySignature for each bar (inf: no signature) + def modeKSForAllTrack(ksMat:np.ndarray): + result = [] + prev = 0 + for col in ksMat.T: + finite_vals = col[~np.isinf(col)] # remove infs + if finite_vals.size > 0: + values, counts = np.unique(finite_vals, return_counts=True) + prev = values[np.argmax(counts)] + result.append(prev) + return np.array(result) + oneInstrumentEachLine: List[str] = [i[0] for i in pageMetadata.getInstrumentEachTrack()] + tone: List[str] = [ins.split(':')[1] if len(ins.split(':')) > 1 else '' for ins in oneInstrumentEachLine] + trackShiftEachLine = [] + for idx in range(len(oneInstrumentEachLine)): + currTone: str = tone[idx] + currIns: str = oneInstrumentEachLine[idx] + if isInstrumentIncluded(currIns): + trackShiftEachLine.append(-toneHelper.getKsShift(currTone)) + else: + trackShiftEachLine.append(-np.inf) + ksBarMatForEachTrack = [] + ksBarShiftForEachTrack = [] + trackRanges = pageMetadata.getTrackRange() + for trackRange in trackRanges: + numTrack = trackRange[1]-trackRange[0] + 1 # inclusive of the start and end + numBars = len(barList[trackRange[0]]) + ksBarMat = np.ones((numTrack, numBars))*np.inf + ksBarMatForEachTrack.append(ksBarMat) + ksBarShift = np.array(trackShiftEachLine[trackRange[0]:trackRange[1]+1])[:, None] * np.ones(numBars) + ksBarShiftForEachTrack.append(ksBarShift) + + for (lineNo, oneLine) in enumerate(barList): + # the line #lineOfThatTrackNo of the track #trackGroupNo + trackGroupNo, lineOfThatTrackNo = pageMetadata.getTrackIdxForLineIdx(lineNo) + for (barNo, oneBar) in enumerate(oneLine): + accumulatedAccidentals = [] + new_elements = [] + prevIsAcc = False + for elem in oneBar.elementList: + if isinstance(elem, Accidentals) and elem.isKeySignature: + accumulatedAccidentals.append(elem) + prevIsAcc = True + else: + new_elements, accumulatedAccidentals, ksBarMatForEachTrack = checkAndAssignKS(prevIsAcc, new_elements, accumulatedAccidentals, trackGroupNo, lineOfThatTrackNo, barNo, ksBarMatForEachTrack) + new_elements.append(elem) + prevIsAcc = False + new_elements, accumulatedAccidentals, ksBarMatForEachTrack = checkAndAssignKS(prevIsAcc, new_elements, accumulatedAccidentals, trackGroupNo, lineOfThatTrackNo, barNo, ksBarMatForEachTrack) + # assign the basic to 0 if there's no changes + if barNo == 0 and ksBarMatForEachTrack[trackGroupNo][lineOfThatTrackNo][barNo] == np.inf: + ksBarMatForEachTrack[trackGroupNo][lineOfThatTrackNo][barNo] = 0 + oneBar.elementList = new_elements + # now ksBarMatForEachTrack is exactly what we see on the score + ksEachTrack = [] + ksMatEachTrackSameAsVisual = [] + for idx in range(len(ksBarMatForEachTrack)): + ksBarMatShifted = ksBarMatForEachTrack[idx]-ksBarShiftForEachTrack[idx] + modeKSArray = modeKSForAllTrack(ksBarMatShifted) + ksEachTrack.append(modeKSArray) + ksMatEachTrackSameAsVisual.append(np.tile(modeKSArray, (ksBarMatForEachTrack[idx].shape[0],1)) + ksBarShiftForEachTrack[idx]) + return ksMatEachTrackSameAsVisual, ksEachTrack, barList +def readCsvAndEditViolin(csvPath: str): + df = pd.read_csv(csvPath) + # Ensure part1 is nullable int + if 'part1' in df.columns: + df['part1'] = df['part1'].astype('Int64') + else: + df['part1'] = pd.Series([pd.NA]*len(df), dtype="Int64") + n = len(df) + for i in range(n): + if df.at[i, 'ins1'] == 'viola': + if i - 2 >= 0: + cond = ( + pd.notna(df.at[i-2, 'ins1']) and df.at[i-2, 'ins1'] == 'violin' and pd.notna(df.at[i-2, 'part1']) and df.at[i-2, 'part1'] == 1 and + pd.notna(df.at[i-1, 'ins1']) and df.at[i-1, 'ins1'] == 'violin' and pd.notna(df.at[i-1, 'part1']) and df.at[i-1, 'part1'] == 2 + + ) + if not cond: + df.at[i-2, 'ins1'] = 'violin' + df.at[i-2, 'part1'] = 1 + df.at[i-1, 'ins1'] = 'violin' + df.at[i-1, 'part1'] = 2 + df = df.copy() + # Ensure nullable int + df['part1'] = df['part1'].astype('Int64') + n = len(df) + for i in range(n): + if pd.notna(df.at[i, 'part1']) and df.at[i, 'part1'] == 1: + # Check if next row exists and is matching part 2 + if not ( + i + 1 < n and + df.at[i+1, 'ins1'] == df.at[i, 'ins1'] and + df.at[i+1, 'part1'] == 2 + ): + df.at[i, 'part1'] = pd.NA + return df + +def exportXML(barList:List[List[Bar]], + instrumentEachLine: List[str], + toneHelper: ToneHelper, + instrument_dict: dict, + ksListAllTrack: np.ndarray, # array([-4., inf, inf, inf, inf, inf, 2, inf, inf]) + image:np.ndarray|None = None, + imgName: str = "Title"): + + def getTrackKsAndNoteShift(toneHelper: ToneHelper, instrumentList: List[str]) -> Tuple[List[int|None], List[int]]: + tone = [ins.split(':')[1] if len(ins.split(':')) > 1 else '' for ins in instrumentList] + trackKsShift = [] + trackNoteShift = [] + for idx, tn in enumerate(tone): + ksShift, noteShift = toneHelper.getKsAndNoteShift(tn) + if isInstrumentIncluded(instrumentList[idx]): + trackKsShift.append(-ksShift) + else: + trackKsShift.append(None) + trackNoteShift.append(noteShift) + return trackKsShift, trackNoteShift + numTrack = len(instrumentEachLine) + # Stem's label -> rhythm meaning + # class_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40), (245, 220, 255),(230,130,175),(165,170, 70)] + if image is None: + debugXMLImg = None + else: + debugXMLImg = image.copy() + def initializeScore(instrumentList: List[str], title): + score = stream.Score() + score.metadata = metadata.Metadata() + score.metadata.title = title + part = [] + for instr_name in instrumentList: + p = stream.Part() + # Assign instrument + instr_obj = get_instrument_from_string(instr_name) + p.insert(0, instr_obj) + p.partName = instr_name + p.partAbbreviation = instr_name + part.append(p) + return score, part + score, part = initializeScore(instrumentEachLine, imgName) + score2, part2 = initializeScore(instrumentEachLine, f"{imgName} Shifted") + keyName = ['C','D','E','F','G','A','B'] + keyCharFlat = ['C','D-','D','E-','E','F','G-','G','A-','A','B-','B'] + keyCharSharp = ['C','C#','D','D#','E','F','F#','G','G#','A','A#','B'] + currentClef = [None]*numTrack + sharps = [3,0,4,1,5,2,6] # F C G D A E B + flats = [6,2,5,1,4,0,3] # B E A D G C F + ksSharp = [set() for _ in range(numTrack)] + ksFlat = [set() for _ in range(numTrack)] + trksOriginal = [[] for _ in range (numTrack)] # for tracking all original notes we see + trksShifted = [[] for _ in range (numTrack)] # for tracking all shifted notes + + # trackKsShift will be None for Timpani, Horn and Trumpet + # # trackNoteShift works for everything except for Timpani + trackKsShift, trackNoteShift = getTrackKsAndNoteShift(toneHelper, instrumentEachLine) + + cleanedInstrumentList = [s.split('__')[0].split(':')[0] for s in instrumentEachLine] + clefOptionEachLine = [instrument_dict.get(instr, [1,0,-1,-2]) for instr in cleanedInstrumentList] + + def setKS(ks:float, trkNo): + ks = int(ks) + nonlocal ksSharp + nonlocal ksFlat + if ks>0: + ksSharp[trkNo] = [keyName[i] for i in [sharps[q] for q in range(ks)]] + ksFlat[trkNo] = set() + else: + ksFlat[trkNo] = [keyName[i] for i in [flats[q] for q in range(-ks)]] + ksSharp[trkNo] = set() + def setKSNeutral(Oriks:float, trkNo, trackShift): + Oriks = int(Oriks) + nonlocal ksSharp + nonlocal ksFlat + ks = Oriks+trackShift + if ks>0: + ksSharp[trkNo] = [keyName[i] for i in [sharps[q] for q in range(ks)]] + ksFlat[trkNo] = set() + else: + ksFlat[trkNo] = [keyName[i] for i in [flats[q] for q in range(-ks)]] + ksSharp[trkNo] = set() + def parseClefOne(elem:Clef, lineNo:int): + nonlocal currentClef + currentClef[lineNo%numTrack] = elem.type + return elem.type + def getClefFromType(clefType:int): + clf = clef.TrebleClef() + if clefType == 1: + clf = clef.TrebleClef() + elif clefType == 0: + clf = clef.AltoClef() + elif clefType == -1: #-1 + clf = clef.BassClef() + elif clefType == -2: + clf = clef.TenorClef() + return clf + def calculatePitch(inputPitch:str): + outputNum = keyCharFlat.index(inputPitch[0])+12*(int(inputPitch[-1])+1) + if len(inputPitch)==3: + if inputPitch[1] == '#': + outputNum+=1 + elif inputPitch[1] == '-' : + outputNum-=1 + return outputNum + def pitchListShift(inputPitchList:List[str], shift: int, sharp = True): # shift: how many half notes + returnList = [] + for pitch in inputPitchList: + pitchNum = calculatePitch(pitch) + pitchNum = pitchNum + shift + returnList.append(pitchNum) + referenceList = keyCharFlat + if sharp: + referenceList = keyCharSharp + for idx,pitchNo in enumerate(returnList): + clefNum = pitchNo//12-1 + pitchName = referenceList[pitchNo%12] + returnList[idx] = pitchName + str(clefNum) + return returnList + def parseNoteGroupShift(elem:NoteGroup, flatSet, sharpSet, naturalSet, currClef, flatSharp, linNo, noteValShift): + # trkShift: + # 0 if original: -2, new: -2 (two flats), + # 1 if original: -2, new: -1 + # flatSharp: how many flats/sharp, -2: two flats + # flatSharp = 2, trkShift = 0: (-2), + # flatSharp = 2, trkShift = 1: (-1) + # ksShift: + # if original key signature is -2, ksShift is 1: -> actually it's -1 (the key signature we actually hear) + keyLst = [] + currLength = 1 + regNoteCount = 0 + def setRemove(currSet, currElm): + if currElm in currSet: + currSet.remove(currElm) + return currSet + for stem in elem.noteStemList: + if not stem.isOrnament: + regNoteCount+=1 + else: + continue + actualPitch = stem.pitchSoprano + if currClef == 0: #viola + actualPitch -= 6 + elif currClef == -1: #cello + actualPitch -= 12 + elif currClef == -2: #tenor + actualPitch -= 8 + currKeyName = keyName[(actualPitch-1)%7] + currKey = currKeyName+str((actualPitch-1)//7+5) + if stem.accidentals is not None: # it has keySignature + sharpSet = setRemove(sharpSet, currKey) + naturalSet = setRemove(naturalSet, currKey) + flatSet = setRemove(flatSet, currKey) + if stem.accidentals == -1 or currKey in flatSet: + flatSet.add(currKey) + currKey = currKey[0]+'-'+currKey[1] + elif stem.accidentals == 1 or currKey in sharpSet: + sharpSet.add(currKey) + currKey = currKey[0]+'#'+currKey[1] + else: # stem.accidentals == 0: + naturalSet.add(currKey) + else: + if currKey in naturalList: + currKey = currKey + elif currKey in flatSet or currKeyName in ksFlat[linNo]: + currKey = currKey[0]+'-'+currKey[1] + elif currKey in sharpSet or currKeyName in ksSharp[linNo]: + currKey = currKey[0]+'#'+currKey[1] + # llen = float(Fraction(class_names[stem.rhythm])) + # if stem.hasdot: + # llen = llen*1.5 + # if llen0) + trksShifted[linNo].append(keyShiftedList) + if len(keyShiftedList) > 1: + return flatSet, sharpSet, chord.Chord(keyShiftedList, quarterLength = currLength*4) + elif len(keyShiftedList) == 1: + return flatSet, sharpSet, note.Note(keyShiftedList[0], quarterLength=currLength*4) + else: + return flatSet, sharpSet, None + # actualPitch, 0: B4, 1: C5, 2: D5 + + def parseNoteGroup(elem:NoteGroup, flatSet, sharpSet, naturalSet, currClef, linNo, trkShift = 0): + keyLst = [] + currLength = 1 + regNoteCount = 0 + def setRemove(currSet, currElm): + if currElm in currSet: + currSet.remove(currElm) + return currSet + for stem in elem.noteStemList: + if not stem.isOrnament: + regNoteCount+=1 + else: + continue + actualPitch = stem.pitchSoprano + if currClef == 0: #viola + actualPitch -= 6 + elif currClef == -1: #cello + actualPitch -= 12 + elif currClef == -2: #tenor + actualPitch -= 8 + currKeyName = keyName[(actualPitch-1)%7] + currKey = currKeyName+str((actualPitch-1)//7+5) + if stem.accidentals is not None: # it has keySignature + sharpSet = setRemove(sharpSet, currKey) + naturalSet = setRemove(naturalSet, currKey) + flatSet = setRemove(flatSet, currKey) + if stem.accidentals == -1: + flatSet.add(currKey) + currKey = currKey[0]+'-'+currKey[1] + elif stem.accidentals == 1 or currKey in sharpSet: + sharpSet.add(currKey) + currKey = currKey[0]+'#'+currKey[1] + else: # stem.accidentals == 0: + naturalSet.add(currKey) + else: + if currKey in naturalList: + currKey = currKey + elif currKey in flatSet or currKeyName in ksFlat[linNo]: + currKey = currKey[0]+'-'+currKey[1] + elif currKey in sharpSet or currKeyName in ksSharp[linNo]: + currKey = currKey[0]+'#'+currKey[1] + # llen = float(Fraction(class_names[stem.rhythm])) + # if stem.hasdot: + # llen = llen*1.5 + # if llen 1: + for k in keyLst: + if k[-1] == '-': + keyLst.remove(k) + keyLst.append(k[:-1]+'4') + return flatSet, sharpSet, chord.Chord(keyLst, quarterLength = currLength*4) + elif len(keyLst) == 1: + return flatSet, sharpSet, note.Note(keyLst[0], quarterLength=currLength*4) + else: + return flatSet, sharpSet, None # ornament + # actualPitch, 0: B4, 1: C5, 2: D5 + def parseRest(elem:Rest): + restLength = float(elem.tunedLength) + if restLength>0: + # restLength = float(Fraction(restClassNames[elem.rhythm])) + # if elem.hasdot: + # restLength = restLength*1.5 + return note.Rest(quarterLength=restLength*4) + else: + pass + # print("setting rest to full") + # return note.Rest(quarterLength=4) + numBars = len(barList[0]) + clefMat = np.ones((numTrack, numBars))*np.inf + currentClef = [None]*numTrack + for currBarNumber in range(numBars): + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): + if len(clefOptionEachLine[lineNo]) == 1: + clefMat[lineNo, currBarNumber] = clefOptionEachLine[lineNo][0] + else: + currBar = barList[lineNo][currBarNumber] + for elem in currBar.elementList: + if type(elem) == Clef: + newClef = parseClefOne(elem, lineNo) + if newClef in clefOptionEachLine[lineNo]: + clefMat[lineNo, currBarNumber] = newClef + if clefMat[lineNo, currBarNumber] == np.inf: + if currBarNumber == 0: + clefMat[lineNo, currBarNumber] = clefOptionEachLine[lineNo][0] + else: + clefMat[lineNo, currBarNumber] = clefMat[lineNo, currBarNumber-1] + global DEBUGIMG + ksListAllTrackNoInf = ksListAllTrack.copy() + for i in range(1, numBars): + if np.isinf(ksListAllTrackNoInf[i]): + ksListAllTrackNoInf[i] = ksListAllTrackNoInf[i - 1] + ksBarMatNeutral = np.tile(ksListAllTrackNoInf, (numTrack,1)) + ksBarMatNeutral = ksBarMatNeutral.astype(int) + trackShiftInf = [t if t is not None else np.inf for t in trackKsShift] + ksBarMatNew = ksBarMatNeutral + np.tile(np.array(trackShiftInf),(numBars,1)).T + # if we want to replace it then + ksBarMatNeutral[np.isinf(ksBarMatNew)] = 0 + ksBarMatNew[np.isinf(ksBarMatNew)] = 0 + ksBarMatNew = ksBarMatNew.astype(int) + clefMat = clefMat.astype(int) + tssList = [b.ts for b in barList[0]] + for currBarNumber in range(numBars): # how many bars each line + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): # each track + currBar = barList[lineNo][currBarNumber] + measure = stream.Measure(number=barNumber) + flatList = set() #put here because reset in measures + sharpList = set() + naturalList = set() + clefType = clefMat[lineNo, currBarNumber] + ks = ksBarMatNew[lineNo, currBarNumber] + if currBarNumber == 0: + measure.append(meter.TimeSignature(f'{currBar.ts[0]}/{currBar.ts[1]}')) + elif tssList[currBarNumber]!=tssList[currBarNumber-1]: + measure.append(meter.TimeSignature(f'{currBar.ts[0]}/{currBar.ts[1]}')) + if currBarNumber == 0: + measure.append(getClefFromType(clefType)) + setKS(ks, lineNo) + measure.append(key.KeySignature(ks)) + elif clefType != clefMat[lineNo, currBarNumber-1]: + measure.append(getClefFromType(clefType)) + elif ks != ksBarMatNew[lineNo, currBarNumber-1]: + setKS(ks, lineNo) + measure.append(key.KeySignature(ks)) + if currBar.getTunedTotalBeat() == 0: + measure.append(note.Rest(quarterLength = currBar.ts[0]/currBar.ts[1]*4)) + else: + for elem in currBar.elementList: + if type(elem) == NoteGroup: + flatList, sharpList, currNote = parseNoteGroup(elem, + flatList, + sharpList, + naturalList, + clefMat[lineNo, currBarNumber], + lineNo, + trkShift=0) + if currNote is not None: + measure.append(currNote) + else: + print() + elif type(elem) == Rest: + currRest = parseRest(elem) + if currRest is not None: + measure.append(currRest) + part[lineNo%numTrack].append(measure) + for p in part: + score.append(p) + ''' + # Part 2: with shift + ksBarMatNeutral + ksSharp = [set() for _ in range(numTrack)] + ksFlat = [set() for _ in range(numTrack)] + for currBarNumber in range(numBars): # for the new one shifted + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): + currBar = barList[lineNo][currBarNumber] + measure = stream.Measure(number=barNumber) + # barNumber = barNumber+1 + flatList = set() #put here because reset in measures + sharpList = set() + naturalList = set() + clefType = clefMat[lineNo, currBarNumber] + ks = ksBarMatNeutral[lineNo, currBarNumber] + if currBarNumber == 0: + measure.append(getClefFromType(clefType)) + setKSNeutral(ks, lineNo, ks) + measure.append(key.KeySignature(ks)) + elif clefType != clefMat[lineNo, currBarNumber-1]: + measure.append(getClefFromType(clefType)) + elif ks != ksBarMatNeutral[lineNo, currBarNumber-1]: + setKSNeutral(ks, lineNo, ks) + measure.append(key.KeySignature(ks)) + if currBar.getTunedTotalBeat() == 0: + measure.append(note.Rest(quarterLength = currBar.ts[0]/currBar.ts[1]*4)) + for elem in currBar.elementList: + if type(elem) == NoteGroup: + flatList, sharpList, currNote = parseNoteGroupShift(elem, + flatList, + sharpList, + naturalList, + clefMat[lineNo, currBarNumber], + ks, + lineNo, + trackNoteShift[lineNo]) + if currNote is not None: + measure.append(currNote) + else: + print() + elif type(elem) == Rest: + currRest = parseRest(elem) + if currRest is not None: + measure.append(currRest) + part2[lineNo%numTrack].append(measure) + for p in part2: + score2.append(p) + ''' + return score, {'ksAssigned':debugXMLImg} + +def numberToString(num: int, strLen: int = 3): + stringNum = str(num) + strAppend = strLen-len(stringNum) + return '0'*strAppend + stringNum + +def getTrackKsAndNoteShift(toneHelper: ToneHelper, instrumentList: List[str]) -> Tuple[List[int|None], List[int]]: + tone = [ins.split(':')[1] if len(ins.split(':')) > 1 else '' for ins in instrumentList] + trackKsShift = [] + trackNoteShift = [] + for idx, tn in enumerate(tone): + ksShift, noteShift = toneHelper.getKsAndNoteShift(tn) + if isInstrumentIncluded(instrumentList[idx]): + trackKsShift.append(-ksShift) + else: + trackKsShift.append(None) + trackNoteShift.append(noteShift) + return trackKsShift, trackNoteShift + +# time signatures +def getMeasuresEachStaff(image: np.ndarray, beamMapImg: np.ndarray, staffList: List[Staff], barEachTrack:List[List[int]], trackRange: List[Tuple[int,int]]): + allRanges = [] + for barThisTrack in barEachTrack: + prevIdx = 0 + rangeThisTrack = [] + if len(barThisTrack) > 1: + while prevIdx < len(barThisTrack)-1: + if barThisTrack[prevIdx+1] - barThisTrack[prevIdx] > BAR_MAX_GAP: + rangeThisTrack.append((barThisTrack[prevIdx], barThisTrack[prevIdx+1])) + prevIdx += 1 + allRanges.append(rangeThisTrack) + retList = [] + for trackIdx, track in enumerate(trackRange): + start = track[0] + end = track[1]+1 + for i in range(start, end): + sf = staffList[i] + retList.append([[rng[0], rng[1], sf.ys[0], sf.ys[1]] for rng in allRanges[trackIdx]]) + return retList + + img = image.copy() + # 第一維：哪一行 staff (長度等於 len(staffList)) + allStavesMeasures = [[] for _ in range(len(staffList))] + + # 取得類別 Map 並只關注小節線類別 (4) + _, itemMap, _ = cv2.split(beamMapImg) + staffCenters = [sf.ys[2] for sf in staffList] + staffImgBinary = (itemMap == 4)[staffCenters, :] + + # 利用 TrackRange 偵測，因為同一個 Track 的小節線通常是垂直對齊的 + for trkRange in pageMetadata.getTrackRange(): + staves_in_track = range(trkRange[0], trkRange[1] + 1) + + # 投影判定小節線位置 + barSum = np.sum(staffImgBinary[trkRange[0]:trkRange[1]+1, :], axis=0) + numStaves = len(staves_in_track) + barPos = np.where(barSum > numStaves * 0.7)[0] + + if len(barPos) > 0: + # --- 1. 篩選小節線：連續像素內 (<=5) 僅保留最右邊的一個值 --- + barLinesX = [] + if len(barPos) > 0: + temp_group_max = barPos[0] + for i in range(1, len(barPos)): + if barPos[i] - barPos[i-1] <= BAR_MAX_GAP: + temp_group_max = barPos[i] # 持續更新，保留最右邊 + else: + barLinesX.append(temp_group_max) + temp_group_max = barPos[i] + barLinesX.append(temp_group_max) + + # --- 2. 根據小節線生成「小節 (Measures)」 --- + # 如果有 N 條線 (barLinesX)，產生 N-1 個小節 + if len(barLinesX) >= 2: + for m_idx in range(len(barLinesX) - 1): + m_left = barLinesX[m_idx] + m_right = barLinesX[m_idx+1] + + # 分配給該 Track 裡的每一行 Staff + for s_idx in staves_in_track: + target_staff = staffList[s_idx] + top = target_staff.ys[0] + bottom = target_staff.ys[-1] + + # 建立小節 Bounding Box [left, right, top, bottom] + measure_bbox = [int(m_left), int(m_right), int(top), int(bottom)] + allStavesMeasures[s_idx].append(measure_bbox) + + # 視覺化：畫出小節範圍 (用藍色框表示小節) + cv2.rectangle(img, (m_left, top), (m_right, bottom), (255, 0, 0), 1) + + cv2.imwrite("measures_detected.jpg", img) + return allStavesMeasures + +def detect_time_signatures(model_path: str, img_path: str, imgsz: int = 1792, conf: float = 0.5) -> List[TimeSignature]: + from ultralytics import YOLO + time_signatures = [] + + model = YOLO(str(model_path)) + results = model.predict( + source=str(img_path), + imgsz=imgsz, + conf=conf, + save=False, + verbose=False + ) + + r = results[0] + if r.boxes is not None and len(r.boxes) > 0: + clss = r.boxes.cls.cpu().tolist() + confs = r.boxes.conf.cpu().tolist() + xyxys = r.boxes.xyxy.cpu().tolist() + + for i in range(len(clss)): + cls_id = int(clss[i]) + conf_val = float(confs[i]) + x1, y1, x2, y2 = xyxys[i] + bbox = (int(x1), int(y1), int(x2), int(y2)) + ts_obj = TimeSignature(yolo_class=cls_id, bbox=bbox, confidence=conf_val) + time_signatures.append(ts_obj) + + return time_signatures + +def map_time_signatures_to_score(time_signature_list, staffList, allStavesMeasures, allItemInScore, imgResizeRatio = 1): + """ + 將 TimeSignature 物件映射到對應的 Staff 和 Measure。 + """ + for ts in time_signature_list: + # if not ts.isValid(): + # continue + + tx1, ty1, tx2, ty2 = [int(s*imgResizeRatio) for s in ts.getBbox()] + + ts_cx = (tx1 + tx2) / 2 # 中心 X + ts_cy = (ty1 + ty2) / 2 # 中心 Y + ts_h = ty2 - ty1 # bbox 高度 + + # --- 規則 1: 縱向尋找最靠近的 staff --- + best_staff_idx = -1 + min_dist = float('inf') + + for s_idx, staff in enumerate(staffList): + # 計算該 staff 的中心 Y (五條線的平均值) + staff_cy = sum(staff.ys) / len(staff.ys) + dist = abs(ts_cy - staff_cy) + + if dist < min_dist: + min_dist = dist + best_staff_idx = s_idx + + # 檢查垂直相差距離是否大過本身 bbox 高度 + if best_staff_idx == -1 or min_dist > ts_h: + ts.setValid(False) + continue + + ts.setstaffline(best_staff_idx) + + # --- 規則 2: 水平尋找小節 (Measure) --- + measures = allStavesMeasures[best_staff_idx] + found_bar_idx = -2 # 預設未找到 + + # 檢查是否落在某個小節內 + for m_idx, m_bbox in enumerate(measures): + mx1, mx2, my1, my2 = m_bbox + if mx1 <= ts_cx <= mx2: + found_bar_idx = m_idx + break + + # 如果沒在小節內，檢查是否在最後一個小節的右邊 + if found_bar_idx == -2: + last_mx2 = measures[-1][1] # 最後一個小節的 x2 + if ts_cx > last_mx2: + found_bar_idx = -1 + else: + # 既不在小節內，也不在右邊（可能在第一小節左邊邊緣外） + ts.setValid(False) + continue + + ts.setBarLoc(found_bar_idx) + + # --- 規則 3: 檢查小節內的 Rest 和 NoteGroup --- + # 如果 barLoc 為 -1 (在最右邊之外)，通常不具備音樂意義上的音符檢查需求，可視情況跳過或判定無效 + if found_bar_idx == -1: + # 依據一般邏輯，拍號不應出現在所有音符之後的最右側，建議設為無效 + ts.setValid(False) + continue + + # 獲取該 staff 的所有物件 + staff_items = allItemInScore[best_staff_idx] + mx1, mx2, _, _ = measures[found_bar_idx] + + is_invalid_by_context = False + for item in staff_items: + # 只考慮 Rest 和 NoteGroup (透過類別名稱判斷) + class_name = item.__class__.__name__ + if class_name in ["Rest", "NoteGroup"]: + ix1, iy1, ix2, iy2 = item.boundingBox + item_cx = (ix1 + ix2) / 2 + + # 判斷該物件是否屬於當前小節 (中心在小節範圍內) + if mx1 <= item_cx <= mx2: + # 如果物件的中心在拍號中心的左邊 -> 拍號位置錯誤 + if item_cx < ts_cx: + is_invalid_by_context = True + break + + if is_invalid_by_context: + ts.setValid(False) + continue + +def map_to_track(a, trackList): + for i, (start, end) in enumerate(trackList): + if start <= a <= end: + return i + return None # in case a doesn't fall into any range +def prepareTSBarChangeList(data, trackList): + grouped = defaultdict(list) + + # Group values + for (a, b), value in data: + track_index = map_to_track(a, trackList) + key = (track_index, b) + grouped[key].append(value) + + result = {} + + # Compute most common per group + for key, values in grouped.items(): + most_common, common_count = Counter(values).most_common(1)[0] + if 'c' in most_common: + most_common = '4,4' + currentTrack = key[0] + if common_count > (trackList[currentTrack][1] - trackList[currentTrack][0]+1)* 0.5: + result[key] = list(map(int, most_common.split(','))) + + # Find the "last" group based on sorted (track_index, b) + if result: + last_key = sorted(result.keys())[-1] + last_value = result[last_key] + else: + last_value = None + + # Convert keys to "x,y" + formatted_result = { + f"{k[0]},{k[1]}": v for k, v in result.items() + } + + return formatted_result, last_value + +if __name__ == '__main__': + noteGroupMap: np.ndarray + stemIdxMap: np.ndarray + noteGroupVerticallyMerged: List[NoteGroup | None] + restMap: np.ndarray + restList: List[Rest | None] + sfnClefMap: np.ndarray + sfnClefList: List[Union[Accidentals, Clef, None]] + beamMapImg: np.ndarray + staffList: List[Staff] + symNum = 6 + sheetName = f'Bee_{symNum}_challenge' + prevBarTS = [12,8] + lenString = 3 + runWholeModel = True + fullBarList = [] + fullksListAllTrack = np.array([]) + dataSepList = dict() + dataSepList['bee5'] = [1, 12, 33, 53, 77, 98, 119, 141, 160, 170, 181, 194, 223, 248, 267, 287, 309, 333, 356, 376,386, 397, 408, 418, 429, 441, 455, 469, 480, 491, + 1, 8, 25, 38, 57, 73, 82, 92, 102, 110, 118, 133, 148, 164, 172, 183, 187, 191, 204, 215, 230, + 1, 12, 38, 61, 82, 83, 104, 123, 131,150, 166, 182, 190, 205, 231, 263, 293, 323, 349, + 1, 6, 12, 17, 21, 26, 33, 39, 44, 49, 54, 59, 62, 69, 74, 81, 85, 90, 94, 104, 112, 117, 122, 127, 132, 136, 140, 145, 150, 161, 186, 207, 214, 219, 224, 228, 234, 240, 247, 252, 257, 262, 268, 272, 279, 285, 294, 300, 305, 309, 316, 324, 330, 336, 343, 349, 354, 364, 374, 383, 391, 397, 405, 415, 422, 432] + dataSepList['bee6'] = [1,17,31,43,57,83,95,111,124,139,154,166,177,196,209,221,241,260,276,298,317,329,354,368,381,395,407,426,440,452,464,479,493, + 1,4,8,10,12,16,21,25,29,31,35,39,42,44,46,49,51,53,57,61,65,70,74,79,83,87,89,91,93,95,97,99,101,103,107,111,116,118,121,123,125,127,133, + 1,19,39,59,80,111,141,161,176,191,205,224,244,125, + 1,14,22,25,28,31,34,40,17,51,56,65,72,78,82,87,93,103,107,110,114,125,137,145, + 1,14,23,26,32,36,40,44,48,52,57,28,77,85,93,101,105,109,117,131,134,137,141,149,153,157,161,170,179,191,196,204,212,219,225,231,245,260] + dataSepList['bee7'] = [1,8,16,19,22,30,36,39,46,53,63,75,87,91,95,99,103,108,118,127,138,148,157,162,167,171,184,196,205,209,213,225,235,245,255,260,270,279,283,287,291,295,300,311,323,331,335,346,358,366,370,376,380,384,397,409,416,422,428,433,439,444, + 1,32,63,77,84,91,106,120,134,146,158,170,182,192,212,217,230,244,261, + 1,16,34,56,76,97,118,135,143,160,181,198,214,233,250,259,277,293,317,337,357,377,396,404,424,441,459,477,496,513,531,554,574,593,613,629,637,645, + 1,7,12,17,21,28,34,39,52,60,73,81,90,104,113,119,122,129,146,151,154,159,163,177,191,203,218,226,231,238,245,251,257,263,271,278,290,302,314,320,328,335,340,347,358,370,382,394,400,406,414,421,429,437,445,453,459] + dataSepList['bee8'] = [1,8,13,20,26,34,48,64,80,92,98,112,124,138,151,163,169,175,182,187,193,200,213,219,226,241,255,263,277,289,295,307,317,328,334,345,349,353,357,364, + 1,8,16,24,32,41,48,55,63,71,78, + 1,6,11,16,21,31,38,52,66, + 1,8,22,31,42,54,65,75,85,99,112,123,135,148,161,173,183,191,201,211,225,238,244,250,260,274,291,302,312,324,336,348,361,373,383,391,394,401,407,412,417,428,439,449,454,461,474,487] + dataSeperate = dataSepList[f'bee{symNum}'] + for number in range(39,40): #(51,53): + isInit = dataSeperate[number-1] == 1 + jsonPath = rf"orch_dataset\{sheetName}\csv\{sheetName}.json" + csvPath = rf"orch_dataset\{sheetName}\csv\{sheetName}_{numberToString(number, lenString)}.csv" + df = readCsvAndEditViolin(csvPath) + pageMetadata = ScoreMetaData(df, jsonPath, isInit) + instrumentList = ScoreMetaData.get_instruments() + if (isInit): + print(f"initing the instrument List to {pageMetadata}") + print(f"updated instrument list: {instrumentList}") + + imgName = rf"{sheetName}_{number}" + imgPath = rf"orch_dataset\{sheetName}\imgs\{sheetName}_{number}\{sheetName}_{number}.png" + pklPath = rf"orch_dataset\{sheetName}\imgs\{sheetName}_{number}\{sheetName}_{number}.pkl" + scorePath = rf"orch_dataset\{sheetName}\xmls\{sheetName}_{number}.musicxml" + scoreShiftedPath = rf"orch_dataset\{sheetName}\xmls\{sheetName}_{number}_shifted.musicxml" + if not os.path.isdir(rf"orch_dataset\{sheetName}\xmls"): + os.mkdir(rf"orch_dataset\{sheetName}\xmls") + if not os.path.exists(pklPath) or runWholeModel: + ( + noteGroupMap, + stemIdxMap, + noteGroupVerticallyMerged, + restMap, + restList, + sfnClefMap, + sfnClefList, + beamMapImg, + staffList, + dataDict + ) = png2decode(imgName, imgPath) + with open(pklPath, "wb") as f: + pickle.dump( + ( + noteGroupMap, + stemIdxMap, + noteGroupVerticallyMerged, + restMap, + restList, + sfnClefMap, + sfnClefList, + beamMapImg, + staffList, + dataDict + ), + f + ) + print(f"finishing processing sheet {sheetName}_{number}") + # to save time running previous step (Debug only) + else: + with open(pklPath, "rb") as f: + ( + noteGroupMap, + stemIdxMap, + noteGroupVerticallyMerged, + restMap, + restList, + sfnClefMap, + sfnClefList, + beamMapImg, + staffList, + dataDict + ) = pickle.load(f) + + # # !!! decoded stuff from score (by bar) + # # !!! note object by staff line (object not just decoded) + allItemInScore = getAllObjectInEachLine(noteGroupMap, noteGroupVerticallyMerged, restMap, restList, sfnClefMap, sfnClefList, beamMapImg, staffList) + + # patch the object to make it up to date + for obj in noteGroupVerticallyMerged: + if obj and not hasattr(obj, "acrossLineNGID"): + obj.acrossLineNGID = None + for obj in restList: + if obj and not hasattr(obj, "acrossLineNGID"): + obj.acrossLineNGID = None + image = dataDict['image'] + # get Barline locations -> bar center for each track: List[List[int]] + + barEachTrack = getBarsEachTrack(image, beamMapImg, staffList, tolerance = 20) + + allStavesMeasures = getMeasuresEachStaff(image, beamMapImg, staffList, barEachTrack, pageMetadata.getTrackRange()) + + # load in image + img = cv2.imread(imgPath) + img_height, img_width, channels = img.shape + + # yolo detect time signature + yolo_model_path = "best_time_signature.pt" + time_signature_list = detect_time_signatures(yolo_model_path, imgPath) + + # filter for valid time signatures only + map_time_signatures_to_score(time_signature_list, staffList, allStavesMeasures, allItemInScore, imgResizeRatio=image.shape[0]/img.shape[0]) + valid_ts_only = time_signature_list #[ts for ts in time_signature_list if ts.isValid()] + time_sig_thres = 0.5 + final_time_sig_list = [ + [(ts.staffline, ts.barLoc if not ts.barLoc == None else 0), ts.getString()] + for ts in valid_ts_only + if ts.getConfidence() >= time_sig_thres + ] + final_time_sig_list.sort(key=lambda x: (x[0][0], x[0][1])) + print("Detected Time Signatures (after filtering):", final_time_sig_list) + barChangeList, lastTS = prepareTSBarChangeList(final_time_sig_list, pageMetadata.getTrackRange()) + # TODO: add in the bar time signature, [[(9.8),(9,8)...], [(9.8),(9,8),(4,4)...]] etc. + if not barChangeList.get('0,0'): + barChangeList['0,0'] = prevBarTS + if lastTS: + prevBarTS = lastTS + # get the list of barList (untuned) + barList,allRanges, numBarsEachLine = constructBar(noteGroupMap, noteGroupVerticallyMerged, restMap ,restList, sfnClefMap, sfnClefList, beamMapImg, staffList, pageMetadata, barEachTrack, barChangeList) + + numBarsEachTrack = [numBarsEachLine[p[0]] for p in pageMetadata.getTrackRange()] + + # noteRestMap, retImg = stackItemVertically(image, barList, pageMetadata, staffList, noteGroupVerticallyMerged, restList) + # outputImWrite(f"{imgName}_stack.jpg", retImg) + # cv2.imwrite(f"test{number}.jpg",retImg) + + rest_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40), (245, 220, 255),(230,130,175),(165,170, 70)] + knn_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40), (245, 220, 255),(230,130,175),(165,170, 70)] + imgrgb = image.copy() + beamWidth = 9 + for oneTrack in barList: + for oneBar in oneTrack: + for elem in oneBar.elementList: + if type(elem) == NoteGroup: + for stem in elem.noteStemList: + x0,y0,x1,y1 = stem.noteBox + imgrgb = cv2.rectangle(imgrgb, (stem.getX()-beamWidth//3, stem.getTopCoord()[1]), (stem.getX()+beamWidth//3, stem.getBottomCoord()[1]), knn_colors[stem.rhythm], 1, cv2.LINE_AA) + imgrgb = cv2.rectangle(imgrgb, (x0,y0),(x1,y1), knn_colors[stem.rhythm], 1, cv2.LINE_AA) + elif type(elem) == Rest: + x0,y0,x1,y1 = elem.boundingBox + imgrgb = cv2.rectangle(imgrgb, (x0,y0),(x1,y1),rest_colors[elem.rhythm], 2, cv2.LINE_AA) + cv2.imwrite('test.jpg',imgrgb) + # tune bar list based on timeSignature + barBreakPoints = tuneBarList(barList, numBarsEachTrack) + + # get all the elements on that line and continue changing currX + toneHelper = ToneHelper("keySignatureMapping.json") + + # returned BarList is exactly what we see in the score (two instrument in one line etc.) + ksMatEachTrackDirectMap, ksEachTrack, barList = getKSMatAndKSList(pageMetadata, barList, toneHelper) + + # add instruments tracks for those not appearing + # all instrument seperated (and added if not present) + barDictPerInstrument, barListPerInstrument, instrumentEachLine = assignBarlistInstrument(barList, pageMetadata) + + ksListAllTrack = np.concatenate(ksEachTrack) + assert len(ksListAllTrack) == len(barListPerInstrument[0]) + + instrument_dict = constructInstrumentMappingDict("instrumentMapping.json") + score, debugImages = exportXML(barListPerInstrument, instrumentEachLine, toneHelper, instrument_dict, ksListAllTrack, image, imgName) + score.write('musicxml', scorePath) + print(f"score write to {scorePath}") + # scoreShifted.write('musicxml',scoreShiftedPath) + # print(f"shifted score write to {scoreShiftedPath}") + # if isInit: + # fullBarList = barListPerInstrument + # else: + # for i in range(len(barListPerInstrument)): + # fullBarList[i]+=barListPerInstrument[i] + fullksListAllTrack = np.append(fullksListAllTrack, ksListAllTrack) + print() + # fullscore, fulldebugImages = exportXML(fullBarList, instrumentEachLine, toneHelper, instrument_dict, fullksListAllTrack) + # fullscore.write('musicxml',f'{imgName}.musicxml') + + print() + + + + + + + + + +# todo: +# Timpani only use one row + + + # current data structure documentation: + # noteChunkList: list of None|NoteChunks + # by noteChunkList[n].noteGroupIdxs you can get the Ids of them + # noteGroupMap, stemIdxMap <-> noteGroupVerticallyMerged + # noteGroupMap value == 0: no note, + # >0: is the noteGroupVerticallyMerged[idx] + # stemIdxMap value == -1: no noteBox + # >=0: is the index of noteGroupVerticallyMerged[idx].noteStemList[index] + # restMap <-> restList + # RestMap value == 0: no rest + # >0: is the restList[idx] + # sfnClefMap <-> sfnClefList + # sfnClefMap value == 0: no clef/sfn + # >0: is the sfnClefList[idx] + # beamMapImg: bb,gg,rr + # bb: the gradient beam image, 255 where there's beam, + # 254-2*stepSize*n: go how far down to beam + # 253-2*stepSize*n: go how far up to beam + # gg: 0 if there's nothing there + # 1 if it's place of a noteGroup object + # 2 if it's place of an accidentals + # 3 if it's place of a clef + # 4 if it's place of a barline + # 5 if it's place of a rest + # 6 if it's place of a noteGroup ornament? TODO not implemented yet + # rr: staff position map + staff number + how far to the bottom of staff + # x = 0~3 (total of 4): the position of the note (1~24, A is 12, C is 13) + # x = 4: the staffnum of the closest one (starting with 1 instead of 0) + # x = -1: how far pixel to the top of the staff (if it's in the staff range and 1 barheight above, else 0) + # x = -2: how far pixel to the bottom of the staff (if it's in the staff range and 1 barheight below, else 0) + # within the x of staff: the staff number + # if it contains anything in the staff it will be that, else it will diff --git a/pdf2musicXMLsingleLine.py b/pdf2musicXMLsingleLine.py new file mode 100644 index 0000000..2c0b21c --- /dev/null +++ b/pdf2musicXMLsingleLine.py @@ -0,0 +1,3764 @@ +import cv2 +import glob +import os +import numpy as np +from scipy import stats +from omr.part1 import runModel1 +from omr.staffline_extraction import staff_extract_staffobj +from omr.part2 import runModel2 +from omr import layers +from typing import List,Tuple, Union, Set +import statistics +import matplotlib.pyplot as plt +from sklearn.cluster import KMeans +import math +from get_prediction_singleStem import Single_Stem_Classifier +from omr.bbox import ( + BBox, + merge_nearby_bbox, +) +import json +import math +from get_prediction import Sfn_Clef_classifier +from get_prediction_rest import Rest_Classifier +from abc import ABC, abstractmethod +from music21 import stream, note, metadata, chord, meter, clef, key +from fractions import Fraction +from dd_classes import RestNg +from tune_bar import tuneBar +from collections import Counter +from pdf2png import savePdf2Png +import pickle +import itertools + +OUTPUT_BASE_FOLDER = '' +NUM_TRACK = 4 +TRACK_SHIFT = [0,0,0,0] +CLEF_OPTIONS = [[1],[1],[0,1],[-1,-2,1]] +RESIZE_RATIO_PACKED = 2 +TIME_SIGNATURE = [9,8] +DEBUGIMG = None +class Staff: + def __init__(self, left:int, right:int, ys:Tuple[int,int,int,int,int], minMaxDiff:int=0): + self.left = left + self.right = right + self.ys = ys + self.minDiff = minMaxDiff + def get_yOne(self)->int: + x = [self.ys[i+1] - self.ys[i] for i in range(len(self.ys) - 1)] + return sum(x)//len(x) + def get_yOne_float(self)->float: + x = [self.ys[i+1] - self.ys[i] for i in range(len(self.ys) - 1)] + return sum(x)/len(x) + def IsStaffAligned(self, barheight:int)->bool: + return self.minDiffint: + # if hasLineMiddle the number has to be even + if self.hasLineMiddle is None: + return round(self.pitchWideFloat) + upperInt:int = math.ceil(self.pitchWideFloat) + lowerInt:int = math.floor(self.pitchWideFloat) + if upperInt == lowerInt: + return upperInt + if self.hasLineMiddle: + return upperInt if upperInt%2==0 else lowerInt + else: + return upperInt if upperInt%2==1 else lowerInt + + def getX(self): + return self.start[0] + def setYlen(self, ylen:int): + x = self.start[0] + if self.isup: + self.end = (x,self.start[1]-abs(ylen)) + else: + self.end = (x,self.start[1]+abs(ylen)) + def getTopCoord(self): + if self.isup: + return self.end + else: + return self.start + def getBottomCoord(self): + if self.isup: + return self.start + else: + return self.end + def getY0Y1(self): + return (min(self.start[1],self.end[1]), max(self.start[1],self.end[1])) + def getLineX0Y0X1Y1(self, width:int=3): + return (self.start[0]-width//2, self.getTopCoord()[1], self.start[0]+(width-width//2), self.getBottomCoord()[1]) + def getYLen(self)->int: + return int(abs(self.start[1]-self.end[1])) + def setBeam(self, typ:str,top:int|None = None, bottom:int|None = None): + if typ.lower().startswith('l'): + self.setLeftBeam(top=top, bottom=bottom) + else: + self.setRightBeam(top=top, bottom=bottom) + def setLeftBeam(self, top:int|None = None, bottom:int|None = None): + if top is not None: + self.leftBeam[0] = top + if bottom is not None: + self.leftBeam[1] = bottom + def setRightBeam(self, top:int|None = None, bottom:int|None = None): + if top is not None: + self.rightBeam[0] = top + if bottom is not None: + self.rightBeam[1] = bottom + def getBeamHeights(self): + return (self.leftBeam[1]-self.leftBeam[0], self.rightBeam[1]-self.rightBeam[0]) + def getBoundingBox(self): + x0,y0,x1,y1 = self.noteBox + yy0 = self.start[1] + yy2 = self.end[1] + return (x0,min(y0,yy0,yy2), x1, max(y0,yy0,yy2)) + def getString(self): + keyName = ['C','D','E','F','G','A','B'] + currKeyName = keyName[(self.pitchSoprano-1)%7] + currKey = currKeyName+str((self.pitchSoprano-1)//7+5) + if self.accidentals is not None: # it has keySignature + if self.accidentals == -1: + currKey = currKey[0]+'-'+currKey[1] + elif self.accidentals == 1: + currKey = currKey[0]+'#'+currKey[1] + return currKey + +class Rest: + def __init__(self, bbox:Tuple[int,int,int,int], rhythm: int): + self.boundingBox:Tuple[int,int,int,int] = bbox + # rest -1: whole rest or 1/2 depending, 0: 1/4, 1:1/8, 2:1/16 ... (the number of lines) + self.rhythm: int = rhythm + self.hasdot: bool = False + self.noteGroupId: int|None = None + self.tunedLength: Fraction|None = None + def setNgId(self, ngId:int): + self.noteGroupId = ngId + def getLengthFraction(self): + restClassNames = ['1/4','1/8','1/16','1/32','1/2'] # only for >=0 + ratio = Fraction(3,2) if self.hasdot else 1 + return Fraction(restClassNames[self.rhythm])*ratio + def getString(self): + return f"Rest_{self.tunedLength}" + +class NoteGroup: + def __init__(self, noteStemObj:Stem): + # the width of the stem box we want + self.stemWidth:int = 3 + # x0,y0,x1,y1 + self.noteBoxes:List[Tuple[int,int,int,int]] = [noteStemObj.noteBox] + # x0, y0, x1, y1 + self.stemLineBoxes:List[Tuple[int,int,int,int]] = [noteStemObj.getLineX0Y0X1Y1(width=self.stemWidth)] + # x0,y0,x1,y1 + self.boundingBox: Tuple[int,int,int,int]|None = None + self.noteStemList: List[Stem] = [noteStemObj] + self.updateBoundingBox() + self.noteChunkId: int|None = None + # self.pitchLists: List[float] = [] + # for instance C has line, D doesn't + # self.noteLineMiddle: List[bool] = [] + self.restList:List[Rest] = [] + self.tunedLength: Fraction|None = None + def addRest(self, rest:Rest): + self.restList.append(rest) + self.updateBoxRest(rest.boundingBox) + def addNoteStem(self, noteStemObj: Stem): + self.noteStemList.append(noteStemObj) + self.noteBoxes.append(noteStemObj.noteBox) + self.stemLineBoxes.append(noteStemObj.getLineX0Y0X1Y1(width=self.stemWidth)) + self.updateBoundingBox() + def updateBoundingBox(self): + noteMinX = min([n[0] for n in self.noteBoxes]) + noteMaxX = max([n[2] for n in self.noteBoxes]) + noteMinY = min([n[1] for n in self.noteBoxes]) + noteMaxY = max([n[3] for n in self.noteBoxes]) + stemMinX = min([n[0] for n in self.stemLineBoxes]) + stemMaxX = max([n[2] for n in self.stemLineBoxes]) + stemMinY = min([n[1] for n in self.stemLineBoxes]) + stemMaxY = max([n[3] for n in self.stemLineBoxes]) + self.boundingBox = (min(noteMinX, stemMinX), min(noteMinY, stemMinY), max(noteMaxX, stemMaxX), max(noteMaxY, stemMaxY)) + def updateBoxRest(self, restBox:Tuple[int,int,int,int]): + minx,miny,maxx,maxy = self.boundingBox + x0,y0,x1,y1 = restBox + self.boundingBox = (min(minx, x0), min(miny, y0), max(maxx, x1), max(maxy, y1)) + def getMinLength(self): + class_names = ['1/4','1/8','1/16','1/32', '1/64', '1/128', '1/2', '1/1'] + minLength = 1 + for ns in self.noteStemList: + if Fraction(class_names[ns.rhythm])*(1.5 if ns.hasdot else 1) filter out the one with greater beams value and >0.5 + if len(nsl1) == 1 and len(nsl2) ==1: + if nslSum1[0] > nslSum2[0]*2 and nslSum1[0]>thres: + return ng2 + elif nslSum2[0] > nslSum1[0]*2 and nslSum2[0]>thres: + return ng1 + else: + ng1.addNoteStem(ng2.noteStemList[0]) + return ng1 + # if one has more than one notes -> check the other one + else: + if len(nsl1)>len(nsl2): + if nslSum2[0]>thres: + return ng1 + elif nslSum1[0]>nslSum2[0]*2 and nslSum1[0]>thres: + return ng2 + else: + if nslSum1[0]>thres: + return ng2 + elif nslSum2[0]>nslSum1[0]*2 and nslSum2[0]>thres: + return ng1 + for ns in nsl2: + ng1.addNoteStem(ns) + return ng1 + +class NoteChunk: + def __init__(self, noteGroups:Set[int]): + self.noteGroupIdxs:Set[int] = noteGroups + def mergeNoteChunk(self, noteGroup2: Set[int]): + self.noteGroupIdxs:Set[int] = self.noteGroupIdxs.union(noteGroup2) + def getBoundingBox(self,noteGroupList:List[NoteGroup]): + x0s = min([noteGroupList[i].boundingBox[0] for i in self.noteGroupIdxs]) + x1s = max([noteGroupList[i].boundingBox[2] for i in self.noteGroupIdxs]) + y0s = min([noteGroupList[i].boundingBox[1] for i in self.noteGroupIdxs]) + y1s = max([noteGroupList[i].boundingBox[3] for i in self.noteGroupIdxs]) + return [x0s, y0s, x1s, y1s] +class sfnClefInterface(ABC): + @abstractmethod + def getString(self): + pass + @abstractmethod + def getBbox(self) -> Tuple[int,int,int,int]: + pass + @abstractmethod + def getValue(self) -> int: + pass + @abstractmethod + def getType(self) -> int: + # 0 is accidental, 1 is clef + pass +class Accidentals(sfnClefInterface): + def __init__(self, bbox:Tuple[int,int,int,int], shift: int): + self.boundingBox:Tuple[int,int,int,int] = bbox + # shift is 1 for sharp, 0 for natural, -1 for flat + self.shift: int = shift + self.isKeySignature:bool = False + self.endKeySignature:bool = False + self.ksKeySop: float|None = None + self.shrinkYs: Tuple[int,int]|None = None + self.ngIndex: int|None = None + def getString(self): + if self.shift == 0: + return 'natural' + elif self.shift == 1: + return 'sharp' + elif self.shift == -1: + return 'flat' + else: + return 'invalid accidentals' + def getColor(self): + clrs = [(255,255,0),(255,0,125),(255,0,255)] + return clrs[self.shift] + def getBbox(self) -> Tuple[int]: + return self.boundingBox + def getValue(self) -> int: + return self.shift + # 0 if is accidentals + def getType(self)->int: + return 0 + def getSign(self)->int: + if self.shift == 0: + return '%' + elif self.shift == 1: + return '#' + elif self.shift == -1: + return 'b' + else: + return 'invalid accidentals' +class Clef(sfnClefInterface): + def __init__(self, bbox:Tuple[int,int,int,int], type: int): + self.boundingBox:Tuple[int,int,int,int] = bbox + self.type:int = type + # how far it should shift if the staff position is 0 in the sidebar + def getString(self): + if self.type == 1: + return 'violin' + elif self.type == 0: + return 'viola' + elif self.type == -1: + return 'bass' + elif self.type == -2: + return 'violaCello' + else: + return 'invalid clef' + def getBbox(self) -> Tuple[int]: + return self.boundingBox + def getValue(self) -> int: + return self.type + # 1 if it's clef + def getType(self)->int: + return 1 + + +class Bar: + def __init__(self, TS:Tuple[int,int]=TIME_SIGNATURE): + self.ts = TS + self.elementList:List[Accidentals|Clef|Rest|NoteGroup] = [] + self.restNgList:List[RestNg] = [] + def getString(self): + retStr = [] + for elem in self.elementList: + retStr.append(elem.getString()) + return retStr + def addElement(self, elem:Union[Accidentals, Clef, Rest, NoteGroup]): + if type(elem) == NoteGroup: + if elem.noteChunkId is not None and not False in [j.rhythm<=0 for j in elem.noteStemList]: + print(f'removing element in noteChunk {elem.noteChunkId} > quarter') + return + self.elementList.append(elem) + def getTunedTotalBeat(self) -> Fraction: + bt = 0 + for elm in self.elementList: + if type(elm) == NoteGroup: + bt += elm.tunedLength + elif type(elm) == Rest: + bt += elm.tunedLength + return bt + + def getTotalBeat(self)->Fraction: + bt = 0 + for elm in self.elementList: + if type(elm) == NoteGroup: + bt += elm.getMinLength() + elif type(elm) == Rest: + bt += elm.getLengthFraction() + return bt + def getRhythmList(self) -> List[Fraction]: + retLst = [] + for elm in self.elementList: + if type(elm) == NoteGroup: + retLst.append(elm.getMinLength()) + elif type(elm) == Rest: + retLst.append(elm.getLengthFraction()) + return retLst + def getRestNg(self) -> List[RestNg]: + if len(self.restNgList)!= 0: + print("returning restNg list from last time") + return self.restNgList + retLst = [] + for elm in self.elementList: + if type(elm) == NoteGroup: + groupId = 0 + if elm.noteChunkId is not None: + groupId = elm.noteChunkId + beamLength = (-1,-1) + beamEnd = [-1,-1] # for grouped notes + length = elm.getMinLength() + if len(elm.noteStemList) == 1: + currStm = elm.noteStemList[0] + beamLength = [int(a) for a in currStm.getBeamHeights()] + if currStm.isup: + beamEnd = [currStm.getX(), int(min(currStm.getY0Y1()))] + else: + beamEnd = [currStm.getX(), int(max(currStm.getY0Y1()))] + newElm = RestNg(length=Fraction(length), + groupId = groupId, + isRest = False, + numNotes = len(elm.noteStemList), + beamLength = beamLength, + beamEnd=beamEnd) + retLst.append(newElm) + elif type(elm) == Rest: + newElm = RestNg(length=elm.getLengthFraction()*Fraction(3,2) if elm.hasdot else elm.getLengthFraction(), + groupId = -1, + isRest = True, + numNotes = 0, + beamLength = (-1,-1), + beamEnd=[-1,-1]) + retLst.append(newElm) + self.restNgList = retLst + return retLst + def reassignLength(self, origLens: List[Fraction], newLens: List[Fraction]): + currNewLenIdx = 0 + for elm in self.elementList: + if type(elm)!=NoteGroup and type(elm)!=Rest: + continue + if newLens[currNewLenIdx] is not None: + elm.tunedLength = newLens[currNewLenIdx] + else: + elm.tunedLength = origLens[currNewLenIdx] + currNewLenIdx+=1 +print_str = True +def printt(str): + if print_str: + print(str) + +debugImg = False + +def imwrite(str,img,strictlyYes=False, startingFolder = f'{OUTPUT_BASE_FOLDER}/'): + debugFolderPath = f'{startingFolder}debug' + if not os.path.exists(debugFolderPath): + os.mkdir(debugFolderPath) + if debugImg or strictlyYes: + print(f'saving {str}') + cv2.imwrite(f'{debugFolderPath}/{str}',img) + + +def outputImWrite(str,img, startingFolder = f'{OUTPUT_BASE_FOLDER}/'): + outputImgPath = f'{startingFolder}output' + if not os.path.exists(outputImgPath): + os.mkdir(outputImgPath) + cv2.imwrite(f'{outputImgPath}/{str}',img) + +# barline_height = 12 + +def getStemBox(dataDict, missing_height = 33): + stems_rests = dataDict['stems_rests'] + clefs_keys = dataDict['clefs_keys'] + notehead = dataDict['notehead'] + staff = dataDict['staff'] + symbols = dataDict['symbols'] + image = dataDict['image'] + + _, img128 = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY_INV) + kernel = np.ones((5,5),dtype=np.uint8) + beam = cv2.erode(img128, kernel, iterations = 1) + imwrite('beam.jpg',beam) + + combine = beam.copy() + notehead_clefkey = notehead+clefs_keys + xs, ys = np.where(notehead_clefkey>0) + combine[xs,ys] = 0 + xs, ys = np.where(symbols>0) + combine[xs,ys] = 255 + combine = cv2.bitwise_not(combine) + imwrite('combine.jpg',combine) + + stem = np.zeros_like(img128) + xs, ys = np.where(stems_rests>0) + stem[xs,ys] = 255 + stem = cv2.erode(stem, np.ones((3,1),dtype=np.uint8),iterations = 1) + stem = cv2.bitwise_not(stem) + imwrite('stem.jpg',stem) + + note_stem = notehead+stems_rests + notestem = np.zeros_like(img128) + xs,ys = np.where(note_stem>0) + notestem[xs,ys] = 255 + notestem = cv2.erode(notestem, np.ones((3,1),dtype=np.uint8),iterations = 1) + notestem = cv2.bitwise_not(notestem) + imwrite('notestem.jpg',notestem) + + ret,mod_stem = cv2.threshold(cv2.cvtColor(stem, cv2.COLOR_BGR2GRAY), 128,255,cv2.THRESH_BINARY_INV) + # now 255 is where the stems etc are + + imwrite('mod_stem.jpg',mod_stem) + # remove circles (the rests) + circle_size = 6 + remove = cv2.erode(mod_stem, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(circle_size,circle_size)), iterations=1) + remove = cv2.dilate(remove, cv2.getStructuringElement(cv2.MORPH_RECT,(circle_size*4,circle_size*6)), iterations=1) + + # remove long beams + remove1 = cv2.dilate(mod_stem, np.ones((1,3),dtype=np.uint8),iterations = 1) + remove1 = cv2.erode(remove1, np.ones((1,7),dtype=np.uint8), iterations=1) + remove1 = cv2.dilate(remove1,np.ones((3,9),dtype=np.uint8), iterations=1) + + mod_stem[remove>0]=0 + mod_stem[remove1>0] = 0 + imwrite('test_remove1.jpg',remove1) + imwrite('test_remove.jpg',remove) + imwrite('test_mod.jpg',mod_stem) + + + # remove noise + single_kernel = np.array([ + [0,255,0], + [0,255,0], + ], dtype=np.uint8) + mod = cv2.erode(mod_stem, single_kernel, iterations = 1) + mod = cv2.dilate(mod, single_kernel, iterations = 1) + imwrite('test_mod2.jpg',mod) + + currLen = missing_height + while currLen>=3: + kernel = np.zeros((currLen,1),dtype=np.uint8) + kernel[0]=1 + kernel[currLen-1]=1 + add = cv2.dilate(mod, kernel, iterations=1) + mod = mod+add + mod[mod>255] = 255 + currLen = currLen//2+1 + + imwrite('test.jpg',cv2.bitwise_not(mod)) + xs,ys = np.where(mod>0) + imgrgb_ori = combine + imgrgb = imgrgb_ori.copy() + assert imgrgb.shape[0] == stem.shape[0] + assert imgrgb.shape[1] == stem.shape[1] + imgrgb[xs,ys] = (0,0,255) + imwrite('add_imgrgb.jpg',imgrgb) + imwrite('stemLocation_mod.jpg',mod) + return image, imgrgb_ori, mod + +def get_bbox(data: np.ndarray) -> List[Tuple[int,int,int,int]]: + contours, _ = cv2.findContours(data.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + bboxes = [] + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + box = (x, y, x+w, y+h) + bboxes.append(box) + return bboxes + +def filter_small_bbox_area(data: np.ndarray, noteimg:np.ndarray, xmin, ymin, areamin): + filtered_Box = [] + for box in data: + x0,y0,x1,y1 = box + w = x1-x0 + h = y1-y0 + if w>=xmin and h>=ymin and w*h>areamin: + filtered_Box.append(box) + else: + noteimg[y0:y1,x0:x1] = 0 + return filtered_Box,noteimg + +def filter_small_bbox(data: np.ndarray, xmin, ymin): + filtered_Box = [] + for box in data: + x0,y0,x1,y1 = box + w = x1-x0 + h = y1-y0 + if w>=xmin and h>=ymin: + filtered_Box.append(box) + return filtered_Box + +def staffs_to_omrStaffList(staffs): + left = min([sf.x_left for sf in staffs[0,:]]) + right = max([sf.x_right for sf in staffs[-1,:]]) + omrstaff_list: List[Staff] = [] + staffRange = [] + mindiffs = [] + maxdiffs = [] + for i in range(staffs.shape[1]): + yuppers = [sf.y_upper for sf in staffs[:,i]] + ybottoms = [sf.y_lower for sf in staffs[:,i]] + top = statistics.median(yuppers) + bottom = statistics.median(ybottoms) + unit_size = statistics.median([sf.unit_size for sf in staffs[:,i]]) + minMaxDiff = min(max(yuppers)-min(yuppers), max(ybottoms)-min(ybottoms)) + maxMaxDiff = max(max(yuppers)-min(yuppers), max(ybottoms)-min(ybottoms)) + ys = [int(top), int(top+unit_size), int((top+bottom)/2),int(bottom-unit_size),int(bottom)] + sf = Staff(int(left), int(right), ys, minMaxDiff) + mindiffs.append(minMaxDiff) + maxdiffs.append(maxMaxDiff) + omrstaff_list.append(sf) + staffRange.append(range(int(top),int(bottom))) + # print(f'MinDiff: {["{:.2f}".format(num) for num in mindiffs]}, average = {np.mean(mindiffs)}') + # print(f'MaxDiff: {["{:.2f}".format(num) for num in maxdiffs]}, average = {np.mean(maxdiffs)}') + # print('') + return omrstaff_list, staffRange + +def init_bar_height(dataDict, min_barheight = 8): + staff = dataDict['staff'] + staffs, zones = staff_extract_staffobj(staff, min_barheight) + staffList, staffRange = staffs_to_omrStaffList(staffs) + barheight = [sf.get_yOne_float() for sf in staffList] + avg_barheight = int(sum(barheight)/len(barheight)) + return avg_barheight, staffList + +def split_connected_notes(bboxList, bar_height,image): + minh = int(bar_height*0.9) + minw = int(bar_height) + retlst = [] + for bbox in bboxList: + x0,y0,x1,y1 = bbox + wrat = (x1-x0)/minw + hrat = (y1-y0)/minh + wnum = int(wrat) + hnum = int(hrat) + note_w = int(minw*1.3) + note_h = int(minh*1.3) + if wnum*hnum <= 1: + retlst.append(bbox) + elif wnum>=2: + if y1-y0<=bar_height or wnum>=3: + xwid = (x1-x0)//wnum + for i in range(wnum): + retlst.append((x0+i*xwid,y0,x0+(i+1)*xwid,y1)) + # it's a scatter of three vertically stacked notes with two adjacent notes + elif hnum>2.5 and hnum>wnum: + # first get the top and bottom ones + # if the top part has more black on the left (x0) + option2s = [] + if np.sum(image[y0:y0+note_h,x0:x0+note_w])>np.sum(image[y0:y0+note_h,x1-note_w:x1]): + retlst.append((x0,y0,x0+note_w, y0+note_h)) + # the other side go down once + option2s.append((x1-note_w,y0+note_h//2,x1, y0+int(note_h*1.5))) + else: + retlst.append((x1-note_w,y0,x1, y0+note_h)) + option2s.append((x0,y0+note_h//2,x0+note_w, y0+int(note_h*1.5))) + # if the bottom part has more black on the left (x0) + if np.sum(image[y1-note_h:y1,x0:x0+note_w])>np.sum(image[y1-note_h:y1,x1-note_w:x1]): + retlst.append((x0,y1-note_h,x0+note_w, y1)) + option2s.append((x1-note_w,y1-int(note_h*1.5),x1, y1-note_h//2)) + else: + retlst.append((x1-note_w,y1-note_h,x1, y1)) + option2s.append((x0,y1-int(note_h*1.5),x0+note_w, y1-note_h//2)) + # deal with the middle one + if np.sum(image[option2s[0][1]:option2s[0][3], option2s[0][0]:option2s[0][2]]) > np.sum(image[option2s[1][1]:option2s[1][3], option2s[1][0]:option2s[1][2]]): + retlst.append(option2s[0]) + else: + retlst.append(option2s[1]) + else: + # if the left part has more black on top(y0) + if np.sum(image[y0:y0+note_h,x0:x0+note_w])>np.sum(image[y1-note_h:y1,x0:x0+note_w]): + retlst.append((x0,y0,x0+note_w, y0+note_h)) + else: + retlst.append((x0,y1-note_h,x0+note_w, y1)) + # if the right part has more black on top(y1) + if np.sum(image[y0:y0+note_h,x1-note_w:x1])>np.sum(image[y1-note_h:y1,x1-note_w:x1]): + retlst.append((x1-note_w,y0,x1, y0+note_h)) + else: + retlst.append((x1-note_w,y1-note_h,x1, y1)) + + elif hnum>=2: + ywid = (y1-y0)//hnum + for i in range(hnum): + retlst.append((x0,y0+i*ywid,x1,y0+(i+1)*ywid)) + return retlst + + + +def assignBeamLengthBeamImg(stem_list:List[Stem], image:np.ndarray, beam:np.ndarray, barheight:int): + x_diff = barheight//2 + beam = cv2.cvtColor(beam, cv2.COLOR_BGR2GRAY) + # if it's completely white in the white_buffer_max area then stop + white_buffer_max = barheight + beam_start_buffer_max = barheight//3 + for stem in stem_list: + xCenter,yCenter = stem.start + xLeft = xCenter-x_diff + xRight = min(image.shape[1]-1,xCenter+x_diff) + yMax = stem.getYLen() + if stem.getYLen() == 0: + continue + if stem.isup: + for xtype in ['left', 'right']: + if xtype=='left': + currX = xLeft + else: + currX = xRight + currY = stem.getTopCoord()[1] + # if the starting point of beam is not white, meaning that it might be a symbol, staff line etc + # keep going down until it's at the right place + if sum(np.max(beam[currY:currY+beam_start_buffer_max, xCenter-barheight//2:xCenter+barheight//2],1)) < 255*beam_start_buffer_max: + while(sum(np.max(beam[currY:currY+beam_start_buffer_max, xCenter-barheight//2:xCenter+barheight//2],1)) < 255*beam_start_buffer_max and currYstem.getBottomCoord()[1]+bar_height//2): + currY = currY+1 + else: + while beam[currY-1, currX] == 255: + currY = currY-1 + # now the topStartY is the "real start" + stem.setBeam(typ=xtype, top=currY) + # go down until it's wholely black + while sum(beam[currY:currY+white_buffer_max, currX])>255: + currY+=1 + stem.setBeam(typ=xtype, bottom=currY) + else: + for xtype in ['left', 'right']: + if xtype=='left': + currX = xLeft + else: + currX = xRight + currY = stem.getBottomCoord()[1] + + # if the starting point of beam is not white, meaning that it might be a symbol, staff line etc + # keep going up until it's at the right place + if sum(np.max(beam[currY-beam_start_buffer_max:currY, xCenter-barheight//2:xCenter+barheight//2],1)) < 255*beam_start_buffer_max: + while(sum(np.max(beam[currY-beam_start_buffer_max:currY, xCenter-barheight//2:xCenter+barheight//2],1)) < 255*beam_start_buffer_max and currY>stem.getTopCoord()[1]+bar_height//2): + currY = currY-1 + stem.end = (stem.end[0], currY) + + # if it's black (need to go up) + if beam[currY, currX] == 0: + while(beam[currY, currX] == 0 and currY255: + currY-=1 + stem.setBeam(typ=xtype, top=currY) + imgBgr = image.copy() + beam_heights = [] + for stem in stem_list: + beam_heights.append(stem.leftBeam[1]-stem.leftBeam[0]) + beam_heights.append(stem.rightBeam[1]-stem.rightBeam[0]) + imgBgr = cv2.rectangle(imgBgr, (stem.start[0]-x_diff, stem.leftBeam[0]), (stem.start[0]-x_diff, stem.leftBeam[1]),(0,0,255),3,cv2.LINE_AA) + imgBgr = cv2.rectangle(imgBgr, (stem.start[0]+x_diff, stem.rightBeam[0]), (stem.start[0]+x_diff, stem.rightBeam[1]),(255,255,0),3, cv2.LINE_AA) + imwrite('leftrightRectBeam.jpg', imgBgr) + return imgBgr, stem_list, beam_heights + +def save_beam_hist(beam_heights: List[int], img_name:str, barheight): + plt.hist([beam_height/bar_height for beam_height in beam_heights], max(beam_heights)) + plt.xlim(0,3.5) + plt.savefig(f'hist_{img_name}.jpg', format='jpg') + plt.close() + count, _ = np.histogram(beam_heights, max(beam_heights)) + whole_thres = [1, int(barheight*0.6), int(barheight*1.5), int(barheight*2.7), int(barheight*3.2)] + # expand the list to be able to access list[init_thres[-1]] + init_thres = whole_thres[:sum([0 if thres>len(count) else 1 for thres in whole_thres])+1] + expanded_count = list(count) + [0]*(init_thres[-1]+1-len(count)) + while sum(expanded_count[init_thres[-1]:])len(count) else 1 for thres in whole_thres])+1] + expanded_count = list(count) + [0]*(init_thres[-1]+1-len(count)) + while len(init_thres)>0 and sum(expanded_count[init_thres[-1]:])tooBig else bh for bh in beam_heights] + expanded_count[tooBig:] = [0]*len(expanded_count[tooBig:]) + init_thres.append(len(expanded_count)) + centers = [0]*len(init_thres) + for i in range(1,len(init_thres)): + centers[i] = np.argmax(expanded_count[init_thres[i-1]:init_thres[i]])+init_thres[i-1] + kmeans = KMeans(n_clusters = len(centers), init = np.array(centers, dtype=np.uint8).reshape(-1, 1), n_init = 1) + kmeans.fit(np.array(beam_heights,dtype=np.uint8).reshape(-1,1)) + return kmeans.labels_.tolist() + +keyCharMap = {'z':'n2', + 'x': 'n4', + 'c': 'n8', + 'v': 'n16', + 's':'noClass', + 'b': 'n1'} +def init_label_folder(rootFolder = 'training'): + for saveFolderName in ['StemUp', 'StemDown']: + for saveFolderNameAdd in ['img200', 'beam']: + folderPath = os.path.join(rootFolder,f'{saveFolderName}_{saveFolderNameAdd}') + if not os.path.exists(folderPath): + os.mkdir(folderPath) + for one_dir in list(keyCharMap.values()): + if not os.path.exists(os.path.join(folderPath,one_dir)): + os.mkdir(os.path.join(folderPath,one_dir)) + +def labelStemData(stem: Stem, img200bwInv: np.ndarray, beamImgBwInv: np.ndarray, idx:int=0, rootFolder = 'training') -> bool: + sy0, sy1 = stem.getY0Y1() + nx0, ny0, nx1, ny1 = stem.noteBox + if stem.isup: + subFolder='StemUp' + x0 = nx0 + x1 = min(nx1+(nx1-nx0), img200bwInv.shape[1]-1) + y0 = sy0 + y1 = ny1 + else: + subFolder = 'StemDown' + x0 = max(nx0-(nx1-nx0)//2,0) + x1 = nx1 + y0 = ny0 + y1 = sy1 + img200Crop = img200bwInv[y0:y1, x0:x1] + beamCrop = beamImgBwInv[y0:y1, x0:x1] + assert beamCrop.shape[0] == beamCrop.shape[0] + cv2.imshow(f'{idx}', np.hstack((img200Crop, beamCrop))) + k=cv2.waitKey() + cv2.destroyAllWindows() + if chr(k) == 'a': + print(f"stopping at img idx: {idx}") + return False + else: + filname = f'{img_name}_{idx}.jpg' + classFolder = 'x' + key_chr = chr(k) + try: + classFolder = keyCharMap[key_chr] + except: + print(f'invalid keychr: {key_chr}') + cv2.imwrite(f'{rootFolder}/{subFolder}_img200/{classFolder}/{filname}', img200Crop) + cv2.imwrite(f'{rootFolder}/{subFolder}_beam/{classFolder}/{filname}', beamCrop) + return True +def getSingleStemPred(img, model:Single_Stem_Classifier): + # ['n2', 'n4', 'n816'] (0,1,2)if it's flat then it should be n1 3 + if img.shape[1]/img.shape[0]>1.5: + return 3 + else: + return model.predict(img) +def knnRhythmAndDraw(stem_list_assigned:List[Stem], beam_heights, image, bar_height, beamMapImg, stemUpClassifier, stemDownClassifier,img_name): + # save_beam_hist(beam_heights, img_name, bar_height) + beam_type = get_beam_height(beam_heights, bar_height) + class_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40), (245, 220, 255),(230,130,175),(165,170, 70)] + class_names = ['1/4','1/8','1/16','1/32', '1/64', '1/128', '1/2', '1/1'] + imgrgb = image.copy() + x_diff = bar_height//2 + mapBeamVal, middle, mapStaffNum = cv2.split(beamMapImg) + # the first one is a empty one just for easier indexing + noteGroupStemMap = np.zeros((beamMapImg.shape[0], beamMapImg.shape[1]), dtype=np.uint16) + noteGroupList:List[NoteGroup|None] = [None] + img200 =cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + _,img200bw = cv2.threshold(img200, 200, 255, cv2.THRESH_BINARY_INV) + for j in range(0,len(class_names)): + imgrgb = cv2.putText(imgrgb,class_names[j], (30, 30+j*30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, class_colors[j], 2, cv2.LINE_AA) + # stillLabel = True + # init_label_folder() + predLabelConvert = [-2,0,1,-1] + for idx,stem in enumerate(stem_list_assigned): + stem.rhythm = max(beam_type[idx*2: idx*2+2]) + leftHeight, rightHeight = stem.getBeamHeights() + # solve the issue of misclassify something to no stem when there's one (since knn also train on 0s) + if stem.rhythm==0 and max(leftHeight, rightHeight)>0: + stem.rhythm = 1 + elif stem.rhythm == 0: + sy0, sy1 = stem.getY0Y1() + nx0, ny0, nx1, ny1 = stem.noteBox + if stem.isup: + x0 = nx0 + x1 = min(nx1+(nx1-nx0), img200bw.shape[1]-1) + y0 = sy0 + y1 = ny1 + else: + x0 = max(nx0-(nx1-nx0)//2,0) + x1 = nx1 + y0 = ny0 + y1 = sy1 + img200Crop = img200bw[y0:y1, x0:x1] + # ['n2', 'n4', 'n816'] (0,1,2), 3 if the box doesn't have stem + predLabel = getSingleStemPred(img200Crop, model=stemUpClassifier if stem.isup else stemDownClassifier) + stem.rhythm = predLabelConvert[predLabel] + stem.isSingle = True + xcenter = stem.start[0] + if len(stem.alternativeBox)==0: + stemX0 = xcenter - bar_height//3 #(bar_height*2//3 if stem.isup else bar_height//3) + stemX1 = xcenter + bar_height//3 #(bar_height//3 if stem.isup else bar_height*2//3) + stemY0,stemY1 = stem.getY0Y1() + else: + # x0,y0,x1,y1, x0 == x1 + x00, stemY0, xcenter2, stemY1 =stem.alternativeBox[0] + assert x00 == xcenter2 + stemX0 = xcenter2 - bar_height//3 + stemX1 = xcenter2 + bar_height//3 + + if stemY0 == stemY1: + stemY1 = stemY0+1 + elif len(stem.alternativeBox)==0: + # y0 is the top, smaller one + stemY0 = stemY0 - beamMapImg[stemY0, -1, 2] + stemY1 = stemY1 + beamMapImg[stemY1, -2, 2] + cropPart = noteGroupStemMap[stemY0:stemY1, stemX0:stemX1] + staffNumPart = mapStaffNum[stemY0:stemY1, stemX0:stemX1] + staffNum = np.max(staffNumPart) + # there's no overlapping group + if np.max(cropPart) == 0: + noteGroupStemMap[stemY0:stemY1, stemX0:stemX1] = len(noteGroupList) + mapStaffNum[stemY0:stemY1, stemX0:stemX1] = staffNum + noteGroupList.append(NoteGroup(stem)) + else: + overlappingIndex = np.unique(cropPart).tolist() + if 0 in overlappingIndex: + overlappingIndex.remove(0) + if len(overlappingIndex)==1: + currGroupIdx = overlappingIndex[0] + # xcenter-bar_height//2:xcenter+bar_height//2 + noteGroupStemMap[stemY0:stemY1, stemX0:stemX1] = currGroupIdx + yy,xx = np.where(noteGroupStemMap == currGroupIdx) + mapStaffNum[yy,xx] = np.max(mapStaffNum[yy,xx]) + noteGroupList[currGroupIdx] = mergeNoteGroup(noteGroupList[currGroupIdx], NoteGroup(stem), beamMapImg) + # noteGroupList[currGroupIdx].addNoteStem(stem) + else: + currGroupIdx = overlappingIndex[0] + noteGroupStemMap[stemY0:stemY1, stemX0:stemX1] = currGroupIdx + for gIdx in overlappingIndex[1:]: + ys,xs = np.where(cropPart == gIdx) + noteGroupStemMap[ys,xs] = currGroupIdx + noteGroupList[currGroupIdx] = mergeNoteGroup(noteGroupList[currGroupIdx],noteGroupList[gIdx], beamMapImg) + noteGroupList[gIdx] = None + yy,xx = np.where(noteGroupStemMap == currGroupIdx) + mapStaffNum[yy,xx] = np.max(mapStaffNum[yy,xx]) + noteGroupList[currGroupIdx] = mergeNoteGroup(noteGroupList[currGroupIdx], NoteGroup(stem), beamMapImg) + + noteSizes = [] + for ng in noteGroupList: + if ng is None: + continue + for stem in ng.noteStemList: + imgrgb = cv2.rectangle(imgrgb, (stem.getX()-bar_height//3, stem.getTopCoord()[1]), (stem.getX()+bar_height//3, stem.getBottomCoord()[1]), + class_colors[stem.rhythm], 1, cv2.LINE_AA) + x0,y0,x1,y1 = stem.noteBox + noteSizes.append((x1-x0)*(y1-y0)) + imgrgb = cv2.rectangle(imgrgb, (x0,y0),(x1,y1), class_colors[stem.rhythm], 1, cv2.LINE_AA) + if len(stem.alternativeBox) >0: + alterBoxes = stem.alternativeBox + for alterBox in alterBoxes: + imgrgb = cv2.rectangle(imgrgb, (alterBox[0], alterBox[1]),(alterBox[2],alterBox[3]),(120,150,255), 3, cv2.LINE_AA) + kmeans = KMeans(n_clusters = 2, init = np.array([min(noteSizes), max(noteSizes)], dtype=np.uint16).reshape(-1, 1), n_init = 1) + kmeans.fit(np.array(noteSizes,dtype=np.uint16).reshape(-1,1)) + + if kmeans.cluster_centers_[1]/kmeans.cluster_centers_[0]>2: + imgrgb2 = image.copy() + currentCount = -1 + minSize = int(kmeans.cluster_centers_[1]//2) + for ngId, ng in enumerate(noteGroupList): + if ng is None: + continue + stmToRemove = [] + for stemId, stem in enumerate(ng.noteStemList): + x0,y0,x1,y1 = stem.noteBox + currentCount = currentCount+1 + # if kmeans.labels_[currentCount] == 0: + # break + if (x1-x0)*(y1-y0)0: + alterBoxes = stem.alternativeBox + for alterBox in alterBoxes: + imgrgb2 = cv2.rectangle(imgrgb2, (alterBox[0], alterBox[1]),(alterBox[2],alterBox[3]),(120,150,255), 3, cv2.LINE_AA) + stmToRemove.reverse() + for rmId in stmToRemove: + del ng.noteStemList[rmId] + if len(ng.noteStemList) == 0: + noteGroupList[ngId] = None + else: + imgrgb2 = imgrgb + noteGroupMap = np.zeros((beamMapImg.shape[0], beamMapImg.shape[1]), dtype=np.uint16) + ngImg = image.copy() + + colors = [(0,0,255),(0,255,0),(255,255,0), (0,120,230)] + for idx,ng in enumerate(noteGroupList): + if ng is not None: + x0,y0,x1,y1 = ng.boundingBox + noteGroupMap = cv2.rectangle(noteGroupMap, (x0,y0),(x1,y1), idx, -1) + staffnum = np.max(mapStaffNum[y0:y1, x0:x1]) + mapStaffNum[y0:y1+1, x0:x1+1] = staffnum + coloridx = -1 if staffnum ==0 else staffnum%3 + ngImg = cv2.rectangle(ngImg, (x0,y0),(x1,y1), colors[coloridx], 1, cv2.LINE_AA) + for nb in ng.noteBoxes: + xx0,yy0,xx1,yy1 = nb + ngImg = cv2.rectangle(ngImg, (xx0,yy0),(xx1,yy1), colors[coloridx], 1, cv2.LINE_AA) + beamMapImg = cv2.merge([mapBeamVal, middle, mapStaffNum]) + imwrite('ngImg.jpg',ngImg) + imwrite('knnbeams.jpg',imgrgb) + imwrite('knnbeams2.jpg',imgrgb2) + global DEBUGIMG + DEBUGIMG = imgrgb.copy() + return noteGroupList, beamMapImg, noteGroupStemMap, noteGroupMap, {'knnbeams':imgrgb,'ngImg': ngImg, 'knnbeams2': imgrgb2} + +# savebeam is a black image with white on the place of beam and notes and clefs +def generateBeamStaffImg(savebeam:np.ndarray, staffObjList:List[Staff], barheight:int,stepSize=1): + # beamThick = cv2.dilate(savebeam, np.ones((3,1), dtype=np.uint8), iterations=1) + mappingImgRgb = np.zeros((savebeam.shape[0], savebeam.shape[1], 3)) + savebeambw = cv2.cvtColor(savebeam, cv2.COLOR_BGR2GRAY) + contours, _ = cv2.findContours(savebeambw.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + selectedContours = [] + bb,gg,rr = cv2.split(mappingImgRgb) + staffYCenters = [] + for idx,staff in enumerate(staffObjList): + if staff.left <= 4: + staff.left = 5 + if staff.right >= savebeam.shape[1]-3: + staff.right = savebeam.shape[1]-4 + staffYs = staff.ys + oneY:float = staff.get_yOne_float() + staffYCenter = staffYs[2] + rr = cv2.rectangle(rr, (staff.left, staffYs[0]), (staff.right, staffYs[-1]), idx+1,-1) + # -1: how far to the top [0] + rr[staffYs[0]:min(staffYs[-1]+barheight, savebeam.shape[0]),-1] = np.array(range(0, min(staffYs[-1]+barheight, savebeam.shape[0])-staffYs[0])) + rr[max(staffYs[0]-barheight, 0):staffYs[-1],-2] = np.array(range(staffYs[-1]-max(staffYs[0]-barheight, 0),0,-1)) + # line one's index will be 1~10:1, 2s will be 20~200:20 + indexRatio = (idx%2)*20+(1-idx%2)*1 + staffYCenter = staffYs[2] + staffYCenters.append(staffYCenter) + + for i in range(1,13): + if staffYCenter-i*oneY>0: + rr[round(staffYCenter-i*oneY):round(staffYCenter-(i-1)*oneY),(idx+1)%4] = i+12 + else: + break + for i in range(1,13): + if staffYCenter+i*oneYbarheight*1.5 and ww and not (w<2*barheight and h/w>0.5)and w>h: + selectedContours.append(cnt) + bb = cv2.drawContours(bb, selectedContours,-1, 255,thickness=-1) + imwrite('beamOri.jpg',savebeambw) + mappingImgRgb = cv2.merge([bb,gg,rr]) + imwrite('beamOricontours.jpg',mappingImgRgb) + bbUint8 = bb.astype(np.uint8) + kernel = np.ones((1,barheight//3), dtype=np.uint8) + bbUint8 = cv2.dilate(bbUint8, kernel, iterations=1) + mappingImgUint8= cv2.merge([bbUint8,gg.astype(np.uint8),rr.astype(np.uint8)]) + print('preparing for beam image') + for xIdx in range(0,savebeam.shape[1], stepSize*2): + # no white in this place + if sum(mappingImgUint8[:,xIdx,0]) == 0: + continue + startingY = np.where(mappingImgUint8[:,xIdx,0]==255)[0].tolist() + # the ending of the bottommost one + startingY.append(min(mappingImgUint8.shape[0],startingY[-1]+barheight*5)) + for i in range(0,len(startingY)-1): + yStartVal = 253 + for yIdx in range((startingY[i]+stepSize)//(2*stepSize)*(2*stepSize)+stepSize, startingY[i+1],stepSize*2): + if yStartVal<=0: + break + mappingImgUint8[yIdx,xIdx,0]=yStartVal + yStartVal-=stepSize*2 + # the ending of the topmost one + startingY.pop() + startingY.insert(0, max(startingY[0]-barheight*5, 0)) + for i in range(len(startingY)-1, 0,-1): + yStartVal = 254 + for yIdx in range((startingY[i]-1)//(2*stepSize)*(2*stepSize), startingY[i-1],-stepSize*2): + if yStartVal<=0: + break + mappingImgUint8[yIdx,xIdx,0]=yStartVal + yStartVal-=stepSize*2 + onlyBImg = mappingImgUint8.copy() + onlyBImg[:,:,1] = onlyBImg[:,:,0] + onlyBImg[:,:,2] = onlyBImg[:,:,0] + imwrite('onlyBImg.jpg',onlyBImg) + saveImages = {} + saveImages['gradImg']=onlyBImg + saveImages['beamContour']=mappingImgRgb + return mappingImgUint8, saveImages + +def getBeamImage(dataDict, bar_height, staffObjList:List[Staff]): + stems_rests = dataDict['stems_rests'] + clefs_keys = dataDict['clefs_keys'] + notehead = dataDict['notehead'] + symbols = dataDict['symbols'] + image:np.ndarray = dataDict['image'] + + _, img128 = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY_INV) + kernel = np.ones((bar_height//3, bar_height//3),dtype=np.uint8) + beam = cv2.erode(img128, kernel, iterations = 1) + imwrite('note_beam1.jpg',beam) + + for ll in range(3): + haslinecount = 0 + totalCount = 0 + for staff in staffObjList: + for b in range(len(staff.ys)): + curr = sum(np.max(beam[max(staff.ys[b]-bar_height//3,0):min(staff.ys[b]+bar_height//3, beam.shape[0]), staff.left:staff.right,1],0)==255) + if curr>(staff.right-staff.left)//3: + haslinecount+=1 + totalCount+=1 + if haslinecount/totalCount>0.6: + kernel = np.ones((bar_height//4, bar_height//4),dtype=np.uint8) + beam = cv2.erode(beam, kernel, iterations = 1) + else: + break + imwrite('note_beam1.5.jpg',beam) + xs,ys = np.where(clefs_keys>0) + beam[xs,ys] = (0,0,0) + beamWithoutStemRest = beam.copy() + imwrite('note_beam2.jpg',beamWithoutStemRest) + # beamret is beamNoClefKey + xs,ys = np.where(stems_rests>0) + beam[xs,ys] = (255,255,255) + imwrite('note_beam3.jpg', beam) + # beam is beam with stem_rests + + kernel_tall = np.ones((1,bar_height*2), np.uint8) + beam_nostem = cv2.erode(beam, kernel_tall) + beam_nostem = cv2.dilate(beam_nostem, kernel_tall) + imwrite('note_beam4.jpg', beam_nostem) + beam_nostem_nostaff = beam_nostem.copy() + savebeambw = cv2.cvtColor(beam_nostem, cv2.COLOR_BGR2GRAY) + toDel = np.where(np.sum(savebeambw>127,axis=1)>savebeambw.shape[1]*0.4)[0] # VARIABLE threshold to delete + for d in toDel: + beam_nostem_nostaff[d,:] = 0 + imwrite('note_beam5.jpg', beam_nostem_nostaff) + # beam nostem is beam with stemRest + erode (also has notes) + return beam, beamWithoutStemRest, beam_nostem_nostaff + +def filter_boxes_on_beam(notehead_boxes, beamMapImg, areamin): + filtered_Boxes = [] + for box in notehead_boxes: + x0,y0,x1,y1 = box + if np.max(beamMapImg[y0:y1, x0:x1, 0])==255: + w = x1-x0 + h = y1-y0 + if w*h0) + notehead_mod = notehead.copy()*255 + notehead_mod[xs,ys] = 0 + + imwrite('notehead_ori.jpg', notehead_mod) + dilation_ratio1 = 0.33 + dilation_ratio2 = 0.4 + # dilate->erode twice to clear image + get clear noteheads + size1 = int(round(bar_height*dilation_ratio1)) + note_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size1, size1)) + notehead_mod = cv2.erode(cv2.dilate(notehead_mod.astype(np.uint8), note_kernel), note_kernel) + size2 = int(round(bar_height*dilation_ratio2)) + + note_kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size2,size2)) + notehead_mod2 = cv2.erode(notehead_mod, note_kernel2) + note_kernel3 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size2+1, size2+1)) + notehead_mod2 = cv2.dilate(notehead_mod2, note_kernel3) + + # both b&w image, white is where there's stuff + imwrite('notehead_mod.jpg',notehead_mod) + imwrite('notehead_mod2.jpg',notehead_mod2) + # notehead mod is basically the cleaned beams + + # get the boxes, if the boxes is in + notehead_boxes_all = get_bbox(notehead_mod2) + notehead_boxes_big, notehead_mod3= filter_small_bbox_area(notehead_boxes_all, notehead_mod2,xmin = bar_height*0.5,ymin = bar_height*0.2, areamin = bar_height*bar_height*0.4) + imwrite('notehead_mod3.jpg',notehead_mod3) + notehead_boxes = split_connected_notes(notehead_boxes_big, bar_height,notehead_mod2) + notehead_boxes_filtered = filter_boxes_on_beam(notehead_boxes, beamMapImg,areamin = bar_height*bar_height*0.8) + return notehead_boxes_filtered, notehead_mod2 + +# noteheadInitial : b&w 2d image, 255: notehead, +# beamMapImg: gradient image, the 1st dimension is what we care about +def getStemList(dataDict, notehead_boxes:List[Tuple[int,int,int,int]], beamWithStemRest): + image:np.ndarray = dataDict['image'] + + drawimg = image.copy() + # 255 is where there's stuff, 0 is empty + _, img200 = cv2.threshold(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY), 200, 255, cv2.THRESH_BINARY_INV) + imgh = image.shape[0] + notehead_rectangle = np.zeros((image.shape[0],image.shape[1])) + stem_init_lst:List[Stem]=[] + # beambw is suppose to be white where there's beam + beambw = cv2.cvtColor(beamWithStemRest, cv2.COLOR_BGR2GRAY) + imwrite('beambw.jpg',beambw) + # get the stem's directions + for idx,boxOriginal in enumerate(notehead_boxes): + x0,y0,x1,y1 = boxOriginal + xleft = (x0+x1*2)//3 + xright = (x0*2+x1)//3 + + leftRange = np.where(img200[y0:y1,xleft]==255)[0].tolist() + rightRange = np.where(img200[y0:y1, xright] ==255)[0].tolist() + if len(leftRange)*len(rightRange) != 0: + y1 = y0+max(max(leftRange),max(rightRange)) + y0 += min(min(leftRange), min(rightRange)) + if y1<=y0: + x0,y0,x1,y1 = boxOriginal + box = (x0,y0,x1,y1) + notehead_rectangle[y0:y1,x0:x1] = 1 + length = bar_height*3 + width = (x1-x0)//3 + leftXrange = range(x0-width//2,x0+width+1) + rightXrange = range(x1-width, x1+width//2+1) + topYrange = range(max(y0-length, 0),y0) + bottomYrange = range(y1,min(y1+length, imgh)) + left_area = beambw[bottomYrange.start:bottomYrange.stop, + leftXrange.start: leftXrange.stop] + left_centerX = np.argmax(np.sum(left_area,0)) + right_area = beambw[topYrange.start:topYrange.stop, + rightXrange.start:rightXrange.stop] + right_centerX = np.argmax(np.sum(right_area,0)) + # has to be more strict not too far to the left + leftup_area = beambw[topYrange.start:topYrange.stop, + leftXrange.start: leftXrange.stop] + leftup_centerX = np.argmax(np.sum(leftup_area,0)) + rightdn_area = beambw[bottomYrange.start:bottomYrange.stop, + rightXrange.start:rightXrange.stop] + rightdn_centerX = np.argmax(np.sum(rightdn_area,0)) + isup = True + hasStem = True + alterBox = [] + leftRightRatio = np.sum(left_area[:,left_centerX])/np.sum(right_area[:, right_centerX]) + if leftRightRatio>1: + # stem is in left bottom + maxRange = [leftXrange, bottomYrange] + isup = False + if leftRightRatio<1.25: + alterBox.append([rightXrange.start+right_centerX,topYrange.start,rightXrange.start+right_centerX,topYrange.stop]) + else: + # it's either close enough or in right bottom + maxRange = [rightXrange, topYrange] + # they are close enough -> alterbox will always record the left bottom one + if leftRightRatio>0.8: + alterBox.append([leftXrange.start+left_centerX,bottomYrange.start,leftXrange.start+left_centerX,bottomYrange.stop]) + maxArea = max(np.sum(left_area[:,left_centerX]),np.sum(right_area[:, right_centerX])) + maxAlterArea = max(np.sum(leftup_area[:,leftup_centerX]),np.sum(rightdn_area[:,rightdn_centerX])) + if maxAlterArea>255*length//2 and maxAlterArea/maxArea>1.5: + if np.sum(leftup_area[:,leftup_centerX])>np.sum(rightdn_area[:,rightdn_centerX]): + alterBox.append([leftXrange.start+leftup_centerX, topYrange.start, leftXrange.start+leftup_centerX, topYrange.stop]) + else: + alterBox.append([rightXrange.start+rightdn_centerX, bottomYrange.start, rightXrange.start+rightdn_centerX, bottomYrange.stop]) + if len(alterBox)==0 and maxArea < 255*length//3: + hasStem = False + # hasStem is a flag for later grouping notes + xrange, yrange = maxRange + crop = img200[yrange.start:yrange.stop, xrange.start:xrange.stop] + a = (np.where(np.sum(crop,0)==np.max(np.sum(crop,0)))[0]).tolist() + chosenx = a[len(a)//2] + x_y = (xrange.start+chosenx, y0 if isup else y1) + stem_init_lst.append(Stem(x_y, isup,boxOriginal,box, hasStem=hasStem, alterbox=alterBox)) + if len(alterBox)==0 and hasStem: + drawimg = cv2.rectangle(drawimg, (xrange.start+chosenx-1, yrange.start),(xrange.start+chosenx+1,yrange.stop),(255,0,120), 1, cv2.LINE_AA) + elif hasStem: + drawimg = cv2.putText(drawimg, str(idx), (x0,y0),cv2.FONT_HERSHEY_SIMPLEX, 1, (120,0,120), 2, cv2.LINE_AA) + drawimg = cv2.rectangle(drawimg, (xrange.start+chosenx-1, yrange.start),(xrange.start+chosenx+1,yrange.stop),(255,255,0), 2, cv2.LINE_AA) + for ab in alterBox: + drawimg = cv2.rectangle(drawimg, (ab[0], ab[1]),(ab[2],ab[3]),(0,255,0), 3, cv2.LINE_AA) + drawimg = cv2.rectangle(drawimg, (x0,y0),(x1,y1),(0,0,255), 2, cv2.LINE_AA) + imwrite('notehead_drawimg.jpg',drawimg) + return stem_init_lst, image, {'notehead_drawimg':drawimg} + +def assignStemLength(stem_init_lst:List[Stem], image, beamMapImg:np.ndarray, stepsize: int, bar_height): + # get the stem's length + img200 =cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + _,img200bw = cv2.threshold(img200, 200, 255, cv2.THRESH_BINARY_INV) + # img200 is black and white, white is where there's stem + imwrite('img200inv.jpg', img200bw) + # wid = 1 + topthres = bar_height//2 + ending_black_height = 3 + # make it whiter for better visualization + drawimg2 = np.ones_like(img200bw)*255 - img200bw.copy()//4 + drawimg2 = cv2.cvtColor(drawimg2, cv2.COLOR_GRAY2BGR) + bigStep = stepSize*2 + for idx,coordStart in enumerate(stem_init_lst): + x,yOri = coordStart.start + wid = (coordStart.noteBox[2]-coordStart.noteBox[0])//3 + yend = yOri + sign = 1 if coordStart.isup else -1 + # is in staff +- 3.5 barheight, no alternative box, has stem + # +beamMapImg[max(yOri-int(2.5*bar_height)*sign,0),x,2] + isTypical:bool = (len(coordStart.alternativeBox) == 0) and coordStart.hasStem and min([abs(posIdx-11.5) for posIdx in beamMapImg[yOri,0:4,2]])<5 + + # if the beamMap is less than 4*barheight away and the beam itself is somewhere in a staffArea, assign it + # elif it's not too far away (2*4*barheight), see if the area has at least 1/2 blacks + # else it's gonna go through the clssification model to see if it's a single quarter/eighth/sixteenth + xLeft = x//bigStep*bigStep + xRight = min(beamMapImg.shape[1]-1,x//bigStep*bigStep+bigStep) + staffLineNumber:int = beamMapImg[yOri, 4, 2] + barPositionI: int = beamMapImg[yOri, staffLineNumber%4, 2] + stemUpLongest:int = 0 if (barPositionI<8 or barPositionI>17) else 17-barPositionI + stemDownLongest:int = 0 if (barPositionI<8 or barPositionI>17) else barPositionI-8 + if coordStart.isup: + longestNormal = stemUpLongest + # go how far is considered normal and ok + y = yOri//bigStep*bigStep-stepsize + leftUpValue = beamMapImg[y,xLeft, 0] + rightUpValue = beamMapImg[y,xRight, 0] + if leftUpValue>rightUpValue: + maxMapValue = leftUpValue + else: + maxMapValue = rightUpValue + assert maxMapValue%2==1 or maxMapValue == 0 + if maxMapValue != 0: + if isTypical and (253-maxMapValue)+bigStep<=(longestNormal)*bar_height: + if maxMapValue < 255: + yend = y-(253-maxMapValue) + assert max(beamMapImg[yend, xLeft,0],beamMapImg[yend, xRight,0]) >= 253 + while max(beamMapImg[yend, xLeft,0],beamMapImg[yend, xRight,0]) >= 253-bar_height//2: + yend -= bigStep + while max(beamMapImg[yend, xLeft,0],beamMapImg[yend, xRight,0]) == 255: + yend -= 1 + # the end of the stem doesn't have enough "stem (white)" (the image is inversed) + if (253-maxMapValue)+bigStep>(longestNormal-3)*bar_height and (sum(np.max(img200bw[yend:y, x-wid:x+wid+1],1))<(y-yend)*255*0.8 or sum(np.max(img200bw[yend:(y+yend)//2, x-wid:x+wid+1],1))<(y-yend)*255*0.4): + yend = yOri + if yend==yOri: + if isTypical and coordStart.hasStem and len(coordStart.alternativeBox)==0: + yend -= min(3,longestNormal)*bar_height + # while there is still things in the inversed bw original image + while sum(np.max(img200bw[yend-ending_black_height:yend, x-wid:x+wid+1],1))!=0: + yend -= 1 + else: + while sum(np.max(img200bw[yend-ending_black_height:yend, x-1:x+2],1))!=0: # and max([(10-beamMapImg[yend,x,1]%11)%10,(10-beamMapImg[yend,x,1]//20)%10]): + yend -= 1 + else: + longestNormal = stemDownLongest + y = min(yOri//bigStep*bigStep+bigStep,beamMapImg.shape[0]-1) + leftUpValue = beamMapImg[y,xLeft, 0] + rightUpValue = beamMapImg[y,xRight, 0] + if leftUpValue>rightUpValue: + maxMapValue = leftUpValue + else: + maxMapValue = rightUpValue + xRight = x//bigStep*bigStep+bigStep + assert maxMapValue%2==0 or maxMapValue == 255 + if isTypical and (254-maxMapValue)+bigStep<=longestNormal*bar_height: + if maxMapValue < 255: + yend = y+(254-maxMapValue) + assert max(beamMapImg[yend, xLeft,0],beamMapImg[yend, xRight,0]) >=254 + while max(beamMapImg[yend, xLeft,0],beamMapImg[yend, xRight,0]) >= 254-bar_height//2 and (yend-yOri)<=(longestNormal)*bar_height: + yend += bigStep + while max(beamMapImg[yend, xLeft,0],beamMapImg[yend, xRight,0]) == 255 and (yend-yOri)<=(longestNormal)*bar_height: + yend += 1 + # the end of the stem doesn't have enough "stem (white)" (the image is inversed) + if (254-maxMapValue)+bigStep>(longestNormal-3)*bar_height and (sum(np.max(img200bw[y:yend, x-wid:x+wid+1],1))<(yend-y)*128 or sum(np.max(img200bw[(y+yend)//2:yend, x-wid:x+wid+1],1))<(yend-y)*64): + yend = yOri + if yend==yOri: + if isTypical and coordStart.hasStem and len(coordStart.alternativeBox)==0: + yend += min(3,longestNormal)*bar_height + # while there is still things in the inversed bw original image + while sum(np.max(img200bw[yend:yend+ending_black_height, x-wid:x+wid+1],1))!=0 and (yend-yOri)<=(longestNormal)*bar_height: + yend += 1 + else: + while sum(np.max(img200bw[yend:yend+ending_black_height, x-1:x+2],1))!=0: # and min([beamMapImg[yend,x,1]%11,beamMapImg[yend,x,1]//20])<2: + yend += 1 + if abs(yend-y)0) # where there's note but no beam + beamNoNotes = beamNoClefKeyWithStemRest.copy() + beamNoNotes[xs,ys] = (0,0,0) + imwrite('beamNoNotes1.jpg',beamNoNotes) + xs,ys = np.where(beambw==255) + beamNoNotes[xs,ys] = (255,255,255) + imwrite('beamNoNotes2.jpg',beamNoNotes) + + return beamNoNotes + +def mergeVerticalNoteGroups(staffObjList:List[Staff],noteGroupList:List[NoteGroup], noteGroupMap:np.ndarray,beamMapImg:np.ndarray, image:np.ndarray, + restMap, restList:List[Rest]): + for staff in staffObjList: + barh = staff.get_yOne() + oneStep = barh//3 + y0 = max(staff.ys[0]-int(barh*2.5),0) + y1 = min(staff.ys[-1]+int(barh*2.5), noteGroupMap.shape[0]) + tempRestMap = restMap.copy() + for x in range(staff.left, staff.right-oneStep, oneStep): + indexes = set(np.unique(noteGroupMap[y0:y1, x])) + indexesNext = set(np.unique(noteGroupMap[y0:y1, x+oneStep])) + rIndex = set(np.unique(tempRestMap[y0:y1, x])) + rIndexNext = set(np.unique(tempRestMap[y0:y1, x+oneStep])) + line = set(np.unique(beamMapImg[y0:y1, x:x+oneStep,2])) + indexes -= {0} + indexesNext-={0} + line-={0} + rIndex-={0} + rIndexNext-={0} + if len(line)>1: + continue + if len(indexes) == 0: + continue + # the overlapping x width is at least oneStep width + noteGroupId = list(indexes)[0] + lineList = list(line) + idxList = list(indexes) + + if len(indexes)>1 and indexes==indexesNext: # two noteGroup overlap + noteGroupId = idxList.pop() + for gid in idxList: + ys,xs = np.where(noteGroupMap == gid) + noteGroupMap[ys,xs] = noteGroupId + beamMapImg[ys, xs, 2] = lineList[0] + noteGroupList[noteGroupId]=mergeNoteGroup(noteGroupList[noteGroupId], noteGroupList[gid], beamMapImg) + noteGroupList[gid] = None + if len(rIndex)>0 and rIndex == rIndexNext: + ridLst = list(rIndex) + for rid in ridLst: + ys,xs = np.where(tempRestMap == rid) + if restList[rid].rhythm == -1: + continue + else: + tempRestMap[ys,xs] = 0 + beamMapImg[ys,xs,2] = lineList[0] + noteGroupList[noteGroupId].addRest(restList[rid]) + restList[rid].setNgId(noteGroupId) + ngImg = image.copy() + colors = [(0,0,255),(0,255,0),(255,255,0), (0,120,230)] + for ng in noteGroupList: + if ng is not None: + x0,y0,x1,y1 = ng.boundingBox + ngImg = cv2.rectangle(ngImg, (x0,y0),(x1,y1), colors[(np.max(beamMapImg[y0:y1, x0:x1, 2])%3 if np.max(beamMapImg[y0:y1, x0:x1, 2])>0 else 3)], 2, cv2.LINE_AA) + imwrite('vertically_merged.jpg', ngImg) + return noteGroupList, noteGroupMap, beamMapImg, {'vertically_merged':ngImg} + +def getbbox(clefs_keys: np.ndarray, bar_height:int)-> List[Tuple[int,int,int,int]]: + contours, _ = cv2.findContours(clefs_keys.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + bboxes = [] + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + if w>3 and h>bar_height*1.6 and h>w: + box = (x, y, x+w, y+h) + bboxes.append(box) + return bboxes + + +def getBestRange(avg_color_lst: list, desire_length:int): + if desire_length>=len(avg_color_lst)-1: + return 0, len(avg_color_lst)-1 + most_white_value = 0 + most_white_ending_idx = 0 + for i in range(desire_length,len(avg_color_lst)-1): + currSum = sum(avg_color_lst[i-desire_length:i]) + if currSum>most_white_value: + most_white_value=currSum + most_white_ending_idx = i + return most_white_ending_idx-desire_length, most_white_ending_idx +def sfnClefToDataType(bbox:Tuple[int,int,int,int], predictionId: int) -> Union[Accidentals, Clef]: + # 'BassF', 'ViolaC', 'flat', 'natural', 'noClass', 'sharp', 'trebleG' + isClef = [True, True, False, False, False, False, True] + mapId = [-1, 0, -1, 0, -3, 1, 1] + # we should never try to assign a noClass + assert predictionId != 4 + if isClef[predictionId]: + return Clef(bbox, mapId[predictionId]) + else: + return Accidentals(bbox, mapId[predictionId]) + + +def symbol_classification(dataDict: dict, model:Sfn_Clef_classifier, bar_height:int): + # output_dir will be images/tch/tch SYMBOL + clefs_keys_ori:np.ndarray = dataDict['clefs_keys'] + clefs_keys = (clefs_keys_ori*255).astype(np.uint8) + clefs_keys_expand = cv2.dilate(clefs_keys, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(4,4))) + image:np.ndarray = dataDict['image'] + round1_sfn_img = image.copy() + ori_bboxes = getbbox(clefs_keys_expand, bar_height) + bboxes = merge_nearby_bbox(ori_bboxes, bar_height*3) + class_names = ['BassF', 'ViolaC', 'flat', 'natural', 'noClass', 'sharp', 'trebleG','wide'] # Replace with actual class names + class_symbols = ['Bass','Viola','b','n','x','#','Treble','Wide'] + class_colors = [(255,0,0),(0,255,0),(255,255,0),(255,0,125),(0,120,255),(255,0,255),(0,0,255),(101,134,168)] + for j in range(0,len(class_colors)): + round1_sfn_img = cv2.putText(round1_sfn_img,class_names[j], (30, 30+j*30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, class_colors[j], 2, cv2.LINE_AA) + noClass_bbox = [] + sfn_height_lst = [] + sfn_width_lst = [] + # the 0th is a stub for better alignment + returnSfnList:List[Union[Accidentals,Clef, None]] = [None] + for b in bboxes: + if (b[3]-b[1])<(b[2]-b[0]): # height likely it's flat instead + predict_idx = 2 + if predict_idx == 2 or predict_idx==5 or predict_idx==3: + # this is flat | sharp | natural + sfn_height_lst.append(b[3]-b[1]) + sfn_width_lst.append(b[2]-b[0]) + round1_sfn_img = cv2.rectangle(round1_sfn_img, (b[0],b[1]),(b[2],b[3]), class_colors[predict_idx],2,cv2.LINE_AA) + round1_sfn_img = cv2.putText(round1_sfn_img, class_symbols[predict_idx], (b[0],b[3]+10),cv2.FONT_HERSHEY_SIMPLEX, 1, class_colors[predict_idx], 1, cv2.LINE_AA) + if predict_idx == 4 or predict_idx==7: + noClass_bbox.append(b) + else: + returnSfnList.append(sfnClefToDataType(b, predict_idx)) + noClass_bbox2 = [] + round2_sfn_img = round1_sfn_img.copy() + sfn_median_height = bar_height*2 + sfn_median_width = bar_height + if len(sfn_height_lst)>0: + sfn_median_height = int(np.median(sfn_height_lst)) + sfn_median_width = int(np.median(sfn_width_lst)) + + for box1 in noClass_bbox: + x0 = box1[0] + y0 = box1[1] + clef_key_crop = clefs_keys[box1[1]:box1[3], box1[0]:box1[2]] + smallbbox = getbbox(clef_key_crop, bar_height) + if len(smallbbox)<=1: + if len(smallbbox)==1 and ((box1[2]-box1[0])>sfn_median_width*1.5 or (box1[3]-box1[1])>sfn_median_height*1.5): + noClass_bbox2.append(box1) + predict_idx = 4 + round2_sfn_img = cv2.rectangle(round2_sfn_img, (box1[0],box1[1]),(box1[2],box1[3]), class_colors[predict_idx],2,cv2.LINE_AA) + round2_sfn_img = cv2.putText(round2_sfn_img, class_symbols[predict_idx], (box1[0],box1[3]+10),cv2.FONT_HERSHEY_SIMPLEX, 1, class_colors[predict_idx], 1, cv2.LINE_AA) + continue + for bb in smallbbox: + b = [x0+bb[0],y0+bb[1],x0+bb[2],y0+bb[3]] + small_crop = clefs_keys[b[1]:b[3], b[0]:b[2]] + predict_idx = model.predict(small_crop) + round2_sfn_img = cv2.rectangle(round2_sfn_img, (b[0],b[1]),(b[2],b[3]), class_colors[predict_idx],2,cv2.LINE_AA) + round2_sfn_img = cv2.putText(round2_sfn_img, class_symbols[predict_idx], (b[0],b[3]+10),cv2.FONT_HERSHEY_SIMPLEX, 1, class_colors[predict_idx], 1, cv2.LINE_AA) + + if predict_idx == 4: + if (small_crop.shape[0]>sfn_median_height*1.5 and small_crop.shape[1]>sfn_median_width*0.8) or (small_crop.shape[0]>sfn_median_height*0.8 and small_crop.shape[1]>sfn_median_width*1.5): + noClass_bbox2.append(b) + else: + continue + else: + returnSfnList.append(sfnClefToDataType(b, predict_idx)) + for box1 in noClass_bbox2: + x0 = box1[0] + y0 = box1[1] + w = box1[2]-x0 + h = box1[3]-y0 + seg_img = clefs_keys[y0:y0+h, x0:x0+w] + width_ratio =w/sfn_median_width + height_ratio = h/sfn_median_height + num_symbol = round(max(width_ratio, height_ratio)) + if num_symbol<2: + continue + if width_ratio>height_ratio: + left_center = sfn_median_width//2 + right_center = w-sfn_median_width//2 + avg_width:float = (right_center-left_center)/(num_symbol-1) + for i in range(num_symbol): + img_left = int(i*avg_width) + crop_img = seg_img[:,img_left:min(img_left+sfn_median_width,seg_img.shape[1])] + vertical_avg = np.mean(crop_img,1) + front,back = getBestRange(vertical_avg, sfn_median_height) + # b: x0, y0, x1, y1 relative to the croped image + b = [max(img_left,0),max(front,0), + min(img_left+sfn_median_width,seg_img.shape[1]), min(back,seg_img.shape[0])] + crop_crop_img = seg_img[b[1]:b[3], b[0]:b[2]] + # predict_idx = model.predict(crop_crop_img) + if sum(np.max(crop_crop_img,1))<=0: + continue + if sum(np.max(crop_crop_img,1))/255/crop_crop_img.shape[0]<=0.6: + continue + predict_lst = model.get_prediction_vector(crop_crop_img) + pred_filter = [x for x in predict_lst if x not in [0,1,4,6,7]] + predict_idx = pred_filter[0] + returnSfnList.append(sfnClefToDataType((x0+b[0], y0+b[1], x0+b[2],y0+b[3]), predict_idx)) + else: + seg_img_to_delete = seg_img.copy() + vertical_boxes = [] + pred_idx = [] + + b = [0,0,w,sfn_median_height] #x0, y0, x1, y1 + hor_avg_top = np.mean(seg_img[b[1]:b[3],:],0) + b[0],b[2] = getBestRange(hor_avg_top, sfn_median_width) + seg_img_to_delete[b[1]:b[3],b[0]:b[2]] = 0 + vertical_boxes.append(b) + + b = [0,h-sfn_median_height-1,w,h-1] + hor_avg_bot = np.mean(seg_img[b[1]:b[3],:],0) + b[0],b[2] = getBestRange(hor_avg_bot, sfn_median_width) + seg_img_to_delete[b[1]:b[3],b[0]:b[2]] = 0 + vertical_boxes.append(b) + + if h>sfn_median_height*2: + b = [0,0,0,0] + # middle_del = seg_img_to_delete[sfn_median_height//2:h-sfn_median_height//2,:] + ver_avg_mid = np.mean(seg_img_to_delete,1) + b[1],b[3] = getBestRange(ver_avg_mid, sfn_median_height) + expanded_left_right = [min(vertical_boxes[0][0],vertical_boxes[1][0]), + max(vertical_boxes[0][2],vertical_boxes[1][2])] + if expanded_left_right[0]>=w-expanded_left_right[1]-sfn_median_width//5: + b[2] = sfn_median_width + else: + b[0] = w-sfn_median_width-1 + b[2] = w-1 + vertical_boxes.append(b) + seg_img_bgr = cv2.cvtColor(seg_img,cv2.COLOR_GRAY2BGR) + seg_img_bgr = cv2.rectangle(seg_img_bgr, (b[0],b[1]),(b[2],b[3]),(0,255,0),2,cv2.LINE_AA) + + for idx, b in enumerate(vertical_boxes): + predict_lst = model.get_prediction_vector(seg_img[b[1]:b[3],b[0]:b[2]]) + pred_filter = [x for x in predict_lst if x not in [0,1,4,6,7]] + predict_idx = pred_filter[0] + if idx == 2 and predict_lst[0] == 4: + continue + returnSfnList.append(sfnClefToDataType((x0+b[0], y0+b[1], x0+b[2],y0+b[3]), predict_idx)) + sfnClefImg = image.copy() + class_colors2 = [(255,255,0),(255,0,125),(255,0,255),(255,0,0),(0,255,0),(0,0,255)] + class_names2 = ['flat', 'natural', 'sharp','BassF', 'ViolaC', 'trebleG'] + for j in range(0,len(class_colors2)): + sfnClefImg = cv2.putText(sfnClefImg,class_names2[j], (30, 30+j*30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, class_colors2[j], 2, cv2.LINE_AA) + # class_names = ['BassF', 'ViolaC', 'flat', 'natural', 'noClass', 'sharp', 'trebleG','wide'] + + sfnClefMap = np.zeros((sfnClefImg.shape[0], sfnClefImg.shape[1]), dtype=np.uint16) + for idx,clefAccidentals in enumerate(returnSfnList): + if clefAccidentals is not None: + x0,y0,x1,y1 = clefAccidentals.getBbox() + # 0 if it's accidentals + predict_idx = clefAccidentals.getType()*3+clefAccidentals.getValue()+1 + sfnClefImg = cv2.rectangle(sfnClefImg, (x0,y0),(x1,y1), class_colors2[predict_idx],2,cv2.LINE_AA) + sfnClefMap = cv2.rectangle(sfnClefMap, (x0,y0),(x1,y1), idx, -1) + + imwrite('round1Sfn.jpg',round1_sfn_img) + imwrite('round2Sfn.jpg',round2_sfn_img) + imwrite('round3Sfn.jpg', sfnClefImg) + + return returnSfnList, sfnClefMap, {'sfnClef':sfnClefImg} + +# filter out the sfn that is overlapping with noteGroupList +def filterSfnClefModifyBeamMap(sfnClefList:List[Union[Accidentals, Clef, None]], + noteGroupMap: np.ndarray, + beamMapImg:np.ndarray): + sfnClefMap = np.zeros((beamMapImg.shape[0], beamMapImg.shape[1]), dtype=np.uint16) + bb,gg,rr = cv2.split(beamMapImg) + assert len(np.unique(gg)) <=2 + for idx, sfnClef in enumerate(sfnClefList): + if sfnClef is not None: + x0,y0,x1,y1 = sfnClef.getBbox() + if np.sum(noteGroupMap[y0:y1,x0:x1]>0)/((x1-x0)*(y1-y0))>0.5: + sfnClefList[idx] = None + else: + sfnClefMap[y0:y1,x0:x1] = idx + # 2 for accidentals, 3 for clef + gg[y0:y1,x0:x1] = sfnClef.getType()+2 + # will want the noteGroup to be "more dominent" than the sfnClef, so it overwrites if it's place of a sfn + ys,xs = np.where(noteGroupMap>0) + gg[ys,xs] = 1 + beamMapImg = cv2.merge([bb,gg,rr]) + return sfnClefList,sfnClefMap, beamMapImg + +def findBarlines(image:np.ndarray, beamMapImg:np.ndarray, staffObjList:List[Staff], noteGroupStemMap:np.ndarray, thres:float)->np.ndarray: + img200 =cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + barlineImg = np.zeros((image.shape[0], image.shape[1]),dtype=np.uint8) + # (1) where there's stuff, (0) where there's no + _,img200bin = cv2.threshold(img200, 200, 1, cv2.THRESH_BINARY_INV) + ys,xs = np.where(beamMapImg[:,:,1]>0) + img200bin[ys,xs] = 0 + ys,xs = np.where(noteGroupStemMap>0) + img200bin[ys,xs] = 0 + if len(staffObjList)%NUM_TRACK!= 0: + print(f"staff List is not divisible: {len(staffObjList)}%{NUM_TRACK}") + for idx,staff in enumerate(staffObjList): + ys = staff.ys + y0 = ys[0] + y1 = ys[-1] + xs = np.where(np.sum(img200bin[y0:y1, :],0)>(y1-y0)*thres)[0] + beamMapImg[y0:y1,xs,1] = 4 + beamMapImg[y0:y1,xs-1,1] = 4 + beamMapImg[y0:y1,xs+1,1] = 4 + barlineImg[y0:y1, xs] = 1 + contours, _ = cv2.findContours(barlineImg, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + barlineboxes:List[Tuple[int,int,int,int]] = [] + imgbgr = image.copy() + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + barlineboxes.append((x,y,x+w,y+h)) + imgbgr = cv2.rectangle(imgbgr, (x,y), (x+w, y+h), (0,0,255),1,cv2.LINE_AA) + else: + for linNo in range(len(staffObjList)//NUM_TRACK): + y0 = staffObjList[NUM_TRACK*linNo].ys[0] + y1 = staffObjList[NUM_TRACK*(linNo+1)-1].ys[-1] + xs = np.where(np.sum(img200bin[y0:y1, :],0)>(y1-y0)*thres)[0] + if xs[0] == 0: + xs = xs[1:] + while xs[-1] >= img200bin.shape[1]-1: + xs = xs[:-1] + beamMapImg[y0:y1,xs,1] = 4 + beamMapImg[y0:y1,xs-1,1] = 4 + beamMapImg[y0:y1,xs+1,1] = 4 + barlineImg[y0:y1, xs] = 1 + contours, _ = cv2.findContours(barlineImg, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + barlineboxes:List[Tuple[int,int,int,int]] = [] + imgbgr = image.copy() + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + barlineboxes.append((x,y,x+w,y+h)) + imgbgr = cv2.rectangle(imgbgr, (x,y), (x+w, y+h), (0,0,255),1,cv2.LINE_AA) + imwrite("barLineImg.jpg",imgbgr) + return beamMapImg, barlineboxes, imgbgr + +def filterBarlineBoxes(image:np.ndarray, barlineboxes:List[Tuple[int,int,int,int]]): + imgbgr = image.copy() + img200 =cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + _,img200bin = cv2.threshold(img200, 200, 1, cv2.THRESH_BINARY_INV) + for b in barlineboxes: + thres = (b[3]-b[1])/4 + if np.sum(img200bin[b[1]:b[3],b[0]-2:b[0]])0) + mask[ys,xs] = 0 + stem_rests[ys,xs] = 0 + stem_rests = cv2.dilate(stem_rests.astype(np.uint8), np.ones((barheight//3, barheight//3), dtype= np.uint8)) + + # remaining = cv2.bitwise_or(cv2.bitwise_and(mask,symbol), stem_rests)*255 + remaining = cv2.bitwise_and(mask,symbol)*255 + remaining = cv2.cvtColor(remaining,cv2.COLOR_GRAY2BGR) + symbol = cv2.cvtColor(symbol*255,cv2.COLOR_GRAY2BGR) + + contours, _ = cv2.findContours(stem_rests.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + stem_rests = cv2.cvtColor((stem_rests*255).astype(np.uint8), cv2.COLOR_GRAY2BGR) + + # save the image version (later can give img128 version), stem_rests itself, remaining + bboxes = [] + longBoxes = [] + # imageClean = image.copy() + remainingClean = remaining.copy() + class_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40), (245, 220, 255),(230,130,175),(165,170, 70)] + restClassNames = ['X', 'r16', 'r32', 'r4', 'r8'] + restToRhythm = [0,2,3,0,1] + imgrgb = dataDict['image'].copy() + for j in range(0,len(restClassNames)): + imgrgb = cv2.putText(imgrgb,restClassNames[j], (30, 30+j*30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, class_colors[j], 2, cv2.LINE_AA) + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + if (w>barheight*0.6 and h>barheight): + box = (max(x-barheight//2, 0), max(y-barheight*2, 0), min(x+w+barheight//2, symbol.shape[1]-1), min(y+h+barheight*2, symbol.shape[0]-1)) + # shrink the box so it centers around the rest + yTopBlank = max(np.max(np.where(np.sum(remainingClean[box[1]:(box[1]+box[3])//2, box[0]:box[2],1],1)<255)[0].tolist()+[0])-barheight//4, 0) + # the index of the ending (the new height) + yBottomBlank = min(np.min(np.where(np.sum(remainingClean[(box[1]+box[3])//2:box[3], box[0]:box[2],1],1)<255)[0].tolist()+[box[3]-box[1]])+(box[3]-box[1])//2+barheight//4, box[3]-box[1]) + # xLeftBlank = max(np.max(np.where(np.sum(remainingClean[box[1]:box[3], box[0]:(box[0]+box[2])//2,1],0)<255)[0].tolist()+[0]), 0) + # yRightBlank = min(np.min(np.where(np.sum(remainingClean[box[1]:box[3], (box[0]+box[2])//2:box[2],1],0)<255)[0].tolist()+[box[2]-box[0]])+(box[2]-box[0])//2, box[2]-box[0]) + # redo the training? No + predict_idx = model.predict(remainingClean[box[1]:box[3], box[0]:box[2],:]) + # predict_idx = model.predict(remainingClean[box[1]+yTopBlank:box[1]+yBottomBlank, box[0]+xLeftBlank:box[0]+yRightBlank,:]) + box = (x, box[1]+yTopBlank, x+w, box[1]+yBottomBlank) + if predict_idx>0: + bboxes.append(box) + restMap[box[1]:box[3],box[0]:box[2]] = len(restList) + restList.append(Rest(box,restToRhythm[predict_idx])) + gg = cv2.rectangle(gg, (box[0], box[1]), (box[2],box[3]),5, -1) + imgrgb = cv2.rectangle(imgrgb,(box[0], box[1]), (box[2],box[3]), class_colors[predict_idx], 2, cv2.LINE_AA) + # remaining = cv2.rectangle(remaining, (box[0], box[1]), (box[2],box[3]), (0,255,0), 2, cv2.LINE_AA) + # stem_rests = cv2.rectangle(stem_rests, (box[0], box[1]), (box[2],box[3]), (0,255,0), 2, cv2.LINE_AA) + # symbol = cv2.rectangle(symbol, (box[0], box[1]), (box[2],box[3]), (0,255,0), 2, cv2.LINE_AA) + elif (w>barheight*0.8 and hbarheight*0.3): + box = (x,y,x+w,y+h) + longBoxes.append(box) + gg = cv2.rectangle(gg, (box[0], box[1]), (box[2],box[3]),5, -1) + imgrgb = cv2.rectangle(imgrgb,(box[0], box[1]), (box[2],box[3]), (255,0,255), 2, cv2.LINE_AA) + restMap[box[1]:box[3],box[0]:box[2]] = len(restList) + restList.append(Rest(box,-1)) + # remaining = cv2.rectangle(remaining, (box[0], box[1]), (box[2],box[3]), (0,0,255), 2, cv2.LINE_AA) + # stem_rests = cv2.rectangle(stem_rests, (box[0], box[1]), (box[2],box[3]), (0,0,255), 2, cv2.LINE_AA) + # symbol = cv2.rectangle(symbol, (box[0], box[1]), (box[2],box[3]), (0,0,255), 2, cv2.LINE_AA) + # init_rest_folder() + # labelRestData([imageClean, stemRestClean, remainingClean], bboxes, imgname) + + imwrite('remaining.jpg',remaining) + imwrite('remainingStemRests.jpg',stem_rests) + imwrite('RestClassification.jpg',imgrgb) + beamMapImg = cv2.merge([bb,gg,rr]) + # return {'RestBarline1': remaining, 'RestBarline2': symbol} + return restMap, restList, beamMapImg, {'RestClassification': imgrgb} + +def assignPitch(image:np.ndarray, noteGroupList:List[NoteGroup|None], beamMapImg:np.ndarray, barheight:int): + # assign the "pitch" relative to the center of the staff (12.5), + # ex: in violin, B will be 0, C:1,D:2, A: -1 etc + # if it doesn't have a staff line id yet, group it using the beamMapImg[:,:,4] + # image is for outputting debug images + pitchImg = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + _, imgBin = cv2.threshold(pitchImg, 200, 1, cv2.THRESH_BINARY_INV) + _,pitchImg = cv2.threshold(pitchImg, 200, 255, cv2.THRESH_BINARY) + pitchLineImg = pitchImg.copy() + pitchLineImg = cv2.cvtColor(pitchLineImg, cv2.COLOR_GRAY2BGR) + pitchImg = pitchImg//4+191 + pitchImg = cv2.cvtColor(pitchImg, cv2.COLOR_GRAY2BGR) + pitch_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40), (255,0,255),(230,130,175),(165,170, 70)] + pitch_names = ['B','C','D','E','F','G','A'] + for j in range(0,len(pitch_names)): + pitchImg = cv2.putText(pitchImg,pitch_names[j], (30, 30+j*30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, pitch_colors[j], 2, cv2.LINE_AA) + for ng in noteGroupList: + if ng is None: + continue + for stemObj in ng.noteStemList: + x0,y0,x1,y1 = stemObj.smallNoteBox + staffNum = np.max(beamMapImg[y0:y1,x0:x1,2]) + if staffNum == 0: + staffNum = round(np.sum(beamMapImg[y0:y1,4,2]/(y1-y0))) + height = np.sum((beamMapImg[y0:y1,staffNum%4,2]-12.5)*2)/(y1-y0) + stemObj.pitchFloat = height + + x0,y0,x1,y1 = stemObj.noteBox + height2 = np.sum((beamMapImg[y0:y1,staffNum%4,2]-12.5)*2)/(y1-y0) + if height2-int(height2) == 0: + stemObj.pitchWideFloat = height + else: + stemObj.pitchWideFloat = height2 + + pitchAndLineImg = pitchImg.copy() + modifiedPitchImg = pitchImg.copy() + + for ngIdx, ng in enumerate(noteGroupList): + if ng is None: + continue + for idx,stemObj in enumerate(ng.noteStemList): + x0,y0,x1,y1 = stemObj.smallNoteBox + _,y0B, _, y1B = stemObj.noteBox + pitch = stemObj.pitchFloat + xx0 = max(x0-barheight//2, 0) + xx1 = min(x1+barheight//2, beamMapImg.shape[1]-1) + cc = np.sum(imgBin[max(y0B,y0-2):min(y1B,y1+3), xx0:xx1],1).tolist() + ccNozero = [c for c in cc if c>0] + maxIdx = [i for i in range(len(ccNozero)) if ccNozero[i]==max(ccNozero)] + # center that was tilted and thick + if (maxIdx[0]>len(ccNozero)/2 and maxIdx[0]len(ccNozero)*0.8) and maxIdx[-1]-maxIdx[0]>=3: # TODO!!! + stemObj.hasLineMiddle = True + pitchLineImg=cv2.rectangle(pitchLineImg,(xx0, y0+maxIdx[0]),(xx1, y0+maxIdx[-1]),(255,0,255), 2, cv2.LINE_AA) + # center + if len(set.intersection(set([len(ccNozero)//2,len(ccNozero)//2-1, len(ccNozero)//2+1]), maxIdx))>0: + stemObj.hasLineMiddle = True + pitchLineImg=cv2.rectangle(pitchLineImg,(xx0, y0+maxIdx[0]),(xx1, y0+maxIdx[-1]),(255,0,255), 2, cv2.LINE_AA) + # top or bottom + elif y1-y0>barheight//2 and (maxIdx[0]<=3 or maxIdx[-1]>=len(ccNozero)-4): + stemObj.hasLineMiddle = False + pitchLineImg=cv2.rectangle(pitchLineImg,(xx0, y0),(xx1, y1),(0,255,0), 1, cv2.LINE_AA) + # if it has line in the middle + elif ccNozero[maxIdx[0]]>(x1-x0): + stemObj.hasLineMiddle = True + pitchLineImg=cv2.rectangle(pitchLineImg,(xx0, y0+maxIdx[0]),(xx1, y0+maxIdx[-1]),(255,0,255), 2, cv2.LINE_AA) + else: + stemObj.hasLineMiddle = True + pitchLineImg=cv2.rectangle(pitchLineImg,(xx0, y0+maxIdx[0]),(xx1, y0+maxIdx[-1]),(255,255,0), 2, cv2.LINE_AA) + + p1 = math.ceil(pitch) + p2 = math.floor(pitch) + pitchLineImg = cv2.putText(pitchLineImg, str(ngIdx), (x0,y0-barheight*2), cv2.FONT_HERSHEY_SIMPLEX, 0.2, (0,0,0), 1, cv2.LINE_AA) + pitchAndLineImg = cv2.putText(pitchAndLineImg, str(ngIdx), (x0,y0-barheight*2), cv2.FONT_HERSHEY_SIMPLEX, 0.2, (0,0,0), 1, cv2.LINE_AA) + pitchAndLineImg = cv2.putText(pitchAndLineImg, str(pitch_names[p1%7]), (x0,y0), cv2.FONT_HERSHEY_SIMPLEX, 0.5, pitch_colors[p1%7], 2, cv2.LINE_AA) + pitchAndLineImg = cv2.putText(pitchAndLineImg, str(pitch_names[p2%7]), (x0,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, pitch_colors[p2%7], 2, cv2.LINE_AA) + pitchAndLineImg = cv2.rectangle(pitchAndLineImg, (x0,y0),(x1,y1), (30,30,30), 1,cv2.LINE_AA) + pitchImg = cv2.putText(pitchImg, str(pitch_names[round(pitch)%7]), (x0,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, pitch_colors[round(pitch)%7], 2, cv2.LINE_AA) + pitchImg = cv2.rectangle(pitchImg, (x0,y0),(x1,y1), (30,30,30), 1,cv2.LINE_AA) + + xend = pitchLineImg.shape[1]-1 + for i in range(pitchLineImg.shape[0]-1): + currStaffNum = beamMapImg[i,4,2] + if beamMapImg[i,currStaffNum%4, 2] != beamMapImg[i+1,currStaffNum%4, 2]: + pitchLineImg = cv2.line(pitchLineImg,(0,i),(xend,i),pitch_colors[currStaffNum%4],thickness=1, lineType=cv2.LINE_AA) + pitchAndLineImg = cv2.line(pitchAndLineImg,(0,i),(xend,i),pitch_colors[currStaffNum%4],thickness=1, lineType=cv2.LINE_AA) + + for idx, ng in enumerate(noteGroupList): + if ng is None: + continue + for stem in ng.noteStemList: + pitchInt = stem.getBestPitchInt() + x0,y0,x1,y1 = stem.noteBox + # modifiedPitchImg = cv2.putText(modifiedPitchImg, str(idx), (x0,y0), cv2.FONT_HERSHEY_SIMPLEX, 0.5, pitch_colors[pitchInt%7], 2, cv2.LINE_AA) + modifiedPitchImg = cv2.putText(modifiedPitchImg, str(pitch_names[pitchInt%7]), (x0,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, pitch_colors[pitchInt%7], 2, cv2.LINE_AA) + modifiedPitchImg = cv2.rectangle(modifiedPitchImg, (x0,y0),(x1,y1), (30,30,30), 1,cv2.LINE_AA) + stem.pitchSoprano = pitchInt + + + imwrite('pitchline.jpg', pitchLineImg) + imwrite('pitchAndLine.jpg', pitchAndLineImg) + imwrite('pitch.jpg', pitchImg) + imwrite('pitchModified.jpg', modifiedPitchImg) + return noteGroupList, {'pitch':pitchImg, + 'pitchAndLine':pitchAndLineImg, + 'pitchline': pitchLineImg, + 'pitchModified':modifiedPitchImg} + +def enhanceStaff(image:np.ndarray, staffObjList:List[Staff],barheight:int): + _, img200 = cv2.threshold(image, 200, 255, cv2.THRESH_BINARY) + for staff in staffObjList: + if staff.IsStaffAligned(barheight): + for y in staff.ys: + img200 = cv2.line(img200, (staff.left, y), (staff.right, y), (0,0,0), 1, cv2.LINE_AA) + imwrite('enhanceStaff.jpg',img200) + return img200 + +def assignDots(noteGroupVerticallyMerged:List[NoteGroup],beamMapImg:np.ndarray, image:np.ndarray,bar_height:int,StaffObjList:List[Staff]): + global DEBUGIMG + imgbgr = image.copy() #bgr + _,gg,_ = cv2.split(beamMapImg) + params = cv2.SimpleBlobDetector_Params() + + params.filterByArea = True + params.minArea = bar_height*bar_height//16 + params.maxArea = bar_height*bar_height//2 + + params.filterByCircularity = False + params.filterByConvexity = False + params.filterByInertia = True + params.minInertiaRatio = 0.5 + + detector = cv2.SimpleBlobDetector_create(params) + _, thresh_image = cv2.threshold(image[:,:,1], 150, 255, cv2.THRESH_BINARY) + thresh_origin = thresh_image.copy() + for sf in staffObjList: + for yOne in sf.ys: + thresh_image[yOne-math.ceil(bar_height/8):yOne+bar_height//8+1,:] = 255 + + for ng in noteGroupVerticallyMerged: + if ng is None: + continue + for sm in ng.noteStemList: + oldbox = sm.noteBox + newbox = (oldbox[2],max(0,oldbox[1]-2*bar_height), + min(oldbox[2]+bar_height, image.shape[1]-1), min(oldbox[1]+2*bar_height, image.shape[0]-1)) + realWidth = np.max(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:newbox[2]],0)==0)[0],0)) + # since the barline usually isn't thick enough + realWidth = min(realWidth, np.min(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:newbox[2]],0)==4)[0],realWidth))) + if realWidth>=bar_height-1: + realWidth = np.max(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:min(newbox[2]+bar_height,image.shape[1]-1)],0)==0)[0],0)) + realWidth = min(realWidth, np.min(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:min(newbox[2]+bar_height,image.shape[1]-1)],0)==4)[0],realWidth))) + if realWidth>bar_height*0.5: + dotBox = (oldbox[2], max(0,oldbox[1]-bar_height), + min(oldbox[2]+realWidth,image.shape[1]-1),min(oldbox[1]+int(bar_height*1.5), image.shape[0]-1)) + keypoints = detector.detect(thresh_image[dotBox[1]:dotBox[3], dotBox[0]:dotBox[2]]) + if len(keypoints)==0: + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(0,255,255),1,cv2.LINE_AA) + elif (np.where(np.min(thresh_origin[dotBox[1]:dotBox[3], dotBox[0]+bar_height//2:dotBox[2]],1)==255)[0]).shape[0]>0 and np.min(np.where(np.min(thresh_origin[dotBox[1]:dotBox[3], dotBox[0]+bar_height//2:dotBox[2]],1)==255))>bar_height: + DEBUGIMG = cv2.rectangle(DEBUGIMG,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(160,100,240),2,cv2.LINE_AA) + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(160,100,240),2,cv2.LINE_AA) + else: + sm.hasdot = True + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(255,255,0),2,cv2.LINE_AA) + DEBUGIMG = cv2.rectangle(DEBUGIMG,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(255,255,0),2,cv2.LINE_AA) + + imwrite('dotBox.jpg',imgbgr) + return noteGroupVerticallyMerged,{'dotBox':imgbgr} + +def assignRestDots(restList:List[Rest], beamMapImg:np.ndarray, noteGroupStemMap:np.ndarray, image:np.ndarray, bar_height:int, staffObjList:List[Staff]): + imgbgr = image.copy() #bgr + _,gg,_ = cv2.split(beamMapImg) + params = cv2.SimpleBlobDetector_Params() + + params.filterByArea = True + params.minArea = bar_height*bar_height//16 + params.maxArea = bar_height*bar_height//2 + + params.filterByCircularity = False + params.filterByConvexity = False + params.filterByInertia = True + params.minInertiaRatio = 0.5 + + detector = cv2.SimpleBlobDetector_create(params) + _, thresh_image = cv2.threshold(image[:,:,1], 150, 255, cv2.THRESH_BINARY) + thresh_origin = thresh_image.copy() + for sf in staffObjList: + for yOne in sf.ys: + thresh_image[yOne-math.ceil(bar_height/8):yOne+bar_height//8+1,:] = 255 + for rs in restList: + if rs is None: + continue + elif rs.rhythm<0: + continue + newbox = (rs.boundingBox[2],rs.boundingBox[1], + min(rs.boundingBox[2]+bar_height,image.shape[1]),min(rs.boundingBox[1]+2*bar_height,image.shape[0])) + # 先用一個barheight寬度，高度從最高點往下兩個barheight，往上一個，用stem跟rest來縮減 + realWidth = np.max(np.append(np.where(np.max(noteGroupStemMap[newbox[1]:newbox[3],newbox[0]:newbox[2]],0)==0)[0],0)) + realWidth = min(realWidth, np.max(np.append(np.where(np.max(beamMapImg[newbox[1]:newbox[3],newbox[0]:newbox[2]],0)!=5)[0],0))) + realWidth = min(realWidth, np.min(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:newbox[2]],0)==4)[0],realWidth))) + # 如果寬度還是滿格的話再往右延長到2*barheight用beamMapImg 來縮減 + if realWidth>=bar_height-1: + realWidth = np.max(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:min(newbox[2]+bar_height,image.shape[1]-1)],0)==0)[0],0)) + realWidth = min(realWidth, np.min(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:min(newbox[2]+bar_height,image.shape[1]-1)],0)==4)[0],realWidth))) + elif realWidth>bar_height*0.5: + realWidth = realWidth+bar_height//3 + if realWidth>bar_height*0.5: + dotBox = newbox + keypoints = detector.detect(thresh_image[dotBox[1]:dotBox[3], dotBox[0]:dotBox[2]]) + if len(keypoints)==0: + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(0,255,200),1,cv2.LINE_AA) + elif len(np.where(np.min(thresh_origin[dotBox[1]:dotBox[3], dotBox[0]+bar_height//2:dotBox[2]],1)==255)[0]) == 0: + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(255,255,0),2,cv2.LINE_AA) + elif np.min(np.where(np.min(thresh_origin[dotBox[1]:dotBox[3], dotBox[0]+bar_height//2:dotBox[2]],1)==255))>bar_height: + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(160,100,240),2,cv2.LINE_AA) + else: + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(255,255,0),2,cv2.LINE_AA) + rs.hasdot = True + imwrite('dotRestBox.jpg',imgbgr) + return restList,{'dotRestBox':imgbgr} +def getNoteChunks(image: np.ndarray, noteGroupMap:np.ndarray, beamMapImg:np.ndarray, noteGroupVerticallyMerged:List[NoteGroup]): + imgrgb = image.copy() + beamBinary = np.zeros((beamMapImg.shape[0],beamMapImg.shape[1])) + xs,ys = np.where(beamMapImg[:,:,0]==255) + beamBinary[xs,ys] = 1 + contours, _ = cv2.findContours(beamBinary.astype(np.uint8),cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + beamBoxes:List[Tuple[int,int,int,int]] = [] + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + beamBoxes.append((x,y,x+w,y+h)) + imgrgb = cv2.rectangle(imgrgb,(x,y), (x+w, y+h), (0,0,255), 2, cv2.LINE_AA) + imwrite('beamContour.jpg',imgrgb) + + noteChunkList:List[NoteChunk] = [None] + noteChunkMap = np.zeros_like(noteGroupMap) + for box in beamBoxes: + currentNoteChunkIdxs = set(np.unique(noteChunkMap[box[1]:box[3], box[0]:box[2]])) + allNoteGroupIdxs = set(np.unique(noteGroupMap[box[1]:box[3], box[0]:box[2]])) + allNoteGroupIdxs.add(0) + allNoteGroupIdxs.remove(0) + currentNoteChunkIdxs.add(0) + currentNoteChunkIdxs.remove(0) + if len(allNoteGroupIdxs) == 0: + continue + if len(currentNoteChunkIdxs)==0: + n = NoteChunk(allNoteGroupIdxs) + for a in allNoteGroupIdxs: + ys,xs = np.where(noteGroupMap==a) + noteChunkMap[ys,xs] = len(noteChunkList) + noteChunkList.append(n) + elif len(currentNoteChunkIdxs) == 1: + noteChunkList[min(currentNoteChunkIdxs)].mergeNoteChunk(allNoteGroupIdxs) + for a in allNoteGroupIdxs: + ys,xs = np.where(noteGroupMap==a) + noteChunkMap[ys,xs] = min(currentNoteChunkIdxs) + else: + minChunkIdx = min(currentNoteChunkIdxs) + currentNoteChunkIdxs.remove(minChunkIdx) + for chunkIdx in list(currentNoteChunkIdxs): + ys,xs = np.where(noteChunkMap==chunkIdx) + noteChunkMap[ys,xs] = minChunkIdx + noteChunkList[minChunkIdx].mergeNoteChunk(noteChunkList[chunkIdx].noteGroupIdxs) + noteChunkList[chunkIdx] = None + imggg = image.copy() + for ii, nc in enumerate(noteChunkList): + if nc is None: + continue + for id in nc.noteGroupIdxs: + noteGroupVerticallyMerged[id].noteChunkId = ii + bx = nc.getBoundingBox(noteGroupList) + imggg = cv2.rectangle(imggg, (bx[0],bx[1]),(bx[2], bx[3]),(0,0,255), 2, cv2.LINE_AA) + imwrite("horizontalGrouping.jpg", imggg) + return noteChunkList, imggg + +def getMaskedImageTimeSignature(image:np.ndarray, beamMapImg: np.ndarray, staffList:List[Staff], barheight: int,img_name:str): + bb,gg,_ = cv2.split(beamMapImg) + imgMasked = image.copy() + topBound = 0 + for sf in staffList: + lowerBound = sf.ys[0] + imgMasked = cv2.rectangle(imgMasked, (0, topBound),(imgMasked.shape[1], lowerBound), (255,255,255), -1) + topBound = sf.ys[-1] + # imgCrop = image[sf.ys[0]+1:sf.ys[-1],:,:] + # cv2.imwrite(f'TimeSignature/tsLong/{img_name}{topBound}{lowerBound}.jpg', imgCrop) + lowerBound = imgMasked.shape[0] + imgMasked = cv2.rectangle(imgMasked, (0, topBound),(imgMasked.shape[1], lowerBound), (255,255,255), -1) + imwrite('imgMasked.jpg', imgMasked) + return imgMasked + +def getCTimeSignature(image:np.ndarray, imgMasked:np.ndarray, staffList:List[Staff]): + tsCImage = image.copy() + # create a matrix of zeros for later (add 1 for each matching boxes and then count + threshold) + tsCount = np.zeros((image.shape[0], image.shape[1])) + match_CThick = cv2.imread("training/CpatternThick.jpg",cv2.IMREAD_GRAYSCALE) + match_COri = cv2.imread("training/CpatternLong.jpg",cv2.IMREAD_GRAYSCALE) + for staff in staffList: + sf = staff.ys + seg_img = image[sf[0]:sf[-1],:,1] + resize_ratio = seg_img.shape[0]/match_CThick.shape[0] + match_c = cv2.resize(match_CThick, None, fx=resize_ratio, fy=resize_ratio, interpolation = cv2.INTER_AREA) + newHeight = match_c.shape[0] + match_c = match_c[newHeight//12:newHeight-newHeight//12] + res1 = cv2.matchTemplate(seg_img, match_c, cv2.TM_SQDIFF_NORMED) + + resize_ratio2 = seg_img.shape[0]/match_COri.shape[0] + match_c2 = cv2.resize(match_COri, None, fx=resize_ratio2, fy=resize_ratio2, interpolation = cv2.INTER_AREA) + newHeight2 = match_c2.shape[0] + match_c2 = match_c2[newHeight2//12:newHeight2-newHeight2//12] + res2 = cv2.matchTemplate(seg_img, match_c2, cv2.TM_SQDIFF_NORMED) + resLst = [res1, res2] + refLst = [match_c, match_c2] + + for idx, res in enumerate(resLst): + w,h = refLst[idx].shape[::-1] + threshold = 0.15 + loc = np.where(res <= threshold) + if len(loc[0])>0: + for pt in zip(*loc[::-1]): + tsCount[pt[1]+sf[0]:pt[1]+sf[0]+h, pt[0]:pt[0]+w]+=1 + # tsCImage = cv2.rectangle(tsCImage, (pt[0],pt[1]+sf[0]), (pt[0] + w, pt[1] + h + sf[0]), (0,0,255), 1) + + _,cPositionFiltered = cv2.threshold(tsCount, 5, 255, cv2.THRESH_BINARY) + contours, _ = cv2.findContours(cPositionFiltered.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + tsCImage = cv2.rectangle(tsCImage, (x,y), (x + w,y+h), (0,0,255), 2) + imwrite('tsCImage.jpg',tsCImage) + return tsCImage + +def getTimeSignature4(image:np.ndarray, imgMasked:np.ndarray, staffList:List[Staff]): + tsCImage = image.copy() + # create a matrix of zeros for later (add 1 for each matching boxes and then count + threshold) + tsCount = np.zeros((image.shape[0], image.shape[1])) + match_8Ori = cv2.imread("training/8Pattern.jpg",cv2.IMREAD_GRAYSCALE) + match_4Ori = cv2.imread("training/4Pattern4x3.jpg",cv2.IMREAD_GRAYSCALE) + thres = [0.15, 0.13] + for staff in staffList: + sf = staff.ys + seg_img = image[sf[2]:sf[-1]+1,:,1] + for currId, match_ori in enumerate([match_4Ori, match_8Ori]): + resize_ratio = seg_img.shape[0]/match_ori.shape[0] + match_c = cv2.resize(match_ori, None, fx=resize_ratio, fy=resize_ratio, interpolation = cv2.INTER_AREA) + newHeight = match_c.shape[0] + match_c = match_c[newHeight//12:newHeight-newHeight//12] + res1 = cv2.matchTemplate(seg_img, match_c, cv2.TM_SQDIFF_NORMED) + + resLst = [res1] + refLst = [match_c] + + for idx, res in enumerate(resLst): + w,h = refLst[idx].shape[::-1] + threshold = thres[currId] + loc = np.where(res <= threshold) + if len(loc[0])>0: + for pt in zip(*loc[::-1]): + tsCount[pt[1]+sf[0]:pt[1]+sf[2]+h, pt[0]:pt[0]+w]+=1 + # tsCImage = cv2.rectangle(tsCImage, (pt[0],pt[1]+sf[0]), (pt[0] + w, pt[1] + h + sf[0]), (0,0,255), 1) + + _,cPositionFiltered = cv2.threshold(tsCount, 1, 255, cv2.THRESH_BINARY) + contours, _ = cv2.findContours(cPositionFiltered.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + tsCImage = cv2.rectangle(tsCImage, (x,y), (x + w,y+h), (0,0,255), 2) + imwrite('ts4Image.jpg',tsCImage) + return tsCImage + +def assignSfnToNote(image:np.ndarray, noteGroupMap:np.ndarray, noteGroupVerticallyMerged:List[NoteGroup],sfnClefMap:np.ndarray,sfnClefList:List[Union[Accidentals, Clef, None]]): + stemIdxMap = np.ones((noteGroupMap.shape[0], noteGroupMap.shape[1]), dtype=np.uint8)*(-1) + accidentalsImg = image.copy() + for ng in noteGroupVerticallyMerged: + if ng is None: + continue + for idx, stem in enumerate(ng.noteStemList): + x0,y0,x1,y1 = stem.noteBox + stemIdxMap[y0:y1, x0:x1] = idx + sfnGroupList = [] + for currSfnIdx, sfnc in enumerate(sfnClefList): + if sfnc is None: + continue + if sfnc.getType() == 1: # is Clef + continue + sfnc:Accidentals + x0,y_0,x1,y_1 = sfnc.getBbox() # y0: smaller (top), y1: bigger (bottom) + if sfnc.shift >=0: # sharp or natural + y0 = (y_0+y_1)//2-(y_1-y_0)//6 + y1 = (y_0+y_1)//2+(y_1-y_0)//6 + else: + y0 = (y_0+y_1)//2 + y1 = y_1 + sfnc.shrinkYs = (y0,y1) + stemIdxLst = np.unique(stemIdxMap[y0:y1,x1:x1+(x1-x0)]).tolist() + if -1 in stemIdxLst: + stemIdxLst.remove(-1) + ngIdxLst = np.unique(noteGroupMap[y0:y1,x1:x1+(x1-x0)]).tolist() + if 0 in ngIdxLst: + ngIdxLst.remove(0) + if len(stemIdxLst)==0 or len(ngIdxLst)==0: + # is Key signature or has overlapping sfns + sfnList = np.unique(sfnClefMap[y_0:y_1,x1:x1+(x1-x0)//2]).tolist() + if sfnList == [0]: + sfnc.isKeySignature = True + else: + if 0 in sfnList: + sfnList.remove(0) + sfnNext = sfnList[0] # sfnNext is the index of the next sfn + if type(sfnClefList[sfnNext]) is not Accidentals: + continue + hasAssigned = False + for sIdx in range(len(sfnGroupList)): + if sfnNext in sfnGroupList[sIdx]: + sfnGroupList[sIdx] = [currSfnIdx]+sfnGroupList[sIdx] + hasAssigned = True + continue + elif currSfnIdx in sfnGroupList[sIdx]: + sfnGroupList[sIdx] = sfnGroupList[sIdx]+[sfnNext] + hasAssigned = True + continue + if not hasAssigned: + sfnGroupList.append([currSfnIdx, sfnNext]) + continue + stemIdx = stemIdxLst[0] + ngIdx = ngIdxLst[0] + if len(ngIdxLst)>1: + cntLst = [np.sum(noteGroupMap[y0:y1,x1:x1+(x1-x0)]==i) for i in ngIdxLst] + ngIdx = ngIdxLst[cntLst.index(max(cntLst))] + if len(stemIdxLst)>1: + ngx0, _, ngx1, _ = noteGroupVerticallyMerged[ngIdx].boundingBox + cntLst = [np.sum(np.sum(stemIdxMap[y0:y1,ngx0:ngx1]==i,0)>0) for i in stemIdxLst] + stemIdx = stemIdxLst[cntLst.index(max(cntLst))] + noteGroupVerticallyMerged[ngIdx].noteStemList[stemIdx].accidentals = sfnc.getValue() + sfnc.ngIndex = ngIdx + for grps in sfnGroupList: + currNgIdx = sfnClefList[grps[-1]].ngIndex + if currNgIdx is None: + sfnClefList[grps[-1]].endKeySignature = True + for grr in grps: + sfnClefList[grr].isKeySignature = True + continue + currNg:NoteGroup = noteGroupVerticallyMerged[currNgIdx] + if len(currNg.noteStemList)==1: + continue + ngx0, _, ngx1, _ = currNg.boundingBox + for grr in grps[:-1]: + currSfn = sfnClefList[grr] + y0,y1 = currSfn.shrinkYs + stemIdxLst = np.unique(stemIdxMap[y0:y1,ngx0:ngx1]).tolist() + if -1 in stemIdxLst: + stemIdxLst.remove(-1) + if len(stemIdxLst)>=1: + cntLst = [np.sum(np.sum(stemIdxMap[y0:y1,ngx0:ngx1]==i,0)>0) for i in stemIdxLst] + stemIdx = stemIdxLst[cntLst.index(max(cntLst))] + if currNg.noteStemList[stemIdx].accidentals is None: + noteGroupVerticallyMerged[currNgIdx].noteStemList[stemIdx].accidentals = currSfn.getValue() + noteGroupVerticallyMerged[currNgIdx].noteStemList[stemIdx].accidentalBox = currSfn.boundingBox + else: + print('Error') + + + accidentalsColors = [(255,255,0),(255,0,125),(255,0,255)] # flat, natural, sharp + for ng in noteGroupVerticallyMerged: + if ng is None: + continue + for idx, stem in enumerate(ng.noteStemList): + if stem.accidentals is not None: + x0,y0,x1,y1 = stem.noteBox + currColor = accidentalsColors[stem.accidentals+1] + accidentalsImg = cv2.rectangle(accidentalsImg, (x0,y0),(x1,y1),currColor,3, cv2.LINE_AA) + imwrite("assignedAccidentals.jpg",accidentalsImg,strictlyYes=True) + return stemIdxMap, noteGroupVerticallyMerged, {'assignedAccidentals':accidentalsImg} + +def constructBar(noteGroupMap:np.ndarray, + stemIdxMap: np.ndarray, + noteGroupVerticallyMerged: List[NoteGroup|None], + restMap: np.ndarray, + restList:List[Rest|None], + sfnClefMap:np.ndarray, + sfnClefList:List[Union[Accidentals,Clef, None]], + beamMapImg:np.ndarray, + staffList:List[Staff]): + #TODO + bb,gg,rr = cv2.split(beamMapImg) + barList:List[List[Bar]] = [[] for _ in range(NUM_TRACK)] + mapForMatching:List[np.ndarray|None] = [None,noteGroupMap,sfnClefMap, sfnClefMap, None, restMap] + listForMatching:List[List] = [None, noteGroupVerticallyMerged,sfnClefList, sfnClefList, None, restList] + notFinishedBar = None + if len(staffList)%NUM_TRACK != 0: + print(f"staff List is not divisible: {len(staffList)}%{NUM_TRACK}") + numBarsEachLine = [] + allRanges = [] + for kk in range(len(staffList)//NUM_TRACK): + allBars = [] + for j in range(NUM_TRACK): + sf0 = staffList[kk*NUM_TRACK+j] + barPlace = np.unique(np.where(gg[sf0.ys[0]:sf0.ys[-1], sf0.left:]==4)[1]) + allBars.append(barPlace) + barsFlat = Counter(np.concatenate(allBars)) + isBar = np.sort(np.array([k for k, v in barsFlat.items() if v > 1])) + ranges = [] + points = [0,isBar[0]] + i = 1 + while i < len(isBar): + while isBar[i] == isBar[i-1]+1 and ipoints[i]+sf0.get_yOne(): + ranges.append((points[i]+sf0.left, points[i+1]+sf0.left)) + allRanges.append(ranges) + for t in range(NUM_TRACK): # trackNo + for j in range(len(staffList)//NUM_TRACK): # number of lines + printedLineNo = j*NUM_TRACK+t # the + currBarList:List[Bar] = [] + sf0 = staffList[printedLineNo] + ranges = allRanges[j] + for ridx, rng in enumerate(ranges): + if notFinishedBar is not None: + bar = notFinishedBar + notFinishedBar = None + else: + bar = Bar() + currX = rng[0] + while currX1: + for i in [1,5,3,2]: + if i in lineLst: + lineLst = [i] + break + if len(lineLst)==0 or 4 in lineLst: + currX+=1 + else: + typeId = lineLst[0] + currMap = mapForMatching[typeId] + currLst = listForMatching[typeId] + inMapId = np.unique(currMap[sf0.ys[0]-sf0.get_yOne():sf0.ys[-1]+sf0.get_yOne(), currX]).tolist() + if 0 in inMapId: + inMapId.remove(0) + if len(inMapId)==1: + sanityCheck = True + if typeId == 3: # clef + currClef:Clef = sfnClefList[inMapId[0]] + _,yy0,_,yy1 = currClef.boundingBox + if yy1-yy0 < sf0.get_yOne_float()*3: + sanityCheck = False + if currClef.type ==0: + if yy00)[1])) + if nextIdx>5: + rng = (isBar[-1]+sf0.left+1, rng[1]+nextIdx) + bar = Bar() + currX = rng[0] + while currX1: + for i in [1,5,3,2]: + if i in lineLst: + lineLst = [i] + break + if len(lineLst)==0 or 4 in lineLst: + currX+=1 + else: + typeId = lineLst[0] + currMap = mapForMatching[typeId] + currLst = listForMatching[typeId] + inMapId = np.unique(currMap[sf0.ys[0]-sf0.get_yOne():sf0.ys[-1]+sf0.get_yOne(), currX]).tolist() + if 0 in inMapId: + inMapId.remove(0) + if len(inMapId)==1: + bar.addElement(currLst[inMapId[0]]) + currX = currLst[inMapId[0]].boundingBox[2]+1 + else: + currX+=1 + print("has more than 1 id") + notFinishedBar = bar + barList[t]+=currBarList + if t == 0: + numBarsEachLine.append(len(currBarList)) + # in order of vln1's 1st line, 2nd line ... | vln2's 1st line, 2nd line ... + return barList,allRanges, numBarsEachLine + +def exportXML(barList:List[List[Bar]], numTrack:int, image:np.ndarray|None = None, + beamMapImg:np.ndarray|None = None, + barsBreakPoints: List[int] = [0], + beamMapList:List[np.ndarray]|None = None, + beamMapRefList:List[int]|None = None, + lineNoList:List[int] | None = None): + # Stem's label -> rhythm meaning + # class_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40), (245, 220, 255),(230,130,175),(165,170, 70)] + if image is None: + debugXMLImg = None + else: + debugXMLImg = image.copy() + if beamMapImg is not None: + bb,gg,rr = cv2.split(beamMapImg) + score = stream.Score() + score.metadata = metadata.Metadata() + score.metadata.title = "Example MusicXML" + score2 = stream.Score() # for those with shift + score2.metadata = metadata.Metadata() + score2.metadata.title = "Example MusicXML" + + part = [stream.Part() for n in range(numTrack)] + part2 = [stream.Part() for n in range(numTrack)] + keyName = ['C','D','E','F','G','A','B'] + keyCharFlat = ['C','D-','D','E-','E','F','G-','G','A-','A','B-','B'] + keyCharSharp = ['C','C#','D','D#','E','F','F#','G','G#','A','A#','B'] + class_names = ['1/4','1/8','1/16','1/32', '1/64', '1/128', '1/2', '1/1'] + restClassNames = ['1/4','1/8','1/16','1/32'] # only for >=0 + currentClef = [None]*numTrack + sharps = [3,0,4,1,5,2,6] # F C G D A E B + flats = [6,2,5,1,4,0,3] # B E A D G C F + ksSharp = [set() for _ in range(numTrack)] + ksFlat = [set() for _ in range(numTrack)] + currentKs = [0 for n in range(numTrack)] + trksOriginal = [[] for _ in range (numTrack)] # for tracking all original notes we see + trksShifted = [[] for _ in range (numTrack)] # for tracking all shifted notes + def setKS(ks:float, trkNo): + ks = int(ks) + nonlocal ksSharp + nonlocal ksFlat + if ks>0: + ksSharp[trkNo] = [keyName[i] for i in [sharps[q] for q in range(ks)]] + ksFlat[trkNo] = set() + else: + ksFlat[trkNo] = [keyName[i] for i in [flats[q] for q in range(-ks)]] + ksSharp[trkNo] = set() + def setKSNeutral(Oriks:float, trkNo, trackShift): + Oriks = int(Oriks) + nonlocal ksSharp + nonlocal ksFlat + ks = Oriks+trackShift + if ks>0: + ksSharp[trkNo] = [keyName[i] for i in [sharps[q] for q in range(ks)]] + ksFlat[trkNo] = set() + else: + ksFlat[trkNo] = [keyName[i] for i in [flats[q] for q in range(-ks)]] + ksSharp[trkNo] = set() + def parseClefOne(elem:Clef, lineNo:int): + nonlocal currentClef + currentClef[lineNo%numTrack] = elem.type + return elem.type + def getClefFromType(clefType:int): + clf = clef.TrebleClef() + if clefType == 1: + clf = clef.TrebleClef() + elif clefType == 0: + clf = clef.AltoClef() + elif clefType == -1: #-1 + clf = clef.BassClef() + elif clefType == -2: + clf = clef.TenorClef() + return clf + + def parseClef(elem:Clef, lineNo:int): + nonlocal currentClef + if elem.type == currentClef[lineNo%numTrack]: + return None + clf = None + if elem.type == 1: + clf = clef.TrebleClef() + elif elem.type == 0: + clf = clef.AltoClef() + elif elem.type == -1: #-1 + clf = clef.BassClef() + elif elem.type == -2: + clf = clef.TenorClef() + currentClef[lineNo%numTrack] = elem.type + return clf + def parseAccidentals(elem:Accidentals, lineNo:int): + if not elem.isKeySignature: # it's accidentals + return None + ys = elem.shrinkYs + if elem.ksKeySop is None: + keyNum = np.sum(rr[ys[0]:ys[1],(lineNo+1)%4])/((ys[1]-ys[0]))*2-25 + elem.ksKeySop= keyNum + return elem + def calculatePitch(inputPitch:str): + outputNum = keyCharFlat.index(inputPitch[0])+12*(int(inputPitch[-1])+1) + if len(inputPitch)==3: + if inputPitch[1] == '#': + outputNum+=1 + elif inputPitch[1] == '-' : + outputNum-=1 + return outputNum + def pitchListShift(inputPitchList:List[str], shift: int, sharp = True): # shift: how many half notes + returnList = [] + for pitch in inputPitchList: + pitchNum = calculatePitch(pitch) + pitchNum = pitchNum + shift + returnList.append(pitchNum) + referenceList = keyCharFlat + if sharp: + referenceList = keyCharSharp + for idx,pitchNo in enumerate(returnList): + clefNum = pitchNo//12-1 + pitchName = referenceList[pitchNo%12] + returnList[idx] = pitchName + str(clefNum) + return returnList + def parseNoteGroupShift(elem:NoteGroup, flatSet, sharpSet, naturalSet, currClef, trkShift, flatSharp, linNo): + # trkShift: 0 if original: -2, new: -2 (two flats), 1 if original: -2, new: -1 + # flatSharp: how many flats/sharp, -2: two flats + # flatSharp = 2, trkShift = 0: (-2), flatSharp = 2, trkShift = 1: (-1) + keyLst = [] + currLength = 1 + regNoteCount = 0 + def setRemove(currSet, currElm): + if currElm in currSet: + currSet.remove(currElm) + return currSet + for stem in elem.noteStemList: + if not stem.isOrnament: + regNoteCount+=1 + else: + continue + actualPitch = stem.pitchSoprano + if currClef == 0: #viola + actualPitch -= 6 + elif currClef == -1: #cello + actualPitch -= 12 + elif currClef == -2: #tenor + actualPitch -= 8 + currKeyName = keyName[(actualPitch-1)%7] + currKey = currKeyName+str((actualPitch-1)//7+5) + if stem.accidentals is not None: # it has keySignature + sharpSet = setRemove(sharpSet, currKey) + naturalSet = setRemove(naturalSet, currKey) + flatSet = setRemove(flatSet, currKey) + if stem.accidentals == -1 or currKey in flatSet: + flatSet.add(currKey) + currKey = currKey[0]+'-'+currKey[1] + elif stem.accidentals == 1 or currKey in sharpSet: + sharpSet.add(currKey) + currKey = currKey[0]+'#'+currKey[1] + else: # stem.accidentals == 0: + naturalSet.add(currKey) + else: + if currKey in naturalList: + currKey = currKey + elif currKey in flatSet or currKeyName in ksFlat[linNo]: + currKey = currKey[0]+'-'+currKey[1] + elif currKey in sharpSet or currKeyName in ksSharp[linNo]: + currKey = currKey[0]+'#'+currKey[1] + # llen = float(Fraction(class_names[stem.rhythm])) + # if stem.hasdot: + # llen = llen*1.5 + # if llen0) + trksShifted[linNo].append(keyShiftedList) + if len(keyShiftedList) > 1: + return flatSet, sharpSet, chord.Chord(keyShiftedList, quarterLength = currLength*4) + elif len(keyShiftedList) == 1: + return flatSet, sharpSet, note.Note(keyShiftedList[0], quarterLength=currLength*4) + else: + return flatSet, sharpSet, None + # actualPitch, 0: B4, 1: C5, 2: D5 + + def parseNoteGroup(elem:NoteGroup, flatSet, sharpSet, naturalSet, currClef, linNo, trkShift = 0): + keyLst = [] + currLength = 1 + regNoteCount = 0 + def setRemove(currSet, currElm): + if currElm in currSet: + currSet.remove(currElm) + return currSet + for stem in elem.noteStemList: + if not stem.isOrnament: + regNoteCount+=1 + else: + continue + actualPitch = stem.pitchSoprano + if currClef == 0: #viola + actualPitch -= 6 + elif currClef == -1: #cello + actualPitch -= 12 + elif currClef == -2: #tenor + actualPitch -= 8 + currKeyName = keyName[(actualPitch-1)%7] + currKey = currKeyName+str((actualPitch-1)//7+5) + if stem.accidentals is not None: # it has keySignature + sharpSet = setRemove(sharpSet, currKey) + naturalSet = setRemove(naturalSet, currKey) + flatSet = setRemove(flatSet, currKey) + if stem.accidentals == -1: + flatSet.add(currKey) + currKey = currKey[0]+'-'+currKey[1] + elif stem.accidentals == 1 or currKey in sharpSet: + sharpSet.add(currKey) + currKey = currKey[0]+'#'+currKey[1] + else: # stem.accidentals == 0: + naturalSet.add(currKey) + else: + if currKey in naturalList: + currKey = currKey + elif currKey in flatSet or currKeyName in ksFlat[linNo]: + currKey = currKey[0]+'-'+currKey[1] + elif currKey in sharpSet or currKeyName in ksSharp[linNo]: + currKey = currKey[0]+'#'+currKey[1] + # llen = float(Fraction(class_names[stem.rhythm])) + # if stem.hasdot: + # llen = llen*1.5 + # if llen 1: + return flatSet, sharpSet, chord.Chord(keyLst, quarterLength = currLength*4) + elif len(keyLst) == 1: + return flatSet, sharpSet, note.Note(keyLst[0], quarterLength=currLength*4) + else: + return flatSet, sharpSet, None # ornament + # actualPitch, 0: B4, 1: C5, 2: D5 + def parseRest(elem:Rest): + restLength = float(elem.tunedLength) + if restLength>0: + # restLength = float(Fraction(restClassNames[elem.rhythm])) + # if elem.hasdot: + # restLength = restLength*1.5 + return note.Rest(quarterLength=restLength*4) + else: + pass + # print("setting rest to full") + # return note.Rest(quarterLength=4) + def assignKsCurrClef(accList:List[Accidentals], currentClef:int): + pitchShift = 0 + if currentClef == 0: + pitchShift -= 6 + elif currentClef== -1: + pitchShift -= 12 + elif currentClef== -2: + pitchShift -= 8 + i = 0 + while i= len(accList): + return None + totalLength = len(accList)-i + pitchList = [(accList[r].ksKeySop+pitchShift-1)%7 for r in range(i, len(accList))] + if not False in [abs(pitchList[k] - sharps[k%7])<1.2 or pitchList[k]-sharps[k%7]>5.8 for k in range(totalLength)]: + return totalLength + elif not False in [abs(pitchList[k] - flats[k%7])<1.2 or pitchList[k]-flats[k%7]>5.8 for k in range(totalLength)]: + return -totalLength + else: + return None + def assignKS(accList:List[Accidentals]): + pitchShift = 0 + if currentClef[lineNo%numTrack] == 0: + pitchShift -= 6 + elif currentClef[lineNo%numTrack] == -1: + pitchShift -= 12 + elif currentClef[lineNo%numTrack] == -2: + pitchShift -= 8 + i = 0 + while i= len(accList): + return None + totalLength = len(accList)-i + pitchList = [(accList[r].ksKeySop+pitchShift-1)%7 for r in range(i, len(accList))] + if not False in [abs(pitchList[k] - sharps[k%7])<1.2 or pitchList[k]-sharps[k%7]>5.8 for k in range(totalLength)]: + return totalLength + elif not False in [abs(pitchList[k] - flats[k%7])<1.2 or pitchList[k]-flats[k%7]>5.8 for k in range(totalLength)]: + return -totalLength + else: + return None + numBars = len(barList[0]) + ksBarMat = np.ones((numTrack, numBars))*np.inf + clefMat = np.ones((numTrack, numBars))*np.inf + currentClef = [None]*numTrack + for currBarNumber in range(numBars): + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): + if len(CLEF_OPTIONS[lineNo]) == 1: + clefMat[lineNo, currBarNumber] = CLEF_OPTIONS[lineNo][0] + else: + currBar = barList[lineNo][currBarNumber] + for elem in currBar.elementList: + if type(elem) == Clef: + newClef = parseClefOne(elem, lineNo) + if newClef in CLEF_OPTIONS[lineNo]: + clefMat[lineNo, currBarNumber] = newClef + if clefMat[lineNo, currBarNumber] == np.inf: + if currBarNumber == 0: + clefMat[lineNo, currBarNumber] = CLEF_OPTIONS[lineNo][0] + else: + clefMat[lineNo, currBarNumber] = clefMat[lineNo, currBarNumber-1] + global DEBUGIMG + beamMapRefIndex = -1 + for currBarNumber in range(numBars): + if beamMapRefList is not None: + if beamMapRefList[currBarNumber] != beamMapRefIndex: + beamMapRefIndex = beamMapRefList[currBarNumber] + beamMapImg = beamMapList[beamMapRefIndex] + bb,gg,rr = cv2.split(beamMapImg) + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): + currBar = barList[lineNo][currBarNumber] + accumAcc: List[Accidentals] = [] # will have the list of accidentals to process + for elem in currBar.elementList: + if type(elem) == Accidentals: + if lineNoList is not None: + currAcc = parseAccidentals(elem,lineNoList[currBarNumber]) # return none if it's accidentals + else: + currAcc = parseAccidentals(elem, lineNo) + if currAcc is None: + if debugXMLImg is not None: + x0,y0,x1,y1 = elem.boundingBox + debugXMLImg = cv2.rectangle(debugXMLImg, (x0,y0),(x1,y1),elem.getColor(), 1, cv2.LINE_AA) + DEBUGIMG = cv2.rectangle(DEBUGIMG, (x0,y0),(x1,y1),elem.getColor(), 1, cv2.LINE_AA) + if len(accumAcc)>0: + ksCurr = assignKsCurrClef(accumAcc, clefMat[lineNo, currBarNumber]) + if ksCurr is None: + for ac in accumAcc: + x0,y0,x1,y1 = ac.boundingBox + debugXMLImg = cv2.circle(debugXMLImg, ((x0+x1)//2, (y0+y1)//2), (x1-x0)//2, ac.getColor(), 3, cv2.LINE_AA) + DEBUGIMG = cv2.circle(DEBUGIMG, ((x0+x1)//2, (y0+y1)//2), (x1-x0)//2, ac.getColor(), 3, cv2.LINE_AA) + if barNumber == 1: + modShift = statistics.mode([a.shift for a in accumAcc]) + currentKs[lineNo%numTrack] = modShift*len(accumAcc) + ksBarMat[lineNo, currBarNumber] = modShift*len(accumAcc) + debugXMLImg = cv2.putText(debugXMLImg,str(modShift*len(accumAcc)), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + DEBUGIMG = cv2.putText(DEBUGIMG,str(modShift*len(accumAcc)), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + else: + for ac in accumAcc: + x0,y0,x1,y1 = ac.boundingBox + debugXMLImg = cv2.rectangle(debugXMLImg, (x0,y0),(x1,y1),ac.getColor(), 3, cv2.LINE_AA) + DEBUGIMG = cv2.rectangle(DEBUGIMG, (x0,y0),(x1,y1),ac.getColor(), 3, cv2.LINE_AA) + debugXMLImg = cv2.putText(debugXMLImg,str(ksCurr), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + DEBUGIMG = cv2.putText(DEBUGIMG,str(ksCurr), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + if ksCurr is not None and ksCurr != currentKs[lineNo%numTrack]: + currentKs[lineNo%numTrack] = ksCurr + ksBarMat[lineNo, currBarNumber] = ksCurr + elif currAcc.endKeySignature: + accumAcc.append(currAcc) + ksCurr = assignKsCurrClef(accumAcc, clefMat[lineNo, currBarNumber]) + if ksCurr is None: + for ac in accumAcc: + x0,y0,x1,y1 = ac.boundingBox + debugXMLImg = cv2.circle(debugXMLImg, ((x0+x1)//2, (y0+y1)//2), (x1-x0)//2, ac.getColor(), 3, cv2.LINE_AA) + DEBUGIMG = cv2.circle(DEBUGIMG, ((x0+x1)//2, (y0+y1)//2), (x1-x0)//2, ac.getColor(), 3, cv2.LINE_AA) + if barNumber == 1: + modShift = statistics.mode([a.shift for a in accumAcc]) + currentKs[lineNo%numTrack] = modShift*len(accumAcc) + ksBarMat[lineNo, currBarNumber] = modShift*len(accumAcc) + debugXMLImg = cv2.putText(debugXMLImg,str(modShift*len(accumAcc)), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + DEBUGIMG = cv2.putText(DEBUGIMG,str(modShift*len(accumAcc)), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + else: + for ac in accumAcc: + x0,y0,x1,y1 = ac.boundingBox + debugXMLImg = cv2.rectangle(debugXMLImg, (x0,y0),(x1,y1),ac.getColor(), 3, cv2.LINE_AA) + DEBUGIMG = cv2.rectangle(DEBUGIMG, (x0,y0),(x1,y1),ac.getColor(), 3, cv2.LINE_AA) + debugXMLImg = cv2.putText(debugXMLImg,str(ksCurr), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + DEBUGIMG = cv2.putText(DEBUGIMG,str(ksCurr), (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.7, ac.getColor(), 2, cv2.LINE_AA) + if ksCurr is not None and ksCurr != currentKs[lineNo%numTrack]: + currentKs[lineNo%numTrack] = ksCurr + ksBarMat[lineNo, currBarNumber] = ksCurr + else: + accumAcc.append(currAcc) + if len(accumAcc)>0: + ksCurr = assignKsCurrClef(accumAcc, clefMat[lineNo, currBarNumber]) + if ksCurr is not None: + currentKs[lineNo%numTrack] = ksCurr + ksBarMat[lineNo, currBarNumber] = ksCurr + if ksBarMat[lineNo, currBarNumber] == np.inf: + if currBarNumber in barsBreakPoints: + ksBarMat[lineNo, currBarNumber] = 0 + else: + ksBarMat[lineNo, currBarNumber] = ksBarMat[lineNo, currBarNumber-1] + kk = ksBarMat.copy() + for idx, trShift in enumerate(TRACK_SHIFT): + kk[idx,:] = kk[idx,:]-trShift + ksBarMatNeutral = np.zeros_like(ksBarMat) # the "standard" orchestra clef we want + ksBarMatNeutral[:,:] = stats.mode(kk, axis=0)[0] + ksBarMatNew = np.zeros_like(ksBarMat) + for idx, trShift in enumerate(TRACK_SHIFT): + ksBarMatNew[idx,:] = ksBarMatNeutral[idx,:]+trShift + ksBarMatNeutral = ksBarMatNeutral.astype(int) + ksBarMatNew = ksBarMatNew.astype(int) + clefMat = clefMat.astype(int) + tssList = [b.ts for b in barList[0]] + for currBarNumber in range(numBars): + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): + currBar = barList[lineNo][currBarNumber] + measure = stream.Measure(number=barNumber) + flatList = set() #put here because reset in measures + sharpList = set() + naturalList = set() + accumAcc = [] # will have the list of accidentals to process + clefType = clefMat[lineNo, currBarNumber] + ks = ksBarMatNew[lineNo, currBarNumber] + if currBarNumber == 0: + measure.append(meter.TimeSignature(f'{currBar.ts[0]}/{currBar.ts[1]}')) + elif tssList[currBarNumber]!=tssList[currBarNumber-1]: + measure.append(meter.TimeSignature(f'{currBar.ts[0]}/{currBar.ts[1]}')) + if currBarNumber == 0: + measure.append(getClefFromType(clefType)) + setKS(ks, lineNo) + measure.append(key.KeySignature(ks)) + elif clefType != clefMat[lineNo, currBarNumber-1]: + measure.append(getClefFromType(clefType)) + elif ks != ksBarMatNew[lineNo, currBarNumber-1]: + setKS(ks, lineNo) + measure.append(key.KeySignature(ks)) + if currBar.getTunedTotalBeat() == 0: + measure.append(note.Rest(quarterLength = currBar.ts[0]/currBar.ts[1]*4)) + else: + for elem in currBar.elementList: + if type(elem) == NoteGroup: + flatList, sharpList, currNote = parseNoteGroup(elem, + flatList, + sharpList, + naturalList, + clefMat[lineNo, currBarNumber], + lineNo, + trkShift=0) + if currNote is not None: + measure.append(currNote) + else: + print() + elif type(elem) == Rest: + currRest = parseRest(elem) + if currRest is not None: + measure.append(currRest) + part[lineNo%numTrack].append(measure) + for p in part: + score.append(p) + # Part 2: with shift + ksSharp = [set() for _ in range(numTrack)] + ksFlat = [set() for _ in range(numTrack)] + for currBarNumber in range(numBars): # for the new one shifted + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): + currBar = barList[lineNo][currBarNumber] + measure = stream.Measure(number=barNumber) + # barNumber = barNumber+1 + flatList = set() #put here because reset in measures + sharpList = set() + naturalList = set() + accumAcc = [] # will have the list of accidentals to process + clefType = clefMat[lineNo, currBarNumber] + ks = ksBarMatNeutral[lineNo, currBarNumber] + if currBarNumber == 0: + measure.append(getClefFromType(clefType)) + setKSNeutral(ks, lineNo, TRACK_SHIFT[lineNo]) + measure.append(key.KeySignature(ks)) + elif clefType != clefMat[lineNo, currBarNumber-1]: + measure.append(getClefFromType(clefType)) + elif ks != ksBarMatNeutral[lineNo, currBarNumber-1]: + setKSNeutral(ks, lineNo, TRACK_SHIFT[lineNo]) + measure.append(key.KeySignature(ks)) + if currBar.getTunedTotalBeat() == 0: + measure.append(note.Rest(quarterLength = currBar.ts[0]/currBar.ts[1]*4)) + for elem in currBar.elementList: + if type(elem) == NoteGroup: + flatList, sharpList, currNote = parseNoteGroupShift(elem, + flatList, + sharpList, + naturalList, + clefMat[lineNo, currBarNumber], + TRACK_SHIFT[lineNo], + ksBarMatNeutral[0,currBarNumber], + lineNo) + if currNote is not None: + measure.append(currNote) + else: + print() + elif type(elem) == Rest: + currRest = parseRest(elem) + if currRest is not None: + measure.append(currRest) + part2[lineNo%numTrack].append(measure) + for p in part2: + score2.append(p) + return score, score2, {'ksAssigned':debugXMLImg} + +def saveBarToCsv(imgName:str, barList:List[List[Bar]], desiredLength=1): + retBarLst = [] + for lineId, oneLine in enumerate(barList): + for barId, bar in enumerate(oneLine): + if bar.getTotalBeat() != desiredLength: + currBar = dict(sheetName=imgName, + lineNo=lineId, + barNo=barId, + targetLength=desiredLength, + elms=[]) + for elm in bar.elementList: + if type(elm) == NoteGroup: + groupId = 0 + if elm.noteChunkId is not None: + groupId = elm.noteChunkId + beamLength = (-1,-1) + beamEnd = [-1,-1] # for grouped notes + length = elm.getMinLength() + if len(elm.noteStemList) == 1: + currStm = elm.noteStemList[0] + if currStm.hasdot: + length = length*1.5 + beamLength = [int(a) for a in currStm.getBeamHeights()] + if currStm.isup: + beamEnd = [currStm.getX(), int(min(currStm.getY0Y1()))] + else: + beamEnd = [currStm.getX(), int(max(currStm.getY0Y1()))] + newElm = dict(length=str(Fraction(length)), + groupId = groupId, + isRest = False, + numNotes = len(elm.noteStemList), + beamLength = beamLength, + beamEnd=beamEnd) + currBar['elms'].append(newElm) + elif type(elm) == Rest: + newElm = dict(length=str(elm.getLengthFraction()*1.5 if elm.hasdot else elm.getLengthFraction()), + groupId = -1, + isRest = True, + numNotes = 0, + beamLength = (-1,-1), + beamEnd=[-1,-1]) + currBar['elms'].append(newElm) + retBarLst.append(currBar) + with open(f"debugBars/{imgName}.json", "w") as file: + json.dump(retBarLst, file, indent=4) + +def tuneBarList(barList:List[List[Bar]], numBarsPerLine:List[int]): + assert len(np.unique([len(barr) for barr in barList])) == 1 + emptyBarsIndexs = np.where(np.sum(np.array([[len(b.elementList) for b in oneTrack] for oneTrack in barList]),axis=0)==0)[0].tolist() + emptyBarsIndexs.reverse() + for emptyBarsIndex in emptyBarsIndexs: + for i in range(NUM_TRACK): + del barList[i][emptyBarsIndex] + qq = 0 + while emptyBarsIndex+1>sum(numBarsPerLine[0:qq]): + qq+=1 + numBarsPerLine[qq-1]-=1 + # delete all elements that's ornament + for trackId, oneTrack in enumerate(barList): + for barId, bar in enumerate(oneTrack): + bar.elementList = [ + elem for elem in bar.elementList + if not (isinstance(elem, NoteGroup) and all(s.isOrnament for s in elem.noteStemList)) + ] + # now all bars are suppose to be valid + for trackId, oneTrack in enumerate(barList): + for barId, bar in enumerate(oneTrack): + origLengths = bar.getRhythmList() + newLengths = [None]*len(origLengths) + if bar.getTotalBeat() == Fraction(bar.ts[0],bar.ts[1]): + newLengths = [None]*len(origLengths) + elif bar.getTotalBeat() == 0: + continue # will be later added a rest + else: + newLengths = tuneBar(bar.getRestNg(), bar.ts) + reassignedLengths = [origLengths[i] if newLengths[i] is None else newLengths[i] for i in range(len(newLengths))] + bar.reassignLength(origLengths, newLengths) + if bar.getTunedTotalBeat() != Fraction(bar.ts[0], bar.ts[1]): + print("bar doesn't have right length") + + for lineii, oneTrack in enumerate(barList): + for barii, bb in enumerate(oneTrack): + if bb.getTunedTotalBeat() != Fraction(bb.ts[0],bb.ts[1]): + print(f"bar at {lineii},{barii} has total beat {bb.getTotalBeat()}") + assert sum(numBarsPerLine) == len(barList[0]) + barBreakPoints = [0] + list(itertools.accumulate(numBarsPerLine)) + return barBreakPoints + +def extendNotegroupsToStaff(noteGroupMap:np.ndarray, + noteGroupVerticallyMerged:List[NoteGroup], + staffObjList:List[Staff], + beamMapImg:np.ndarray): + bb,gg,rr = cv2.split(beamMapImg) + ngZero = np.zeros_like(noteGroupMap) + for sf in staffObjList: + ngZero[sf.ys[0]:sf.ys[-1], sf.left:sf.right] = 1 + for idx, ng in enumerate(noteGroupVerticallyMerged): + if ng is None: + continue + x0,y0,x1,y1 = ng.boundingBox + if np.sum(ngZero[y0:y1,x0:x1])==0: + clefNo = round(np.mean(rr[y0:y1,4])) + if len(np.unique(noteGroupMap[y0:y1,x0:x1])) == 1: + noteNo =np.unique(noteGroupMap[y0:y1,x0:x1])[0] + if y0>staffObjList[clefNo-1].ys[2]: # is at the bottom + y00 = staffObjList[clefNo-1].ys[-2] + y11 = y1 + else: + y00 = y0 + y11 = staffObjList[clefNo-1].ys[1] + gg[y00:y11,x0:x1] = 1 + noteGroupMap[y00:y11,x0:x1]=noteNo + rr[y00:y11,x0:x1] = clefNo + ng.boundingBox = (x0,y00,x1,y11) + + return cv2.merge([bb,gg,rr]), noteGroupMap + +def getAccidentalsChanges(barList:List[List[Bar]]): + # barList: [[track 1's bars], [track 2's bars], ...] + numTracks = len(barList) + numBars = len(barList[0]) + accTracks = [[0]*numBars for _ in range(numTracks)] + clefTracks = [[None]*numBars for _ in range(numTracks)] + imgCircleAcc = image.copy() + clrs = [(0,0,255),(0,255,0),(255,255,0)] + for lineNo,currBarList in enumerate(barList): + for barNo,currBar in enumerate(currBarList): + for elem in currBar.elementList: + if type(elem) == Clef: + # 1: treble, 0: alto, -1: bass, -2: tenor + clefTracks[lineNo][barNo] = elem.type + print(f"at line {lineNo}, bar {barNo}, element has type {elem.type}") + if clefTracks[lineNo][barNo] is None and barNo>0: + clefTracks[lineNo][barNo] = clefTracks[lineNo][barNo-1] + # TODO: fill in if there's none in clefTracks + if [True] in [[None in f] for f in clefTracks]: + print("need to reassign!") + for lineNo,currBarList in enumerate(barList): + for barNo,currBar in enumerate(currBarList): + for elem in currBar.elementList: + if type(elem) == Accidentals and elem.isKeySignature: + x0,y0,x1,y1 = elem.boundingBox + currClef = clefTracks[lineNo][barNo] + # ys = elem.shrinkYs + # keyNum = np.sum(rr[ys[0]:ys[1],(lineNo+1)%4])/((ys[1]-ys[0]))*2-25 + # elem.ksKeySop = keyNum + imgCircleAcc = cv2.rectangle(imgCircleAcc,(x0,y0),(x1,y1),clrs[elem.shift],3, cv2.LINE_AA) + +def createMask(barList:List[List[Bar]], barRanges:List[List[Tuple[int,int]]], staffObjList:List[Staff], image:np.ndarray,beamMapImg:np.ndarray): + maskImg = image.copy() + brWidth = 2 # VARIABLE: 1 third of the size of mask + barlineCenters = [] + tsBounding1 = image.copy() + tsBounding2 = image.copy() + + for lineIdx,brList in enumerate(barRanges): + yStart = staffObjList[lineIdx*NUM_TRACK].ys[0] + yEnd = staffObjList[(lineIdx+1)*NUM_TRACK-1].ys[-1] + brCenters = [br[0] for br in brList]+[brList[-1][-1]] + barlineCenters.append(brCenters) + for br in brCenters: + maskImg = cv2.rectangle(maskImg, (br-brWidth, yStart),(br+2*brWidth, yEnd), (255,255,255), lineType=cv2.LINE_AA, thickness=-1) + tsBounding2 = cv2.rectangle(tsBounding2, (br-brWidth, yStart),(br+2*brWidth, yEnd), (0,255,0), lineType=cv2.LINE_AA, thickness=-1) + for tracks in barList: + for bar in tracks: + for elem in bar.elementList: + if type(elem) == Accidentals: + x0,y0,x1,y1 = elem.boundingBox + maskImg = cv2.rectangle(maskImg, (x0,y0),(x1,y1), (255,255,255), lineType=cv2.LINE_AA, thickness=-1) + elif type(elem) == NoteGroup: + x0,y0,x1,y1 = elem.boundingBox + maskImg = cv2.rectangle(maskImg, (x0,y0),(x1,y1), (255,255,255), lineType=cv2.LINE_AA, thickness=-1) + for stm in elem.noteStemList: + if stm.accidentalBox is not None: + x0,y0,x1,y1 = stm.accidentalBox + maskImg = cv2.rectangle(maskImg, (x0,y0),(x1,y1), (255,255,255), lineType=cv2.LINE_AA, thickness=-1) + elif type(elem) == Rest: + x0,y0,x1,y1 = elem.boundingBox + maskImg = cv2.rectangle(maskImg, (x0,y0),(x1,y1), (255,255,255), lineType=cv2.LINE_AA, thickness=-1) + elif type(elem) == Clef: + x0,y0,x1,y1 = elem.boundingBox + maskImg = cv2.rectangle(maskImg, (x0,y0),(x1,y1), (255,255,255), lineType=cv2.LINE_AA, thickness=-1) + beamMapBW = beamMapImg[:,:,0] + _, bmINV = cv2.threshold(beamMapBW, 127,1, cv2.THRESH_BINARY_INV) + maskImgBW = cv2.cvtColor(maskImg, cv2.COLOR_BGR2GRAY) + _, maskImgINV = cv2.threshold(maskImgBW, 127, 1, cv2.THRESH_BINARY_INV) + imgBW = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + maskImgINV = maskImgINV*bmINV + maskWhole = np.zeros_like(maskImgBW) + yPrev = 0 + for sfId,sf in enumerate(staffObjList): + y0 = sf.ys[0] + y1 = sf.ys[-1] + maskWhole[yPrev:y0,:] = 0 + x0 = sf.left + x1 = sf.right + currLine = maskImgINV[y0:y1,:] + h,w = currLine.shape + sumCurr = np.sum(currLine,axis=0) + val,cnts = np.unique(sumCurr, return_counts = True) + sorted_indices = np.argsort(-cnts) + minIdx = np.where(sorted_indices>=np.percentile(np.sum(currLine,axis=0),75))[0][0] + thres = np.max(sorted_indices[:3]) + thres = max(thres, sorted_indices[minIdx]) + xPos = np.where(np.sum(currLine,axis=0)>thres)[0] + mask = np.zeros(w, dtype=np.uint8) + mask[xPos] = 1 + mask[:x0] = 0 + mask[x1:] = 0 + mask = np.tile(mask, (h,1)) + maskWhole[y0:y1,:] = mask + yPrev = y1 + maskWhole[yPrev:,:] = 0 + retImgBlack = cv2.bitwise_not(imgBW)*maskWhole + retImgWhite = cv2.bitwise_not(maskWhole*maskImgINV*255) + contours, _ = cv2.findContours(retImgBlack, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + rett = image.copy() + whiteBG = np.zeros_like(imgBW) # put 1s on where there's residual stuff + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + if h>sf.get_yOne(): + rett = cv2.rectangle(rett, (x,y),(x+w,y+h),(0,0,255),-1,cv2.LINE_AA) + whiteBG = cv2.rectangle(whiteBG, (x,y),(x+w,y+h),1,-1,cv2.LINE_AA) + sfTrack = np.zeros((len(staffObjList),image.shape[1])) + for sfId,sf in enumerate(staffObjList): + y0 = sf.ys[0] + y1 = sf.ys[-1] + currLine = whiteBG[y0:y1,:] + xLoc = np.max(currLine, axis=0) + sfTrack[sfId,:] = xLoc + windowWidth = sf.get_yOne() #VARIABLE: how much we want the sliding window to be (width) + windowThres = 0.5 + boxList = [(0,0,0,0,-1)] # x0,y0,x1,y1,lineNo(starting 0) + for linNo in range(len(staffObjList)//NUM_TRACK): # go through window and record + currSFTrack = sfTrack[linNo*NUM_TRACK:(linNo+1)*(NUM_TRACK),:] + for xLeft in range(0, image.shape[1]-windowWidth): + if np.mean(currSFTrack[:,xLeft:xLeft+windowWidth])>windowThres: + if xLeft=0: # no overlap for sure + continue + while barlineCenters[currBrCenter[0]][currBrCenter[1]]0: + tsBounding2 = cv2.rectangle(tsBounding2, (x0,y0), (x1,y1), (0,0,255),3,cv2.LINE_AA) + for idx,crpImg in enumerate(cropImgLst): + cv2.imwrite(f"ts_dataset/{img_name}_{x0}_{y0}_{idx}.jpg", crpImg) + print(f"writing dataset to ts_dataset/{img_name}_{x0}_{y0}_{idx}.jpg") + imwrite("TSboundingBox_.jpg", tsBounding1) + imwrite("TSboundingBoxNoBr_.jpg", tsBounding2) + imwrite("filteredTS_.jpg",rett) + return maskImg, boxList, boxListFiltered,{'tsboundingBox':tsBounding1,'tsboundingBoxNoBr':tsBounding2, 'filteredTS':rett} + + for box in boxList[1:]: + x0,y0,x1,y1,currLine = box + tsBounding1 = cv2.rectangle(tsBounding1, (x0,y0), (x1,y1), (0,0,255),3,cv2.LINE_AA) + cropImgLst = [] + for trkNo in range(NUM_TRACK): + yss = staffObjList[currLine*NUM_TRACK+trkNo].ys + crpImg = imgBW[yss[0]:yss[-1],x0:x1] + cropImgLst.append(crpImg) + matchScores = [] + for i in range(4): + for j in range(i+1,4): + result = cv2.matchTemplate(cropImgLst[i], cropImgLst[j], cv2.TM_CCOEFF_NORMED) + _, max_val, _, max_loc = cv2.minMaxLoc(result) + matchScores.append(max_val) + if min(matchScores)>0: + tsBounding2 = cv2.rectangle(tsBounding2, (x0,y0), (x1,y1), (0,0,255),3,cv2.LINE_AA) + boxListFiltered.append(box) + for idx,crpImg in enumerate(cropImgLst): + cv2.imwrite(f"ts_dataset/{img_name}_{x0}_{y0}_{idx}.jpg", crpImg) + imwrite("TSboundingBox_.jpg", tsBounding1) + imwrite("TSboundingBoxNoBr_.jpg", tsBounding2) + imwrite("filteredTS_.jpg",rett) + return maskImg, boxList, boxListFiltered,{'tsboundingBox':tsBounding1,'tsboundingBoxNoBr':tsBounding2, 'filteredTS':rett} +# pagenum starts with 1 +def editBarTS(barList:List[List[Bar]], allChanges:List[dict], pageNum, numBarsPerLine:List[int]): + prev = max([i for i, val in enumerate([allChanges[i]['page'] for i in range(len(allChanges))]) if val < pageNum]+[0]) + TIME_SIGNATURE = allChanges[prev]['time_signature'] + indexes = [i+1 for i, val in enumerate([allChanges[i]['page'] for i in range(1,len(allChanges))]) if val == pageNum] + if len(indexes) > 0: + breakPoints = [0] + list(itertools.accumulate(numBarsPerLine)) + numBarsThisPage = len(barList[0]) # how many bars are in this page + locBarNumChanges = [allChanges[ind]['loc'] for ind in indexes] + barNumChanges = [breakPoints[l[0]]+l[1] for l in locBarNumChanges]+[numBarsThisPage*2] # [0,6, xx (too large)] if we have ts change in bar 0, 6 + tss = [TIME_SIGNATURE]+[allChanges[ind]['time_signature'] for ind in indexes] # [[orig],[3/4],[9/8]] + # from 0 to barNumChanges[0] : TIME_SIGNATURE + # from barNumChanges[0] to barNumChanges[1]: tss[0] + for currTrack in barList: + currTsIndex = 0 # tss[currTsIndex] is the one we want to assign + TIME_SIGNATURE = tss[0] + for barNum, bar in enumerate(currTrack): + if barNum == barNumChanges[currTsIndex]: + currTsIndex += 1 + TIME_SIGNATURE = tss[currTsIndex] + bar.ts = TIME_SIGNATURE + else: + for currTrack in barList: + for barNum, bar in enumerate(currTrack): + bar.ts = TIME_SIGNATURE +def exportYolo(sfnClefList:List[Accidentals|Clef], image:np.ndarray,img_name:str, barheight: int): + if not os.path.isdir("yolo"): + os.mkdir("yolo") + if not os.path.isdir("yolo/debug"): + os.mkdir("yolo/debug") + height, width, _ = image.shape + outputString = "" + imgg = image.copy() + for sfnClef in sfnClefList: + if type(sfnClef) == Clef: + if sfnClef.getValue() == 0 or sfnClef.getValue() == -2: + x0,y0,x1,y1 = sfnClef.boundingBox + if y1-x1 < 3*barheight: + continue + cv2.rectangle(imgg, (x0,y0),(x1,y1),(0,0,255), 2, cv2.LINE_AA) + xPerc = (x1+x0)/2/width + yPerc = (y1+y0)/2/height + xWidth = (x1-x0)/width + yHeight = (y1-y0)/height + print(f"{1} {xPerc:.6f} {yPerc:.6f} {xWidth:.6f} {yHeight:.6f}") + outputString += f"{1} {xPerc:.6f} {yPerc:.6f} {xWidth:.6f} {yHeight:.6f}\n" + elif type(sfnClef) == Accidentals: + if sfnClef.getValue() == -1: + # is flat + x0,y0,x1,y1 = sfnClef.boundingBox + cv2.rectangle(imgg, (x0,y0),(x1,y1),(0,255,0), 2, cv2.LINE_AA) + xPerc = (x1+x0)/2/width + yPerc = (y1+y0)/2/height + xWidth = (x1-x0)/width + yHeight = (y1-y0)/height + print(f"{0} {xPerc:.6f} {yPerc:.6f} {xWidth:.6f} {yHeight:.6f}") + outputString += f"{0} {xPerc:.6f} {yPerc:.6f} {xWidth:.6f} {yHeight:.6f}\n" + with open(f"yolo/{img_name}.txt","w") as f: + f.write(outputString) + cv2.imwrite(f"yolo/{img_name}.jpg", image) + cv2.imwrite(f"yolo/debug/{img_name}.jpg",imgg) + +if __name__ == '__main__': + root_folder = 'string_dataset' + base_folder = f'{root_folder}/pdf_data/' + base_output_folder = f'{root_folder}/output/' + pieces = [] + if not os.path.isdir(base_folder): + os.mkdir(base_folder) + if not os.path.isdir(base_output_folder): + os.mkdir(base_output_folder) + with open(f'{root_folder}/piecesToRun.json','r') as f: + pieces = json.load(f) + for piece_name in pieces: + OUTPUT_BASE_FOLDER = os.path.join(base_output_folder, piece_name) + if not os.path.isdir(OUTPUT_BASE_FOLDER): + os.mkdir(OUTPUT_BASE_FOLDER) + if not os.path.isdir(f"{OUTPUT_BASE_FOLDER}/output"): + os.mkdir(f"{OUTPUT_BASE_FOLDER}/output") + piece_base_folder = os.path.join(base_folder, piece_name) # 'string_dataset/pdf_data/beethoven1' + with open(f"{piece_base_folder}/{piece_name}.json") as f: + config = json.load(f) + NUM_TRACK = config['numTrack'] + TRACK_SHIFT = config['track_shift'] + CLEF_OPTIONS = config['clef_options'] + TIME_SIGNATURE = config['tsChange'][0]['time_signature'] + + run_img_folder = os.path.join(piece_base_folder, 'imgs') + if not os.path.isdir(run_img_folder): + savePdf2Png(piece_base_folder,config['numPerPage'],config['rotate']) + + stemUpPth = "training/stemupImg32x32_best.pth" + stemDownPth = "training/stemdownImg32x32_best.pth" + stemUpModel = Single_Stem_Classifier(stemUpPth, 3) + stemDownModel = Single_Stem_Classifier(stemDownPth, 3) + + img_name_list = [f'{piece_name}_{i}' for i in range(1,config['numPage']+1)] + wholeBarList:List[List[Bar]] = [[] for _ in range(NUM_TRACK)] + beamRefList: List[int] = [] # which beamMapImg should it refer to (which page was it in), start with 0 + allBeamMaps: List[np.ndarray] = [] + lineNoList: List[int] = [] + allBarBreakPoints: List[int] = [] + for imgIdx,img_name in enumerate(img_name_list): + MIN_BAR_HEIGHT = 8 + img_path = f"{run_img_folder}/{img_name}/{img_name}.png" + base_path = f"{run_img_folder}/{img_name}/{img_name}" + npy_path = f"{run_img_folder}/{img_name}/{img_name}.npy" + pkl_path = f"{run_img_folder}/{img_name}/{img_name}_staffList.pkl" + barList_path = f"{run_img_folder}/{img_name}/{img_name}_barlist.pkl" + # beamMapImg_path = f"{run_img_folder}/{img_name}/{img_name}_beamMapInit.jpg" + if not os.path.exists(npy_path): + printt(f"missing npy for {img_name}") + dataDictOrigin = runModel1(img_path,outputPath = base_path,dodewarp=False,save_npy=True) + if not os.path.exists(pkl_path): + printt(f"missing pkl for {img_name}") + staffListOrigin = runModel2(npy_path, img_path, base_path) + if os.path.exists(npy_path) and os.path.exists(pkl_path): + printt(f"working on {img_path}") + dataDict = np.load(npy_path,allow_pickle=True) + dataDict = dataDict.tolist() + resize_ratio = 2 + # the minimum pixel height between staffline should be higher than 8 to ensure proper processing of image + if img_name.startswith('packed_'): + for k in dataDict.keys(): + img = dataDict[k] + resized_image = cv2.resize(img, None, fx=resize_ratio, fy=resize_ratio, interpolation=cv2.INTER_NEAREST) + dataDict[k] = resized_image + else: + resize_ratio = 2 + for k in dataDict.keys(): + img = dataDict[k] + resized_image = cv2.resize(img, None, fx=resize_ratio, fy=resize_ratio, interpolation=cv2.INTER_NEAREST) + dataDict[k] = resized_image + + img = np.ones_like(dataDict['image'])*255 + ys,xs = np.where(dataDict['stems_rests']>0) + img[ys,xs] = (0,0,255) + # outputImWrite(f'{img_name}_stemrests.jpg', img) + imwrite(f'stemrests_.jpg', img) + + img = np.ones_like(dataDict['image'])*255 + ys,xs = np.where(dataDict['symbols']>0) + img[ys,xs] = (255,0,0) + # outputImWrite(f'{img_name}_symbols.jpg', img) + imwrite(f'symbols_.jpg', img) + + # ori_img, binary_img, symbol = getStemBox(dataDict) + bar_height, staffObjList = init_bar_height(dataDict, min_barheight=MIN_BAR_HEIGHT) + print(f'barheight: {bar_height}') + print(bar_height) + stepSize = int(bar_height/4) + + # get the beam image + beamNoClefKeyWithStemRest, beamWithoutStemRest, beam_nostem = getBeamImage(dataDict, bar_height, staffObjList) + # get the gradient beamMap and (dont filter noteheadInitial with longer beams cause might remove noteheads) + beamMapImg, debugImages = generateBeamStaffImg(beam_nostem, staffObjList, bar_height,stepSize=stepSize) + # get the inital black and white image of notehead (white: has note), it's b&w, depth=1 + # noteBwImage is the cleaned but not filtered note image, + + # if os.path.exists(barList_path): + # with open(barList_path, "rb") as f: + # barLists = pickle.load(f) + # for tr in range(NUM_TRACK): + # wholeBarList[tr] += barLists[tr] + # beamRefList += [imgIdx]*len(barLists[0]) + # cv2.imwrite(beamMapImg_path, beamMapImg) + # lineNoList += [i for i in range(len(barLists[0]))] + # allBeamMaps.append(beamMapImg) + # continue + + noteheadBoxesInitList, noteBwImageOrigin = getInitialNoteheadBoxList(dataDict, beam_nostem, beamMapImg, bar_height) + + # mask the note image with the notehead boxes + maskedNoteBwImage = maskImage(noteheadBoxesInitList, noteBwImageOrigin) + + stem_init_list, image, debugImages= getStemList(dataDict, noteheadBoxesInitList, beamNoClefKeyWithStemRest) + # {'notehead_drawimg':drawimg} + for si in debugImages.keys(): + # outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + imwrite(f'{si}.jpg', debugImages[si]) + stem_list_assigned_height, debugImages = assignStemLength(stem_init_list, image, beamMapImg, stepSize, bar_height) + # {'notestem_drawimg': drawimg2} + for si in debugImages.keys(): + # outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + imwrite(f'{si}.jpg', debugImages[si]) + + beamRemoveNotes = removeNotes(maskedNoteBwImage, beamNoClefKeyWithStemRest, beamMapImg) + beam_img2, stem_list_assigned, beam_heights = assignBeamLengthBeamImg(stem_list_assigned_height, image, beamRemoveNotes, bar_height) + # outputImWrite(f'{img_name}_knn_beam.jpg', beamRemoveNotes) + # outputImWrite(f'{img_name}_knn_beamImg.jpg', beam_img2) + imwrite(f'knn_beam.jpg', beamRemoveNotes) + imwrite(f'knn_beamImg.jpg', beam_img2) + + noteGroupList, beamMapImg, noteGroupStemMap, noteGroupMap, debugImages = knnRhythmAndDraw(stem_list_assigned, beam_heights, image, bar_height, beamMapImg, stemUpClassifier=stemUpModel, stemDownClassifier=stemDownModel,img_name=img_name) + # {'knnbeams':imgrgb,'ngImg': ngImg, 'knnbeams2': knnbeams2} + for si in ['ngImg','knnbeams2']:#debugImages.keys(): + # outputImWrite(f'{img_name}_notehead_Boxes.jpg', drawimg) + outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + imwrite(f'{si}.jpg',debugImages[si]) + + ys,xs = np.where(noteGroupStemMap>0) + zz = image.copy() + zz[ys,xs,1:2] = 100 + # outputImWrite(f'{img_name}_noteGroupStemMap.jpg', zz) + imwrite(f'noteGroupStemMap.jpg', zz) + + + ys,xs = np.where(noteGroupMap>0) + zz = image.copy() + zz[ys,xs,1:2] = 100 + # outputImWrite(f'{img_name}_noteGroupStemMap.jpg', zz) + imwrite(f'noteGroupMap.jpg', zz) + + colors = [(0,0,255),(0,255,0),(255,255,0), (0,120,230)] + _,_,rr = cv2.split(beamMapImg) + zz = image.copy() + for i in range(len(staffObjList)): + ys,xs = np.where(rr==(i+1)) + zz[ys,xs] = colors[i%4] + imwrite('GroupMap.jpg',zz) + # outputImWrite(f'{img_name}_GroupMap_1.jpg', zz) + + pth_path = r'training\rest_remain2x1_best.pth' + num_classes = 5 + restClassifier = Rest_Classifier(pth_path, num_classes) + restMap, restList, beamMapImg, debugImages = findRests(dataDict, beamMapImg, bar_height, restClassifier) + # {'RestClassification': imgrgb} + # for si in debugImages.keys(): + # outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + # pass + + # beamMapImg: modified beamMap1 with vertically grouped ones + noteGroupVerticallyMerged, noteGroupMapWithIssues, beamMapImg, debugImages = mergeVerticalNoteGroups(staffObjList,noteGroupList, noteGroupMap, beamMapImg, image,restMap, restList) + + noteGroupMap = np.zeros_like(noteGroupMapWithIssues) + for i in range(len(noteGroupVerticallyMerged)): + if noteGroupVerticallyMerged[i] is not None: + ng = noteGroupVerticallyMerged[i] + x0,y0,x1,y1 = ng.boundingBox + noteGroupMap[y0:y1, x0:x1] = i + + # {'vertically_merged':ngImg} + for si in debugImages.keys(): + # outputImWrite(f'{img_name}_notehead_Boxes.jpg', drawimg) + outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + imwrite(f'{si}.jpg',debugImages[si]) + colors = [(0,0,255),(0,255,0),(255,255,0), (0,120,230)] + _,_,rr = cv2.split(beamMapImg) + zz = image.copy() + for i in range(len(staffObjList)): + ys,xs = np.where(rr==(i+1)) + zz[ys,xs] = colors[i%4] + # outputImWrite(f'{img_name}_GroupMap_2.jpg', zz) + imwrite(f'GroupMap2.jpg', zz) + + + pth_path = 'training/best_model32x32_seg_dil4x4.pth' + num_classes = 7 + sfnClassifier = Sfn_Clef_classifier(pth_path, num_classes) + sfnClefList, sfnClefMap, debugImages = symbol_classification(dataDict, sfnClassifier, bar_height) + exportYolo(sfnClefList, image,img_name, bar_height) + # {'sfnClef':sfnClefImg} + for si in debugImages.keys(): + outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + imwrite(f'{si}.jpg',debugImages[si]) + + # filter the sfn and clef that are overlapping with beam map + sfnClefList, sfnClefMap, beamMapImg = filterSfnClefModifyBeamMap(sfnClefList, noteGroupMap, beamMapImg) + bb,gg,_ = cv2.split(beamMapImg) + _,zz = cv2.threshold(image, 200, 255, cv2.THRESH_BINARY) + zzmask = zz.copy() + zzmask[:max(staffObjList[0].ys[0]-bar_height*2,0),:] = (255,255,255) + zzmask[min(staffObjList[-1].ys[-1]+bar_height*2,zzmask.shape[0]):,:] = (255,255,255) + for sf in staffObjList: + xMaskOut = np.where(np.max(noteGroupMap[max(sf.ys[0]-2*bar_height, 0):min(sf.ys[-1]+2*bar_height, zzmask.shape[0]), :],0)>0)[0] + zzmask[max(sf.ys[0]-2*bar_height, 0):min(sf.ys[-1]+2*bar_height,zzmask.shape[0]),xMaskOut] = (255,255,255) + ys,xs = np.where(gg>0) + zzmask[ys,xs] = (255,255,255) + bb = cv2.dilate(bb, np.ones((bar_height//2, bar_height//2), dtype= np.uint8), iterations=1) + ys,xs = np.where(bb>=255) + zzmask[ys,xs] = (255,255,255) + # zzmask = cv2.dilate(zzmask, np.ones((bar_height//3,1),dtype=np.uint8),iterations=1) + # zzmask = cv2.erode(zzmask, np.ones((bar_height//3,bar_height//3),dtype=np.uint8),iterations=1) + # cv2.imwrite('test.jpg',zzmask) + imwrite('SfnClefNoteWhite.jpg', zzmask) + # outputImWrite(f'{img_name}_SfnClefNoteWhite.jpg', zzmask) + + for i in range(1,4): + ys,xs = np.where(gg==i) + zz[ys,xs,(i-2)%3] = 100 + zz[ys,xs,(i-3)%3] = 0 + ys,xs = np.where(image[:,:,0]<128) + zz[ys,xs,:] = (0,0,0) + # outputImWrite(f'{img_name}_SfnClefNote.jpg', zz) + imwrite(f'SfnClefNote.jpg', zz) + + + staffEnhancedImg = enhanceStaff(image, staffObjList,bar_height) + noteGroupPitchList, debugImages = assignPitch(staffEnhancedImg,noteGroupList, beamMapImg, bar_height) + # {'pitch':pitchImg, 'pitchAndLine':pitchAndLineImg, 'pitchline': pitchLineImg} + for si in debugImages.keys(): + # outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + imwrite(f'{si}.jpg',debugImages[si]) + outputImWrite(f'{img_name}_pitchline.jpg', debugImages['pitchline']) + + + + imgMasked = getMaskedImageTimeSignature(image, beamMapImg, staffObjList, bar_height, img_name) + # outputImWrite(f'{img_name}_imgMasked.jpg', imgMasked) + # outputImWrite(f'{img_name}_tsContour.jpg', img2) + + CBounding = getCTimeSignature(image, imgMasked, staffObjList) + outputImWrite(f'{img_name}_CBounding.jpg', CBounding) + + Bounding4 = getTimeSignature4(image, imgMasked, staffObjList) + outputImWrite(f'{img_name}_4Bounding.jpg', Bounding4) + noteChunkList, horizontalGroupImg = getNoteChunks(image, noteGroupMap, beamMapImg, noteGroupVerticallyMerged) + # outputImWrite(f'{img_name}_beamContour.jpg',retimg) + outputImWrite(f'{img_name}_horizontalGroupImg.jpg',horizontalGroupImg) + + beamMapImg, barlineboxes, barlineQuart = findBarlines(image, beamMapImg, staffObjList, noteGroupStemMap, thres=0.8) # VARIABLE thres + outputImWrite(f'{img_name}_barlineQuart.jpg',barlineQuart) + + # barlineImages = filterBarlineBoxes(image, barlineboxes) + # outputImWrite(f'{img_name}_barlineFiltered.jpg', barlineImages) + + zz = image.copy() + bb,gg,_ = cv2.split(beamMapImg) + colors = [(190,120,0),(255,50,0),(50,240,0),(0,120,230),(0,0,255)] + for i in range(1,6): + ys,xs = np.where(gg==i) + zz[ys,xs] = colors[i%5] + + imwrite(f'SfnClefNoteBarlineRest.jpg', zz) + + colors = [(230,200,130),(230,130,100),(120,170,140),(235,176,113),(0,0,255)] + zz = image.copy() + for i in [1,2,3,5]: + ys,xs = np.where(gg==i) + zz[ys,xs] = colors[i%5] + ys,xs = np.where(image[:,:,1]<128) + zz[ys,xs] = (0,0,0) + i = 4 + ys,xs = np.where(gg==i) + zz[ys,xs] = colors[i%5] + outputImWrite(f'{img_name}_DotPrevious.jpg', zz) + imwrite(f'DotPrevious.jpg', zz) + + noteGroupVerticallyMerged, debugImages = assignDots(noteGroupVerticallyMerged,beamMapImg, image,bar_height,staffObjList) + # {'dotBox':imgbgr} + # for si in debugImages.keys(): + # outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + + restList, debugImages = assignRestDots(restList, beamMapImg, noteGroupStemMap, image, bar_height, staffObjList) + # {'dotRestBox':imgbgr} + # for si in debugImages.keys(): + # outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + + stemIdxMap, noteGroupVerticallyMerged, debugImages = assignSfnToNote(image, noteGroupMap, noteGroupVerticallyMerged,sfnClefMap,sfnClefList) + # {'assignedAccidentals':accidentalsImg} + for si in debugImages.keys(): + outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + # current issue: + # far away ones can't be assigned -> do it in the 2nd round + # tuneRhythm(noteGroupMap, stemIdxMap, noteGroupVerticallyMerged, restMap,restList,sfnClefMap,sfnClefList,beamMapImg,staffObjList) + beamMapImg,noteGroupMap = extendNotegroupsToStaff(noteGroupMap, noteGroupVerticallyMerged,staffObjList,beamMapImg) + barList, barRanges, numBarsPerLine = constructBar(noteGroupMap, stemIdxMap, noteGroupVerticallyMerged, restMap,restList,sfnClefMap,sfnClefList,beamMapImg,staffObjList) + # maskImg, tsBoxes, tsBoxesFiltered, debugImages = createMask(barList, barRanges, staffObjList, image, beamMapImg) + for si in debugImages.keys(): + outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + # saveBarToCsv(img_name, barList, desiredLengtdh=1) + # trackNo = 1 + # if img_wholename in is2: + # trackNo = 2 + # elif img_wholename in is5: + # trackNo = 5 + # elif img_wholename.startswith("quart"): + # trackNo = 4 + editBarTS(barList, config['tsChange'], imgIdx+1, numBarsPerLine) + barBreakPoints = tuneBarList(barList, numBarsPerLine) # modifies barList + trackNo = NUM_TRACK + for tr in range(NUM_TRACK): + wholeBarList[tr] += barList[tr] + # getAccidentalsChanges(barList) + score, scoreShifted, debugImages = exportXML(barList, trackNo, image, beamMapImg=beamMapImg, barsBreakPoints = barBreakPoints) + if len(allBarBreakPoints) == 0: + allBarBreakPoints+=barBreakPoints + else: + sumPrevBar = allBarBreakPoints[-1] + allBarBreakPoints+=[bp+sumPrevBar for bp in barBreakPoints] + with open(barList_path, "wb") as f: + pickle.dump(barList, f) + for si in debugImages.keys(): + outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + imwrite(f'{si}.jpg', debugImages[si]) + score.write('musicxml', fp=f'{OUTPUT_BASE_FOLDER}/{img_name}.xml') + print(f"MusicXML written to '{OUTPUT_BASE_FOLDER}/{img_name}.xml'") + if len(np.unique(TRACK_SHIFT))>1: + scoreShifted.write('musicxml', fp=f'{OUTPUT_BASE_FOLDER}/s_{img_name}.xml') + print(f"MusicXML written to '{OUTPUT_BASE_FOLDER}/s_{img_name}.xml'") + outputImWrite(f'DEBUG_{img_name}.jpg', DEBUGIMG) + print(f'finishing parsing {img_name}') + + print('finishing all') + scoreWhole, scoreWholeShifted, _= exportXML(wholeBarList, NUM_TRACK,barsBreakPoints=allBarBreakPoints) + # scoreWhole, scoreWholeShifted, _= exportXML(wholeBarList, NUM_TRACK, beamMapList=allBeamMaps, beamMapRefList=beamRefList,lineNoList=lineNoList) + scoreWhole.write('musicxml', fp=f'{OUTPUT_BASE_FOLDER}/all_{img_name}.xml') + if len(np.unique(TRACK_SHIFT))>1: + scoreWholeShifted.write('musicxml', fp=f'{OUTPUT_BASE_FOLDER}/whole_s_{img_name}.xml') + print(f"MusicXML written to '{OUTPUT_BASE_FOLDER}/whole_s_{img_name}.xml'") + + print(f"MusicXML written to '{OUTPUT_BASE_FOLDER}/all_{img_name}.xml'") + with open(f"{base_folder}{piece_name}/{piece_name}_barList.pkl","wb") as f: + pickle.dump(wholeBarList, f) +# ''' + # TODO + # go through each staffline and get all elements on it: + # assign length (for triplets etc.) based on horizontal grouping + # if length adds to 1 or 2 -> keep it as it is + # if approx 1 or 2: go through each's + + + # current data structure documentation: + # noteChunkList: list of None|NoteChunks + # by noteChunkList[n].noteGroupIdxs you can get the Ids of them + # noteGroupMap, stemIdxMap <-> noteGroupVerticallyMerged + # noteGroupMap value == 0: no note, + # >0: is the noteGroupVerticallyMerged[idx] + # stemIdxMap value == -1: no noteBox + # >=0: is the index of noteGroupVerticallyMerged[idx].noteStemList[index] + # restMap <-> restList + # RestMap value == 0: no rest + # >0: is the restList[idx] + # sfnClefMap <-> sfnClefList + # sfnClefMap value == 0: no clef/sfn + # >0: is the sfnClefList[idx] + # beamMapImg: bb,gg,rr + # bb: the gradient beam image, 255 where there's beam, + # 254-2*stepSize*n: go how far down to beam + # 253-2*stepSize*n: go how far up to beam + # gg: 0 if there's nothing there + # 1 if it's place of a noteGroup object + # 2 if it's place of an accidentals + # 3 if it's place of a clef + # 4 if it's place of a barline + # 5 if it's place of a rest + # 6 if it's place of a noteGroup ornament? TODO not implemented yet + # rr: staff position map + staff number + how far to the bottom of staff + # x = 0~3 (total of 4): the position of the note (1~24, A is 12, C is 13) + # x = 4: the staffnum of the closest one (starting with 1 instead of 0) + # x = -1: how far pixel to the top of the staff (if it's in the staff range and 1 barheight above, else 0) + # x = -2: how far pixel to the bottom of the staff (if it's in the staff range and 1 barheight below, else 0) + # within the x of staff: the staff number + # if it contains anything in the staff it will be that, else it will + + +''' +NOTES +1. I've commented out the filters in part2.py +''' \ No newline at end of file diff --git a/pdf2musicXMLsq.py b/pdf2musicXMLsq.py new file mode 100644 index 0000000..01da427 --- /dev/null +++ b/pdf2musicXMLsq.py @@ -0,0 +1,1505 @@ +from typing import List +import numpy as np +import pandas as pd +import csv +import os +import json +import pickle +import itertools +import statistics + +from collections import defaultdict, Counter +from music21 import stream, note, metadata, chord, meter, clef, key, instrument +from scipy import stats + +from png2decode import png2decode +from utils import * +from tune_bar import tuneBar + + +BAR_MAX_GAP = 5 # if two "lines" are within this pixel then they are considered the same barline +DEBUGIMG = None +class ScoreMetaData: + # static + instruments: List[str] = [] + + def __init__(self, numInstrument: int, numLines: int, instrumentList: List[str]): + self.numInstrument = numInstrument + self.numLines = numLines + assert self.numLines%self.numInstrument == 0 + self.numTrack = self.numLines // self.numInstrument + self.trackRange = [(i*self.numInstrument, (i+1)*self.numInstrument-1) for i in range(self.numTrack)] + self.set_instruments(instrumentList) + + + def getTrackRange(self) -> List[Tuple[int,int]]: + return self.trackRange + + def getTrackXYRange(self, staffList:List[Staff]): + ranges = self.getTrackRange() + retRangeList = [] + for r in ranges: + start = r[0] + end = r[1] # included + retRangeList.append((staffList[start].left, staffList[start].right, staffList[start].ys[0], staffList[end].ys[-1])) + return retRangeList + + def getInstrumentEachTrack(self) -> List[List[str]]: + return [[i] for i in self.instruments] * self.numTrack + + # get the number of track that line belongs to + def getTrackForLineIdx(self, lineIdx: int) -> int: + for i, (start, end) in enumerate(self.trackRange): + if start <= lineIdx <= end: + return i + return -1 + + # get the line number of that line inside that track. + # let's say [3,6] are the 2nd group (inclusive), then + # fn(3) -> (1,0), 2nd group(count starts at 0), index 0 + # fn(6) -> (1,3), 2nd group, index 3 + def getTrackIdxForLineIdx(self, lineIdx: int) -> Tuple[int,int]: + for i, (start, end) in enumerate(self.trackRange): + if start <= lineIdx <= end: + return (i, lineIdx - start) + return (-1,-1) + + + # static + @classmethod + def set_instruments(cls, instrumentList: List[str]): + cls.instruments = instrumentList + + + @classmethod + def add_instrument(cls, instrument: str): + cls.instruments.append(instrument) + + @classmethod + def get_instruments(cls) -> List[str]: + return cls.instruments.copy() + +def parseCsvData(csv_path: str): + with open(csv_path) as f: + csvData = list(csv.reader(f)) + return csvData + +def findBarPosition(staffImgBinary: np.ndarray, tolerance, row_ratio:float = 0.7): + kernel = np.ones((1, 2*tolerance+1), dtype=np.uint8) + expanded = cv2.dilate(staffImgBinary.astype(np.uint8), kernel) + acc = np.sum(expanded, axis=0) + threshold = staffImgBinary.shape[0] * row_ratio + barPos = np.where(acc > threshold)[0] + return barPos + +def getBarsEachTrack(image:np.ndarray, beamMapImg:np.ndarray, staffList:List[Staff], tolerance:int = 20): + img = image.copy() + staffCenters = [sf.ys[2] for sf in staffList] + _,itemMap,_ = cv2.split(beamMapImg) + staffImgBinary = (itemMap == 4)[staffCenters,:] + barEachTrack = [] + for trkRange in pageMetadata.getTrackRange(): + staffStart = staffList[trkRange[0]].ys[0] + staffEnd = staffList[trkRange[1]].ys[-1] + barPos = findBarPosition(staffImgBinary[trkRange[0]:trkRange[1],:], tolerance) + img[staffStart:staffEnd, barPos] = (0,0,255) + barEachTrack += [barPos.tolist()] + imwrite(f"barForTracks{tolerance}.jpg", img) + return barEachTrack + + +def getAllObjectInEachLine( + noteGroupMap: np.ndarray, + noteGroupVerticallyMerged: List[NoteGroup | None], + restMap: np.ndarray, + restList: List[Rest | None], + sfnClefMap: np.ndarray, + sfnClefList: List[Union[Accidentals, Clef, None]], + beamMapImg: np.ndarray, + staffList: List[Staff] + ): + _, objMap, _ = cv2.split(beamMapImg) + mapForMatching:List[np.ndarray|None] = [None,noteGroupMap,sfnClefMap, sfnClefMap, None, restMap] + listForMatching:List[List] = [None, noteGroupVerticallyMerged,sfnClefList, sfnClefList, None, restList] + allItems = [] + for sf in staffList: + allItemInLine = [] + currX = sf.left + while currX < sf.right: + lineLst = np.unique(objMap[sf.ys[0]-sf.get_yOne():sf.ys[-1]+sf.get_yOne(), currX]).tolist() + if 0 in lineLst: + lineLst.remove(0) + if len(lineLst)>1: + for i in [1,5,3,2]: + if i in lineLst: + lineLst = [i] + break + if len(lineLst) == 0 or 4 in lineLst: + currX += 1 + else: + typeId = lineLst[0] + currMap = mapForMatching[typeId] + currLst = listForMatching[typeId] + inMapId = np.unique(currMap[sf.ys[0]-sf.get_yOne():sf.ys[-1]+sf.get_yOne(), currX]).tolist() + if 0 in inMapId: + inMapId.remove(0) + if (len(inMapId) == 1): + currentElementId = inMapId[0] + allItemInLine.append(currLst[currentElementId]) + currX = max(currX, currLst[currentElementId].boundingBox[2])+1 + else: + currX += 1 + allItems.append(allItemInLine) + return allItems + +def constructBar(noteGroupMap:np.ndarray, + noteGroupVerticallyMerged: List[NoteGroup|None], + restMap: np.ndarray, + restList:List[Rest|None], + sfnClefMap:np.ndarray, + sfnClefList:List[Union[Accidentals,Clef, None]], + beamMapImg:np.ndarray, + staffList:List[Staff], + pageMetadata:ScoreMetaData, + barEachTrack:List[List[int]], + barChangeList:dict[str, tuple[int, int]] #(track, num),(TStop, TSbottom) + ): + numLines = len(staffList) + bb,gg,rr = cv2.split(beamMapImg) + barList:List[List[Bar]] = [[] for _ in range(numLines)] + numBarsEachLine:List[int] = [0 for _ in range(numLines)] + mapForMatching:List[np.ndarray|None] = [None,noteGroupMap,sfnClefMap, sfnClefMap, None, restMap] + listForMatching:List[List] = [None, noteGroupVerticallyMerged,sfnClefList, sfnClefList, None, restList] + notFinishedBar = None + allRanges = [] + for barThisTrack in barEachTrack: + prevIdx = 0 + rangeThisTrack = [] + if len(barThisTrack) > 1: + while prevIdx < len(barThisTrack)-1: + if barThisTrack[prevIdx+1] - barThisTrack[prevIdx] > BAR_MAX_GAP: + rangeThisTrack.append((barThisTrack[prevIdx], barThisTrack[prevIdx+1])) + prevIdx += 1 + allRanges.append(rangeThisTrack) + + currTS = barChangeList.get('0,0') + for t in range(numLines): # trackNo + printedLineNo = t # the current line number + currBarList:List[Bar] = [] + sf0 = staffList[printedLineNo] + trackForThatLine = pageMetadata.getTrackForLineIdx(t) + ranges = allRanges[trackForThatLine] + isBar = barEachTrack[trackForThatLine] + + for ridx, rng in enumerate(ranges): + if barChangeList.get(f'{trackForThatLine},{ridx}'): + currTS = barChangeList.get(f'{trackForThatLine},{ridx}') + if currTS != [9,8]: + print(f'ridx: {ridx}, rng: {rng}') + if notFinishedBar is not None: + bar = notFinishedBar + notFinishedBar = None + else: + bar = Bar(currTS) + currX = rng[0] + while currX1: + for i in [1,5,3,2]: # noteGroup(1), rest(5), clef(3), accidentals(2) + if i in lineLst: + lineLst = [i] + break + if len(lineLst)==0 or 4 in lineLst: + currX+=1 + else: + typeId = lineLst[0] + currMap = mapForMatching[typeId] + currLst = listForMatching[typeId] + inMapId = np.unique(currMap[sf0.ys[0]-sf0.get_yOne():sf0.ys[-1]+sf0.get_yOne(), currX]).tolist() + if 0 in inMapId: + inMapId.remove(0) + if len(inMapId) > 1 and typeId == 1: # if it's multiple notegroups in that line + # get the noteGroup that's closer to the center of the staff + yCenter = sf0.ys[2] + newInMapIdIdx = -1 + currSmallestDist = np.inf + for idid in range(0,len(inMapId)): + x0,y0,x1,y1 = currLst[inMapId[idid]].boundingBox + if abs((y1+y0)/2-yCenter) < currSmallestDist: + currSmallestDist = abs((y1+y0)/2-yCenter) + newInMapIdIdx = idid + inMapId = [inMapId[newInMapIdIdx]] + if len(inMapId)==1: + sanityCheck = True + if typeId == 3: # clef + currClef:Clef = sfnClefList[inMapId[0]] + _,yy0,_,yy1 = currClef.boundingBox + if yy1-yy0 < sf0.get_yOne_float()*3: + sanityCheck = False + if currClef.type ==0: + if yy00)[1] + # nextIdx = 0 + # if len(notfinishedBarStuff) > 0: + # nextIdx = np.max(np.unique(np.where(gg[sf0.ys[0]-sf0.get_yOne():sf0.ys[-1]+sf0.get_yOne(), rng[1]:]>0)[1])) + # if nextIdx>5: + # rng = (isBar[-1]+sf0.left+1, rng[1]+nextIdx) + # bar = Bar(currTS) + # currX = rng[0] + # while currX1: + # for i in [1,5,3,2]: + # if i in lineLst: + # lineLst = [i] + # break + # if len(lineLst)==0 or 4 in lineLst: + # currX+=1 + # else: + # typeId = lineLst[0] + # currMap = mapForMatching[typeId] + # currLst = listForMatching[typeId] + # inMapId = np.unique(currMap[sf0.ys[0]-sf0.get_yOne():sf0.ys[-1]+sf0.get_yOne(), currX]).tolist() + # if 0 in inMapId: + # inMapId.remove(0) + # if len(inMapId)==1: + # bar.addElement(currLst[inMapId[0]]) + # currX = currLst[inMapId[0]].boundingBox[2]+1 + # else: + # currX+=1 + # print("has more than 1 id") + # notFinishedBar = bar + barList[t] = currBarList + numBarsEachLine[t] = len(currBarList) + # in order of vln1's 1st line, 2nd line ... | vln2's 1st line, 2nd line ... + return barList,allRanges, numBarsEachLine + +# TODO: rotate the image at here? as long as all maps are rotated it should be fine +def stackItemVertically(image:np.ndarray, + barList: List[List[Bar]], + pageMetadata: ScoreMetaData, + staffList:List[Staff], + noteGroupVerticallyMerged: List[NoteGroup], + restList: List[Rest]): + noteRestMap = np.zeros((image.shape[0], image.shape[1]), dtype=np.int16) + for oneLine in barList: + for oneBar in oneLine: + for elem in oneBar.elementList: + if type(elem) == NoteGroup: + x0,y0,x1,y1 = elem.boundingBox + noteRestMap = cv2.rectangle(noteRestMap, (x0,y0),(x1,y1),elem.indexNumber, -1) + elif type(elem) == Rest: + x0,y0,x1,y1 = elem.boundingBox + if elem.rhythm != -1: + noteRestMap = cv2.rectangle(noteRestMap, (x0,y0),(x1,y1),-elem.indexNumber, -1) + img2 = image.copy() + img2[noteRestMap>0] = (0,0,255) + img2[noteRestMap<0] = (255,255,0) + imwrite("finalNoteRest.jpg", img2) + retImg = image.copy() + oneLineDist = staffList[0].get_yOne() + acrossLineNGID = 1 + for (xStart, xEnd, yStart, yEnd) in pageMetadata.getTrackXYRange(staffList): + currX = xStart + while currX < xEnd: + inLineIds = np.unique(noteRestMap[yStart:yEnd, currX]).tolist() + if inLineIds == [0]: + currX += 1 + continue + allElementLists:List[NoteGroup|Rest] = [] + allXCenters = [currX] + allXEnds = [currX] + for inLineId in inLineIds: + if inLineId == 0: + continue + if inLineId > 0: # note + currElem = noteGroupVerticallyMerged[inLineId] + elif inLineId < 0: # rest + currElem = restList[-inLineId] + allElementLists.append(currElem) + if currElem.boundingBox[2]-currElem.boundingBox[1] <= oneLineDist*2: + allXCenters.append((currElem.boundingBox[0]+currElem.boundingBox[2])//2) + allXEnds.append(currElem.boundingBox[2]) + xCenterAvg = sum(allXCenters)//len(allXCenters) + newInLineIdx = np.unique(noteRestMap[yStart:yEnd, xCenterAvg]).tolist() + if set(inLineIds) == set(newInLineIdx): + # current Center is the center of this whole noteGroup thingy + # !!! I'm gonna draw on the image for now but other things have to be done here + # cv2.rectangle(sfnClefImg, (x0,y0),(x1,y1), class_colors2[predict_idx],2,cv2.LINE_AA) + retImg = cv2.rectangle(retImg, (xCenterAvg, yStart), (xCenterAvg, yEnd), (255,100,0), 3, cv2.LINE_AA) + for elem in allElementLists: + x0,y0,x1,y1 = elem.boundingBox + retImg = cv2.rectangle(retImg, (x0,y0), (x1,y1), (255,255,0), -1, cv2.LINE_AA) + noteRestMap = cv2.rectangle(noteRestMap, (x0,y0), (x1,y1), 0, -1, cv2.LINE_AA) + elem.setAcrossLineNGID(acrossLineNGID) + acrossLineNGID += 1 + currX = max(max(allXEnds), currX)+1 + else: + currX = max(xCenterAvg+1, currX+1) + imwrite("stackingNotes.jpg", retImg) + return noteRestMap, retImg + +# originally we pass in a list of each track [vln1's bars: [bar1, bar2 ...], vln2's bars: [bar1, bar2 ...]] +# now we have each line differently +# return the breakPoint (aka which bar is the starting of next line) +def tuneBarList(barList:List[List[Bar]], numBarsPerTrackLine:List[int], ): + trackRange = pageMetadata.getTrackRange() + + for lineRangeForTrack in trackRange: + startIdx = lineRangeForTrack[0] + endIdx = lineRangeForTrack[1]+1 + emptyBarsIndexs = np.where(np.sum(np.array([[len(b.elementList) for b in oneTrack] for oneTrack in barList[startIdx:endIdx]]),axis=0)==0)[0].tolist() + emptyBarsIndexs.reverse() + for emptyBarsIndex in emptyBarsIndexs: + for i in range(startIdx, endIdx): + del barList[i][emptyBarsIndex] + qq = 0 + while emptyBarsIndex+1>sum(numBarsPerTrackLine[0:qq]): + qq+=1 + numBarsPerTrackLine[qq-1]-=1 + + # delete all elements that's ornament + for trackId, oneTrack in enumerate(barList): + for barId, bar in enumerate(oneTrack): + bar.elementList = [ + elem for elem in bar.elementList + if not (isinstance(elem, NoteGroup) and all(s.isOrnament for s in elem.noteStemList)) + ] + # now all bars are suppose to be valid + for trackId, oneTrack in enumerate(barList): + for barId, bar in enumerate(oneTrack): + origLengths = bar.getRhythmList() + newLengths = [None]*len(origLengths) + if bar.getTotalBeat() == Fraction(bar.ts[0],bar.ts[1]): + newLengths = [None]*len(origLengths) + elif bar.getTotalBeat() == 0: + continue # will be later added a rest + else: + newLengths = tuneBar(bar.getRestNg(), bar.ts) + reassignedLengths = [origLengths[i] if newLengths[i] is None else newLengths[i] for i in range(len(newLengths))] + bar.reassignLength(origLengths, newLengths) + if bar.getTunedTotalBeat() != Fraction(bar.ts[0], bar.ts[1]): + print("bar doesn't have right length") + + for lineii, oneTrack in enumerate(barList): + for barii, bb in enumerate(oneTrack): + if bb.getTunedTotalBeat() != Fraction(bb.ts[0],bb.ts[1]): + print(f"bar at {lineii},{barii} has total beat {bb.getTotalBeat()}") + barBreakPoints = [0] + list(itertools.accumulate(numBarsPerTrackLine)) + return barBreakPoints + +def assignBarlistInstrument(barList:List[List[Bar]], pageMetadata:ScoreMetaData): + def getEmptyBarDefaultList(barTrack: List[Bar]): + return [Bar(b.ts) for b in barTrack] + def build_grouped_lookup(instrument_list): + grouped = defaultdict(list) + for item in instrument_list: + if ":" in item: + base, suffix = item.split(":", 1) + key_suffix = ":" + suffix + else: + base = item + key_suffix = "" + base_name = base.split("__")[0] + key = base_name + key_suffix + grouped[key].append(item) + return dict(grouped) + + def build_instrument_lookup_no_section(instrument_list): + grouped = defaultdict(list) + for item in instrument_list: + base = item.split(":", 1)[0] + base_name = base.split("__")[0] + grouped[base_name].append(item) + return dict(grouped) + def getInstruments(instrumentLookup: dict, instrumentLookupNoSec: dict, ins: str): + if instrumentLookup.get(ins): + return instrumentLookup.get(ins) + if instrumentLookupNoSec.get(ins): + return instrumentLookupNoSec.get(ins) + return [ins] + instrumentTrack = pageMetadata.getInstrumentEachTrack() + trackRange = pageMetadata.getTrackRange() + retBarList = dict() + numBarsTotal = 0 + for trackLineIdx, currTrackRange in enumerate(trackRange): + startIdx = currTrackRange[0] + endIdx = currTrackRange[1]+1 + instrumentList = ScoreMetaData.get_instruments() + if trackLineIdx == 0: + for ins in instrumentList: + retBarList[ins] = [] + instrumentLookup = build_grouped_lookup(instrumentList) + instrumentLookupNoSec = build_instrument_lookup_no_section(instrumentList) + numBarsTotal += len(barList[startIdx]) + for i in range(startIdx, endIdx): + instrumentInThisLineList = [getInstruments(instrumentLookup,instrumentLookupNoSec,ins) for ins in instrumentTrack[i]] + instrumentInThisLine = [item for sublist in instrumentInThisLineList for item in sublist] + print(f"instrument in this line: {instrumentInThisLine}") + for ins in instrumentInThisLine: + if not ins in instrumentList: + print(f'WARNING: instrument {ins} not in list') + continue + if trackLineIdx == 0: + retBarList[ins] = list(barList[i]) + else: + if len(retBarList[ins]) == numBarsTotal: + print(f"double instrument: {ins}") + N = len(barList[i]) + retBarList[ins][-N:] = barList[i] + else: + retBarList[ins] += barList[i] + if ins in instrumentList: + instrumentList.remove(ins) + else: + print(f"instrument {ins} not in list") + for resIns in instrumentList: + if trackLineIdx == 0: + retBarList[resIns] = getEmptyBarDefaultList(barList[startIdx]) + else: + retBarList[resIns] += getEmptyBarDefaultList(barList[startIdx]) + assert len(np.unique([len(retBarList[b]) for b in retBarList])) == 1 + barListPerInstrument = [retBarList[k] for k in list(retBarList.keys())] + instrumentEachLine = list(retBarList.keys()) + return retBarList, barListPerInstrument, instrumentEachLine + +# return (1) ksMat for each track (exactly as how it's presented visually), metric of [number of lines, bars] +# (2) the keySignature of each track (concert pitch) as an array +def getKSMatAndKSList(pageMetadata: ScoreMetaData, barList: List[List[Bar]], toneHelper: ToneHelper): + def checkAndAssignKS( + prevIsAcc: bool, + new_elements: List[Accidentals|Clef|Rest|NoteGroup|KeySignature], + accumulatedAccidentals: List[KeySignature], + trackGroupNo: int, + lineOfThatTrackNo: int, + barNo: int, + ksBarMatForEachTrack: List[np.ndarray]): + if prevIsAcc: + newKS = KeySignature(accumulatedAccidentals) + new_elements.append(newKS) + ksBarMatForEachTrack[trackGroupNo][lineOfThatTrackNo][barNo] = newKS.getSharpFlatInt() + accumulatedAccidentals = [] + return new_elements, accumulatedAccidentals, ksBarMatForEachTrack + # return the list of concert pitch keySignature for each bar (inf: no signature) + def modeKSForAllTrack(ksMat:np.ndarray): + result = [] + prev = 0 + for col in ksMat.T: + finite_vals = col[~np.isinf(col)] # remove infs + if finite_vals.size > 0: + values, counts = np.unique(finite_vals, return_counts=True) + prev = values[np.argmax(counts)] + result.append(prev) + return np.array(result) + oneInstrumentEachLine: List[str] = [i[0] for i in pageMetadata.getInstrumentEachTrack()] + tone: List[str] = [ins.split(':')[1] if len(ins.split(':')) > 1 else '' for ins in oneInstrumentEachLine] + trackShiftEachLine = [] + for idx in range(len(oneInstrumentEachLine)): + currTone: str = tone[idx] + currIns: str = oneInstrumentEachLine[idx] + if isInstrumentIncluded(currIns): + trackShiftEachLine.append(-toneHelper.getKsShift(currTone)) + else: + trackShiftEachLine.append(-np.inf) + ksBarMatForEachTrack = [] + ksBarShiftForEachTrack = [] + trackRanges = pageMetadata.getTrackRange() + for trackRange in trackRanges: + numTrack = trackRange[1]-trackRange[0] + 1 # inclusive of the start and end + numBars = len(barList[trackRange[0]]) + ksBarMat = np.ones((numTrack, numBars))*np.inf + ksBarMatForEachTrack.append(ksBarMat) + ksBarShift = np.array(trackShiftEachLine[trackRange[0]:trackRange[1]+1])[:, None] * np.ones(numBars) + ksBarShiftForEachTrack.append(ksBarShift) + + for (lineNo, oneLine) in enumerate(barList): + # the line #lineOfThatTrackNo of the track #trackGroupNo + trackGroupNo, lineOfThatTrackNo = pageMetadata.getTrackIdxForLineIdx(lineNo) + for (barNo, oneBar) in enumerate(oneLine): + accumulatedAccidentals = [] + new_elements = [] + prevIsAcc = False + for elem in oneBar.elementList: + if isinstance(elem, Accidentals) and elem.isKeySignature: + accumulatedAccidentals.append(elem) + prevIsAcc = True + else: + new_elements, accumulatedAccidentals, ksBarMatForEachTrack = checkAndAssignKS(prevIsAcc, new_elements, accumulatedAccidentals, trackGroupNo, lineOfThatTrackNo, barNo, ksBarMatForEachTrack) + new_elements.append(elem) + prevIsAcc = False + new_elements, accumulatedAccidentals, ksBarMatForEachTrack = checkAndAssignKS(prevIsAcc, new_elements, accumulatedAccidentals, trackGroupNo, lineOfThatTrackNo, barNo, ksBarMatForEachTrack) + # assign the basic to 0 if there's no changes + if barNo == 0 and ksBarMatForEachTrack[trackGroupNo][lineOfThatTrackNo][barNo] == np.inf: + ksBarMatForEachTrack[trackGroupNo][lineOfThatTrackNo][barNo] = 0 + oneBar.elementList = new_elements + # now ksBarMatForEachTrack is exactly what we see on the score + ksEachTrack = [] + ksMatEachTrackSameAsVisual = [] + for idx in range(len(ksBarMatForEachTrack)): + ksBarMatShifted = ksBarMatForEachTrack[idx]-ksBarShiftForEachTrack[idx] + modeKSArray = modeKSForAllTrack(ksBarMatShifted) + ksEachTrack.append(modeKSArray) + ksMatEachTrackSameAsVisual.append(np.tile(modeKSArray, (ksBarMatForEachTrack[idx].shape[0],1)) + ksBarShiftForEachTrack[idx]) + return ksMatEachTrackSameAsVisual, ksEachTrack, barList +def readCsvAndEditViolin(csvPath: str): + df = pd.read_csv(csvPath) + # Ensure part1 is nullable int + if 'part1' in df.columns: + df['part1'] = df['part1'].astype('Int64') + else: + df['part1'] = pd.Series([pd.NA]*len(df), dtype="Int64") + n = len(df) + for i in range(n): + if df.at[i, 'ins1'] == 'viola': + if i - 2 >= 0: + cond = ( + pd.notna(df.at[i-2, 'ins1']) and df.at[i-2, 'ins1'] == 'violin' and pd.notna(df.at[i-2, 'part1']) and df.at[i-2, 'part1'] == 1 and + pd.notna(df.at[i-1, 'ins1']) and df.at[i-1, 'ins1'] == 'violin' and pd.notna(df.at[i-1, 'part1']) and df.at[i-1, 'part1'] == 2 + + ) + if not cond: + df.at[i-2, 'ins1'] = 'violin' + df.at[i-2, 'part1'] = 1 + df.at[i-1, 'ins1'] = 'violin' + df.at[i-1, 'part1'] = 2 + df = df.copy() + # Ensure nullable int + df['part1'] = df['part1'].astype('Int64') + n = len(df) + for i in range(n): + if pd.notna(df.at[i, 'part1']) and df.at[i, 'part1'] == 1: + # Check if next row exists and is matching part 2 + if not ( + i + 1 < n and + df.at[i+1, 'ins1'] == df.at[i, 'ins1'] and + df.at[i+1, 'part1'] == 2 + ): + df.at[i, 'part1'] = pd.NA + return df + +def exportXML(barList:List[List[Bar]], + instrumentEachLine: List[str], + toneHelper: ToneHelper, + instrument_dict: dict, + ksListAllTrack: np.ndarray, # array([-4., inf, inf, inf, inf, inf, 2, inf, inf]) + image:np.ndarray|None = None, + imgName: str = "Title"): + + def getTrackKsAndNoteShift(toneHelper: ToneHelper, instrumentList: List[str]) -> Tuple[List[int|None], List[int]]: + tone = [ins.split(':')[1] if len(ins.split(':')) > 1 else '' for ins in instrumentList] + trackKsShift = [] + trackNoteShift = [] + for idx, tn in enumerate(tone): + ksShift, noteShift = toneHelper.getKsAndNoteShift(tn) + if isInstrumentIncluded(instrumentList[idx]): + trackKsShift.append(-ksShift) + else: + trackKsShift.append(None) + trackNoteShift.append(noteShift) + return trackKsShift, trackNoteShift + numTrack = len(instrumentEachLine) + # Stem's label -> rhythm meaning + # class_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40), (245, 220, 255),(230,130,175),(165,170, 70)] + if image is None: + debugXMLImg = None + else: + debugXMLImg = image.copy() + def initializeScore(instrumentList: List[str], title): + score = stream.Score() + score.metadata = metadata.Metadata() + score.metadata.title = title + part = [] + for instr_name in instrumentList: + p = stream.Part() + # Assign instrument + instr_obj = get_instrument_from_string(instr_name) + p.insert(0, instr_obj) + p.partName = instr_name + p.partAbbreviation = instr_name + part.append(p) + return score, part + score, part = initializeScore(instrumentEachLine, imgName) + score2, part2 = initializeScore(instrumentEachLine, f"{imgName} Shifted") + keyName = ['C','D','E','F','G','A','B'] + keyCharFlat = ['C','D-','D','E-','E','F','G-','G','A-','A','B-','B'] + keyCharSharp = ['C','C#','D','D#','E','F','F#','G','G#','A','A#','B'] + currentClef = [None]*numTrack + sharps = [3,0,4,1,5,2,6] # F C G D A E B + flats = [6,2,5,1,4,0,3] # B E A D G C F + ksSharp = [set() for _ in range(numTrack)] + ksFlat = [set() for _ in range(numTrack)] + trksOriginal = [[] for _ in range (numTrack)] # for tracking all original notes we see + trksShifted = [[] for _ in range (numTrack)] # for tracking all shifted notes + + # trackKsShift will be None for Timpani, Horn and Trumpet + # # trackNoteShift works for everything except for Timpani + trackKsShift, trackNoteShift = getTrackKsAndNoteShift(toneHelper, instrumentEachLine) + + cleanedInstrumentList = [s.split('__')[0].split(':')[0] for s in instrumentEachLine] + clefOptionEachLine = [instrument_dict.get(instr, [1,0,-1,-2]) for instr in cleanedInstrumentList] + + def setKS(ks:float, trkNo): + ks = int(ks) + nonlocal ksSharp + nonlocal ksFlat + if ks>0: + ksSharp[trkNo] = [keyName[i] for i in [sharps[q] for q in range(ks)]] + ksFlat[trkNo] = set() + else: + ksFlat[trkNo] = [keyName[i] for i in [flats[q] for q in range(-ks)]] + ksSharp[trkNo] = set() + def setKSNeutral(Oriks:float, trkNo, trackShift): + Oriks = int(Oriks) + nonlocal ksSharp + nonlocal ksFlat + ks = Oriks+trackShift + if ks>0: + ksSharp[trkNo] = [keyName[i] for i in [sharps[q] for q in range(ks)]] + ksFlat[trkNo] = set() + else: + ksFlat[trkNo] = [keyName[i] for i in [flats[q] for q in range(-ks)]] + ksSharp[trkNo] = set() + def parseClefOne(elem:Clef, lineNo:int): + nonlocal currentClef + currentClef[lineNo%numTrack] = elem.type + return elem.type + def getClefFromType(clefType:int): + clf = clef.TrebleClef() + if clefType == 1: + clf = clef.TrebleClef() + elif clefType == 0: + clf = clef.AltoClef() + elif clefType == -1: #-1 + clf = clef.BassClef() + elif clefType == -2: + clf = clef.TenorClef() + return clf + def calculatePitch(inputPitch:str): + outputNum = keyCharFlat.index(inputPitch[0])+12*(int(inputPitch[-1])+1) + if len(inputPitch)==3: + if inputPitch[1] == '#': + outputNum+=1 + elif inputPitch[1] == '-' : + outputNum-=1 + return outputNum + def pitchListShift(inputPitchList:List[str], shift: int, sharp = True): # shift: how many half notes + returnList = [] + for pitch in inputPitchList: + pitchNum = calculatePitch(pitch) + pitchNum = pitchNum + shift + returnList.append(pitchNum) + referenceList = keyCharFlat + if sharp: + referenceList = keyCharSharp + for idx,pitchNo in enumerate(returnList): + clefNum = pitchNo//12-1 + pitchName = referenceList[pitchNo%12] + returnList[idx] = pitchName + str(clefNum) + return returnList + def parseNoteGroupShift(elem:NoteGroup, flatSet, sharpSet, naturalSet, currClef, flatSharp, linNo, noteValShift): + # trkShift: + # 0 if original: -2, new: -2 (two flats), + # 1 if original: -2, new: -1 + # flatSharp: how many flats/sharp, -2: two flats + # flatSharp = 2, trkShift = 0: (-2), + # flatSharp = 2, trkShift = 1: (-1) + # ksShift: + # if original key signature is -2, ksShift is 1: -> actually it's -1 (the key signature we actually hear) + keyLst = [] + currLength = 1 + regNoteCount = 0 + def setRemove(currSet, currElm): + if currElm in currSet: + currSet.remove(currElm) + return currSet + for stem in elem.noteStemList: + if not stem.isOrnament: + regNoteCount+=1 + else: + continue + actualPitch = stem.pitchSoprano + if currClef == 0: #viola + actualPitch -= 6 + elif currClef == -1: #cello + actualPitch -= 12 + elif currClef == -2: #tenor + actualPitch -= 8 + currKeyName = keyName[(actualPitch-1)%7] + currKey = currKeyName+str((actualPitch-1)//7+5) + if stem.accidentals is not None: # it has keySignature + sharpSet = setRemove(sharpSet, currKey) + naturalSet = setRemove(naturalSet, currKey) + flatSet = setRemove(flatSet, currKey) + if stem.accidentals == -1 or currKey in flatSet: + flatSet.add(currKey) + currKey = currKey[0]+'-'+currKey[1] + elif stem.accidentals == 1 or currKey in sharpSet: + sharpSet.add(currKey) + currKey = currKey[0]+'#'+currKey[1] + else: # stem.accidentals == 0: + naturalSet.add(currKey) + else: + if currKey in naturalList: + currKey = currKey + elif currKey in flatSet or currKeyName in ksFlat[linNo]: + currKey = currKey[0]+'-'+currKey[1] + elif currKey in sharpSet or currKeyName in ksSharp[linNo]: + currKey = currKey[0]+'#'+currKey[1] + # llen = float(Fraction(class_names[stem.rhythm])) + # if stem.hasdot: + # llen = llen*1.5 + # if llen0) + trksShifted[linNo].append(keyShiftedList) + if len(keyShiftedList) > 1: + return flatSet, sharpSet, chord.Chord(keyShiftedList, quarterLength = currLength*4) + elif len(keyShiftedList) == 1: + return flatSet, sharpSet, note.Note(keyShiftedList[0], quarterLength=currLength*4) + else: + return flatSet, sharpSet, None + # actualPitch, 0: B4, 1: C5, 2: D5 + + def parseNoteGroup(elem:NoteGroup, flatSet, sharpSet, naturalSet, currClef, linNo, trkShift = 0): + keyLst = [] + currLength = 1 + regNoteCount = 0 + def setRemove(currSet, currElm): + if currElm in currSet: + currSet.remove(currElm) + return currSet + for stem in elem.noteStemList: + if not stem.isOrnament: + regNoteCount+=1 + else: + continue + actualPitch = stem.pitchSoprano + if currClef == 0: #viola + actualPitch -= 6 + elif currClef == -1: #cello + actualPitch -= 12 + elif currClef == -2: #tenor + actualPitch -= 8 + currKeyName = keyName[(actualPitch-1)%7] + currKey = currKeyName+str((actualPitch-1)//7+5) + if stem.accidentals is not None: # it has keySignature + sharpSet = setRemove(sharpSet, currKey) + naturalSet = setRemove(naturalSet, currKey) + flatSet = setRemove(flatSet, currKey) + if stem.accidentals == -1: + flatSet.add(currKey) + currKey = currKey[0]+'-'+currKey[1] + elif stem.accidentals == 1 or currKey in sharpSet: + sharpSet.add(currKey) + currKey = currKey[0]+'#'+currKey[1] + else: # stem.accidentals == 0: + naturalSet.add(currKey) + else: + if currKey in naturalList: + currKey = currKey + elif currKey in flatSet or currKeyName in ksFlat[linNo]: + currKey = currKey[0]+'-'+currKey[1] + elif currKey in sharpSet or currKeyName in ksSharp[linNo]: + currKey = currKey[0]+'#'+currKey[1] + # llen = float(Fraction(class_names[stem.rhythm])) + # if stem.hasdot: + # llen = llen*1.5 + # if llen 1: + keyLst = list(set(keyLst)) + for k in keyLst: + if k[-1] == '-': + keyLst.remove(k) + keyLst.append(k[:-1]+'4') + return flatSet, sharpSet, chord.Chord(keyLst, quarterLength = currLength*4) + elif len(keyLst) == 1: + for k in keyLst: + if k[-1] == '-': + keyLst.remove(k) + keyLst.append(k[:-1]+'4') + return flatSet, sharpSet, note.Note(keyLst[0], quarterLength=currLength*4) + else: + return flatSet, sharpSet, None # ornament + # actualPitch, 0: B4, 1: C5, 2: D5 + def parseRest(elem:Rest): + restLength = float(elem.tunedLength) + if restLength>0: + # restLength = float(Fraction(restClassNames[elem.rhythm])) + # if elem.hasdot: + # restLength = restLength*1.5 + return note.Rest(quarterLength=restLength*4) + else: + pass + # print("setting rest to full") + # return note.Rest(quarterLength=4) + numBars = len(barList[0]) + clefMat = np.ones((numTrack, numBars))*np.inf + currentClef = [None]*numTrack + for currBarNumber in range(numBars): + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): + if len(clefOptionEachLine[lineNo]) == 1: + clefMat[lineNo, currBarNumber] = clefOptionEachLine[lineNo][0] + else: + currBar = barList[lineNo][currBarNumber] + for elem in currBar.elementList: + if type(elem) == Clef: + newClef = parseClefOne(elem, lineNo) + if newClef in clefOptionEachLine[lineNo]: + clefMat[lineNo, currBarNumber] = newClef + if clefMat[lineNo, currBarNumber] == np.inf: + if currBarNumber == 0: + clefMat[lineNo, currBarNumber] = clefOptionEachLine[lineNo][0] + else: + clefMat[lineNo, currBarNumber] = clefMat[lineNo, currBarNumber-1] + global DEBUGIMG + ksListAllTrackNoInf = ksListAllTrack.copy() + for i in range(1, numBars): + if np.isinf(ksListAllTrackNoInf[i]): + ksListAllTrackNoInf[i] = ksListAllTrackNoInf[i - 1] + ksBarMatNeutral = np.tile(ksListAllTrackNoInf, (numTrack,1)) + ksBarMatNeutral = ksBarMatNeutral.astype(int) + trackShiftInf = [t if t is not None else np.inf for t in trackKsShift] + ksBarMatNew = ksBarMatNeutral + np.tile(np.array(trackShiftInf),(numBars,1)).T + # if we want to replace it then + ksBarMatNeutral[np.isinf(ksBarMatNew)] = 0 + ksBarMatNew[np.isinf(ksBarMatNew)] = 0 + ksBarMatNew = ksBarMatNew.astype(int) + clefMat = clefMat.astype(int) + tssList = [b.ts for b in barList[0]] + for currBarNumber in range(numBars): # how many bars each line + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): # each track + currBar = barList[lineNo][currBarNumber] + measure = stream.Measure(number=barNumber) + flatList = set() #put here because reset in measures + sharpList = set() + naturalList = set() + clefType = clefMat[lineNo, currBarNumber] + ks = ksBarMatNew[lineNo, currBarNumber] + if currBarNumber == 0: + measure.append(meter.TimeSignature(f'{currBar.ts[0]}/{currBar.ts[1]}')) + elif tssList[currBarNumber]!=tssList[currBarNumber-1]: + measure.append(meter.TimeSignature(f'{currBar.ts[0]}/{currBar.ts[1]}')) + if currBarNumber == 0: + measure.append(getClefFromType(clefType)) + setKS(ks, lineNo) + measure.append(key.KeySignature(ks)) + elif clefType != clefMat[lineNo, currBarNumber-1]: + measure.append(getClefFromType(clefType)) + elif ks != ksBarMatNew[lineNo, currBarNumber-1]: + setKS(ks, lineNo) + measure.append(key.KeySignature(ks)) + if currBar.getTunedTotalBeat() == 0: + measure.append(note.Rest(quarterLength = currBar.ts[0]/currBar.ts[1]*4)) + else: + for elem in currBar.elementList: + if type(elem) == NoteGroup: + flatList, sharpList, currNote = parseNoteGroup(elem, + flatList, + sharpList, + naturalList, + clefMat[lineNo, currBarNumber], + lineNo, + trkShift=0) + if currNote is not None: + measure.append(currNote) + else: + print() + elif type(elem) == Rest: + currRest = parseRest(elem) + if currRest is not None: + measure.append(currRest) + part[lineNo%numTrack].append(measure) + for p in part: + score.append(p) + ''' + # Part 2: with shift + ksBarMatNeutral + ksSharp = [set() for _ in range(numTrack)] + ksFlat = [set() for _ in range(numTrack)] + for currBarNumber in range(numBars): # for the new one shifted + barNumber = currBarNumber+1 + for lineNo in range(len(barList)): + currBar = barList[lineNo][currBarNumber] + measure = stream.Measure(number=barNumber) + # barNumber = barNumber+1 + flatList = set() #put here because reset in measures + sharpList = set() + naturalList = set() + clefType = clefMat[lineNo, currBarNumber] + ks = ksBarMatNeutral[lineNo, currBarNumber] + if currBarNumber == 0: + measure.append(getClefFromType(clefType)) + setKSNeutral(ks, lineNo, ks) + measure.append(key.KeySignature(ks)) + elif clefType != clefMat[lineNo, currBarNumber-1]: + measure.append(getClefFromType(clefType)) + elif ks != ksBarMatNeutral[lineNo, currBarNumber-1]: + setKSNeutral(ks, lineNo, ks) + measure.append(key.KeySignature(ks)) + if currBar.getTunedTotalBeat() == 0: + measure.append(note.Rest(quarterLength = currBar.ts[0]/currBar.ts[1]*4)) + for elem in currBar.elementList: + if type(elem) == NoteGroup: + flatList, sharpList, currNote = parseNoteGroupShift(elem, + flatList, + sharpList, + naturalList, + clefMat[lineNo, currBarNumber], + ks, + lineNo, + trackNoteShift[lineNo]) + if currNote is not None: + measure.append(currNote) + else: + print() + elif type(elem) == Rest: + currRest = parseRest(elem) + if currRest is not None: + measure.append(currRest) + part2[lineNo%numTrack].append(measure) + for p in part2: + score2.append(p) + ''' + return score, {'ksAssigned':debugXMLImg} + +def numberToString(num: int, strLen: int = 3): + stringNum = str(num) + strAppend = strLen-len(stringNum) + return '0'*strAppend + stringNum + +def getTrackKsAndNoteShift(toneHelper: ToneHelper, instrumentList: List[str]) -> Tuple[List[int|None], List[int]]: + tone = [ins.split(':')[1] if len(ins.split(':')) > 1 else '' for ins in instrumentList] + trackKsShift = [] + trackNoteShift = [] + for idx, tn in enumerate(tone): + ksShift, noteShift = toneHelper.getKsAndNoteShift(tn) + if isInstrumentIncluded(instrumentList[idx]): + trackKsShift.append(-ksShift) + else: + trackKsShift.append(None) + trackNoteShift.append(noteShift) + return trackKsShift, trackNoteShift + +# time signatures +def getMeasuresEachStaff(image: np.ndarray, beamMapImg: np.ndarray, staffList: List[Staff], barEachTrack:List[List[int]], trackRange: List[Tuple[int,int]]): + allRanges = [] + for barThisTrack in barEachTrack: + prevIdx = 0 + rangeThisTrack = [] + if len(barThisTrack) > 1: + while prevIdx < len(barThisTrack)-1: + if barThisTrack[prevIdx+1] - barThisTrack[prevIdx] > BAR_MAX_GAP: + rangeThisTrack.append((barThisTrack[prevIdx], barThisTrack[prevIdx+1])) + prevIdx += 1 + allRanges.append(rangeThisTrack) + retList = [] + for trackIdx, track in enumerate(trackRange): + start = track[0] + end = track[1]+1 + for i in range(start, end): + sf = staffList[i] + retList.append([[rng[0], rng[1], sf.ys[0], sf.ys[1]] for rng in allRanges[trackIdx]]) + return retList + + img = image.copy() + # 第一維：哪一行 staff (長度等於 len(staffList)) + allStavesMeasures = [[] for _ in range(len(staffList))] + + # 取得類別 Map 並只關注小節線類別 (4) + _, itemMap, _ = cv2.split(beamMapImg) + staffCenters = [sf.ys[2] for sf in staffList] + staffImgBinary = (itemMap == 4)[staffCenters, :] + + # 利用 TrackRange 偵測，因為同一個 Track 的小節線通常是垂直對齊的 + for trkRange in pageMetadata.getTrackRange(): + staves_in_track = range(trkRange[0], trkRange[1] + 1) + + # 投影判定小節線位置 + barSum = np.sum(staffImgBinary[trkRange[0]:trkRange[1]+1, :], axis=0) + numStaves = len(staves_in_track) + barPos = np.where(barSum > numStaves * 0.7)[0] + + if len(barPos) > 0: + # --- 1. 篩選小節線：連續像素內 (<=5) 僅保留最右邊的一個值 --- + barLinesX = [] + if len(barPos) > 0: + temp_group_max = barPos[0] + for i in range(1, len(barPos)): + if barPos[i] - barPos[i-1] <= BAR_MAX_GAP: + temp_group_max = barPos[i] # 持續更新，保留最右邊 + else: + barLinesX.append(temp_group_max) + temp_group_max = barPos[i] + barLinesX.append(temp_group_max) + + # --- 2. 根據小節線生成「小節 (Measures)」 --- + # 如果有 N 條線 (barLinesX)，產生 N-1 個小節 + if len(barLinesX) >= 2: + for m_idx in range(len(barLinesX) - 1): + m_left = barLinesX[m_idx] + m_right = barLinesX[m_idx+1] + + # 分配給該 Track 裡的每一行 Staff + for s_idx in staves_in_track: + target_staff = staffList[s_idx] + top = target_staff.ys[0] + bottom = target_staff.ys[-1] + + # 建立小節 Bounding Box [left, right, top, bottom] + measure_bbox = [int(m_left), int(m_right), int(top), int(bottom)] + allStavesMeasures[s_idx].append(measure_bbox) + + # 視覺化：畫出小節範圍 (用藍色框表示小節) + cv2.rectangle(img, (m_left, top), (m_right, bottom), (255, 0, 0), 1) + + cv2.imwrite("measures_detected.jpg", img) + return allStavesMeasures + +def detect_time_signatures(model_path: str, img_path: str, imgsz: int = 1792, conf: float = 0.5) -> List[TimeSignature]: + from ultralytics import YOLO + time_signatures = [] + + model = YOLO(str(model_path)) + results = model.predict( + source=str(img_path), + imgsz=imgsz, + conf=conf, + save=False, + verbose=False + ) + + r = results[0] + if r.boxes is not None and len(r.boxes) > 0: + clss = r.boxes.cls.cpu().tolist() + confs = r.boxes.conf.cpu().tolist() + xyxys = r.boxes.xyxy.cpu().tolist() + + for i in range(len(clss)): + cls_id = int(clss[i]) + conf_val = float(confs[i]) + x1, y1, x2, y2 = xyxys[i] + bbox = (int(x1), int(y1), int(x2), int(y2)) + ts_obj = TimeSignature(yolo_class=cls_id, bbox=bbox, confidence=conf_val) + time_signatures.append(ts_obj) + + return time_signatures + +def map_time_signatures_to_score(time_signature_list, staffList, allStavesMeasures, allItemInScore, imgResizeRatio = 1): + """ + 將 TimeSignature 物件映射到對應的 Staff 和 Measure。 + """ + for ts in time_signature_list: + # if not ts.isValid(): + # continue + + tx1, ty1, tx2, ty2 = [int(s*imgResizeRatio) for s in ts.getBbox()] + + ts_cx = (tx1 + tx2) / 2 # 中心 X + ts_cy = (ty1 + ty2) / 2 # 中心 Y + ts_h = ty2 - ty1 # bbox 高度 + + # --- 規則 1: 縱向尋找最靠近的 staff --- + best_staff_idx = -1 + min_dist = float('inf') + + for s_idx, staff in enumerate(staffList): + # 計算該 staff 的中心 Y (五條線的平均值) + staff_cy = sum(staff.ys) / len(staff.ys) + dist = abs(ts_cy - staff_cy) + + if dist < min_dist: + min_dist = dist + best_staff_idx = s_idx + + # 檢查垂直相差距離是否大過本身 bbox 高度 + if best_staff_idx == -1 or min_dist > ts_h: + ts.setValid(False) + continue + + ts.setstaffline(best_staff_idx) + + # --- 規則 2: 水平尋找小節 (Measure) --- + measures = allStavesMeasures[best_staff_idx] + found_bar_idx = -2 # 預設未找到 + + # 檢查是否落在某個小節內 + for m_idx, m_bbox in enumerate(measures): + mx1, mx2, my1, my2 = m_bbox + if mx1 <= ts_cx <= mx2: + found_bar_idx = m_idx + break + + # 如果沒在小節內，檢查是否在最後一個小節的右邊 + if found_bar_idx == -2: + last_mx2 = measures[-1][1] # 最後一個小節的 x2 + if ts_cx > last_mx2: + found_bar_idx = -1 + else: + # 既不在小節內，也不在右邊（可能在第一小節左邊邊緣外） + ts.setValid(False) + continue + + ts.setBarLoc(found_bar_idx) + + # --- 規則 3: 檢查小節內的 Rest 和 NoteGroup --- + # 如果 barLoc 為 -1 (在最右邊之外)，通常不具備音樂意義上的音符檢查需求，可視情況跳過或判定無效 + if found_bar_idx == -1: + # 依據一般邏輯，拍號不應出現在所有音符之後的最右側，建議設為無效 + ts.setValid(False) + continue + + # 獲取該 staff 的所有物件 + staff_items = allItemInScore[best_staff_idx] + mx1, mx2, _, _ = measures[found_bar_idx] + + is_invalid_by_context = False + for item in staff_items: + # 只考慮 Rest 和 NoteGroup (透過類別名稱判斷) + class_name = item.__class__.__name__ + if class_name in ["Rest", "NoteGroup"]: + ix1, iy1, ix2, iy2 = item.boundingBox + item_cx = (ix1 + ix2) / 2 + + # 判斷該物件是否屬於當前小節 (中心在小節範圍內) + if mx1 <= item_cx <= mx2: + # 如果物件的中心在拍號中心的左邊 -> 拍號位置錯誤 + if item_cx < ts_cx: + is_invalid_by_context = True + break + + if is_invalid_by_context: + ts.setValid(False) + continue + +def map_to_track(a, trackList): + for i, (start, end) in enumerate(trackList): + if start <= a <= end: + return i + return None # in case a doesn't fall into any range +def prepareTSBarChangeList(data, trackList): + grouped = defaultdict(list) + + # Group values + for (a, b), value in data: + track_index = map_to_track(a, trackList) + key = (track_index, b) + grouped[key].append(value) + + result = {} + + # Compute most common per group + for key, values in grouped.items(): + most_common, common_count = Counter(values).most_common(1)[0] + if 'c' in most_common: + most_common = '4,4' + currentTrack = key[0] + if common_count > (trackList[currentTrack][1] - trackList[currentTrack][0]+1)* 0.5: + result[key] = list(map(int, most_common.split(','))) + + # Find the "last" group based on sorted (track_index, b) + if result: + last_key = sorted(result.keys())[-1] + last_value = result[last_key] + else: + last_value = None + + # Convert keys to "x,y" + formatted_result = { + f"{k[0]},{k[1]}": v for k, v in result.items() + } + + return formatted_result, last_value +def clearExtraMapping(noteGroupMap: np.ndarray, + stemIdxMap: np.ndarray, + noteGroupVerticallyMerged: List[NoteGroup | None], + restMap: np.ndarray, + restList: List[Rest | None], + sfnClefMap: np.ndarray, + sfnClefList: List[Union[Accidentals, Clef, None]], + beamMapImg: np.ndarray, + staffList: List[Staff], + dataDict, + pageMetadata: ScoreMetaData + ): + rangeInPair = [0] + trackRange = pageMetadata.getTrackRange() + for trk in trackRange: + start, end = trk + sf0 = staffList[start] + rangeInPair.append(sf0.ys[0]-int(sf0.get_yOne_float()*5)) + sf0 = staffList[end] + rangeInPair.append(sf0.ys[-1]+int(sf0.get_yOne_float()*5)) + pageheight = stemIdxMap.shape[0] + rangeInPair.append(pageheight) + pairs = list(zip(rangeInPair[::2], rangeInPair[1::2])) + for currMap in [noteGroupMap, stemIdxMap, restMap, sfnClefMap, beamMapImg]: + for (st,ed) in pairs: + if st<0 or ed<0 or st>=pageheight or ed>=pageheight: + continue + currMap[st:ed,:] = currMap[0,0] + for ddKey in dataDict: + for (st,ed) in pairs: + dataDict[ddKey][st:ed,:] = dataDict[ddKey][0,0] + return noteGroupMap, stemIdxMap,noteGroupVerticallyMerged, restMap, restList, sfnClefMap, sfnClefList, beamMapImg, staffList,dataDict + +if __name__ == '__main__': + noteGroupMap: np.ndarray + stemIdxMap: np.ndarray + noteGroupVerticallyMerged: List[NoteGroup | None] + restMap: np.ndarray + restList: List[Rest | None] + sfnClefMap: np.ndarray + sfnClefList: List[Union[Accidentals, Clef, None]] + beamMapImg: np.ndarray + staffList: List[Staff] + rootFolder = "sq_dataset" + sheetName = 'issue' + scoreInstruments = ['violin__1','violin__2','viola','cello'] + prevBarTS = [4,4] + lenString = 3 + runWholeModel = False + fullBarList = [[] for _ in range(len(scoreInstruments))] + fullksListAllTrack = np.array([]) + clearExtra = True # for piano quartet etc. if there's different size of lines + packedImg = True + for number in range(1,2): #(51,53): + imgName = rf"{sheetName}_{number}" + imgPath = rf"{rootFolder}\{sheetName}\imgs\{sheetName}_{number}\{sheetName}_{number}.png" + pklPath = rf"{rootFolder}\{sheetName}\imgs\{sheetName}_{number}\{sheetName}_{number}.pkl" + scorePath = rf"{rootFolder}\{sheetName}\xmls\{sheetName}_{number}.musicxml" + scoreShiftedPath = rf"{rootFolder}\{sheetName}\xmls\{sheetName}_{number}_shifted.musicxml" + if not os.path.isdir(rf"{rootFolder}\{sheetName}\xmls"): + os.mkdir(rf"{rootFolder}\{sheetName}\xmls") + if not os.path.exists(pklPath) or runWholeModel: + ( + noteGroupMap, + stemIdxMap, + noteGroupVerticallyMerged, + restMap, + restList, + sfnClefMap, + sfnClefList, + beamMapImg, + staffList, + dataDict + ) = png2decode(imgName, imgPath, len(scoreInstruments), packedImg) + with open(pklPath, "wb") as f: + pickle.dump( + ( + noteGroupMap, + stemIdxMap, + noteGroupVerticallyMerged, + restMap, + restList, + sfnClefMap, + sfnClefList, + beamMapImg, + staffList, + dataDict + ), + f + ) + print(f"finishing processing sheet {sheetName}_{number}") + # to save time running previous step (Debug only) + else: + with open(pklPath, "rb") as f: + ( + noteGroupMap, + stemIdxMap, + noteGroupVerticallyMerged, + restMap, + restList, + sfnClefMap, + sfnClefList, + beamMapImg, + staffList, + dataDict + ) = pickle.load(f) + pageMetadata = ScoreMetaData(len(scoreInstruments), len(staffList), scoreInstruments) + instrumentList = ScoreMetaData.get_instruments() + + # if clearExtra: + # noteGroupMap, stemIdxMap,noteGroupVerticallyMerged, restMap, restList, sfnClefMap, sfnClefList, beamMapImg, staffList,dataDict = clearExtraMapping(noteGroupMap, stemIdxMap,noteGroupVerticallyMerged, restMap, restList, sfnClefMap, sfnClefList, beamMapImg, staffList, dataDict, pageMetadata) + # # !!! decoded stuff from score (by bar) + # # !!! note object by staff line (object not just decoded) + allItemInScore = getAllObjectInEachLine(noteGroupMap, noteGroupVerticallyMerged, restMap, restList, sfnClefMap, sfnClefList, beamMapImg, staffList) + + # patch the object to make it up to date + for obj in noteGroupVerticallyMerged: + if obj and not hasattr(obj, "acrossLineNGID"): + obj.acrossLineNGID = None + for obj in restList: + if obj and not hasattr(obj, "acrossLineNGID"): + obj.acrossLineNGID = None + image = dataDict['image'] + # get Barline locations -> bar center for each track: List[List[int]] + + barEachTrack = getBarsEachTrack(image, beamMapImg, staffList, tolerance = 20) + + allStavesMeasures = getMeasuresEachStaff(image, beamMapImg, staffList, barEachTrack, pageMetadata.getTrackRange()) + + # load in image + img = cv2.imread(imgPath) + img_height, img_width, channels = img.shape + + # yolo detect time signature + yolo_model_path = "best_time_signature.pt" + time_signature_list = detect_time_signatures(yolo_model_path, imgPath) + + # filter for valid time signatures only + map_time_signatures_to_score(time_signature_list, staffList, allStavesMeasures, allItemInScore, imgResizeRatio=image.shape[0]/img.shape[0]) + valid_ts_only = time_signature_list #[ts for ts in time_signature_list if ts.isValid()] + time_sig_thres = 0.5 + final_time_sig_list = [ + [(ts.staffline, ts.barLoc if not ts.barLoc == None else 0), ts.getString()] + for ts in valid_ts_only + if ts.getConfidence() >= time_sig_thres + ] + final_time_sig_list = [x for x in final_time_sig_list if x[0][0] is not None] + final_time_sig_list.sort(key=lambda x: (x[0][0], x[0][1])) + print("Detected Time Signatures (after filtering):", final_time_sig_list) + barChangeList, lastTS = prepareTSBarChangeList(final_time_sig_list, pageMetadata.getTrackRange()) + # TODO: add in the bar time signature, [[(9.8),(9,8)...], [(9.8),(9,8),(4,4)...]] etc. + if not barChangeList.get('0,0'): + barChangeList['0,0'] = prevBarTS + if lastTS: + prevBarTS = lastTS + # get the list of barList (untuned) + barList,allRanges, numBarsEachLine = constructBar(noteGroupMap, noteGroupVerticallyMerged, restMap ,restList, sfnClefMap, sfnClefList, beamMapImg, staffList, pageMetadata, barEachTrack, barChangeList) + + numBarsEachTrack = [numBarsEachLine[p[0]] for p in pageMetadata.getTrackRange()] + + # noteRestMap, retImg = stackItemVertically(image, barList, pageMetadata, staffList, noteGroupVerticallyMerged, restList) + # outputImWrite(f"{imgName}_stack.jpg", retImg) + # cv2.imwrite(f"test{number}.jpg",retImg) + + + + # tune bar list based on timeSignature + barBreakPoints = tuneBarList(barList, numBarsEachTrack) + + # get all the elements on that line and continue changing currX + toneHelper = ToneHelper("keySignatureMapping.json") + + # returned BarList is exactly what we see in the score (two instrument in one line etc.) + ksMatEachTrackDirectMap, ksEachTrack, barList = getKSMatAndKSList(pageMetadata, barList, toneHelper) + + # add instruments tracks for those not appearing + # all instrument seperated (and added if not present) + barDictPerInstrument, barListPerInstrument, instrumentEachLine = assignBarlistInstrument(barList, pageMetadata) + + ksListAllTrack = np.concatenate(ksEachTrack) + assert len(ksListAllTrack) == len(barListPerInstrument[0]) + + instrument_dict = constructInstrumentMappingDict("instrumentMapping.json") + score, debugImages = exportXML(barListPerInstrument, instrumentEachLine, toneHelper, instrument_dict, ksListAllTrack, image, imgName) + score.write('musicxml', scorePath) + print(f"score write to {scorePath}") + # scoreShifted.write('musicxml',scoreShiftedPath) + # print(f"shifted score write to {scoreShiftedPath}") + # if isInit: + # fullBarList = barListPerInstrument + # else: + for i in range(len(barListPerInstrument)): + fullBarList[i]+=barListPerInstrument[i] + fullksListAllTrack = np.append(fullksListAllTrack, ksListAllTrack) + print() + fullscore, fulldebugImages = exportXML(fullBarList, instrumentEachLine, toneHelper, instrument_dict, fullksListAllTrack) + fullscore.write('musicxml',f'{imgName}.musicxml') + + print() + + + + + + + + + +# todo: +# Timpani only use one row + + + # current data structure documentation: + # noteChunkList: list of None|NoteChunks + # by noteChunkList[n].noteGroupIdxs you can get the Ids of them + # noteGroupMap, stemIdxMap <-> noteGroupVerticallyMerged + # noteGroupMap value == 0: no note, + # >0: is the noteGroupVerticallyMerged[idx] + # stemIdxMap value == -1: no noteBox + # >=0: is the index of noteGroupVerticallyMerged[idx].noteStemList[index] + # restMap <-> restList + # RestMap value == 0: no rest + # >0: is the restList[idx] + # sfnClefMap <-> sfnClefList + # sfnClefMap value == 0: no clef/sfn + # >0: is the sfnClefList[idx] + # beamMapImg: bb,gg,rr + # bb: the gradient beam image, 255 where there's beam, + # 254-2*stepSize*n: go how far down to beam + # 253-2*stepSize*n: go how far up to beam + # gg: 0 if there's nothing there + # 1 if it's place of a noteGroup object + # 2 if it's place of an accidentals + # 3 if it's place of a clef + # 4 if it's place of a barline + # 5 if it's place of a rest + # 6 if it's place of a noteGroup ornament? TODO not implemented yet + # rr: staff position map + staff number + how far to the bottom of staff + # x = 0~3 (total of 4): the position of the note (1~24, A is 12, C is 13) + # x = 4: the staffnum of the closest one (starting with 1 instead of 0) + # x = -1: how far pixel to the top of the staff (if it's in the staff range and 1 barheight above, else 0) + # x = -2: how far pixel to the bottom of the staff (if it's in the staff range and 1 barheight below, else 0) + # within the x of staff: the staff number + # if it contains anything in the staff it will be that, else it will diff --git a/pdf2png.py b/pdf2png.py index 1ed2ec6..e46c399 100644 --- a/pdf2png.py +++ b/pdf2png.py @@ -1,41 +1,115 @@ -from pdf2image import convert_from_path import os +from pdf2image import convert_from_path, pdfinfo_from_path from PIL import Image -# yyy/xxx/ -> yyy/xxx/imgs/xxx_i/xxx_i.png -def savePdf2Png(root_folder:str, numPngPerPage:int, rotate:bool): +def savePdf2Png(root_folder: str, numPngPerPage: int, rotate: bool, dpi: int = 200): file_name = os.path.basename(root_folder) pdf_path = os.path.join(root_folder, f'{file_name}.pdf') - images = convert_from_path(pdf_path, dpi=300) - imgNum = 1 + + # Get number of pages safely + info = pdfinfo_from_path(pdf_path) + num_pages = info["Pages"] + img_root = os.path.join(root_folder, 'imgs') if not os.path.isdir(img_root): os.mkdir(img_root) - for fullImg in images: + + imgNum = 1 + + # Process ONE page at a time + for i in range(1, num_pages + 1): + print(f"Processing page {i}/{num_pages}...") + + images = convert_from_path( + pdf_path, + dpi=dpi, + first_page=i, + last_page=i, + use_cropbox=True + ) + + fullImg = images[0] + if rotate: fullImg = fullImg.transpose(Image.Transpose.ROTATE_270) + + width, height = fullImg.size + if numPngPerPage == 1: image = fullImg + folderPath = os.path.join(img_root, f'{file_name}_{imgNum}') - if not os.path.isdir(folderPath): - os.mkdir(folderPath) - newroute = os.path.join(folderPath,f'{file_name}_{imgNum}.png') + os.makedirs(folderPath, exist_ok=True) + + newroute = os.path.join(folderPath, f'{file_name}_{imgNum}.png') image.save(newroute, 'PNG') - print(f"image {imgNum} saved to {newroute}") - imgNum = imgNum+1 + + print(f"Image {imgNum} saved to {newroute}") + imgNum += 1 + else: + # Split page horizontally into equal parts + split_width = width // numPngPerPage + for j in range(numPngPerPage): - width, height = fullImg.size - image = fullImg.crop((width//2*j, 0, (width//2)*(j+1), height)) + image = fullImg.crop(( + split_width * j, + 0, + split_width * (j + 1), + height + )) + folderPath = os.path.join(img_root, f'{file_name}_{imgNum}') - if not os.path.isdir(folderPath): - os.mkdir(folderPath) - newroute = os.path.join(folderPath,f'{file_name}_{imgNum}.png') + os.makedirs(folderPath, exist_ok=True) + + newroute = os.path.join(folderPath, f'{file_name}_{imgNum}.png') image.save(newroute, 'PNG') - print(f"image {imgNum} saved to {newroute}") - imgNum = imgNum+1 + + print(f"Image {imgNum} saved to {newroute}") + imgNum += 1 + + +if __name__ == '__main__': + savePdf2Png('sq_dataset\sq1', 1, False, dpi=200) + +# from pdf2image import convert_from_path +# import oszcxf +# from PIL import Image + +# # yyy/xxx/ -> yyy/xxx/imgs/xxx_i/xxx_i.png +# def savePdf2Png(root_folder:str, numPngPerPage:int, rotate:bool): +# file_name = os.path.basename(root_folder) +# pdf_path = os.path.join(root_folder, f'{file_name}.pdf') +# images = convert_from_path(pdf_path, dpi=300) +# imgNum = 1 +# img_root = os.path.join(root_folder, 'imgs') +# if not os.path.isdir(img_root): +# os.mkdir(img_root) +# for fullImg in images: +# if rotate: +# fullImg = fullImg.transpose(Image.Transpose.ROTATE_270) +# if numPngPerPage == 1: +# image = fullImg +# folderPath = os.path.join(img_root, f'{file_name}_{imgNum}') +# if not os.path.isdir(folderPath): +# os.mkdir(folderPath) +# newroute = os.path.join(folderPath,f'{file_name}_{imgNum}.png') +# image.save(newroute, 'PNG') +# print(f"image {imgNum} saved to {newroute}") +# imgNum = imgNum+1 +# else: +# for j in range(numPngPerPage): +# width, height = fullImg.size +# image = fullImg.crop((width//2*j, 0, (width//2)*(j+1), height)) +# folderPath = os.path.join(img_root, f'{file_name}_{imgNum}') +# if not os.path.isdir(folderPath): +# os.mkdir(folderPath) +# newroute = os.path.join(folderPath,f'{file_name}_{imgNum}.png') +# image.save(newroute, 'PNG') +# print(f"image {imgNum} saved to {newroute}") +# imgNum = imgNum+1 # if __name__ == '__main__': -# savePdf2Png('string_dataset/pdf_data/beethoven1',1,False) +# savePdf2Png('orch_dataset/Bee_8_challenge',1,False) # # Path to the PDF file diff --git a/png2decode.py b/png2decode.py new file mode 100644 index 0000000..cbe2ebc --- /dev/null +++ b/png2decode.py @@ -0,0 +1,2081 @@ + + +import os +os.environ["OMP_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" +import statistics + +from sklearn.cluster import KMeans +from collections import Counter +from scipy.ndimage import rotate + +from exportXml import exportXML +from get_prediction_singleStem import Single_Stem_Classifier +from get_prediction_rest import Rest_Classifier +from get_prediction import Sfn_Clef_classifier + +from utils import * +from omr.part1 import runModel1 +from omr.staffline_extraction import staff_extract_staffobj +from omr.part2 import runModel2 +from omr.bbox import merge_nearby_bbox + +STEM_UP_MODEL = Single_Stem_Classifier("training/stemupImg32x32_best.pth", 3) +STEM_DOWN_MODEL = Single_Stem_Classifier("training/stemdownImg32x32_best.pth", 3) +MIN_BAR_HEIGHT = 8 +ORIGINAL_IMAGE_RESIZE_RATIO = 2 +REST_CLASSIFIER = Rest_Classifier(r'training\rest_remain2x1_best.pth', 5) +SFN_CLEF_CLASSIFIER = Sfn_Clef_classifier('training/best_model32x32_seg_dil4x4.pth', 7) + + +# -------------------------------------------------------------------------------------------------- +# debug images +# -------------------------------------------------------------------------------------------------- +def outputDebugStemRestImg(dataDict): + img = np.ones_like(dataDict['image'])*255 + ys,xs = np.where(dataDict['stems_rests']>0) + img[ys,xs] = (0,0,255) + # outputImWrite(f'{img_name}_stemrests.jpg', img) + imwrite(f'stemrests_.jpg', img) + +def outputDebugSymbolsImg(dataDict): + img = np.ones_like(dataDict['image'])*255 + ys,xs = np.where(dataDict['symbols']>0) + img[ys,xs] = (255,0,0) + # outputImWrite(f'{img_name}_symbols.jpg', img) + imwrite(f'symbols_.jpg', img) + +def outputNotegroupStemMapImg(noteGroupStemMap, image): + ys,xs = np.where(noteGroupStemMap>0) + zz = image.copy() + zz[ys,xs,1:2] = 100 + # outputImWrite(f'{img_name}_noteGroupStemMap.jpg', zz) + imwrite(f'noteGroupStemMap.jpg', zz) + +def outputNoteGroupMapImg(noteGroupMap, image): + ys,xs = np.where(noteGroupMap>0) + zz = image.copy() + zz[ys,xs,1:2] = 100 + # outputImWrite(f'{img_name}_noteGroupStemMap.jpg', zz) + imwrite(f'noteGroupMap.jpg', zz) + +def outputNoteMapImg(beamMapImg, image, staffObjList): + colors = [(0,0,255),(0,255,0),(255,255,0), (0,120,230)] + _,_,rr = cv2.split(beamMapImg) + zz = image.copy() + for i in range(len(staffObjList)): + ys,xs = np.where(rr==(i+1)) + zz[ys,xs] = colors[i%4] + imwrite('GroupMap.jpg',zz) + +def outputSfnClefNoteWhiteImg(beamMapImg, image, bar_height, noteGroupMap, staffObjList): + bb,gg,_ = cv2.split(beamMapImg) + _,zz = cv2.threshold(image, 200, 255, cv2.THRESH_BINARY) + zzmask = zz.copy() + zzmask[:max(staffObjList[0].ys[0]-bar_height*2,0),:] = (255,255,255) + zzmask[min(staffObjList[-1].ys[-1]+bar_height*2,zzmask.shape[0]):,:] = (255,255,255) + for sf in staffObjList: + xMaskOut = np.where(np.max(noteGroupMap[max(sf.ys[0]-2*bar_height, 0):min(sf.ys[-1]+2*bar_height, zzmask.shape[0]), :],0)>0)[0] + zzmask[max(sf.ys[0]-2*bar_height, 0):min(sf.ys[-1]+2*bar_height,zzmask.shape[0]),xMaskOut] = (255,255,255) + ys,xs = np.where(gg>0) + zzmask[ys,xs] = (255,255,255) + bb = cv2.dilate(bb, np.ones((bar_height//2, bar_height//2), dtype= np.uint8), iterations=1) + ys,xs = np.where(bb>=255) + zzmask[ys,xs] = (255,255,255) + # zzmask = cv2.dilate(zzmask, np.ones((bar_height//3,1),dtype=np.uint8),iterations=1) + # zzmask = cv2.erode(zzmask, np.ones((bar_height//3,bar_height//3),dtype=np.uint8),iterations=1) + # cv2.imwrite('test.jpg',zzmask) + imwrite('SfnClefNoteWhite.jpg', zzmask) + +def outputSfnClefNoteImg(image, beamMapImg): + _,gg,_ = cv2.split(beamMapImg) + _,zz = cv2.threshold(image, 200, 255, cv2.THRESH_BINARY) + for i in range(1,4): + ys,xs = np.where(gg==i) + zz[ys,xs,(i-2)%3] = 100 + zz[ys,xs,(i-3)%3] = 0 + ys,xs = np.where(image[:,:,0]<128) + zz[ys,xs,:] = (0,0,0) + # outputImWrite(f'{img_name}_SfnClefNote.jpg', zz) + imwrite(f'SfnClefNote.jpg', zz) + +def outputSfnClefNoteBarlineRestImg(image, beamMapImg): + zz = image.copy() + _,gg,_ = cv2.split(beamMapImg) + colors = [(190,120,0),(255,50,0),(50,240,0),(0,120,230),(0,0,255)] + for i in range(1,6): + ys,xs = np.where(gg==i) + zz[ys,xs] = colors[i%5] + + imwrite(f'SfnClefNoteBarlineRest.jpg', zz) + +def outputThingsBeforeDots(image, beamMapImg): + _,gg,_ = cv2.split(beamMapImg) + colors = [(230,200,130),(230,130,100),(120,170,140),(235,176,113),(0,0,255)] + zz = image.copy() + for i in [1,2,3,5]: + ys,xs = np.where(gg==i) + zz[ys,xs] = colors[i%5] + ys,xs = np.where(image[:,:,1]<128) + zz[ys,xs] = (0,0,0) + i = 4 + ys,xs = np.where(gg==i) + zz[ys,xs] = colors[i%5] + imwrite(f'DotPrevious.jpg', zz) + return zz + + + +# -------------------------------------------------------------------------------------------------- +# functions +# -------------------------------------------------------------------------------------------------- +def init_bar_height(dataDict, min_barheight): + def staffs_to_omrStaffList(staffs): + left = min([sf.x_left for sf in staffs[0,:]]) + right = max([sf.x_right for sf in staffs[-1,:]]) + omrstaff_list: List[Staff] = [] + staffRange = [] + mindiffs = [] + maxdiffs = [] + for i in range(staffs.shape[1]): + yuppers = [sf.y_upper for sf in staffs[:,i]] + ybottoms = [sf.y_lower for sf in staffs[:,i]] + top = statistics.median(yuppers) + bottom = statistics.median(ybottoms) + unit_size = statistics.median([sf.unit_size for sf in staffs[:,i]]) + minMaxDiff = min(max(yuppers)-min(yuppers), max(ybottoms)-min(ybottoms)) + maxMaxDiff = max(max(yuppers)-min(yuppers), max(ybottoms)-min(ybottoms)) + ys = [int(top), int(top+unit_size), int((top+bottom)/2),int(bottom-unit_size),int(bottom)] + sf = Staff(int(left), int(right), ys, minMaxDiff, yuppers, ybottoms) + mindiffs.append(minMaxDiff) + maxdiffs.append(maxMaxDiff) + omrstaff_list.append(sf) + staffRange.append(range(int(top),int(bottom))) + return omrstaff_list, staffRange + staff = dataDict['staff'] + staffs, zones = staff_extract_staffobj(staff, min_barheight) + staffList, staffRange = staffs_to_omrStaffList(staffs) + barheight = [sf.get_yOne_float() for sf in staffList] + avg_barheight = int(sum(barheight)/len(barheight)) + return avg_barheight, staffList + +def getBeamImage(dataDict, bar_height, staffObjList:List[Staff]): + stems_rests = dataDict['stems_rests'] + clefs_keys = dataDict['clefs_keys'] + notehead = dataDict['notehead'] + symbols = dataDict['symbols'] + image:np.ndarray = dataDict['image'] + + _, img128 = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY_INV) + kernel = np.ones((bar_height//3, bar_height//3),dtype=np.uint8) + beam = cv2.erode(img128, kernel, iterations = 1) + imwrite('note_beam1.jpg',beam) + + for ll in range(3): + haslinecount = 0 + totalCount = 0 + for staff in staffObjList: + for b in range(len(staff.ys)): + curr = sum(np.max(beam[max(staff.ys[b]-bar_height//3,0):min(staff.ys[b]+bar_height//3, beam.shape[0]), staff.left:staff.right,1],0)==255) + if curr>(staff.right-staff.left)//3: + haslinecount+=1 + totalCount+=1 + if haslinecount/totalCount>0.6: + kernel = np.ones((bar_height//4, bar_height//4),dtype=np.uint8) + beam = cv2.erode(beam, kernel, iterations = 1) + else: + break + imwrite('note_beam1.5.jpg',beam) + xs,ys = np.where(clefs_keys>0) + beam[xs,ys] = (0,0,0) + beamWithoutStemRest = beam.copy() + imwrite('note_beam2.jpg',beamWithoutStemRest) + # beamret is beamNoClefKey + xs,ys = np.where(stems_rests>0) + beam[xs,ys] = (255,255,255) + imwrite('note_beam3.jpg', beam) + # beam is beam with stem_rests + + kernel_tall = np.ones((1,bar_height*2), np.uint8) + beam_nostem = cv2.erode(beam, kernel_tall) + beam_nostem = cv2.dilate(beam_nostem, kernel_tall) + imwrite('note_beam4.jpg', beam_nostem) + beam_nostem_nostaff = beam_nostem.copy() + savebeambw = cv2.cvtColor(beam_nostem, cv2.COLOR_BGR2GRAY) + toDel = np.where(np.sum(savebeambw>127,axis=1)>savebeambw.shape[1]*0.4)[0] # VARIABLE threshold to delete + for d in toDel: + beam_nostem_nostaff[d,:] = 0 + imwrite('note_beam5.jpg', beam_nostem_nostaff) + # beam nostem is beam with stemRest + erode (also has notes) + return beam, beamWithoutStemRest, beam_nostem_nostaff + +def generateBeamStaffImg(savebeam:np.ndarray, staffObjList:List[Staff], barheight:int,stepSize=1): + # beamThick = cv2.dilate(savebeam, np.ones((3,1), dtype=np.uint8), iterations=1) + mappingImgRgb = np.zeros((savebeam.shape[0], savebeam.shape[1], 3)) + savebeambw = cv2.cvtColor(savebeam, cv2.COLOR_BGR2GRAY) + contours, _ = cv2.findContours(savebeambw.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + selectedContours = [] + bb,gg,rr = cv2.split(mappingImgRgb) + staffYCenters = [] + for idx,staff in enumerate(staffObjList): + if staff.left <= 4: + staff.left = 5 + if staff.right >= savebeam.shape[1]-3: + staff.right = savebeam.shape[1]-4 + staffYs = staff.ys + oneY:float = staff.get_yOne_float() + staffYCenter = staffYs[2] + rr = cv2.rectangle(rr, (staff.left, staffYs[0]), (staff.right, staffYs[-1]), idx+1,-1) + # -1: how far to the top [0] + rr[staffYs[0]:min(staffYs[-1]+barheight, savebeam.shape[0]),-1] = np.array(range(0, min(staffYs[-1]+barheight, savebeam.shape[0])-staffYs[0])) + rr[max(staffYs[0]-barheight, 0):staffYs[-1],-2] = np.array(range(staffYs[-1]-max(staffYs[0]-barheight, 0),0,-1)) + # line one's index will be 1~10:1, 2s will be 20~200:20 + indexRatio = (idx%2)*20+(1-idx%2)*1 + staffYCenter = staffYs[2] + staffYCenters.append(staffYCenter) + + for i in range(1,13): + if staffYCenter-i*oneY>0: + rr[round(staffYCenter-i*oneY):round(staffYCenter-(i-1)*oneY),(idx+1)%4] = i+12 + else: + break + for i in range(1,13): + if staffYCenter+i*oneYbarheight*1.5 and ww and not (w<2*barheight and h/w>0.5)and w>h: + selectedContours.append(cnt) + bb = cv2.drawContours(bb, selectedContours,-1, 255,thickness=-1) + imwrite('beamOri.jpg',savebeambw) + mappingImgRgb = cv2.merge([bb,gg,rr]) + imwrite('beamOricontours.jpg',mappingImgRgb) + bbUint8 = bb.astype(np.uint8) + kernel = np.ones((1,barheight//3), dtype=np.uint8) + bbUint8 = cv2.dilate(bbUint8, kernel, iterations=1) + mappingImgUint8= cv2.merge([bbUint8,gg.astype(np.uint8),rr.astype(np.uint8)]) + print('preparing for beam image') + for xIdx in range(0,savebeam.shape[1], stepSize*2): + # no white in this place + if sum(mappingImgUint8[:,xIdx,0]) == 0: + continue + startingY = np.where(mappingImgUint8[:,xIdx,0]==255)[0].tolist() + # the ending of the bottommost one + startingY.append(min(mappingImgUint8.shape[0],startingY[-1]+barheight*5)) + for i in range(0,len(startingY)-1): + yStartVal = 253 + for yIdx in range((startingY[i]+stepSize)//(2*stepSize)*(2*stepSize)+stepSize, startingY[i+1],stepSize*2): + if yStartVal<=0: + break + mappingImgUint8[yIdx,xIdx,0]=yStartVal + yStartVal-=stepSize*2 + # the ending of the topmost one + startingY.pop() + startingY.insert(0, max(startingY[0]-barheight*5, 0)) + for i in range(len(startingY)-1, 0,-1): + yStartVal = 254 + for yIdx in range((startingY[i]-1)//(2*stepSize)*(2*stepSize), startingY[i-1],-stepSize*2): + if yStartVal<=0: + break + mappingImgUint8[yIdx,xIdx,0]=yStartVal + yStartVal-=stepSize*2 + onlyBImg = mappingImgUint8.copy() + onlyBImg[:,:,1] = onlyBImg[:,:,0] + onlyBImg[:,:,2] = onlyBImg[:,:,0] + imwrite('onlyBImg.jpg',onlyBImg) + saveImages = {} + saveImages['gradImg']=onlyBImg + saveImages['beamContour']=mappingImgRgb + return mappingImgUint8, saveImages + +def getInitialNoteheadBoxList(dataDict, beam_nostem, beamMapImg:np.ndarray, bar_height): + + def get_bbox(data: np.ndarray) -> List[Tuple[int,int,int,int]]: + contours, _ = cv2.findContours(data.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + bboxes = [] + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + box = (x, y, x+w, y+h) + bboxes.append(box) + return bboxes + + def filter_small_bbox_area(data: np.ndarray, noteimg:np.ndarray, xmin, ymin, areamin): + filtered_Box = [] + for box in data: + x0,y0,x1,y1 = box + w = x1-x0 + h = y1-y0 + if w>=xmin and h>=ymin and w*h>areamin: + filtered_Box.append(box) + else: + noteimg[y0:y1,x0:x1] = 0 + return filtered_Box,noteimg + + def split_connected_notes(bboxList, bar_height,image): + minh = int(bar_height*0.9) + minw = int(bar_height) + retlst = [] + for bbox in bboxList: + x0,y0,x1,y1 = bbox + wrat = (x1-x0)/minw + hrat = (y1-y0)/minh + wnum = int(wrat) + hnum = int(hrat) + note_w = int(minw*1.3) + note_h = int(minh*1.3) + if wnum*hnum <= 1: + retlst.append(bbox) + elif wnum>=2: + if y1-y0<=bar_height or wnum>=3: + xwid = (x1-x0)//wnum + for i in range(wnum): + retlst.append((x0+i*xwid,y0,x0+(i+1)*xwid,y1)) + # it's a scatter of three vertically stacked notes with two adjacent notes + elif hnum>2.5 and hnum>wnum: + # first get the top and bottom ones + # if the top part has more black on the left (x0) + option2s = [] + if np.sum(image[y0:y0+note_h,x0:x0+note_w])>np.sum(image[y0:y0+note_h,x1-note_w:x1]): + retlst.append((x0,y0,x0+note_w, y0+note_h)) + # the other side go down once + option2s.append((x1-note_w,y0+note_h//2,x1, y0+int(note_h*1.5))) + else: + retlst.append((x1-note_w,y0,x1, y0+note_h)) + option2s.append((x0,y0+note_h//2,x0+note_w, y0+int(note_h*1.5))) + # if the bottom part has more black on the left (x0) + if np.sum(image[y1-note_h:y1,x0:x0+note_w])>np.sum(image[y1-note_h:y1,x1-note_w:x1]): + retlst.append((x0,y1-note_h,x0+note_w, y1)) + option2s.append((x1-note_w,y1-int(note_h*1.5),x1, y1-note_h//2)) + else: + retlst.append((x1-note_w,y1-note_h,x1, y1)) + option2s.append((x0,y1-int(note_h*1.5),x0+note_w, y1-note_h//2)) + # deal with the middle one + if np.sum(image[option2s[0][1]:option2s[0][3], option2s[0][0]:option2s[0][2]]) > np.sum(image[option2s[1][1]:option2s[1][3], option2s[1][0]:option2s[1][2]]): + retlst.append(option2s[0]) + else: + retlst.append(option2s[1]) + else: + # if the left part has more black on top(y0) + if np.sum(image[y0:y0+note_h,x0:x0+note_w])>np.sum(image[y1-note_h:y1,x0:x0+note_w]): + retlst.append((x0,y0,x0+note_w, y0+note_h)) + else: + retlst.append((x0,y1-note_h,x0+note_w, y1)) + # if the right part has more black on top(y1) + if np.sum(image[y0:y0+note_h,x1-note_w:x1])>np.sum(image[y1-note_h:y1,x1-note_w:x1]): + retlst.append((x1-note_w,y0,x1, y0+note_h)) + else: + retlst.append((x1-note_w,y1-note_h,x1, y1)) + + elif hnum>=2: + ywid = (y1-y0)//hnum + for i in range(hnum): + retlst.append((x0,y0+i*ywid,x1,y0+(i+1)*ywid)) + return retlst + + def filter_boxes_on_beam(notehead_boxes, beamMapImg, areamin): + filtered_Boxes = [] + for box in notehead_boxes: + x0,y0,x1,y1 = box + if np.max(beamMapImg[y0:y1, x0:x1, 0])==255: + w = x1-x0 + h = y1-y0 + if w*h0) + notehead_mod = notehead.copy()*255 + notehead_mod[xs,ys] = 0 + + imwrite('notehead_ori.jpg', notehead_mod) + dilation_ratio1 = 0.33 + dilation_ratio2 = 0.4 + # dilate->erode twice to clear image + get clear noteheads + size1 = int(round(bar_height*dilation_ratio1)) + note_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size1, size1)) + notehead_mod = cv2.erode(cv2.dilate(notehead_mod.astype(np.uint8), note_kernel), note_kernel) + size2 = int(round(bar_height*dilation_ratio2)) + + note_kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size2,size2)) + notehead_mod2 = cv2.erode(notehead_mod, note_kernel2) + note_kernel3 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size2+1, size2+1)) + notehead_mod2 = cv2.dilate(notehead_mod2, note_kernel3) + + # both b&w image, white is where there's stuff + imwrite('notehead_mod.jpg',notehead_mod) + imwrite('notehead_mod2.jpg',notehead_mod2) + # notehead mod is basically the cleaned beams + + # get the boxes, if the boxes is in + notehead_boxes_all = get_bbox(notehead_mod2) + notehead_boxes_big, notehead_mod3= filter_small_bbox_area(notehead_boxes_all, notehead_mod2,xmin = bar_height*0.5,ymin = bar_height*0.2, areamin = bar_height*bar_height*0.4) + imwrite('notehead_mod3.jpg',notehead_mod3) + notehead_boxes = split_connected_notes(notehead_boxes_big, bar_height,notehead_mod2) + notehead_boxes_filtered = filter_boxes_on_beam(notehead_boxes, beamMapImg,areamin = bar_height*bar_height*0.8) + return notehead_boxes_filtered, notehead_mod2 + +def maskImage(noteheadBoxesInitList:List[Tuple[int,int,int,int]], noteBwImageOrigin: np.ndarray): + maskedNoteBw = np.zeros_like(noteBwImageOrigin) + for box in noteheadBoxesInitList: + x0,y0,x1,y1 = box + maskedNoteBw[y0:y1, x0:x1] = noteBwImageOrigin[y0:y1, x0:x1] + imwrite('maskedNoteBw.jpg',maskedNoteBw) + return maskedNoteBw + +# noteheadInitial : b&w 2d image, 255: notehead, +# beamMapImg: gradient image, the 1st dimension is what we care about +def getStemList(dataDict, bar_height, notehead_boxes:List[Tuple[int,int,int,int]], beamWithStemRest): + image:np.ndarray = dataDict['image'] + + drawimg = image.copy() + # 255 is where there's stuff, 0 is empty + _, img200 = cv2.threshold(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY), 200, 255, cv2.THRESH_BINARY_INV) + imgh = image.shape[0] + notehead_rectangle = np.zeros((image.shape[0],image.shape[1])) + stem_init_lst:List[Stem]=[] + # beambw is suppose to be white where there's beam + beambw = cv2.cvtColor(beamWithStemRest, cv2.COLOR_BGR2GRAY) + imwrite('beambw.jpg',beambw) + # get the stem's directions + for idx,boxOriginal in enumerate(notehead_boxes): + x0,y0,x1,y1 = boxOriginal + xleft = (x0+x1*2)//3 + xright = (x0*2+x1)//3 + + leftRange = np.where(img200[y0:y1,xleft]==255)[0].tolist() + rightRange = np.where(img200[y0:y1, xright] ==255)[0].tolist() + if len(leftRange)*len(rightRange) != 0: + y1 = y0+max(max(leftRange),max(rightRange)) + y0 += min(min(leftRange), min(rightRange)) + if y1<=y0: + x0,y0,x1,y1 = boxOriginal + box = (x0,y0,x1,y1) + notehead_rectangle[y0:y1,x0:x1] = 1 + length = bar_height*3 + width = (x1-x0)//3 + leftXrange = range(x0-width//2,x0+width+1) + rightXrange = range(x1-width, x1+width//2+1) + topYrange = range(max(y0-length, 0),y0) + bottomYrange = range(y1,min(y1+length, imgh)) + left_area = beambw[bottomYrange.start:bottomYrange.stop, + leftXrange.start: leftXrange.stop] + left_centerX = np.argmax(np.sum(left_area,0)) + right_area = beambw[topYrange.start:topYrange.stop, + rightXrange.start:rightXrange.stop] + right_centerX = np.argmax(np.sum(right_area,0)) + # has to be more strict not too far to the left + leftup_area = beambw[topYrange.start:topYrange.stop, + leftXrange.start: leftXrange.stop] + leftup_centerX = np.argmax(np.sum(leftup_area,0)) + rightdn_area = beambw[bottomYrange.start:bottomYrange.stop, + rightXrange.start:rightXrange.stop] + rightdn_centerX = np.argmax(np.sum(rightdn_area,0)) + isup = True + hasStem = True + alterBox = [] + leftRightRatio = np.sum(left_area[:,left_centerX])/np.sum(right_area[:, right_centerX]) + if leftRightRatio>1: + # stem is in left bottom + maxRange = [leftXrange, bottomYrange] + isup = False + if leftRightRatio<1.25: + alterBox.append([rightXrange.start+right_centerX,topYrange.start,rightXrange.start+right_centerX,topYrange.stop]) + else: + # it's either close enough or in right bottom + maxRange = [rightXrange, topYrange] + # they are close enough -> alterbox will always record the left bottom one + if leftRightRatio>0.8: + alterBox.append([leftXrange.start+left_centerX,bottomYrange.start,leftXrange.start+left_centerX,bottomYrange.stop]) + maxArea = max(np.sum(left_area[:,left_centerX]),np.sum(right_area[:, right_centerX])) + maxAlterArea = max(np.sum(leftup_area[:,leftup_centerX]),np.sum(rightdn_area[:,rightdn_centerX])) + if maxAlterArea>255*length//2 and maxAlterArea/maxArea>1.5: + if np.sum(leftup_area[:,leftup_centerX])>np.sum(rightdn_area[:,rightdn_centerX]): + alterBox.append([leftXrange.start+leftup_centerX, topYrange.start, leftXrange.start+leftup_centerX, topYrange.stop]) + else: + alterBox.append([rightXrange.start+rightdn_centerX, bottomYrange.start, rightXrange.start+rightdn_centerX, bottomYrange.stop]) + if len(alterBox)==0 and maxArea < 255*length//3: + hasStem = False + # hasStem is a flag for later grouping notes + xrange, yrange = maxRange + crop = img200[yrange.start:yrange.stop, xrange.start:xrange.stop] + a = (np.where(np.sum(crop,0)==np.max(np.sum(crop,0)))[0]).tolist() + chosenx = a[len(a)//2] + x_y = (xrange.start+chosenx, y0 if isup else y1) + stem_init_lst.append(Stem(x_y, isup,boxOriginal,box, hasStem=hasStem, alterbox=alterBox)) + if len(alterBox)==0 and hasStem: + drawimg = cv2.rectangle(drawimg, (xrange.start+chosenx-1, yrange.start),(xrange.start+chosenx+1,yrange.stop),(255,0,120), 1, cv2.LINE_AA) + elif hasStem: + drawimg = cv2.putText(drawimg, str(idx), (x0,y0),cv2.FONT_HERSHEY_SIMPLEX, 1, (120,0,120), 2, cv2.LINE_AA) + drawimg = cv2.rectangle(drawimg, (xrange.start+chosenx-1, yrange.start),(xrange.start+chosenx+1,yrange.stop),(255,255,0), 2, cv2.LINE_AA) + for ab in alterBox: + drawimg = cv2.rectangle(drawimg, (ab[0], ab[1]),(ab[2],ab[3]),(0,255,0), 3, cv2.LINE_AA) + drawimg = cv2.rectangle(drawimg, (x0,y0),(x1,y1),(0,0,255), 2, cv2.LINE_AA) + imwrite('notehead_drawimg.jpg',drawimg) + return stem_init_lst, image, {'notehead_drawimg':drawimg} + +def assignStemLength(stem_init_lst:List[Stem], image, beamMapImg:np.ndarray, stepSize: int, bar_height): + # get the stem's length + img200 =cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + _,img200bw = cv2.threshold(img200, 200, 255, cv2.THRESH_BINARY_INV) + # img200 is black and white, white is where there's stem + imwrite('img200inv.jpg', img200bw) + # wid = 1 + topthres = bar_height//2 + ending_black_height = 3 + # make it whiter for better visualization + drawimg2 = np.ones_like(img200bw)*255 - img200bw.copy()//4 + drawimg2 = cv2.cvtColor(drawimg2, cv2.COLOR_GRAY2BGR) + bigStep = stepSize*2 + for idx,coordStart in enumerate(stem_init_lst): + x,yOri = coordStart.start + wid = (coordStart.noteBox[2]-coordStart.noteBox[0])//3 + yend = yOri + sign = 1 if coordStart.isup else -1 + # is in staff +- 3.5 barheight, no alternative box, has stem + # +beamMapImg[max(yOri-int(2.5*bar_height)*sign,0),x,2] + isTypical:bool = (len(coordStart.alternativeBox) == 0) and coordStart.hasStem and min([abs(posIdx-11.5) for posIdx in beamMapImg[yOri,0:4,2]])<5 + + # if the beamMap is less than 4*barheight away and the beam itself is somewhere in a staffArea, assign it + # elif it's not too far away (2*4*barheight), see if the area has at least 1/2 blacks + # else it's gonna go through the clssification model to see if it's a single quarter/eighth/sixteenth + xLeft = x//bigStep*bigStep + xRight = min(beamMapImg.shape[1]-1,x//bigStep*bigStep+bigStep) + staffLineNumber:int = beamMapImg[yOri, 4, 2] + barPositionI: int = beamMapImg[yOri, staffLineNumber%4, 2] + stemUpLongest:int = 0 if (barPositionI<8 or barPositionI>17) else 17-barPositionI + stemDownLongest:int = 0 if (barPositionI<8 or barPositionI>17) else barPositionI-8 + if coordStart.isup: + longestNormal = stemUpLongest + # go how far is considered normal and ok + y = yOri//bigStep*bigStep-stepSize + leftUpValue = beamMapImg[y,xLeft, 0] + rightUpValue = beamMapImg[y,xRight, 0] + if leftUpValue>rightUpValue: + maxMapValue = leftUpValue + else: + maxMapValue = rightUpValue + assert maxMapValue%2==1 or maxMapValue == 0 + if maxMapValue != 0: + if isTypical and (253-maxMapValue)+bigStep<=(longestNormal)*bar_height: + if maxMapValue < 255: + yend = y-(253-maxMapValue) + assert max(beamMapImg[yend, xLeft,0],beamMapImg[yend, xRight,0]) >= 253 + while max(beamMapImg[yend, xLeft,0],beamMapImg[yend, xRight,0]) >= 253-bar_height//2: + yend -= bigStep + while max(beamMapImg[yend, xLeft,0],beamMapImg[yend, xRight,0]) == 255: + yend -= 1 + # the end of the stem doesn't have enough "stem (white)" (the image is inversed) + if (253-maxMapValue)+bigStep>(longestNormal-3)*bar_height and (sum(np.max(img200bw[yend:y, x-wid:x+wid+1],1))<(y-yend)*255*0.8 or sum(np.max(img200bw[yend:(y+yend)//2, x-wid:x+wid+1],1))<(y-yend)*255*0.4): + yend = yOri + if yend==yOri: + if isTypical and coordStart.hasStem and len(coordStart.alternativeBox)==0: + yend -= min(3,longestNormal)*bar_height + # while there is still things in the inversed bw original image + while sum(np.max(img200bw[yend-ending_black_height:yend, x-wid:x+wid+1],1))!=0: + yend -= 1 + else: + while sum(np.max(img200bw[yend-ending_black_height:yend, x-1:x+2],1))!=0: # and max([(10-beamMapImg[yend,x,1]%11)%10,(10-beamMapImg[yend,x,1]//20)%10]): + yend -= 1 + else: + longestNormal = stemDownLongest + y = min(yOri//bigStep*bigStep+bigStep,beamMapImg.shape[0]-1) + leftUpValue = beamMapImg[y,xLeft, 0] + rightUpValue = beamMapImg[y,xRight, 0] + if leftUpValue>rightUpValue: + maxMapValue = leftUpValue + else: + maxMapValue = rightUpValue + xRight = x//bigStep*bigStep+bigStep + assert maxMapValue%2==0 or maxMapValue == 255 + if isTypical and (254-maxMapValue)+bigStep<=longestNormal*bar_height: + if maxMapValue < 255: + yend = y+(254-maxMapValue) + if max(beamMapImg[yend, xLeft,0],beamMapImg[yend, xRight,0]) >=254: + while max(beamMapImg[yend, xLeft,0],beamMapImg[yend, xRight,0]) >= 254-bar_height//2 and (yend-yOri)<=(longestNormal)*bar_height: + yend += bigStep + while max(beamMapImg[yend, xLeft,0],beamMapImg[yend, xRight,0]) == 255 and (yend-yOri)<=(longestNormal)*bar_height: + yend += 1 + # the end of the stem doesn't have enough "stem (white)" (the image is inversed) + if (254-maxMapValue)+bigStep>(longestNormal-3)*bar_height and (sum(np.max(img200bw[y:yend, x-wid:x+wid+1],1))<(yend-y)*128 or sum(np.max(img200bw[(y+yend)//2:yend, x-wid:x+wid+1],1))<(yend-y)*64): + yend = yOri + if yend==yOri: + if isTypical and coordStart.hasStem and len(coordStart.alternativeBox)==0: + yend += min(3,longestNormal)*bar_height + # while there is still things in the inversed bw original image + while sum(np.max(img200bw[yend:yend+ending_black_height, x-wid:x+wid+1],1))!=0 and (yend-yOri)<=(longestNormal)*bar_height: + yend += 1 + else: + while sum(np.max(img200bw[yend:yend+ending_black_height, x-1:x+2],1))!=0: # and min([beamMapImg[yend,x,1]%11,beamMapImg[yend,x,1]//20])<2: + yend += 1 + if abs(yend-y)0) # where there's note but no beam + beamNoNotes = beamNoClefKeyWithStemRest.copy() + beamNoNotes[xs,ys] = (0,0,0) + imwrite('beamNoNotes1.jpg',beamNoNotes) + xs,ys = np.where(beambw==255) + beamNoNotes[xs,ys] = (255,255,255) + imwrite('beamNoNotes2.jpg',beamNoNotes) + + return beamNoNotes + + +def assignBeamLengthBeamImg(stem_list:List[Stem], image:np.ndarray, beam:np.ndarray, barheight:int): + x_diff = barheight//2 + beam = cv2.cvtColor(beam, cv2.COLOR_BGR2GRAY) + # if it's completely white in the white_buffer_max area then stop + white_buffer_max = barheight + beam_start_buffer_max = barheight//3 + for stem in stem_list: + xCenter,yCenter = stem.start + xLeft = xCenter-x_diff + xRight = min(image.shape[1]-1,xCenter+x_diff) + yMax = stem.getYLen() + if stem.getYLen() == 0: + continue + if stem.isup: + for xtype in ['left', 'right']: + if xtype=='left': + currX = xLeft + else: + currX = xRight + currY = stem.getTopCoord()[1] + # if the starting point of beam is not white, meaning that it might be a symbol, staff line etc + # keep going down until it's at the right place + if sum(np.max(beam[currY:currY+beam_start_buffer_max, xCenter-barheight//2:xCenter+barheight//2],1)) < 255*beam_start_buffer_max: + while(sum(np.max(beam[currY:currY+beam_start_buffer_max, xCenter-barheight//2:xCenter+barheight//2],1)) < 255*beam_start_buffer_max and currYstem.getBottomCoord()[1]+barheight//2): + currY = currY+1 + else: + while beam[currY-1, currX] == 255: + currY = currY-1 + # now the topStartY is the "real start" + stem.setBeam(typ=xtype, top=currY) + # go down until it's wholely black + while sum(beam[currY:currY+white_buffer_max, currX])>255: + currY+=1 + stem.setBeam(typ=xtype, bottom=currY) + else: + for xtype in ['left', 'right']: + if xtype=='left': + currX = xLeft + else: + currX = xRight + currY = stem.getBottomCoord()[1] + + # if the starting point of beam is not white, meaning that it might be a symbol, staff line etc + # keep going up until it's at the right place + if sum(np.max(beam[currY-beam_start_buffer_max:currY, xCenter-barheight//2:xCenter+barheight//2],1)) < 255*beam_start_buffer_max: + while(sum(np.max(beam[currY-beam_start_buffer_max:currY, xCenter-barheight//2:xCenter+barheight//2],1)) < 255*beam_start_buffer_max and currY>stem.getTopCoord()[1]+barheight//2): + currY = currY-1 + stem.end = (stem.end[0], currY) + + # if it's black (need to go up) + if beam[currY, currX] == 0: + while(beam[currY, currX] == 0 and currY255: + currY-=1 + stem.setBeam(typ=xtype, top=currY) + imgBgr = image.copy() + beam_heights = [] + for stem in stem_list: + beam_heights.append(stem.leftBeam[1]-stem.leftBeam[0]) + beam_heights.append(stem.rightBeam[1]-stem.rightBeam[0]) + imgBgr = cv2.rectangle(imgBgr, (stem.start[0]-x_diff, stem.leftBeam[0]), (stem.start[0]-x_diff, stem.leftBeam[1]),(0,0,255),3,cv2.LINE_AA) + imgBgr = cv2.rectangle(imgBgr, (stem.start[0]+x_diff, stem.rightBeam[0]), (stem.start[0]+x_diff, stem.rightBeam[1]),(255,255,0),3, cv2.LINE_AA) + imwrite('leftrightRectBeam.jpg', imgBgr) + return imgBgr, stem_list, beam_heights + + +def knnRhythmAndDraw(stem_list_assigned:List[Stem], beam_heights, image, bar_height, beamMapImg, stemUpClassifier, stemDownClassifier,img_name): + def get_beam_height(beam_heights:List[int], barheight): + whole_thres = [1, int(barheight*0.6), int(barheight*1.8), int(barheight*2.7), int(barheight*3.2)] + if max(beam_heights) == 0: + return beam_heights + count, _ = np.histogram(beam_heights, max(beam_heights)) + # expand the list to be able to access list[init_thres[-1]] + init_thres = whole_thres[:sum([0 if thres>len(count) else 1 for thres in whole_thres])+1] + expanded_count = list(count) + [0]*(init_thres[-1]+1-len(count)) + while len(init_thres)>0 and sum(expanded_count[init_thres[-1]:])tooBig else bh for bh in beam_heights] + expanded_count[tooBig:] = [0]*len(expanded_count[tooBig:]) + init_thres.append(len(expanded_count)) + centers = [0]*len(init_thres) + for i in range(1,len(init_thres)): + centers[i] = np.argmax(expanded_count[init_thres[i-1]:init_thres[i]])+init_thres[i-1] + kmeans = KMeans(n_clusters = len(centers), init = np.array(centers, dtype=np.uint8).reshape(-1, 1), n_init = 1) + kmeans.fit(np.array(beam_heights,dtype=np.uint8).reshape(-1,1)) + return kmeans.labels_.tolist() + def getSingleStemPred(img, model:Single_Stem_Classifier): + # ['n2', 'n4', 'n816'] (0,1,2)if it's flat then it should be n1 3 + if img.shape[1]/img.shape[0]>1.5: + return 3 + else: + return model.predict(img) + + beam_type = get_beam_height(beam_heights, bar_height) + class_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40), (245, 220, 255),(230,130,175),(165,170, 70)] + class_names = ['1/4','1/8','1/16','1/32', '1/64', '1/128', '1/2', '1/1'] + imgrgb = image.copy() + x_diff = bar_height//2 + mapBeamVal, middle, mapStaffNum = cv2.split(beamMapImg) + # the first one is a empty one just for easier indexing + noteGroupStemMap = np.zeros((beamMapImg.shape[0], beamMapImg.shape[1]), dtype=np.uint16) + noteGroupList:List[NoteGroup|None] = [None] + img200 =cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + _,img200bw = cv2.threshold(img200, 200, 255, cv2.THRESH_BINARY_INV) + for j in range(0,len(class_names)): + imgrgb = cv2.putText(imgrgb,class_names[j], (30, 30+j*30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, class_colors[j], 2, cv2.LINE_AA) + # stillLabel = True + # init_label_folder() + predLabelConvert = [-2,0,1,-1] + for idx,stem in enumerate(stem_list_assigned): + stem.rhythm = max(beam_type[idx*2: idx*2+2]) + leftHeight, rightHeight = stem.getBeamHeights() + # solve the issue of misclassify something to no stem when there's one (since knn also train on 0s) + if stem.rhythm==0 and max(leftHeight, rightHeight)>0: + stem.rhythm = 1 + elif stem.rhythm == 0: + sy0, sy1 = stem.getY0Y1() + nx0, ny0, nx1, ny1 = stem.noteBox + if stem.isup: + x0 = nx0 + x1 = min(nx1+(nx1-nx0), img200bw.shape[1]-1) + y0 = sy0 + y1 = ny1 + else: + x0 = max(nx0-(nx1-nx0)//2,0) + x1 = nx1 + y0 = ny0 + y1 = sy1 + img200Crop = img200bw[y0:y1, x0:x1] + # ['n2', 'n4', 'n816'] (0,1,2), 3 if the box doesn't have stem + predLabel = getSingleStemPred(img200Crop, model=stemUpClassifier if stem.isup else stemDownClassifier) + stem.rhythm = predLabelConvert[predLabel] + stem.isSingle = True + xcenter = stem.start[0] + if len(stem.alternativeBox)==0: + stemX0 = xcenter - bar_height//3 #(bar_height*2//3 if stem.isup else bar_height//3) + stemX1 = xcenter + bar_height//3 #(bar_height//3 if stem.isup else bar_height*2//3) + stemY0,stemY1 = stem.getY0Y1() + else: + # x0,y0,x1,y1, x0 == x1 + x00, stemY0, xcenter2, stemY1 =stem.alternativeBox[0] + assert x00 == xcenter2 + stemX0 = xcenter2 - bar_height//3 + stemX1 = xcenter2 + bar_height//3 + + if stemY0 == stemY1: + stemY1 = stemY0+1 + elif len(stem.alternativeBox)==0: + # y0 is the top, smaller one + stemY0 = stemY0 - beamMapImg[stemY0, -1, 2] + stemY1 = stemY1 + beamMapImg[stemY1, -2, 2] + cropPart = noteGroupStemMap[stemY0:stemY1, stemX0:stemX1] + staffNumPart = mapStaffNum[stemY0:stemY1, stemX0:stemX1] + staffNum = np.max(staffNumPart) + # there's no overlapping group + if np.max(cropPart) == 0: + noteGroupStemMap[stemY0:stemY1, stemX0:stemX1] = len(noteGroupList) + mapStaffNum[stemY0:stemY1, stemX0:stemX1] = staffNum + noteGroupList.append(NoteGroup(stem)) + else: + overlappingIndex = np.unique(cropPart).tolist() + if 0 in overlappingIndex: + overlappingIndex.remove(0) + if len(overlappingIndex)==1: + currGroupIdx = overlappingIndex[0] + # xcenter-bar_height//2:xcenter+bar_height//2 + noteGroupStemMap[stemY0:stemY1, stemX0:stemX1] = currGroupIdx + yy,xx = np.where(noteGroupStemMap == currGroupIdx) + mapStaffNum[yy,xx] = np.max(mapStaffNum[yy,xx]) + noteGroupList[currGroupIdx] = mergeNoteGroup(noteGroupList[currGroupIdx], NoteGroup(stem), beamMapImg) + # noteGroupList[currGroupIdx].addNoteStem(stem) + else: + currGroupIdx = overlappingIndex[0] + noteGroupStemMap[stemY0:stemY1, stemX0:stemX1] = currGroupIdx + for gIdx in overlappingIndex[1:]: + ys,xs = np.where(cropPart == gIdx) + noteGroupStemMap[ys,xs] = currGroupIdx + noteGroupList[currGroupIdx] = mergeNoteGroup(noteGroupList[currGroupIdx],noteGroupList[gIdx], beamMapImg) + noteGroupList[gIdx] = None + yy,xx = np.where(noteGroupStemMap == currGroupIdx) + mapStaffNum[yy,xx] = np.max(mapStaffNum[yy,xx]) + noteGroupList[currGroupIdx] = mergeNoteGroup(noteGroupList[currGroupIdx], NoteGroup(stem), beamMapImg) + + noteSizes = [] + for ng in noteGroupList: + if ng is None: + continue + for stem in ng.noteStemList: + imgrgb = cv2.rectangle(imgrgb, (stem.getX()-bar_height//3, stem.getTopCoord()[1]), (stem.getX()+bar_height//3, stem.getBottomCoord()[1]), + class_colors[stem.rhythm], 1, cv2.LINE_AA) + x0,y0,x1,y1 = stem.noteBox + noteSizes.append((x1-x0)*(y1-y0)) + imgrgb = cv2.rectangle(imgrgb, (x0,y0),(x1,y1), class_colors[stem.rhythm], 1, cv2.LINE_AA) + if len(stem.alternativeBox) >0: + alterBoxes = stem.alternativeBox + for alterBox in alterBoxes: + imgrgb = cv2.rectangle(imgrgb, (alterBox[0], alterBox[1]),(alterBox[2],alterBox[3]),(120,150,255), 3, cv2.LINE_AA) + kmeans = KMeans(n_clusters = 2, init = np.array([min(noteSizes), max(noteSizes)], dtype=np.uint16).reshape(-1, 1), n_init = 1) + kmeans.fit(np.array(noteSizes,dtype=np.uint16).reshape(-1,1)) + + if kmeans.cluster_centers_[1]/kmeans.cluster_centers_[0]>2: + imgrgb2 = image.copy() + currentCount = -1 + minSize = int(kmeans.cluster_centers_[1]//2) + for ngId, ng in enumerate(noteGroupList): + if ng is None: + continue + stmToRemove = [] + for stemId, stem in enumerate(ng.noteStemList): + x0,y0,x1,y1 = stem.noteBox + currentCount = currentCount+1 + # if kmeans.labels_[currentCount] == 0: + # break + if (x1-x0)*(y1-y0)0: + alterBoxes = stem.alternativeBox + for alterBox in alterBoxes: + imgrgb2 = cv2.rectangle(imgrgb2, (alterBox[0], alterBox[1]),(alterBox[2],alterBox[3]),(120,150,255), 3, cv2.LINE_AA) + stmToRemove.reverse() + for rmId in stmToRemove: + del ng.noteStemList[rmId] + if len(ng.noteStemList) == 0: + noteGroupList[ngId] = None + else: + imgrgb2 = imgrgb + noteGroupMap = np.zeros((beamMapImg.shape[0], beamMapImg.shape[1]), dtype=np.uint16) + ngImg = image.copy() + + colors = [(0,0,255),(0,255,0),(255,255,0), (0,120,230)] + for idx,ng in enumerate(noteGroupList): + if ng is not None: + x0,y0,x1,y1 = ng.boundingBox + noteGroupMap = cv2.rectangle(noteGroupMap, (x0,y0),(x1,y1), idx, -1) + staffnum = np.max(mapStaffNum[y0:y1, x0:x1]) + mapStaffNum[y0:y1+1, x0:x1+1] = staffnum + coloridx = -1 if staffnum ==0 else staffnum%3 + ngImg = cv2.rectangle(ngImg, (x0,y0),(x1,y1), colors[coloridx], 1, cv2.LINE_AA) + for nb in ng.noteBoxes: + xx0,yy0,xx1,yy1 = nb + ngImg = cv2.rectangle(ngImg, (xx0,yy0),(xx1,yy1), colors[coloridx], 1, cv2.LINE_AA) + beamMapImg = cv2.merge([mapBeamVal, middle, mapStaffNum]) + imwrite('ngImg.jpg',ngImg) + imwrite('knnbeams.jpg',imgrgb) + imwrite('knnbeams2.jpg',imgrgb2) + return noteGroupList, beamMapImg, noteGroupStemMap, noteGroupMap, {'knnbeams':imgrgb,'ngImg': ngImg, 'knnbeams2': imgrgb2} + + +def findRests(dataDict:dict, + beamMapImg: np.ndarray,barheight:float, + model: Rest_Classifier,): + restList:List[Rest|None] = [None] + restMap = np.zeros((beamMapImg.shape[0], beamMapImg.shape[1]), dtype=np.uint16) + symbol = dataDict['symbols'].astype(np.uint8) # 1 where there's symbols + stem_rests = dataDict['stems_rests'].astype(np.uint8) + bb,gg,rr = cv2.split(beamMapImg) + mask = np.ones_like(symbol, dtype= np.uint8) + stemRestClean = cv2.cvtColor(stem_rests*255, cv2.COLOR_GRAY2BGR) + ys,xs = np.where(bb==255) + mask[ys,xs] = 0 + stem_rests[ys,xs] = 0 + # mask = cv2.erode(mask.astype(np.uint8), np.ones((barheight//2, barheight//4),dtype=np.uint8)).squeeze() + ys,xs = np.where(gg>0) + mask[ys,xs] = 0 + stem_rests[ys,xs] = 0 + stem_rests = cv2.dilate(stem_rests.astype(np.uint8), np.ones((barheight//3, barheight//3), dtype= np.uint8)) + + # remaining = cv2.bitwise_or(cv2.bitwise_and(mask,symbol), stem_rests)*255 + remaining = cv2.bitwise_and(mask,symbol)*255 + remaining = cv2.cvtColor(remaining,cv2.COLOR_GRAY2BGR) + symbol = cv2.cvtColor(symbol*255,cv2.COLOR_GRAY2BGR) + + contours, _ = cv2.findContours(stem_rests.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + stem_rests = cv2.cvtColor((stem_rests*255).astype(np.uint8), cv2.COLOR_GRAY2BGR) + + # save the image version (later can give img128 version), stem_rests itself, remaining + bboxes = [] + longBoxes = [] + # imageClean = image.copy() + remainingClean = remaining.copy() + class_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40), (245, 220, 255),(230,130,175),(165,170, 70)] + restClassNames = ['X', 'r16', 'r32', 'r4', 'r8'] + restToRhythm = [0,2,3,0,1] + imgrgb = dataDict['image'].copy() + for j in range(0,len(restClassNames)): + imgrgb = cv2.putText(imgrgb,restClassNames[j], (30, 30+j*30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, class_colors[j], 2, cv2.LINE_AA) + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + # filter out too big of boxed (likely some error) + if (w>barheight*2 and h>barheight): + continue + if (w>barheight*0.6 and h>barheight): + box = (max(x-barheight//2, 0), max(y-barheight*2, 0), min(x+w+barheight//2, symbol.shape[1]-1), min(y+h+barheight*2, symbol.shape[0]-1)) + # shrink the box so it centers around the rest + yTopBlank = max(np.max(np.where(np.sum(remainingClean[box[1]:(box[1]+box[3])//2, box[0]:box[2],1],1)<255)[0].tolist()+[0])-barheight//4, 0) + # the index of the ending (the new height) + yBottomBlank = min(np.min(np.where(np.sum(remainingClean[(box[1]+box[3])//2:box[3], box[0]:box[2],1],1)<255)[0].tolist()+[box[3]-box[1]])+(box[3]-box[1])//2+barheight//4, box[3]-box[1]) + # xLeftBlank = max(np.max(np.where(np.sum(remainingClean[box[1]:box[3], box[0]:(box[0]+box[2])//2,1],0)<255)[0].tolist()+[0]), 0) + # yRightBlank = min(np.min(np.where(np.sum(remainingClean[box[1]:box[3], (box[0]+box[2])//2:box[2],1],0)<255)[0].tolist()+[box[2]-box[0]])+(box[2]-box[0])//2, box[2]-box[0]) + # redo the training? No + predict_idx = model.predict(remainingClean[box[1]:box[3], box[0]:box[2],:]) + # predict_idx = model.predict(remainingClean[box[1]+yTopBlank:box[1]+yBottomBlank, box[0]+xLeftBlank:box[0]+yRightBlank,:]) + box = (x, box[1]+yTopBlank, x+w, box[1]+yBottomBlank) + if predict_idx>0: + bboxes.append(box) + restMap[box[1]:box[3],box[0]:box[2]] = len(restList) + restList.append(Rest(box,restToRhythm[predict_idx])) + gg = cv2.rectangle(gg, (box[0], box[1]), (box[2],box[3]),5, -1) + imgrgb = cv2.rectangle(imgrgb,(box[0], box[1]), (box[2],box[3]), class_colors[predict_idx], 2, cv2.LINE_AA) + # remaining = cv2.rectangle(remaining, (box[0], box[1]), (box[2],box[3]), (0,255,0), 2, cv2.LINE_AA) + # stem_rests = cv2.rectangle(stem_rests, (box[0], box[1]), (box[2],box[3]), (0,255,0), 2, cv2.LINE_AA) + # symbol = cv2.rectangle(symbol, (box[0], box[1]), (box[2],box[3]), (0,255,0), 2, cv2.LINE_AA) + elif (w>barheight*0.8 and hbarheight*0.3): + box = (x,y,x+w,y+h) + longBoxes.append(box) + gg = cv2.rectangle(gg, (box[0], box[1]), (box[2],box[3]),5, -1) + imgrgb = cv2.rectangle(imgrgb,(box[0], box[1]), (box[2],box[3]), (255,0,255), 2, cv2.LINE_AA) + restMap[box[1]:box[3],box[0]:box[2]] = len(restList) + restList.append(Rest(box,-1)) + # remaining = cv2.rectangle(remaining, (box[0], box[1]), (box[2],box[3]), (0,0,255), 2, cv2.LINE_AA) + # stem_rests = cv2.rectangle(stem_rests, (box[0], box[1]), (box[2],box[3]), (0,0,255), 2, cv2.LINE_AA) + # symbol = cv2.rectangle(symbol, (box[0], box[1]), (box[2],box[3]), (0,0,255), 2, cv2.LINE_AA) + # init_rest_folder() + # labelRestData([imageClean, stemRestClean, remainingClean], bboxes, imgname) + imwrite('remaining.jpg',remaining) + imwrite('remainingStemRests.jpg',stem_rests) + imwrite('RestClassification.jpg',imgrgb) + beamMapImg = cv2.merge([bb,gg,rr]) + # return {'RestBarline1': remaining, 'RestBarline2': symbol} + return restMap, restList, beamMapImg, {'RestClassification': imgrgb} + +def mergeVerticalNoteGroups(staffObjList:List[Staff],noteGroupList:List[NoteGroup], noteGroupMap:np.ndarray,beamMapImg:np.ndarray, image:np.ndarray, + restMap, restList:List[Rest]): + for staff in staffObjList: + barh = staff.get_yOne() + oneStep = barh//3 + y0 = max(staff.ys[0]-int(barh*2.5),0) + y1 = min(staff.ys[-1]+int(barh*2.5), noteGroupMap.shape[0]) + tempRestMap = restMap.copy() + for x in range(staff.left, staff.right-oneStep, oneStep): + indexes = set(np.unique(noteGroupMap[y0:y1, x])) + indexesNext = set(np.unique(noteGroupMap[y0:y1, x+oneStep])) + rIndex = set(np.unique(tempRestMap[y0:y1, x])) + rIndexNext = set(np.unique(tempRestMap[y0:y1, x+oneStep])) + line = set(np.unique(beamMapImg[y0:y1, x:x+oneStep,2])) + indexes -= {0} + indexesNext-={0} + line-={0} + rIndex-={0} + rIndexNext-={0} + if len(line)>1: + continue + if len(indexes) == 0: + continue + # the overlapping x width is at least oneStep width + noteGroupId = list(indexes)[0] + lineList = list(line) + idxList = list(indexes) + + if len(indexes)>1 and indexes==indexesNext: # two noteGroup overlap + noteGroupId = idxList.pop() + for gid in idxList: + ys,xs = np.where(noteGroupMap == gid) + noteGroupMap[ys,xs] = noteGroupId + beamMapImg[ys, xs, 2] = lineList[0] + noteGroupList[noteGroupId]=mergeNoteGroup(noteGroupList[noteGroupId], noteGroupList[gid], beamMapImg) + noteGroupList[gid] = None + if len(rIndex)>0 and rIndex == rIndexNext: + ridLst = list(rIndex) + for rid in ridLst: + ys,xs = np.where(tempRestMap == rid) + if restList[rid].rhythm == -1: + continue + else: + tempRestMap[ys,xs] = 0 + beamMapImg[ys,xs,2] = lineList[0] + noteGroupList[noteGroupId].addRest(restList[rid]) + restList[rid].setNgId(noteGroupId) + ngImg = image.copy() + colors = [(0,0,255),(0,255,0),(255,255,0), (0,120,230)] + for ng in noteGroupList: + if ng is not None: + x0,y0,x1,y1 = ng.boundingBox + ngImg = cv2.rectangle(ngImg, (x0,y0),(x1,y1), colors[(np.max(beamMapImg[y0:y1, x0:x1, 2])%3 if np.max(beamMapImg[y0:y1, x0:x1, 2])>0 else 3)], 2, cv2.LINE_AA) + imwrite('vertically_merged.jpg', ngImg) + return noteGroupList, noteGroupMap, beamMapImg, {'vertically_merged':ngImg} + +def symbol_classification(dataDict: dict, model:Sfn_Clef_classifier, bar_height:int): + def getbbox(clefs_keys: np.ndarray, bar_height:int)-> List[Tuple[int,int,int,int]]: + contours, _ = cv2.findContours(clefs_keys.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + bboxes = [] + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + if w>3 and h>bar_height*1.6 and h>w: + box = (x, y, x+w, y+h) + bboxes.append(box) + return bboxes + def getBestRange(avg_color_lst: list, desire_length:int): + if desire_length>=len(avg_color_lst)-1: + return 0, len(avg_color_lst)-1 + most_white_value = 0 + most_white_ending_idx = 0 + for i in range(desire_length,len(avg_color_lst)-1): + currSum = sum(avg_color_lst[i-desire_length:i]) + if currSum>most_white_value: + most_white_value=currSum + most_white_ending_idx = i + return most_white_ending_idx-desire_length, most_white_ending_idx + def sfnClefToDataType(bbox:Tuple[int,int,int,int], predictionId: int) -> Union[Accidentals, Clef]: + # 'BassF', 'ViolaC', 'flat', 'natural', 'noClass', 'sharp', 'trebleG' + isClef = [True, True, False, False, False, False, True] + mapId = [-1, 0, -1, 0, -3, 1, 1] + # we should never try to assign a noClass + assert predictionId != 4 + if isClef[predictionId]: + return Clef(bbox, mapId[predictionId]) + else: + return Accidentals(bbox, mapId[predictionId]) + + # output_dir will be images/tch/tch SYMBOL + clefs_keys_ori:np.ndarray = dataDict['clefs_keys'] + clefs_keys = (clefs_keys_ori*255).astype(np.uint8) + clefs_keys_expand = cv2.dilate(clefs_keys, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(4,4))) + image:np.ndarray = dataDict['image'] + round1_sfn_img = image.copy() + ori_bboxes = getbbox(clefs_keys_expand, bar_height) + bboxes = merge_nearby_bbox(ori_bboxes, bar_height*3) + class_names = ['BassF', 'ViolaC', 'flat', 'natural', 'noClass', 'sharp', 'trebleG','wide'] # Replace with actual class names + class_symbols = ['Bass','Viola','b','n','x','#','Treble','Wide'] + class_colors = [(255,0,0),(0,255,0),(255,255,0),(255,0,125),(0,120,255),(255,0,255),(0,0,255),(101,134,168)] + for j in range(0,len(class_colors)): + round1_sfn_img = cv2.putText(round1_sfn_img,class_names[j], (30, 30+j*30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, class_colors[j], 2, cv2.LINE_AA) + noClass_bbox = [] + sfn_height_lst = [] + sfn_width_lst = [] + # the 0th is a stub for better alignment + returnSfnList:List[Union[Accidentals,Clef, None]] = [None] + for b in bboxes: + if (b[3]-b[1])<(b[2]-b[0]): # height likely it's flat instead + predict_idx = 2 + if predict_idx == 2 or predict_idx==5 or predict_idx==3: + # this is flat | sharp | natural + sfn_height_lst.append(b[3]-b[1]) + sfn_width_lst.append(b[2]-b[0]) + round1_sfn_img = cv2.rectangle(round1_sfn_img, (b[0],b[1]),(b[2],b[3]), class_colors[predict_idx],2,cv2.LINE_AA) + round1_sfn_img = cv2.putText(round1_sfn_img, class_symbols[predict_idx], (b[0],b[3]+10),cv2.FONT_HERSHEY_SIMPLEX, 1, class_colors[predict_idx], 1, cv2.LINE_AA) + if predict_idx == 4 or predict_idx==7: + noClass_bbox.append(b) + else: + returnSfnList.append(sfnClefToDataType(b, predict_idx)) + noClass_bbox2 = [] + round2_sfn_img = round1_sfn_img.copy() + sfn_median_height = bar_height*2 + sfn_median_width = bar_height + if len(sfn_height_lst)>0: + sfn_median_height = int(np.median(sfn_height_lst)) + sfn_median_width = int(np.median(sfn_width_lst)) + + for box1 in noClass_bbox: + x0 = box1[0] + y0 = box1[1] + clef_key_crop = clefs_keys[box1[1]:box1[3], box1[0]:box1[2]] + smallbbox = getbbox(clef_key_crop, bar_height) + if len(smallbbox)<=1: + if len(smallbbox)==1 and ((box1[2]-box1[0])>sfn_median_width*1.5 or (box1[3]-box1[1])>sfn_median_height*1.5): + noClass_bbox2.append(box1) + predict_idx = 4 + round2_sfn_img = cv2.rectangle(round2_sfn_img, (box1[0],box1[1]),(box1[2],box1[3]), class_colors[predict_idx],2,cv2.LINE_AA) + round2_sfn_img = cv2.putText(round2_sfn_img, class_symbols[predict_idx], (box1[0],box1[3]+10),cv2.FONT_HERSHEY_SIMPLEX, 1, class_colors[predict_idx], 1, cv2.LINE_AA) + continue + for bb in smallbbox: + b = [x0+bb[0],y0+bb[1],x0+bb[2],y0+bb[3]] + small_crop = clefs_keys[b[1]:b[3], b[0]:b[2]] + predict_idx = model.predict(small_crop) + round2_sfn_img = cv2.rectangle(round2_sfn_img, (b[0],b[1]),(b[2],b[3]), class_colors[predict_idx],2,cv2.LINE_AA) + round2_sfn_img = cv2.putText(round2_sfn_img, class_symbols[predict_idx], (b[0],b[3]+10),cv2.FONT_HERSHEY_SIMPLEX, 1, class_colors[predict_idx], 1, cv2.LINE_AA) + + if predict_idx == 4: + if (small_crop.shape[0]>sfn_median_height*1.5 and small_crop.shape[1]>sfn_median_width*0.8) or (small_crop.shape[0]>sfn_median_height*0.8 and small_crop.shape[1]>sfn_median_width*1.5): + noClass_bbox2.append(b) + else: + continue + else: + returnSfnList.append(sfnClefToDataType(b, predict_idx)) + for box1 in noClass_bbox2: + x0 = box1[0] + y0 = box1[1] + w = box1[2]-x0 + h = box1[3]-y0 + seg_img = clefs_keys[y0:y0+h, x0:x0+w] + width_ratio =w/sfn_median_width + height_ratio = h/sfn_median_height + num_symbol = round(max(width_ratio, height_ratio)) + if num_symbol<2: + continue + if width_ratio>height_ratio: + left_center = sfn_median_width//2 + right_center = w-sfn_median_width//2 + avg_width:float = (right_center-left_center)/(num_symbol-1) + for i in range(num_symbol): + img_left = int(i*avg_width) + crop_img = seg_img[:,img_left:min(img_left+sfn_median_width,seg_img.shape[1])] + vertical_avg = np.mean(crop_img,1) + front,back = getBestRange(vertical_avg, sfn_median_height) + # b: x0, y0, x1, y1 relative to the croped image + b = [max(img_left,0),max(front,0), + min(img_left+sfn_median_width,seg_img.shape[1]), min(back,seg_img.shape[0])] + crop_crop_img = seg_img[b[1]:b[3], b[0]:b[2]] + # predict_idx = model.predict(crop_crop_img) + if sum(np.max(crop_crop_img,1))<=0: + continue + if sum(np.max(crop_crop_img,1))/255/crop_crop_img.shape[0]<=0.6: + continue + predict_lst = model.get_prediction_vector(crop_crop_img) + pred_filter = [x for x in predict_lst if x not in [0,1,4,6,7]] + predict_idx = pred_filter[0] + returnSfnList.append(sfnClefToDataType((x0+b[0], y0+b[1], x0+b[2],y0+b[3]), predict_idx)) + else: + seg_img_to_delete = seg_img.copy() + vertical_boxes = [] + pred_idx = [] + + b = [0,0,w,sfn_median_height] #x0, y0, x1, y1 + hor_avg_top = np.mean(seg_img[b[1]:b[3],:],0) + b[0],b[2] = getBestRange(hor_avg_top, sfn_median_width) + seg_img_to_delete[b[1]:b[3],b[0]:b[2]] = 0 + vertical_boxes.append(b) + + b = [0,h-sfn_median_height-1,w,h-1] + hor_avg_bot = np.mean(seg_img[b[1]:b[3],:],0) + b[0],b[2] = getBestRange(hor_avg_bot, sfn_median_width) + seg_img_to_delete[b[1]:b[3],b[0]:b[2]] = 0 + vertical_boxes.append(b) + + if h>sfn_median_height*2: + b = [0,0,0,0] + if np.max(seg_img_to_delete) == 0: # if there's some issue with the bounding box basically + break + # middle_del = seg_img_to_delete[sfn_median_height//2:h-sfn_median_height//2,:] + ver_avg_mid = np.mean(seg_img_to_delete,1) + b[1],b[3] = getBestRange(ver_avg_mid, sfn_median_height) + expanded_left_right = [min(vertical_boxes[0][0],vertical_boxes[1][0]), + max(vertical_boxes[0][2],vertical_boxes[1][2])] + if expanded_left_right[0]>=w-expanded_left_right[1]-sfn_median_width//5: + b[2] = sfn_median_width + else: + b[0] = w-sfn_median_width-1 + b[2] = w-1 + vertical_boxes.append(b) + seg_img_bgr = cv2.cvtColor(seg_img,cv2.COLOR_GRAY2BGR) + seg_img_bgr = cv2.rectangle(seg_img_bgr, (b[0],b[1]),(b[2],b[3]),(0,255,0),2,cv2.LINE_AA) + + for idx, b in enumerate(vertical_boxes): + predict_lst = model.get_prediction_vector(seg_img[b[1]:b[3],b[0]:b[2]]) + pred_filter = [x for x in predict_lst if x not in [0,1,4,6,7]] + predict_idx = pred_filter[0] + if idx == 2 and predict_lst[0] == 4: + continue + returnSfnList.append(sfnClefToDataType((x0+b[0], y0+b[1], x0+b[2],y0+b[3]), predict_idx)) + sfnClefImg = image.copy() + class_colors2 = [(255,255,0),(255,0,125),(255,0,255),(255,0,0),(0,255,0),(0,0,255)] + class_names2 = ['flat', 'natural', 'sharp','BassF', 'ViolaC', 'trebleG'] + for j in range(0,len(class_colors2)): + sfnClefImg = cv2.putText(sfnClefImg,class_names2[j], (30, 30+j*30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, class_colors2[j], 2, cv2.LINE_AA) + # class_names = ['BassF', 'ViolaC', 'flat', 'natural', 'noClass', 'sharp', 'trebleG','wide'] + + sfnClefMap = np.zeros((sfnClefImg.shape[0], sfnClefImg.shape[1]), dtype=np.uint16) + for idx,clefAccidentals in enumerate(returnSfnList): + if clefAccidentals is not None: + x0,y0,x1,y1 = clefAccidentals.getBbox() + # 0 if it's accidentals + predict_idx = clefAccidentals.getType()*3+clefAccidentals.getValue()+1 + sfnClefImg = cv2.rectangle(sfnClefImg, (x0,y0),(x1,y1), class_colors2[predict_idx],2,cv2.LINE_AA) + sfnClefMap = cv2.rectangle(sfnClefMap, (x0,y0),(x1,y1), idx, -1) + + imwrite('round1Sfn.jpg',round1_sfn_img) + imwrite('round2Sfn.jpg',round2_sfn_img) + imwrite('round3Sfn.jpg', sfnClefImg) + + return returnSfnList, sfnClefMap, {'sfnClef':sfnClefImg} + +def exportYolo(sfnClefList:List[Accidentals|Clef], image:np.ndarray,img_name:str, barheight: int): + if not os.path.isdir("yolo"): + os.mkdir("yolo") + if not os.path.isdir("yolo/debug"): + os.mkdir("yolo/debug") + height, width, _ = image.shape + outputString = "" + imgg = image.copy() + for sfnClef in sfnClefList: + if type(sfnClef) == Clef: + if sfnClef.getValue() == 0 or sfnClef.getValue() == -2: + x0,y0,x1,y1 = sfnClef.boundingBox + if y1-x1 < 3*barheight: + continue + cv2.rectangle(imgg, (x0,y0),(x1,y1),(0,0,255), 2, cv2.LINE_AA) + xPerc = (x1+x0)/2/width + yPerc = (y1+y0)/2/height + xWidth = (x1-x0)/width + yHeight = (y1-y0)/height + print(f"{1} {xPerc:.6f} {yPerc:.6f} {xWidth:.6f} {yHeight:.6f}") + outputString += f"{1} {xPerc:.6f} {yPerc:.6f} {xWidth:.6f} {yHeight:.6f}\n" + elif type(sfnClef) == Accidentals: + if sfnClef.getValue() == -1: + # is flat + x0,y0,x1,y1 = sfnClef.boundingBox + cv2.rectangle(imgg, (x0,y0),(x1,y1),(0,255,0), 2, cv2.LINE_AA) + xPerc = (x1+x0)/2/width + yPerc = (y1+y0)/2/height + xWidth = (x1-x0)/width + yHeight = (y1-y0)/height + print(f"{0} {xPerc:.6f} {yPerc:.6f} {xWidth:.6f} {yHeight:.6f}") + outputString += f"{0} {xPerc:.6f} {yPerc:.6f} {xWidth:.6f} {yHeight:.6f}\n" + with open(f"yolo/{img_name}.txt","w") as f: + f.write(outputString) + cv2.imwrite(f"yolo/{img_name}.jpg", image) + cv2.imwrite(f"yolo/debug/{img_name}.jpg",imgg) + +# filter out the sfn that is overlapping with noteGroupList +def filterSfnClefModifyBeamMap(sfnClefList:List[Union[Accidentals, Clef, None]], + noteGroupMap: np.ndarray, + beamMapImg:np.ndarray): + sfnClefMap = np.zeros((beamMapImg.shape[0], beamMapImg.shape[1]), dtype=np.uint16) + bb,gg,rr = cv2.split(beamMapImg) + assert len(np.unique(gg)) <=2 + for idx, sfnClef in enumerate(sfnClefList): + if sfnClef is not None: + x0,y0,x1,y1 = sfnClef.getBbox() + if np.sum(noteGroupMap[y0:y1,x0:x1]>0)/((x1-x0)*(y1-y0))>0.5: + sfnClefList[idx] = None + else: + sfnClefMap[y0:y1,x0:x1] = idx + # 2 for accidentals, 3 for clef + gg[y0:y1,x0:x1] = sfnClef.getType()+2 + # will want the noteGroup to be "more dominent" than the sfnClef, so it overwrites if it's place of a sfn + ys,xs = np.where(noteGroupMap>0) + gg[ys,xs] = 1 + beamMapImg = cv2.merge([bb,gg,rr]) + return sfnClefList,sfnClefMap, beamMapImg + +def enhanceStaff(image:np.ndarray, staffObjList:List[Staff],barheight:int): + _, img200 = cv2.threshold(image, 200, 255, cv2.THRESH_BINARY) + for staff in staffObjList: + if staff.IsStaffAligned(barheight): + for y in staff.ys: + img200 = cv2.line(img200, (staff.left, y), (staff.right, y), (0,0,0), 1, cv2.LINE_AA) + imwrite('enhanceStaff.jpg',img200) + return img200 + +def assignPitch(image:np.ndarray, noteGroupList:List[NoteGroup|None], beamMapImg:np.ndarray, barheight:int): + # assign the "pitch" relative to the center of the staff (12.5), + # ex: in violin, B will be 0, C:1,D:2, A: -1 etc + # if it doesn't have a staff line id yet, group it using the beamMapImg[:,:,4] + # image is for outputting debug images + pitchImg = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + _, imgBin = cv2.threshold(pitchImg, 200, 1, cv2.THRESH_BINARY_INV) + _,pitchImg = cv2.threshold(pitchImg, 200, 255, cv2.THRESH_BINARY) + pitchLineImg = pitchImg.copy() + pitchLineImg = cv2.cvtColor(pitchLineImg, cv2.COLOR_GRAY2BGR) + pitchImg = pitchImg//4+191 + pitchImg = cv2.cvtColor(pitchImg, cv2.COLOR_GRAY2BGR) + pitch_colors = [(255,0,0),(0,0,255),(255,255,0),(0,120,255),(40,255,40), (255,0,255),(230,130,175),(165,170, 70)] + pitch_names = ['B','C','D','E','F','G','A'] + for j in range(0,len(pitch_names)): + pitchImg = cv2.putText(pitchImg,pitch_names[j], (30, 30+j*30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, pitch_colors[j], 2, cv2.LINE_AA) + for ng in noteGroupList: + if ng is None: + continue + for stemObj in ng.noteStemList: + x0,y0,x1,y1 = stemObj.smallNoteBox + staffNum = np.max(beamMapImg[y0:y1,x0:x1,2]) + if staffNum == 0: + staffNum = round(np.sum(beamMapImg[y0:y1,4,2]/(y1-y0))) + height = np.sum((beamMapImg[y0:y1,staffNum%4,2]-12.5)*2)/(y1-y0) + stemObj.pitchFloat = height + + x0,y0,x1,y1 = stemObj.noteBox + height2 = np.sum((beamMapImg[y0:y1,staffNum%4,2]-12.5)*2)/(y1-y0) + if height2-int(height2) == 0: + stemObj.pitchWideFloat = height + else: + stemObj.pitchWideFloat = height2 + + pitchAndLineImg = pitchImg.copy() + modifiedPitchImg = pitchImg.copy() + + for ngIdx, ng in enumerate(noteGroupList): + if ng is None: + continue + for idx,stemObj in enumerate(ng.noteStemList): + x0,y0,x1,y1 = stemObj.smallNoteBox + _,y0B, _, y1B = stemObj.noteBox + pitch = stemObj.pitchFloat + xx0 = max(x0-barheight//2, 0) + xx1 = min(x1+barheight//2, beamMapImg.shape[1]-1) + cc = np.sum(imgBin[max(y0B,y0-2):min(y1B,y1+3), xx0:xx1],1).tolist() + ccNozero = [c for c in cc if c>0] + maxIdx = [i for i in range(len(ccNozero)) if ccNozero[i]==max(ccNozero)] + # center that was tilted and thick + if (maxIdx[0]>len(ccNozero)/2 and maxIdx[0]len(ccNozero)*0.8) and maxIdx[-1]-maxIdx[0]>=3: # TODO + stemObj.hasLineMiddle = True + pitchLineImg=cv2.rectangle(pitchLineImg,(xx0, y0+maxIdx[0]),(xx1, y0+maxIdx[-1]),(255,0,255), 2, cv2.LINE_AA) + # center + if len(set.intersection(set([len(ccNozero)//2,len(ccNozero)//2-1, len(ccNozero)//2+1]), maxIdx))>0: + stemObj.hasLineMiddle = True + pitchLineImg=cv2.rectangle(pitchLineImg,(xx0, y0+maxIdx[0]),(xx1, y0+maxIdx[-1]),(255,0,255), 2, cv2.LINE_AA) + # top or bottom + elif y1-y0>barheight//2 and (maxIdx[0]<=3 or maxIdx[-1]>=len(ccNozero)-4): + stemObj.hasLineMiddle = False + pitchLineImg=cv2.rectangle(pitchLineImg,(xx0, y0),(xx1, y1),(0,255,0), 1, cv2.LINE_AA) + # if it has line in the middle + elif ccNozero[maxIdx[0]]>(x1-x0): + stemObj.hasLineMiddle = True + pitchLineImg=cv2.rectangle(pitchLineImg,(xx0, y0+maxIdx[0]),(xx1, y0+maxIdx[-1]),(255,0,255), 2, cv2.LINE_AA) + else: + stemObj.hasLineMiddle = True + pitchLineImg=cv2.rectangle(pitchLineImg,(xx0, y0+maxIdx[0]),(xx1, y0+maxIdx[-1]),(255,255,0), 2, cv2.LINE_AA) + + p1 = math.ceil(pitch) + p2 = math.floor(pitch) + pitchLineImg = cv2.putText(pitchLineImg, str(ngIdx), (x0,y0-barheight*2), cv2.FONT_HERSHEY_SIMPLEX, 0.2, (0,0,0), 1, cv2.LINE_AA) + pitchAndLineImg = cv2.putText(pitchAndLineImg, str(ngIdx), (x0,y0-barheight*2), cv2.FONT_HERSHEY_SIMPLEX, 0.2, (0,0,0), 1, cv2.LINE_AA) + pitchAndLineImg = cv2.putText(pitchAndLineImg, str(pitch_names[p1%7]), (x0,y0), cv2.FONT_HERSHEY_SIMPLEX, 0.5, pitch_colors[p1%7], 2, cv2.LINE_AA) + pitchAndLineImg = cv2.putText(pitchAndLineImg, str(pitch_names[p2%7]), (x0,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, pitch_colors[p2%7], 2, cv2.LINE_AA) + pitchAndLineImg = cv2.rectangle(pitchAndLineImg, (x0,y0),(x1,y1), (30,30,30), 1,cv2.LINE_AA) + pitchImg = cv2.putText(pitchImg, str(pitch_names[round(pitch)%7]), (x0,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, pitch_colors[round(pitch)%7], 2, cv2.LINE_AA) + pitchImg = cv2.rectangle(pitchImg, (x0,y0),(x1,y1), (30,30,30), 1,cv2.LINE_AA) + + xend = pitchLineImg.shape[1]-1 + for i in range(pitchLineImg.shape[0]-1): + currStaffNum = beamMapImg[i,4,2] + if beamMapImg[i,currStaffNum%4, 2] != beamMapImg[i+1,currStaffNum%4, 2]: + pitchLineImg = cv2.line(pitchLineImg,(0,i),(xend,i),pitch_colors[currStaffNum%4],thickness=1, lineType=cv2.LINE_AA) + pitchAndLineImg = cv2.line(pitchAndLineImg,(0,i),(xend,i),pitch_colors[currStaffNum%4],thickness=1, lineType=cv2.LINE_AA) + + for idx, ng in enumerate(noteGroupList): + if ng is None: + continue + for stem in ng.noteStemList: + pitchInt = stem.getBestPitchInt() + x0,y0,x1,y1 = stem.noteBox + # modifiedPitchImg = cv2.putText(modifiedPitchImg, str(idx), (x0,y0), cv2.FONT_HERSHEY_SIMPLEX, 0.5, pitch_colors[pitchInt%7], 2, cv2.LINE_AA) + modifiedPitchImg = cv2.putText(modifiedPitchImg, str(pitch_names[pitchInt%7]), (x0,y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, pitch_colors[pitchInt%7], 2, cv2.LINE_AA) + modifiedPitchImg = cv2.rectangle(modifiedPitchImg, (x0,y0),(x1,y1), (30,30,30), 1,cv2.LINE_AA) + stem.pitchSoprano = pitchInt + + + imwrite('pitchline.jpg', pitchLineImg) + imwrite('pitchAndLine.jpg', pitchAndLineImg) + imwrite('pitch.jpg', pitchImg) + imwrite('pitchModified.jpg', modifiedPitchImg) + return noteGroupList, {'pitch':pitchImg, + 'pitchAndLine':pitchAndLineImg, + 'pitchline': pitchLineImg, + 'pitchModified':modifiedPitchImg} + +def getNoteChunks(image: np.ndarray, noteGroupMap:np.ndarray, beamMapImg:np.ndarray, noteGroupVerticallyMerged:List[NoteGroup]): + imgrgb = image.copy() + beamBinary = np.zeros((beamMapImg.shape[0],beamMapImg.shape[1])) + xs,ys = np.where(beamMapImg[:,:,0]==255) + beamBinary[xs,ys] = 1 + contours, _ = cv2.findContours(beamBinary.astype(np.uint8),cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + beamBoxes:List[Tuple[int,int,int,int]] = [] + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + beamBoxes.append((x,y,x+w,y+h)) + imgrgb = cv2.rectangle(imgrgb,(x,y), (x+w, y+h), (0,0,255), 2, cv2.LINE_AA) + imwrite('beamContour.jpg',imgrgb) + + noteChunkList:List[NoteChunk] = [None] + noteChunkMap = np.zeros_like(noteGroupMap) + for box in beamBoxes: + currentNoteChunkIdxs = set(np.unique(noteChunkMap[box[1]:box[3], box[0]:box[2]])) + allNoteGroupIdxs = set(np.unique(noteGroupMap[box[1]:box[3], box[0]:box[2]])) + allNoteGroupIdxs.add(0) + allNoteGroupIdxs.remove(0) + currentNoteChunkIdxs.add(0) + currentNoteChunkIdxs.remove(0) + if len(allNoteGroupIdxs) == 0: + continue + if len(currentNoteChunkIdxs)==0: + n = NoteChunk(allNoteGroupIdxs) + for a in allNoteGroupIdxs: + ys,xs = np.where(noteGroupMap==a) + noteChunkMap[ys,xs] = len(noteChunkList) + noteChunkList.append(n) + elif len(currentNoteChunkIdxs) == 1: + noteChunkList[min(currentNoteChunkIdxs)].mergeNoteChunk(allNoteGroupIdxs) + for a in allNoteGroupIdxs: + ys,xs = np.where(noteGroupMap==a) + noteChunkMap[ys,xs] = min(currentNoteChunkIdxs) + else: + minChunkIdx = min(currentNoteChunkIdxs) + currentNoteChunkIdxs.remove(minChunkIdx) + for chunkIdx in list(currentNoteChunkIdxs): + ys,xs = np.where(noteChunkMap==chunkIdx) + noteChunkMap[ys,xs] = minChunkIdx + noteChunkList[minChunkIdx].mergeNoteChunk(noteChunkList[chunkIdx].noteGroupIdxs) + noteChunkList[chunkIdx] = None + imggg = image.copy() + for ii, nc in enumerate(noteChunkList): + if nc is None: + continue + for id in nc.noteGroupIdxs: + noteGroupVerticallyMerged[id].noteChunkId = ii + bx = nc.getBoundingBox(noteGroupVerticallyMerged) # changed here + imggg = cv2.rectangle(imggg, (bx[0],bx[1]),(bx[2], bx[3]),(0,0,255), 2, cv2.LINE_AA) + imwrite("horizontalGrouping.jpg", imggg) + return noteChunkList, imggg + +def findBarlines(image:np.ndarray, beamMapImg:np.ndarray, staffObjList:List[Staff], noteGroupStemMap:np.ndarray, thres:float)->np.ndarray: + img200 =cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + barlineImg = np.zeros((image.shape[0], image.shape[1]),dtype=np.uint8) + # (1) where there's stuff, (0) where there's no + _,img200bin = cv2.threshold(img200, 200, 1, cv2.THRESH_BINARY_INV) + ys,xs = np.where(beamMapImg[:,:,1]>0) + img200bin[ys,xs] = 0 + ys,xs = np.where(noteGroupStemMap>0) + img200bin[ys,xs] = 0 + for idx,staff in enumerate(staffObjList): + ys = staff.ys + y0 = ys[0] + y1 = ys[-1] + xs = np.where(np.sum(img200bin[y0:y1, :],0)>(y1-y0)*thres)[0] + beamMapImg[y0:y1,xs,1] = 4 + beamMapImg[y0:y1,xs-1,1] = 4 + beamMapImg[y0:y1,xs+1,1] = 4 + barlineImg[y0:y1, xs] = 1 + contours, _ = cv2.findContours(barlineImg, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + barlineboxes:List[Tuple[int,int,int,int]] = [] + imgbgr = image.copy() + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + barlineboxes.append((x,y,x+w,y+h)) + imgbgr = cv2.rectangle(imgbgr, (x,y), (x+w, y+h), (0,0,255),1,cv2.LINE_AA) + imwrite("barLineImg.jpg",imgbgr) + return beamMapImg, barlineboxes, imgbgr + +def assignDots(noteGroupVerticallyMerged:List[NoteGroup],beamMapImg:np.ndarray, image:np.ndarray,bar_height:int,staffObjList:List[Staff]): + imgbgr = image.copy() #bgr + _,gg,_ = cv2.split(beamMapImg) + params = cv2.SimpleBlobDetector_Params() + + params.filterByArea = True + params.minArea = bar_height*bar_height//16 + params.maxArea = bar_height*bar_height//2 + + params.filterByCircularity = False + params.filterByConvexity = False + params.filterByInertia = True + params.minInertiaRatio = 0.5 + + detector = cv2.SimpleBlobDetector_create(params) + _, thresh_image = cv2.threshold(image[:,:,1], 150, 255, cv2.THRESH_BINARY) + thresh_origin = thresh_image.copy() + for sf in staffObjList: + for yOne in sf.ys: + thresh_image[yOne-math.ceil(bar_height/8):yOne+bar_height//8+1,:] = 255 + + for ng in noteGroupVerticallyMerged: + if ng is None: + continue + for sm in ng.noteStemList: + oldbox = sm.noteBox + newbox = (oldbox[2],max(0,oldbox[1]-2*bar_height), + min(oldbox[2]+bar_height, image.shape[1]-1), min(oldbox[1]+2*bar_height, image.shape[0]-1)) + realWidth = np.max(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:newbox[2]],0)==0)[0],0)) + # since the barline usually isn't thick enough + realWidth = min(realWidth, np.min(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:newbox[2]],0)==4)[0],realWidth))) + if realWidth>=bar_height-1: + realWidth = np.max(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:min(newbox[2]+bar_height,image.shape[1]-1)],0)==0)[0],0)) + realWidth = min(realWidth, np.min(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:min(newbox[2]+bar_height,image.shape[1]-1)],0)==4)[0],realWidth))) + if realWidth>bar_height*0.5: + dotBox = (oldbox[2], max(0,oldbox[1]-bar_height), + min(oldbox[2]+realWidth,image.shape[1]-1),min(oldbox[1]+int(bar_height*1.5), image.shape[0]-1)) + keypoints = detector.detect(thresh_image[dotBox[1]:dotBox[3], dotBox[0]:dotBox[2]]) + if len(keypoints)==0: + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(0,255,255),1,cv2.LINE_AA) + elif (np.where(np.min(thresh_origin[dotBox[1]:dotBox[3], dotBox[0]+bar_height//2:dotBox[2]],1)==255)[0]).shape[0]>0 and np.min(np.where(np.min(thresh_origin[dotBox[1]:dotBox[3], dotBox[0]+bar_height//2:dotBox[2]],1)==255))>bar_height: + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(160,100,240),2,cv2.LINE_AA) + else: + sm.hasdot = True + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(255,255,0),2,cv2.LINE_AA) + imwrite('dotBox.jpg',imgbgr) + return noteGroupVerticallyMerged,{'dotBox':imgbgr} + +def assignRestDots(restList:List[Rest], beamMapImg:np.ndarray, noteGroupStemMap:np.ndarray, image:np.ndarray, bar_height:int, staffObjList:List[Staff]): + imgbgr = image.copy() #bgr + _,gg,_ = cv2.split(beamMapImg) + params = cv2.SimpleBlobDetector_Params() + + params.filterByArea = True + params.minArea = bar_height*bar_height//16 + params.maxArea = bar_height*bar_height//2 + + params.filterByCircularity = False + params.filterByConvexity = False + params.filterByInertia = True + params.minInertiaRatio = 0.5 + + detector = cv2.SimpleBlobDetector_create(params) + _, thresh_image = cv2.threshold(image[:,:,1], 150, 255, cv2.THRESH_BINARY) + thresh_origin = thresh_image.copy() + for sf in staffObjList: + for yOne in sf.ys: + thresh_image[yOne-math.ceil(bar_height/8):yOne+bar_height//8+1,:] = 255 + for rs in restList: + if rs is None: + continue + elif rs.rhythm<0: + continue + newbox = (rs.boundingBox[2],rs.boundingBox[1], + min(rs.boundingBox[2]+bar_height,image.shape[1]),min(rs.boundingBox[1]+2*bar_height,image.shape[0])) + # 先用一個barheight寬度，高度從最高點往下兩個barheight，往上一個，用stem跟rest來縮減 + realWidth = np.max(np.append(np.where(np.max(noteGroupStemMap[newbox[1]:newbox[3],newbox[0]:newbox[2]],0)==0)[0],0)) + realWidth = min(realWidth, np.max(np.append(np.where(np.max(beamMapImg[newbox[1]:newbox[3],newbox[0]:newbox[2]],0)!=5)[0],0))) + realWidth = min(realWidth, np.min(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:newbox[2]],0)==4)[0],realWidth))) + # 如果寬度還是滿格的話再往右延長到2*barheight用beamMapImg 來縮減 + if realWidth>=bar_height-1: + realWidth = np.max(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:min(newbox[2]+bar_height,image.shape[1]-1)],0)==0)[0],0)) + realWidth = min(realWidth, np.min(np.append(np.where(np.max(gg[newbox[1]:newbox[3],newbox[0]:min(newbox[2]+bar_height,image.shape[1]-1)],0)==4)[0],realWidth))) + elif realWidth>bar_height*0.5: + realWidth = realWidth+bar_height//3 + if realWidth>bar_height*0.5: + dotBox = newbox + keypoints = detector.detect(thresh_image[dotBox[1]:dotBox[3], dotBox[0]:dotBox[2]]) + if len(keypoints)==0: + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(0,255,200),1,cv2.LINE_AA) + elif len(np.where(np.min(thresh_origin[dotBox[1]:dotBox[3], dotBox[0]+bar_height//2:dotBox[2]],1)==255)[0]) == 0: + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(255,255,0),2,cv2.LINE_AA) + elif np.min(np.where(np.min(thresh_origin[dotBox[1]:dotBox[3], dotBox[0]+bar_height//2:dotBox[2]],1)==255))>bar_height: + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(160,100,240),2,cv2.LINE_AA) + else: + imgbgr = cv2.rectangle(imgbgr,(dotBox[0],dotBox[1]),(dotBox[2],dotBox[3]),(255,255,0),2,cv2.LINE_AA) + rs.hasdot = True + imwrite('dotRestBox.jpg',imgbgr) + return restList,{'dotRestBox':imgbgr} + +def assignSfnToNote(image:np.ndarray, noteGroupMap:np.ndarray, noteGroupVerticallyMerged:List[NoteGroup],sfnClefMap:np.ndarray,sfnClefList:List[Union[Accidentals, Clef, None]]): + stemIdxMap = np.ones((noteGroupMap.shape[0], noteGroupMap.shape[1]), dtype=np.uint8)*(-1) + accidentalsImg = image.copy() + for ng in noteGroupVerticallyMerged: + if ng is None: + continue + for idx, stem in enumerate(ng.noteStemList): + x0,y0,x1,y1 = stem.noteBox + stemIdxMap[y0:y1, x0:x1] = idx + sfnGroupList = [] + for currSfnIdx, sfnc in enumerate(sfnClefList): + if sfnc is None: + continue + if sfnc.getType() == 1: # is Clef + continue + sfnc:Accidentals + x0,y_0,x1,y_1 = sfnc.getBbox() # y0: smaller (top), y1: bigger (bottom) + if sfnc.shift >=0: # sharp or natural + y0 = (y_0+y_1)//2-(y_1-y_0)//6 + y1 = (y_0+y_1)//2+(y_1-y_0)//6 + else: + y0 = (y_0+y_1)//2 + y1 = y_1 + sfnc.shrinkYs = (y0,y1) + stemIdxLst = np.unique(stemIdxMap[y0:y1,x1:x1+(x1-x0)]).tolist() + if -1 in stemIdxLst: + stemIdxLst.remove(-1) + ngIdxLst = np.unique(noteGroupMap[y0:y1,x1:x1+(x1-x0)]).tolist() + if 0 in ngIdxLst: + ngIdxLst.remove(0) + if len(stemIdxLst)==0 or len(ngIdxLst)==0: + # is Key signature or has overlapping sfns + sfnList = np.unique(sfnClefMap[y_0:y_1,x1:x1+(x1-x0)//2]).tolist() + if sfnList == [0]: + sfnc.isKeySignature = True + else: + if 0 in sfnList: + sfnList.remove(0) + sfnNext = sfnList[0] # sfnNext is the index of the next sfn + if type(sfnClefList[sfnNext]) is not Accidentals: + continue + hasAssigned = False + for sIdx in range(len(sfnGroupList)): + if sfnNext in sfnGroupList[sIdx]: + sfnGroupList[sIdx] = [currSfnIdx]+sfnGroupList[sIdx] + hasAssigned = True + continue + elif currSfnIdx in sfnGroupList[sIdx]: + sfnGroupList[sIdx] = sfnGroupList[sIdx]+[sfnNext] + hasAssigned = True + continue + if not hasAssigned: + sfnGroupList.append([currSfnIdx, sfnNext]) + continue + stemIdx = stemIdxLst[0] + ngIdx = ngIdxLst[0] + if len(ngIdxLst)>1: + cntLst = [np.sum(noteGroupMap[y0:y1,x1:x1+(x1-x0)]==i) for i in ngIdxLst] + ngIdx = ngIdxLst[cntLst.index(max(cntLst))] + if len(stemIdxLst)>1: + ngx0, _, ngx1, _ = noteGroupVerticallyMerged[ngIdx].boundingBox + cntLst = [np.sum(np.sum(stemIdxMap[y0:y1,ngx0:ngx1]==i,0)>0) for i in stemIdxLst] + stemIdx = stemIdxLst[cntLst.index(max(cntLst))] + if stemIdx < len(noteGroupVerticallyMerged[ngIdx].noteStemList): + noteGroupVerticallyMerged[ngIdx].noteStemList[stemIdx].accidentals = sfnc.getValue() + sfnc.ngIndex = ngIdx + for grps in sfnGroupList: + currNgIdx = sfnClefList[grps[-1]].ngIndex + if currNgIdx is None: + sfnClefList[grps[-1]].endKeySignature = True + for grr in grps: + sfnClefList[grr].isKeySignature = True + continue + currNg:NoteGroup = noteGroupVerticallyMerged[currNgIdx] + if len(currNg.noteStemList)==1: + continue + ngx0, _, ngx1, _ = currNg.boundingBox + for grr in grps[:-1]: + currSfn = sfnClefList[grr] + y0,y1 = currSfn.shrinkYs + stemIdxLst = np.unique(stemIdxMap[y0:y1,ngx0:ngx1]).tolist() + if -1 in stemIdxLst: + stemIdxLst.remove(-1) + if len(stemIdxLst)>=1: + cntLst = [np.sum(np.sum(stemIdxMap[y0:y1,ngx0:ngx1]==i,0)>0) for i in stemIdxLst] + stemIdx = stemIdxLst[cntLst.index(max(cntLst))] + if currNg.noteStemList[stemIdx].accidentals is None: + noteGroupVerticallyMerged[currNgIdx].noteStemList[stemIdx].accidentals = currSfn.getValue() + noteGroupVerticallyMerged[currNgIdx].noteStemList[stemIdx].accidentalBox = currSfn.boundingBox + else: + print('Error assigning sfn to notes') + + + accidentalsColors = [(255,255,0),(255,0,125),(255,0,255)] # flat, natural, sharp + for ng in noteGroupVerticallyMerged: + if ng is None: + continue + for idx, stem in enumerate(ng.noteStemList): + if stem.accidentals is not None: + x0,y0,x1,y1 = stem.noteBox + currColor = accidentalsColors[stem.accidentals+1] + accidentalsImg = cv2.rectangle(accidentalsImg, (x0,y0),(x1,y1),currColor,3, cv2.LINE_AA) + imwrite("assignedAccidentals.jpg",accidentalsImg,strictlyYes=True) + return stemIdxMap, noteGroupVerticallyMerged, {'assignedAccidentals':accidentalsImg} + +def extendNotegroupsToStaff(noteGroupMap:np.ndarray, + noteGroupVerticallyMerged:List[NoteGroup], + staffObjList:List[Staff], + beamMapImg:np.ndarray): + bb,gg,rr = cv2.split(beamMapImg) + ngZero = np.zeros_like(noteGroupMap) + for sf in staffObjList: + ngZero[sf.ys[0]:sf.ys[-1], sf.left:sf.right] = 1 + for idx, ng in enumerate(noteGroupVerticallyMerged): + if ng is None: + continue + x0,y0,x1,y1 = ng.boundingBox + if np.sum(ngZero[y0:y1,x0:x1])==0: + clefNo = round(np.mean(rr[y0:y1,4])) + if len(np.unique(noteGroupMap[y0:y1,x0:x1])) == 1: + noteNo =np.unique(noteGroupMap[y0:y1,x0:x1])[0] + if y0>staffObjList[clefNo-1].ys[2]: # is at the bottom + y00 = staffObjList[clefNo-1].ys[-2] + y11 = y1 + else: + y00 = y0 + y11 = staffObjList[clefNo-1].ys[1] + gg[y00:y11,x0:x1] = 1 + noteGroupMap[y00:y11,x0:x1]=noteNo + rr[y00:y11,x0:x1] = clefNo + ng.boundingBox = (x0,y00,x1,y11) + + return cv2.merge([bb,gg,rr]), noteGroupMap + +def constructBar(noteGroupMap:np.ndarray, + stemIdxMap: np.ndarray, + noteGroupVerticallyMerged: List[NoteGroup|None], + restMap: np.ndarray, + restList:List[Rest|None], + sfnClefMap:np.ndarray, + sfnClefList:List[Union[Accidentals,Clef, None]], + beamMapImg:np.ndarray, + staffList:List[Staff]): + numLines = len(staffList) + bb,gg,rr = cv2.split(beamMapImg) + barList:List[List[Bar]] = [[] for _ in range(numLines)] + numBarsEachLine:List[int] = [0 for _ in range(numLines)] + mapForMatching:List[np.ndarray|None] = [None,noteGroupMap,sfnClefMap, sfnClefMap, None, restMap] + listForMatching:List[List] = [None, noteGroupVerticallyMerged,sfnClefList, sfnClefList, None, restList] + notFinishedBar = None + allRanges = [] + for kk in range(numLines): + allBars = [] + sf0 = staffList[kk] + barPlace = np.unique(np.where(gg[sf0.ys[0]:sf0.ys[-1], sf0.left:]==4)[1]) + isBar = np.sort(barPlace) + ranges = [] + points = [0,isBar[0]] + i = 1 + while i < len(isBar): + while isBar[i] == isBar[i-1]+1 and ipoints[i]+sf0.get_yOne(): + ranges.append((points[i]+sf0.left, points[i+1]+sf0.left)) + allRanges.append(ranges) + for t in range(numLines): # trackNo + printedLineNo = t # the + currBarList:List[Bar] = [] + sf0 = staffList[printedLineNo] + ranges = allRanges[t] + for ridx, rng in enumerate(ranges): + if notFinishedBar is not None: + bar = notFinishedBar + notFinishedBar = None + else: + bar = Bar() + currX = rng[0] + while currX1: + for i in [1,5,3,2]: + if i in lineLst: + lineLst = [i] + break + if len(lineLst)==0 or 4 in lineLst: + currX+=1 + else: + typeId = lineLst[0] + currMap = mapForMatching[typeId] + currLst = listForMatching[typeId] + inMapId = np.unique(currMap[sf0.ys[0]-sf0.get_yOne():sf0.ys[-1]+sf0.get_yOne(), currX]).tolist() + if 0 in inMapId: + inMapId.remove(0) + if len(inMapId)==1: + sanityCheck = True + if typeId == 3: # clef + currClef:Clef = sfnClefList[inMapId[0]] + _,yy0,_,yy1 = currClef.boundingBox + if yy1-yy0 < sf0.get_yOne_float()*3: + sanityCheck = False + if currClef.type ==0: + if yy00)[1])) + if nextIdx>5: + rng = (isBar[-1]+sf0.left+1, rng[1]+nextIdx) + bar = Bar() + currX = rng[0] + while currX1: + for i in [1,5,3,2]: + if i in lineLst: + lineLst = [i] + break + if len(lineLst)==0 or 4 in lineLst: + currX+=1 + else: + typeId = lineLst[0] + currMap = mapForMatching[typeId] + currLst = listForMatching[typeId] + inMapId = np.unique(currMap[sf0.ys[0]-sf0.get_yOne():sf0.ys[-1]+sf0.get_yOne(), currX]).tolist() + if 0 in inMapId: + inMapId.remove(0) + if len(inMapId)==1: + bar.addElement(currLst[inMapId[0]]) + currX = currLst[inMapId[0]].boundingBox[2]+1 + else: + currX+=1 + print("has more than 1 id") + notFinishedBar = bar + barList[t] = currBarList + numBarsEachLine[t] = len(currBarList) + # in order of vln1's 1st line, 2nd line ... | vln2's 1st line, 2nd line ... + return barList,allRanges, numBarsEachLine + + + +def find_best_rotation(staffObjList): + def compute_line_angle(y_values, x_start, x_end): + n = len(y_values) + x = np.linspace(x_start, x_end, n) + + # Fit line y = mx + b + m, _ = np.polyfit(x, y_values, 1) + + angle = np.arctan(m) # radians + return angle + angles = [] + + for oneStaff in staffObjList: + angle = compute_line_angle( + oneStaff.yUpperList, + oneStaff.left, + oneStaff.right + ) + angles.append(angle) + angle = compute_line_angle( + oneStaff.yLowerList, + oneStaff.left, + oneStaff.right + ) + angles.append(angle) + best_angle = np.median(angles) + return np.degrees(best_angle) + +def clearExtraMapping(staffList: List[Staff], dataDict, numInstrument: int): + insToDelete = len(staffList)%numInstrument + medStaff = statistics.median([sf.get_yOne_float() for sf in staffList]) + toMedDiff = [abs(sf.get_yOne_float()-medStaff) for sf in staffList] + indices = sorted(range(len(toMedDiff)), key=lambda i: toMedDiff[i], reverse=True)[:insToDelete] + for i in sorted(indices, reverse=True): + del staffList[i] + rangeInPair = [0] + for sf0 in staffList: + rangeInPair.append(sf0.ys[0]-int(sf0.get_yOne_float()*5)) + rangeInPair.append(sf0.ys[-1]+int(sf0.get_yOne_float()*5)) + pageheight = dataDict['image'].shape[0] + rangeInPair.append(pageheight) + pairs = list(zip(rangeInPair[::2], rangeInPair[1::2])) + for ddKey in dataDict: + for (st,ed) in pairs: + dataDict[ddKey][st:ed,:] = dataDict[ddKey][0,0] + return dataDict + +# ================================================================================================== +# main function +# ================================================================================================== +def png2decode(img_name: str, img_path: str, instrumentNumTrack: int = 0, packedImg:bool = False): + base_path = img_path.replace('.png','') + npy_path = f"{base_path}.npy" + pkl_path = f"{base_path}_staffList.pkl" + barList_path = f"{base_path}_barlist.pkl" + + # using oemer's output + if not os.path.exists(npy_path): + printt(f"missing npy for {img_name}") + dataDictOrigin = runModel1(img_path,outputPath = base_path,dodewarp=False,save_npy=True) + if not os.path.exists(pkl_path): + printt(f"missing pkl for {img_name}") + staffListOrigin = runModel2(npy_path, img_path, base_path, packedImg) + if os.path.exists(npy_path) and os.path.exists(pkl_path): + printt(f"working on {img_path}") + dataDict = np.load(npy_path,allow_pickle=True) + dataDict = dataDict.tolist() + + # make the image bigger to ensure distance between two stafflines are > 8px + for k in dataDict.keys(): + img = dataDict[k] + resized_image = cv2.resize(img, None, fx=ORIGINAL_IMAGE_RESIZE_RATIO, fy=ORIGINAL_IMAGE_RESIZE_RATIO, interpolation=cv2.INTER_NEAREST) + dataDict[k] = resized_image + + outputDebugStemRestImg(dataDict) # generate stemrests_ in debug + outputDebugSymbolsImg(dataDict) # generate symbols_ in debug + + bar_height, staffObjList = init_bar_height(dataDict, min_barheight=MIN_BAR_HEIGHT) + + # !!! 五線譜 + # staff.left & staff.right: 五線譜開始/結束的位置 + # staff.ys: [y0, y1, y2, y3, y4] 五線譜每行的中心位置 + # !!! add csv decoding for instruments here + print(f'barheight: {bar_height}') + print(bar_height) + bestRotation = find_best_rotation(staffObjList) + if abs(bestRotation) > 0.05: + for k in dataDict.keys(): + img = dataDict[k] + fillVal = 0 + if k == 'image': + fillVal = 255 + resized_image = rotate(img, angle=bestRotation, reshape=True, mode='constant', cval=fillVal) + dataDict[k] = resized_image + + bar_height, staffObjList = init_bar_height(dataDict, min_barheight=MIN_BAR_HEIGHT) + stepSize = int(bar_height/4) + if instrumentNumTrack != 0: + dataDict=clearExtraMapping(staffObjList, dataDict, instrumentNumTrack) + + beamNoClefKeyWithStemRest, beamWithoutStemRest, beam_nostem = getBeamImage(dataDict, bar_height, staffObjList) + + # get the gradient beamMap and (dont filter noteheadInitial with longer beams cause might remove noteheads) + beamMapImg, debugImages = generateBeamStaffImg(beam_nostem, staffObjList, bar_height,stepSize=stepSize) + + noteheadBoxesInitList, noteBwImageOrigin = getInitialNoteheadBoxList(dataDict, beam_nostem, beamMapImg, bar_height) + + # mask the note image with the notehead boxes + maskedNoteBwImage = maskImage(noteheadBoxesInitList, noteBwImageOrigin) + + stem_init_list, image, debugImages= getStemList(dataDict, bar_height, noteheadBoxesInitList, beamNoClefKeyWithStemRest) + writeDebugImagesFromDict(debugImages) + + stem_list_assigned_height, debugImages = assignStemLength(stem_init_list, image, beamMapImg, stepSize, bar_height) + writeDebugImagesFromDict(debugImages) + + beamRemoveNotes = removeNotes(maskedNoteBwImage, beamNoClefKeyWithStemRest, beamMapImg) + imwrite(f'knn_beam.jpg', beamRemoveNotes) + + beam_img2, stem_list_assigned, beam_heights = assignBeamLengthBeamImg(stem_list_assigned_height, image, beamRemoveNotes, bar_height) + imwrite(f'knn_beamImg.jpg', beam_img2) + + noteGroupList, beamMapImg, noteGroupStemMap, noteGroupMap, debugImages = knnRhythmAndDraw(stem_list_assigned, beam_heights, image, bar_height, beamMapImg, stemUpClassifier=STEM_UP_MODEL, stemDownClassifier=STEM_DOWN_MODEL,img_name=img_name) + outputImWrite(f"{img_name}_knnbeams2.jpg", debugImages['knnbeams2']) + writeDebugImagesFromDict(debugImages) + outputNotegroupStemMapImg(noteGroupStemMap, image) + outputNoteGroupMapImg(noteGroupMap, image) + outputNoteMapImg(beamMapImg, image, staffObjList) + + restMap, restList, beamMapImg, debugImages = findRests(dataDict, beamMapImg, bar_height, REST_CLASSIFIER) + + noteGroupVerticallyMerged, noteGroupMapWithIssues, beamMapImg, debugImages = mergeVerticalNoteGroups(staffObjList,noteGroupList, noteGroupMap, beamMapImg, image,restMap, restList) + writeDebugImagesFromDict(debugImages) + + sfnClefList, sfnClefMap, debugImages = symbol_classification(dataDict, SFN_CLEF_CLASSIFIER, bar_height) + writeDebugImagesFromDict(debugImages) + + exportYolo(sfnClefList, image,img_name, bar_height) + + sfnClefList, sfnClefMap, beamMapImg = filterSfnClefModifyBeamMap(sfnClefList, noteGroupMap, beamMapImg) + outputSfnClefNoteWhiteImg(beamMapImg, image, bar_height, noteGroupMap, staffObjList) + outputSfnClefNoteImg(image, beamMapImg) + + staffEnhancedImg = enhanceStaff(image, staffObjList,bar_height) + + noteGroupPitchList, debugImages = assignPitch(staffEnhancedImg,noteGroupList, beamMapImg, bar_height) + writeDebugImagesFromDict(debugImages) + outputImWrite(f'{img_name}_pitchline.jpg', debugImages['pitchline']) + + noteChunkList, horizontalGroupImg = getNoteChunks(image, noteGroupMap, beamMapImg, noteGroupVerticallyMerged) + outputImWrite(f'{img_name}_horizontalGroupImg.jpg',horizontalGroupImg) + + beamMapImg, barlineboxes, barlineSingle = findBarlines(image, beamMapImg, staffObjList, noteGroupStemMap, thres=0.8) # VARIABLE thres + outputImWrite(f'{img_name}_barline.jpg',barlineSingle) + outputSfnClefNoteBarlineRestImg(image, beamMapImg) + dotPrevious = outputThingsBeforeDots(image, beamMapImg) + outputImWrite(f'{img_name}_DotPrevious.jpg', dotPrevious) + + noteGroupVerticallyMerged, debugImages = assignDots(noteGroupVerticallyMerged,beamMapImg, image,bar_height,staffObjList) + writeDebugImagesFromDict(debugImages) + + restList, debugImages = assignRestDots(restList, beamMapImg, noteGroupStemMap, image, bar_height, staffObjList) + writeDebugImagesFromDict(debugImages) + + stemIdxMap, noteGroupVerticallyMerged, debugImages = assignSfnToNote(image, noteGroupMap, noteGroupVerticallyMerged,sfnClefMap,sfnClefList) + writeDebugImagesFromDict(debugImages) + + beamMapImg,noteGroupMap = extendNotegroupsToStaff(noteGroupMap, noteGroupVerticallyMerged,staffObjList,beamMapImg) + + return noteGroupMap, stemIdxMap, noteGroupVerticallyMerged, restMap,restList,sfnClefMap,sfnClefList,beamMapImg,staffObjList, dataDict + + +if __name__ == '__main__': + noteGroupMap: np.ndarray + stemIdxMap: np.ndarray + noteGroupVerticallyMerged: List[NoteGroup | None] + restMap: np.ndarray + restList: List[Rest | None] + sfnClefMap: np.ndarray + sfnClefList: List[Union[Accidentals, Clef, None]] + beamMapImg: np.ndarray + staffList: List[Staff] + + ( + noteGroupMap, + stemIdxMap, + noteGroupVerticallyMerged, + restMap, + restList, + sfnClefMap, + sfnClefList, + beamMapImg, + staffList, + dataDict + ) = png2decode("tchai_4_001", r"orch_dataset\tchai_4\images\001\tchai_4_001.png") + +# 五線譜位置 () \ No newline at end of file diff --git a/seperateByMeasure.py b/seperateByMeasure.py new file mode 100644 index 0000000..fae2bc9 --- /dev/null +++ b/seperateByMeasure.py @@ -0,0 +1,119 @@ +from music21 import converter, stream +import os +import copy + +def slice_score_clean(score, start, end): + new_score = stream.Score() + + for part in score.parts: + new_part = stream.Part() + new_part.insert(0, part.getInstrument()) + + for m in part.measures(start, end): + new_part.append(copy.deepcopy(m)) + + new_score.append(new_part) + + return new_score + +def seperateMeasure(filePaths, numberOfMvts, beginningIndexes, outputFolder): + assert len(filePaths) == numberOfMvts + assert len(beginningIndexes) == numberOfMvts + for idx in range(numberOfMvts): + filePath = filePaths[idx] + beginningIndex = beginningIndexes[idx] + score = converter.parse(filePath) + for i in range(len(beginningIndex) - 1): + start = beginningIndex[i] + end = beginningIndex[i + 1] - 1 # stop before next boundary + segment = slice_score_clean(score, start, end) + outputFileName = '' + if '.musicxml' in filePath: + outputFileName = f"{os.path.basename(filePath).replace('.musicxml',f'_{i+1}.musicxml')}" + elif '.xml' in filePath: + outputFileName = f"{os.path.basename(filePath).replace('.xml',f'_{i+1}.xml')}" + outputPath = os.path.join(outputFolder, outputFileName) + segment.write('musicxml', outputPath) + print(f"saved score to {outputPath}") + start = beginningIndex[-1] + end = score.measures(start, None) + segment = slice_score_clean(score, start, None) + outputFileName = '' + if '.musicxml' in filePath: + outputFileName = f"{os.path.basename(filePath).replace('.musicxml',f'_{i+2}.musicxml')}" + elif '.xml' in filePath: + outputFileName = f"{os.path.basename(filePath).replace('.xml',f'_{i+2}.xml')}" + outputPath = os.path.join(outputFolder, outputFileName) + segment.write('musicxml', outputPath) + print(f"saved score to {outputPath}") +def seperateList(measureLst): + measureNumberLists = [] + current = [] + for x in measureLst: + if x == 1: + if current: + measureNumberLists.append(current) + current = [1] + elif current: + current.append(x) + if current: + measureNumberLists.append(current) + return measureNumberLists + +def addValToList(oriList, startActual, endActual, addValue): + start = startActual-1 + end = endActual + oriList[start:end] = [x + addValue for x in dataSeperate[start:end]] + return oriList +if __name__ == '__main__': + # # BEE7 + # dataSeperate = [1,8,16,19,22,30,36,39,46,53,63,75,87,91,95,99,103,108,118,127,138,148,157,162,167,171,184,196,205,209,213,225,235,245,255,260,270,279,283,287,291,295,300,311,323,331,335,346,358,366,370,376,380,384,397,409,416,422,428,433,439,444, #1~62 + # 1,32,63,77,84,91,106,120,134,146,158,170,182,192,212,217,230,244,261, #63~81 + # 1,16,34,56,76,97,118,135,143,160,181,198,214,233,250,259,277,293,317,337,357,377,396,404,424,441,459,477,496,513,531,554,574,593,613,629,637,645, #82~119 # 84 + 1, 91 + 4+1, 96~+1 111+1 + # 1,7,12,17,21,28,34,39,52,60,73,81,90,104,113,119,122,129,146,151,154,159,163,177,191,203,218,226,231,238,245,251,257,263,271,278,290,302,314,320,328,335,340,347,358,370,382,394,400,406,414,421,429,437,445,453,459 #148+1 #120~ 122+1 124+1 136+5 140+2 142+1 + # ] + # dataSeperate = addValToList(dataSeperate, 91,119,4) + # dataSeperate = addValToList(dataSeperate, 122, 176,1) + # dataSeperate = addValToList(dataSeperate, 124, 176,1) + # dataSeperate = addValToList(dataSeperate, 136, 176,5) + # dataSeperate = addValToList(dataSeperate, 140, 176,2) + # dataSeperate = addValToList(dataSeperate, 142, 176,1) + # dataSeperate = addValToList(dataSeperate, 148, 176,1) + + # #BEE5 + # dataSeperate = [1, 12, 33, 53, 77, 98, 119, 141, 160, 170, 181, 194, 223, 248, 267, 287, 309, 333, 356, 376,386, 397, 408, 418, 429, 441, 455, 469, 480, 491, 1, 8, 25, 38, 57, 73, 82, 92, 102, 110, + # 118, 133, 148, 164, 172, 183, 187, 191, 204, 215, 230, 1, 12, 38, 61, 82, 83, 104, 123, 131,150, 166, 182, 190, 205, 231, 263, 293, 323, 349, 1, 6, 12, 17, 21, 26, 33, 39, 44, 49, + # 54, 59, 62, 69, 74, 81, 85, 90, 94, 104, 112, 117, 122, 127, 132, 136, 140, 145, 150, 161, + # 186, 207, 214, 219, 224, 228, 234, 240, 247, 252, 257, 262, 268, 272, 279, 285, 294, 300, + # 305, 309, 316, 324, 330, 336, 343, 349, 354, 364, 374, 383, 391, 397, 405, 415, 422, 432] + # dataSeperate = addValToList(dataSeperate, 87, 136, 2) + + # # BEE8 + # dataSeperate = [1,8,13,20,26,34,48,64,80,92,98,112, 124,138,151,163,169,175,182,187,193,200,213,219,226,241,255,263,277,289,295,307,317,328,334,345,349,353,357,364, # 1~40 12+1 + # 1, 8,16,24,32,41,48,55,63,71,78,# 41~51 + # 1,6,11,16 , 21,31,38,52, 66, #52~60 53+1 54+1 59+3 + # 1, 8,22,31,42,54,65,75,85,99,112,123,135,148,161,173,183,191,201,211,225,238,244,250,260,274,291,302,312,324,336,348,361,373,383,391,394,401,407,412,417,428,439,449,454,461,474,487 #61~108 62+1 + # ] + # dataSeperate = addValToList(dataSeperate, 12, 40, 1) + # dataSeperate = addValToList(dataSeperate, 53, 60, 1) + # dataSeperate = addValToList(dataSeperate, 54, 60, 1) + # dataSeperate = addValToList(dataSeperate, 59, 60, 3) + # dataSeperate = addValToList(dataSeperate, 62, 108, 1) + + # BEE6 + dataSeperate = [1,17,31,43,57,83,95,111,124,139,154,166,177,196,209,221,241,260,276,298,317,329,354,368,381,395,407,426,440,452,464,479,493, + 1,4,8,10,12,16,21,25,29,31,35,39,42,44,46,49,51,53,57,61,65,70,74,79,83,87,89,91,93,95,97,99,101,103,107,111,116,118,121,123,125,127,133, + 1,19,39,59,80,111,141,161,176,191,205,224,244,125, + 1,14,22,25,28,31,34,40,17,51,56,65,72,78,82,87,93,103,107,110,114,125,137,145, + 1,14,23,26,32,36,40,44,48,52,57,28,77,85,93,101,105,109,117,131,134,137,141,149,153,157,161,170,179,191,196,204,212,219,225,231,245,260] + dataSeperate = addValToList(dataSeperate, 78, 89, 1) + dataSeperate = addValToList(dataSeperate, 87, 89, 1) + + symphonyNum = 7 + numberOfMvts = dataSeperate.count(1) # assume all movements starts at a new page + filePaths = [rf'md_gt\{symphonyNum}_{i}.musicxml' for i in range(1,numberOfMvts+1)] + outputFolderName = rf'xml_gt\val{symphonyNum}' + seperatedList = seperateList(dataSeperate) # separate list by measures + if not os.path.exists(outputFolderName): + os.mkdir(outputFolderName) + seperateMeasure(filePaths, numberOfMvts, seperatedList, outputFolderName) \ No newline at end of file diff --git a/tune_bar.py b/tune_bar.py index f62f3b1..904ca46 100644 --- a/tune_bar.py +++ b/tune_bar.py @@ -32,6 +32,8 @@ def calibOneGroup(elmList:List[RestNg], desiredLength:Fraction): endBeamTrust = [True]*len(elmList) if -1 in stemYs or -1 in stemXs: endBeamTrust = [False]*len(elmList) + elif 0 in [stemXs[i] == stemXs[i-1] for i in range(1,len(stemXs))]: + endBeamTrust = [False] * len(endBeamTrust) else: stemAtan = [math.atan((stemYs[i]-stemYs[i-1])/(stemXs[i]-stemXs[i-1])) for i in range(1,len(stemXs))] irregThresh = math.pi/18 diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..1698fa0 --- /dev/null +++ b/utils.py @@ -0,0 +1,621 @@ +from abc import ABC, abstractmethod +from fractions import Fraction +import os +from typing import List,Tuple, Union, Set +from music21 import instrument +import math + +import cv2 +import numpy as np +import json + +from dd_classes import RestNg + +# -------------------------------------------------------------------------------------------------- +# Setting for debug helper functions +# -------------------------------------------------------------------------------------------------- +DEBUG_IMAGE = True +LOG_MESSAGE = True + +# -------------------------------------------------------------------------------------------------- +# Helper function for loggings +# -------------------------------------------------------------------------------------------------- +def printt(str): + if LOG_MESSAGE: + print(str) + +def imwrite(str,img,strictlyYes=False, startingFolder = ''): + debugFolderPath = f'{startingFolder}debug' + if not os.path.exists(debugFolderPath): + os.mkdir(debugFolderPath) + if DEBUG_IMAGE or strictlyYes: + print(f'saving {str}') + cv2.imwrite(f'{debugFolderPath}/{str}',img) + + +def outputImWrite(str,img, startingFolder = ''): + outputImgPath = f'{startingFolder}output' + if not os.path.exists(outputImgPath): + os.mkdir(outputImgPath) + cv2.imwrite(f'{outputImgPath}/{str}',img) + +def writeDebugImagesFromDict(debugImages, writeToOutput = False, img_name = ''): + for si in debugImages.keys(): + if writeToOutput and img_name != '': + outputImWrite(f'{img_name}_{si}.jpg', debugImages[si]) + imwrite(f'{si}.jpg', debugImages[si]) + +# -------------------------------------------------------------------------------------------------- +# helper function for Key Signatures +# -------------------------------------------------------------------------------------------------- +class ToneHelper: + def __init__(self, toneMapPath: str): + with open(toneMapPath, 'r') as f: + self.toneMap = json.load(f) + # get the actual key signature based on what signature it looks like + # for instance, in B flat clarinet, if it looks like 1 flat it's actually 3 flat, so -2 + def getKsShift(self, toneName: str, includeForKs = True) -> int: + if toneName == "": + return 0 + toneData = self.toneMap.get(toneName) + if not includeForKs: + return np.inf + if toneData is None: + print(f"can't find tone for {toneName}, set ksShift to 0") + return 0 + else: + return int(toneData[0]) + def getNoteShift(self, toneName: str) -> int: + if toneName == "": + return 0 + toneData = self.toneMap.get(toneName) + if toneData is None: + print(f"can't find tone for {toneName}, set noteShift to 0") + return 0 + else: + return int(toneData[1]) + def getKsAndNoteShift(self, toneName: str, includeForKs = True) -> Tuple[int, int]: + if toneName == "": + return (0,0) + toneData = self.toneMap.get(toneName) + noteShift = 0 + ksShift = np.inf + if toneData is None: + print(f"can't find tone for {toneName}, set all shifts to 0") + else: + noteShift = toneData[1] + if includeForKs: + ksShift = toneData[0] + return ksShift, noteShift + +# -------------------------------------------------------------------------------------------------- +# Helper function for instrument +# -------------------------------------------------------------------------------------------------- + +def constructInstrumentMappingDict(json_file) -> dict: + clef_map = {"treble": 1, "alto": 0, "bass": -1, "tenor": -2} + with open(json_file, "r") as f: + data = json.load(f) + mapped = {} + for instrument, clefs in data.items(): + mapped[instrument] = [clef_map[c] for c in clefs] + return mapped + +def isInstrumentIncluded(currIns: str): + notIncluded = ["trumpet", "horn", "timpani"] # instruments that is noted in its own way + return all(word not in currIns.lower() for word in notIncluded) + +instrument_classes = { + "violin": instrument.Violin, + "viola": instrument.Viola, + "cello": instrument.Violoncello, + "double_bass": instrument.Contrabass, + "bass": instrument.Contrabass, + "piccolo": instrument.Piccolo, + "flute": instrument.Flute, + "oboe": instrument.Oboe, + "english_horn": instrument.EnglishHorn, + "clarinet": instrument.Clarinet, + "bass_clarinet": instrument.BassClarinet, + "bassoon": instrument.Bassoon, + "contrabassoon": instrument.Contrabassoon, + "french_horn": instrument.Horn, + "trumpet": instrument.Trumpet, + "trombone": instrument.Trombone, + "bass_trombone": instrument.BassTrombone, + "tuba": instrument.Tuba, + "harp": instrument.Harp, + "piano": instrument.Piano, + "celesta": instrument.Celesta, + "timpani": instrument.Timpani, + "xylophone": instrument.Xylophone, + "marimba": instrument.Marimba, + "glockenspiel": instrument.Glockenspiel, + "vibraphone": instrument.Vibraphone, + "horn": instrument.Horn +} +def get_instrument_from_string(name: str): + name_lower = name.lower() + for key, instr_class in instrument_classes.items(): + if key in name_lower: + return instr_class() + + return instrument.Piano() +# -------------------------------------------------------------------------------------------------- +# Class definitions +# -------------------------------------------------------------------------------------------------- +class Staff: + def __init__(self, left:int, right:int, ys:Tuple[int,int,int,int,int], minMaxDiff:int=0, yUpperList:List[int] = [], yLowerList: List[int]=[]): + self.left = left + self.right = right + self.ys = ys + self.minDiff = minMaxDiff + self.yUpperList = yUpperList + self.yLowerList = yLowerList + def get_yOne(self)->int: + x = [self.ys[i+1] - self.ys[i] for i in range(len(self.ys) - 1)] + return sum(x)//len(x) + def get_yOne_float(self)->float: + x = [self.ys[i+1] - self.ys[i] for i in range(len(self.ys) - 1)] + return sum(x)/len(x) + def IsStaffAligned(self, barheight:int)->bool: + return self.minDiffint: + # if hasLineMiddle the number has to be even + if self.hasLineMiddle is None: + return round(self.pitchWideFloat) + upperInt:int = math.ceil(self.pitchWideFloat) + lowerInt:int = math.floor(self.pitchWideFloat) + if upperInt == lowerInt: + return upperInt + if self.hasLineMiddle: + return upperInt if upperInt%2==0 else lowerInt + else: + return upperInt if upperInt%2==1 else lowerInt + + def getX(self): + return self.start[0] + def setYlen(self, ylen:int): + x = self.start[0] + if self.isup: + self.end = (x,self.start[1]-abs(ylen)) + else: + self.end = (x,self.start[1]+abs(ylen)) + def getTopCoord(self): + if self.isup: + return self.end + else: + return self.start + def getBottomCoord(self): + if self.isup: + return self.start + else: + return self.end + def getY0Y1(self): + return (min(self.start[1],self.end[1]), max(self.start[1],self.end[1])) + def getLineX0Y0X1Y1(self, width:int=3): + return (self.start[0]-width//2, self.getTopCoord()[1], self.start[0]+(width-width//2), self.getBottomCoord()[1]) + def getYLen(self)->int: + return int(abs(self.start[1]-self.end[1])) + def setBeam(self, typ:str,top:int|None = None, bottom:int|None = None): + if typ.lower().startswith('l'): + self.setLeftBeam(top=top, bottom=bottom) + else: + self.setRightBeam(top=top, bottom=bottom) + def setLeftBeam(self, top:int|None = None, bottom:int|None = None): + if top is not None: + self.leftBeam[0] = top + if bottom is not None: + self.leftBeam[1] = bottom + def setRightBeam(self, top:int|None = None, bottom:int|None = None): + if top is not None: + self.rightBeam[0] = top + if bottom is not None: + self.rightBeam[1] = bottom + def getBeamHeights(self): + return (self.leftBeam[1]-self.leftBeam[0], self.rightBeam[1]-self.rightBeam[0]) + def getBoundingBox(self): + x0,y0,x1,y1 = self.noteBox + yy0 = self.start[1] + yy2 = self.end[1] + return (x0,min(y0,yy0,yy2), x1, max(y0,yy0,yy2)) + def getString(self): + keyName = ['C','D','E','F','G','A','B'] + currKeyName = keyName[(self.pitchSoprano-1)%7] + currKey = currKeyName+str((self.pitchSoprano-1)//7+5) + if self.accidentals is not None: # it has keySignature + if self.accidentals == -1: + currKey = currKey[0]+'-'+currKey[1] + elif self.accidentals == 1: + currKey = currKey[0]+'#'+currKey[1] + return currKey + +class Rest: + def __init__(self, bbox:Tuple[int,int,int,int], rhythm: int): + self.boundingBox:Tuple[int,int,int,int] = bbox + # rest -1: whole rest or 1/2 depending, 0: 1/4, 1:1/8, 2:1/16 ... (the number of lines) + self.rhythm: int = rhythm + self.hasdot: bool = False + self.noteGroupId: int|None = None + self.tunedLength: Fraction|None = None + self.indexNumber: int|None = None + self.acrossLineNGID: int|None = None + def setAcrossLineNGID(self, id: int): + self.acrossLineNGID = id + def setIndexNumber(self, index:int): + self.indexNumber = index + def setNgId(self, ngId:int): + self.noteGroupId = ngId + def getLengthFraction(self): + restClassNames = ['1/4','1/8','1/16','1/32','1/2'] # only for >=0 + ratio = Fraction(3,2) if self.hasdot else 1 + return Fraction(restClassNames[self.rhythm])*ratio + def getString(self): + return f"Rest_{self.tunedLength}" + +class NoteGroup: + def __init__(self, noteStemObj:Stem): + # the width of the stem box we want + self.stemWidth:int = 3 + # x0,y0,x1,y1 + self.noteBoxes:List[Tuple[int,int,int,int]] = [noteStemObj.noteBox] + # x0, y0, x1, y1 + self.stemLineBoxes:List[Tuple[int,int,int,int]] = [noteStemObj.getLineX0Y0X1Y1(width=self.stemWidth)] + # x0,y0,x1,y1 + self.boundingBox: Tuple[int,int,int,int]|None = None + self.noteStemList: List[Stem] = [noteStemObj] + self.updateBoundingBox() + self.noteChunkId: int|None = None + # self.pitchLists: List[float] = [] + # for instance C has line, D doesn't + # self.noteLineMiddle: List[bool] = [] + self.restList:List[Rest] = [] + self.tunedLength: Fraction|None = None + self.indexNumber: int|None = None + self.acrossLineNGID: int|None = None + def setAcrossLineNGID(self, id: int): + self.acrossLineNGID = id + def setIndexNumber(self, index:int): + self.indexNumber = index + def addRest(self, rest:Rest): + self.restList.append(rest) + self.updateBoxRest(rest.boundingBox) + def addNoteStem(self, noteStemObj: Stem): + self.noteStemList.append(noteStemObj) + self.noteBoxes.append(noteStemObj.noteBox) + self.stemLineBoxes.append(noteStemObj.getLineX0Y0X1Y1(width=self.stemWidth)) + self.updateBoundingBox() + def updateBoundingBox(self): + noteMinX = min([n[0] for n in self.noteBoxes]) + noteMaxX = max([n[2] for n in self.noteBoxes]) + noteMinY = min([n[1] for n in self.noteBoxes]) + noteMaxY = max([n[3] for n in self.noteBoxes]) + stemMinX = min([n[0] for n in self.stemLineBoxes]) + stemMaxX = max([n[2] for n in self.stemLineBoxes]) + stemMinY = min([n[1] for n in self.stemLineBoxes]) + stemMaxY = max([n[3] for n in self.stemLineBoxes]) + self.boundingBox = (min(noteMinX, stemMinX), min(noteMinY, stemMinY), max(noteMaxX, stemMaxX), max(noteMaxY, stemMaxY)) + def updateBoxRest(self, restBox:Tuple[int,int,int,int]): + minx,miny,maxx,maxy = self.boundingBox + x0,y0,x1,y1 = restBox + self.boundingBox = (min(minx, x0), min(miny, y0), max(maxx, x1), max(maxy, y1)) + def getMinLength(self): + class_names = ['1/4','1/8','1/16','1/32', '1/64', '1/128', '1/2', '1/1'] + minLength = 1 + for ns in self.noteStemList: + if Fraction(class_names[ns.rhythm])*(1.5 if ns.hasdot else 1) filter out the one with greater beams value and >0.5 + if len(nsl1) == 1 and len(nsl2) ==1: + if nslSum1[0] > nslSum2[0]*2 and nslSum1[0]>thres: + return ng2 + elif nslSum2[0] > nslSum1[0]*2 and nslSum2[0]>thres: + return ng1 + else: + ng1.addNoteStem(ng2.noteStemList[0]) + return ng1 + # if one has more than one notes -> check the other one + else: + if len(nsl1)>len(nsl2): + if nslSum2[0]>thres: + return ng1 + elif nslSum1[0]>nslSum2[0]*2 and nslSum1[0]>thres: + return ng2 + else: + if nslSum1[0]>thres: + return ng2 + elif nslSum2[0]>nslSum1[0]*2 and nslSum2[0]>thres: + return ng1 + for ns in nsl2: + ng1.addNoteStem(ns) + return ng1 + +class NoteChunk: + def __init__(self, noteGroups:Set[int]): + self.noteGroupIdxs:Set[int] = noteGroups + def mergeNoteChunk(self, noteGroup2: Set[int]): + self.noteGroupIdxs:Set[int] = self.noteGroupIdxs.union(noteGroup2) + def getBoundingBox(self,noteGroupList:List[NoteGroup]): + x0s = min([noteGroupList[i].boundingBox[0] for i in self.noteGroupIdxs]) + x1s = max([noteGroupList[i].boundingBox[2] for i in self.noteGroupIdxs]) + y0s = min([noteGroupList[i].boundingBox[1] for i in self.noteGroupIdxs]) + y1s = max([noteGroupList[i].boundingBox[3] for i in self.noteGroupIdxs]) + return [x0s, y0s, x1s, y1s] +class sfnClefInterface(ABC): + @abstractmethod + def getString(self): + pass + @abstractmethod + def getBbox(self) -> Tuple[int,int,int,int]: + pass + @abstractmethod + def getValue(self) -> int: + pass + @abstractmethod + def getType(self) -> int: + # 0 is accidental, 1 is clef + pass + +class Accidentals(sfnClefInterface): + def __init__(self, bbox:Tuple[int,int,int,int], shift: int): + self.boundingBox:Tuple[int,int,int,int] = bbox + # shift is 1 for sharp, 0 for natural, -1 for flat + self.shift: int = shift + self.isKeySignature:bool = False + self.endKeySignature:bool = False + self.ksKeySop: float|None = None + self.shrinkYs: Tuple[int,int]|None = None + self.ngIndex: int|None = None + def setIndexNumber(self, index:int): + return + def getString(self): + if self.shift == 0: + return 'natural' + elif self.shift == 1: + return 'sharp' + elif self.shift == -1: + return 'flat' + else: + return 'invalid accidentals' + def getColor(self): + clrs = [(255,255,0),(255,0,125),(255,0,255)] + return clrs[self.shift] + def getBbox(self) -> Tuple[int]: + return self.boundingBox + def getValue(self) -> int: + return self.shift + # 0 if is accidentals + def getType(self)->int: + return 0 + def getSign(self)->int: + if self.shift == 0: + return '%' + elif self.shift == 1: + return '#' + elif self.shift == -1: + return 'b' + else: + return 'invalid accidentals' +class KeySignature(): + def __init__(self, accs: List[Accidentals]): + self.accs = accs + def getSharpFlat(self)->float: + return sum([acc.shift for acc in self.accs])/len(self.accs) + # supposingly if there's 4 flats will be -4, 4 sharp will be +4 + def getSharpFlatInt(self) -> int: + if sum([acc.shift for acc in self.accs]) > 0: + return len(self.accs) + else: + return -len(self.accs) + def setIndexNumber(self, index:int): + return + + +class Clef(sfnClefInterface): + def __init__(self, bbox:Tuple[int,int,int,int], type: int): + self.boundingBox:Tuple[int,int,int,int] = bbox + self.type:int = type + # how far it should shift if the staff position is 0 in the sidebar + def getString(self): + if self.type == 1: + return 'violin' + elif self.type == 0: + return 'viola' + elif self.type == -1: + return 'bass' + elif self.type == -2: + return 'violaCello' + else: + return 'invalid clef' + def getBbox(self) -> Tuple[int]: + return self.boundingBox + def getValue(self) -> int: + return self.type + # 1 if it's clef + def getType(self)->int: + return 1 + + +class Bar: + def __init__(self, TS:Tuple[int,int]=[4,4]): + self.ts = TS + self.elementList:List[Accidentals|Clef|Rest|NoteGroup] = [] + self.restNgList:List[RestNg] = [] + def assignEmptyBar(self): + rng = RestNg(length=Fraction(self.ts[0], self.ts[1]), + groupId = -1, + isRest = True, + numNotes = 0, + beamLength = (-1,-1), + beamEnd=[-1,-1]) + emptyRest = Rest([-1,-1,-1,-1], -1) + self.elementList.append(emptyRest) + self.restNgList.append(rng) + def getString(self): + retStr = [] + for elem in self.elementList: + retStr.append(elem.getString()) + return retStr + def addElement(self, elem:Union[Accidentals, Clef, Rest, NoteGroup]): + if type(elem) == NoteGroup: + if elem.noteChunkId is not None and not False in [j.rhythm<=0 for j in elem.noteStemList]: + print(f'removing element in noteChunk {elem.noteChunkId} > quarter') + return + self.elementList.append(elem) + def getTunedTotalBeat(self) -> Fraction: + bt = 0 + for elm in self.elementList: + if type(elm) == NoteGroup: + bt += elm.tunedLength + elif type(elm) == Rest: + bt += elm.tunedLength + return bt + + def getTotalBeat(self)->Fraction: + bt = 0 + for elm in self.elementList: + if type(elm) == NoteGroup: + bt += elm.getMinLength() + elif type(elm) == Rest: + bt += elm.getLengthFraction() + return bt + def getRhythmList(self) -> List[Fraction]: + retLst = [] + for elm in self.elementList: + if type(elm) == NoteGroup: + retLst.append(elm.getMinLength()) + elif type(elm) == Rest: + retLst.append(elm.getLengthFraction()) + return retLst + def getRestNg(self) -> List[RestNg]: + if len(self.restNgList)!= 0: + print("returning restNg list from last time") + return self.restNgList + retLst = [] + for elm in self.elementList: + if type(elm) == NoteGroup: + groupId = 0 + if elm.noteChunkId is not None: + groupId = elm.noteChunkId + beamLength = (-1,-1) + beamEnd = [-1,-1] # for grouped notes + length = elm.getMinLength() + if len(elm.noteStemList) == 1: + currStm = elm.noteStemList[0] + beamLength = [int(a) for a in currStm.getBeamHeights()] + if currStm.isup: + beamEnd = [currStm.getX(), int(min(currStm.getY0Y1()))] + else: + beamEnd = [currStm.getX(), int(max(currStm.getY0Y1()))] + newElm = RestNg(length=Fraction(length), + groupId = groupId, + isRest = False, + numNotes = len(elm.noteStemList), + beamLength = beamLength, + beamEnd=beamEnd, + acrossLineNGID = elm.acrossLineNGID) + retLst.append(newElm) + elif type(elm) == Rest: + newElm = RestNg(length=elm.getLengthFraction()*Fraction(3,2) if elm.hasdot else elm.getLengthFraction(), + groupId = -1, + isRest = True, + numNotes = 0, + beamLength = (-1,-1), + beamEnd=[-1,-1], + acrossLineNGID = elm.acrossLineNGID) + retLst.append(newElm) + self.restNgList = retLst + return retLst + def reassignLength(self, origLens: List[Fraction], newLens: List[Fraction]): + currNewLenIdx = 0 + for elm in self.elementList: + if type(elm)!=NoteGroup and type(elm)!=Rest: + continue + if newLens[currNewLenIdx] is not None: + elm.tunedLength = newLens[currNewLenIdx] + else: + elm.tunedLength = origLens[currNewLenIdx] + currNewLenIdx+=1 + +class TimeSignature: + def __init__(self, yolo_class:int,bbox:Tuple[int,int,int,int],confidence:float): + self.yolo_class = yolo_class + self.boundingBox = bbox + self.str = None + self.staffline = None + self.barLoc = None + self.confidence = confidence + self.valid = True + # mapping + map_data = [ + "4,4", "3,4", "6,4", "2,4", "c", "6,8", "8,4", "9,4", "13,8", + "10,4", "3,8", "7,8", "9,8", "6,2", "4,2", "ch", "12,8", "5,4" + ] + if yolo_class < len(map_data): + self.str = map_data[yolo_class] + else: + self.str = "invalid" + def getString(self): + return self.str + def getBbox(self) -> Tuple[int,int,int,int]: + return self.boundingBox + def getClass(self) -> int: + return self.yolo_class + def setstaffline(self, staffline:int): + self.staffline = staffline + def setBarLoc(self, barLoc:int): + self.barLoc = barLoc + def getConfidence(self) -> float: + return self.confidence + def setValid(self, valid:bool): + self.valid = valid + def isValid(self) -> bool: + return self.valid