misc/reports/ali_wound_repair_report.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d3643e38",
   "metadata": {
    "tags": []
   },
   "source": [
    "# ALI Wound Repair\n",
    "\n",
    "## Autogenerated Report: ALI Wound Repair Analysis\n",
    "#### Website: https://github.com/AlphonsG/EPIC-BBox-Cell-Tracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac962860",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import math \n",
    "import shutil\n",
    "import sys\n",
    "import os\n",
    "import csv\n",
    "from collections import defaultdict\n",
    "from copy import deepcopy\n",
    "from itertools import chain\n",
    "import random as rd\n",
    "from statistics import mean, pstdev\n",
    "\n",
    "import cv2\n",
    "\n",
    "from IPython.display import Image, Video, display\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import scipy \n",
    "\n",
    "import epic\n",
    "from epic.analysis.cell_migration_analysis import (drnty_vs_euclid_dist,\n",
    "                                                   metric_box_plots, metric_mthd_avgs,\n",
    "                                                   smp_trks_le_dist, smp_trks_le_propn, \n",
    "                                                   smp_trks_len, traj_plots)\n",
    "from epic.analysis.cell_migration_metrics import MetricFactory\n",
    "from epic.utils.cell_migration import detect_leading_edges\n",
    "from epic.detection.deep_extreme_cut import DeepExtremeCut\n",
    "from epic.utils.file_processing import (load_imgs, load_motc_dets, load_motc_tracks,\n",
    "                                        save_imgs, save_motc_tracks, save_video,\n",
    "                                        video_reshape)\n",
    "from epic.utils.image_processing import (draw_bounding_boxes, draw_masks, draw_ids, draw_leading_edges, \n",
    "                                         draw_tracks)\n",
    "from epic.utils.misc import create_tracklets\n",
    "\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4c561ba-9ad4-4916-91fc-ad6995c31918",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# preconfiguration\n",
    "plt.rcParams['figure.figsize'] = [8, 6]\n",
    "plt.rcParams['figure.dpi'] = 100\n",
    "rd.seed(0)\n",
    "DPI = 1000\n",
    "VID_FILENAME = 'video'\n",
    "CELL_AREAS_FIG_FILENAME = 'wound_area_over_time.png'\n",
    "CELL_AREAS_NORM_FIG_FILENAME = 'wound_area_over_time_normalized.png'\n",
    "AUC_CSV_FILENAME = 'wound_area_under_the_curve.csv'\n",
    "AUC_NORM_CSV_FILENAME = 'wound_area_under_the_curve_normalized.csv'\n",
    "AUC_FIG_FILENAME = 'wound_area_under_the_curve.png'\n",
    "AUC_NORM_FIG_FILENAME = 'wound_area_under_the_curve_normalized.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3acb65d-3785-4dbd-bd05-9cf1a9029919",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# start\n",
    "cwd = os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "838c009c-9728-43ed-90ed-6ed7c4c99086",
   "metadata": {},
   "source": [
    "## Image Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b73f95a9-a458-4b3d-9293-1b6b230b2571",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# load images\n",
    "motchallenge_img_dir = os.path.join(cwd, '..', 'img1')\n",
    "img_dir = (motchallenge_img_dir if os.path.isdir(motchallenge_img_dir) else \n",
    "           os.path.join(cwd, '..'))\n",
    "assert os.path.isdir(img_dir), f'Image directory {img_dir} could not be found.'\n",
    "imgs = load_imgs(img_dir) \n",
    "assert len(imgs) > 1, 'Raw image series shorter than 2 frames.'\n",
    "print(f'Loaded {len(imgs)} images.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f411e650-f474-4b4e-bb4f-2e91a95c2245",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for i, img in enumerate(imgs, start=1):\n",
    "    print(f'Image {i}: {img[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db71f2e8-3725-43d0-936b-9f713b3628d2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# show image series\n",
    "output_dir = os.path.join(cwd, 'Raw_Image_Series')\n",
    "if os.path.isdir(output_dir):\n",
    "    shutil.rmtree(output_dir)\n",
    "os.mkdir(output_dir)\n",
    "save_imgs(imgs, output_dir)\n",
    "raw_vid = os.path.join(output_dir, VID_FILENAME)\n",
    "save_video(imgs, raw_vid)\n",
    "raw_vid +=  epic.VID_FILE_EXT\n",
    "dsp_wdh, dsp_hgt = video_reshape(raw_vid, 500)\n",
    "Video(raw_vid, embed=True, width=dsp_wdh, height=dsp_hgt, html_attributes='controls loop autoplay')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7eb2acd4-e9b0-4db2-9f04-e870fbbd2deb",
   "metadata": {},
   "source": [
    "## Wound Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ef41ade-3434-4be6-999f-e78e8a7d9601",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# PARAMETER - None OR (> 0 AND <= 1)\n",
    "dets_min_score = 0.5  # only load detections with this minimum confidence score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33139150-5d16-4fe2-8466-01a563035c9d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# load detections\n",
    "motc_dets_file = os.path.join(os.getcwd(), '..', epic.DETECTIONS_DIR_NAME, \n",
    "                              epic.MOTC_DETS_FILENAME)\n",
    "assert os.path.isfile(motc_dets_file), f'MOTC detections file {motc_dets_file} could not be found.'\n",
    "dets = load_motc_dets(motc_dets_file, dets_min_score)\n",
    "assert len(dets) > 1, 'Detections found in less than 2 frames.'\n",
    "num_frames = min(len(imgs), len(dets))\n",
    "imgs, dets = imgs[0: num_frames], dets[0: num_frames]\n",
    "dets = create_tracklets(dets, imgs)\n",
    "print(f'Loaded {len(list(chain.from_iterable(list(dets))))} detections in total.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f31a4b8b-da88-4300-8085-ffce24ce7bac",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "num_dets = []\n",
    "for i, ds in enumerate(dets, start=1):\n",
    "    num_dets.append(len(ds))\n",
    "    print(f'Image {i} contains {len(ds)} detections')\n",
    "print(f'\\nAverage number of detections per image: {round(mean(num_dets))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ade9710a-d245-4c84-bab9-9243a996142a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# show detections\n",
    "dets_imgs = deepcopy(imgs)\n",
    "for ds in dets:\n",
    "    draw_bounding_boxes(ds, dets_imgs, colour=(255, 0, 0))\n",
    "output_dir = os.path.join(cwd, 'All_Detections')\n",
    "if os.path.isdir(output_dir):\n",
    "    shutil.rmtree(output_dir)\n",
    "os.mkdir(output_dir)\n",
    "save_imgs(dets_imgs, output_dir)\n",
    "dets_vid = os.path.join(output_dir, VID_FILENAME)\n",
    "save_video(dets_imgs, dets_vid)\n",
    "dets_vid += epic.VID_FILE_EXT\n",
    "dsp_wdh, dsp_hgt = video_reshape(dets_vid, 500)\n",
    "Video(dets_vid, embed=True, width=dsp_wdh, height=dsp_hgt, html_attributes='controls loop autoplay')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8577f860-346f-4fc3-b5a4-a61cc80198e0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# load tracks\n",
    "motc_tracks_file = os.path.join(os.getcwd(), '..', epic.TRACKS_DIR_NAME, \n",
    "                                epic.MOTC_TRACKS_FILENAME)\n",
    "assert os.path.isfile(motc_tracks_file), f'MOTC tracks file {motc_tracks_file} could not be found.'\n",
    "auto_tracks = load_motc_tracks(motc_tracks_file)\n",
    "dev = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "mask_generator = DeepExtremeCut(dev)\n",
    "auto_tracks = create_tracklets(auto_tracks, imgs, det_type='gend_mask', mask_generator=mask_generator, device=dev)\n",
    "auto_tracks = list(chain.from_iterable(list(auto_tracks)))\n",
    "assert len(auto_tracks) > 0, 'No tracks loaded.'\n",
    "\n",
    "print(f'Loaded {len(auto_tracks)} tracks in total.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d19d2c17-e6d5-4e39-ad7a-3f98bbd07aa5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "track_lens, track_start_frames, track_end_frames = [], [], []\n",
    "for track in auto_tracks:\n",
    "    track_lens.append(track.num_dets)\n",
    "    track_start_frames.append(track.start_frame)\n",
    "    track_end_frames.append(track.end_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9adb37ab-460a-46af-9bbe-8ff48302b0d5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# show tracks\n",
    "tracks_imgs = deepcopy(imgs)\n",
    "draw_tracks(auto_tracks, tracks_imgs)\n",
    "output_dir = os.path.join(cwd, 'All_Tracks')\n",
    "if os.path.isdir(output_dir):\n",
    "    shutil.rmtree(output_dir)\n",
    "os.mkdir(output_dir)\n",
    "save_imgs(tracks_imgs, output_dir)\n",
    "tracks_vid = os.path.join(output_dir, VID_FILENAME)\n",
    "save_video(tracks_imgs, tracks_vid)\n",
    "tracks_vid += epic.VID_FILE_EXT\n",
    "dsp_wdh, dsp_hgt = video_reshape(tracks_vid, 500)\n",
    "Video(tracks_vid, embed=True, width=dsp_wdh, height=dsp_hgt, html_attributes='controls loop autoplay')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "24005391-f923-4a2d-9580-c9e393a38f4b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Wound Mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09296a70-de0c-45a8-8940-2f4b9567adee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# show all cell masks\n",
    "mask_imgs = deepcopy(imgs)\n",
    "draw_masks(auto_tracks, mask_imgs)\n",
    "draw_ids(mask_imgs, auto_tracks, colour=(0, 0, 255))\n",
    "\n",
    "output_dir = os.path.join(cwd, 'All_Masks')\n",
    "if os.path.isdir(output_dir):\n",
    "    shutil.rmtree(output_dir)\n",
    "os.mkdir(output_dir)\n",
    "save_imgs(mask_imgs, output_dir)\n",
    "tracks_vid = os.path.join(output_dir, VID_FILENAME)\n",
    "save_video(mask_imgs, tracks_vid)\n",
    "tracks_vid += epic.VID_FILE_EXT\n",
    "dsp_wdh, dsp_hgt = video_reshape(tracks_vid, 500)\n",
    "Video(tracks_vid, embed=True, width=dsp_wdh, height=dsp_hgt, html_attributes='controls loop autoplay')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1b0584b1-f75f-4838-8c4b-84002b22c91e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Wound Area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fb67fb1-f75a-4b43-ba49-b527dbb66626",
   "metadata": {},
   "outputs": [],
   "source": [
    "um_px = 1.83\n",
    "start_frame, end_frame = 1, len(imgs)\n",
    "sampled_tracks = [t for t in auto_tracks if t.start_frame == start_frame and t.end_frame == end_frame]\n",
    "for i, t in enumerate(sampled_tracks, start=1):\n",
    "    t.idn = i\n",
    "    \n",
    "areas = defaultdict(list)\n",
    "for t in sampled_tracks:\n",
    "    for det in t.dets:\n",
    "        area = det.area * um_px\n",
    "        areas[t.idn].append(area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ee3d723-d21b-4a15-b0c1-08705c13397b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# show area cell masks \n",
    "area_imgs = deepcopy(imgs)\n",
    "draw_masks(sampled_tracks, area_imgs)\n",
    "draw_ids(area_imgs, sampled_tracks, colour=(0, 0, 255))\n",
    "\n",
    "output_dir = os.path.join(cwd, 'Wound_Areas')\n",
    "if os.path.isdir(output_dir):\n",
    "    shutil.rmtree(output_dir)\n",
    "os.mkdir(output_dir)\n",
    "save_imgs(area_imgs, output_dir)\n",
    "tracks_vid = os.path.join(output_dir, VID_FILENAME)\n",
    "save_video(area_imgs, tracks_vid)\n",
    "tracks_vid += epic.VID_FILE_EXT\n",
    "dsp_wdh, dsp_hgt = video_reshape(tracks_vid, 500)\n",
    "Video(tracks_vid, embed=True, width=dsp_wdh, height=dsp_hgt, html_attributes='controls loop autoplay')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57242dbd-81a0-4f78-b31e-137b80098b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 'Wound Area Over Time (μm²) (per wound ID)'\n",
    "times = list(np.arange(0, (len(areas[1])) * 10, 10))\n",
    "columns = list(areas.keys())\n",
    "columns = list(zip([k] * len(list(areas.keys())), columns))             \n",
    "columns = pd.MultiIndex.from_tuples(columns) \n",
    "\n",
    "rows = times\n",
    "rows = list(zip(['Time (Minutes)'] * len(times), rows))             \n",
    "rows = pd.MultiIndex.from_tuples(rows) \n",
    "pd.set_option('display.precision', 2)\n",
    "df = pd.DataFrame({(k, idn): a for idn, a in areas.items()}, index=rows, columns=columns)\n",
    "df.style.set_table_styles(\n",
    "    [dict(selector=\"th\",props=[('max-width', '80px')]),\n",
    "        dict(selector=\"th.col_heading\",\n",
    "                 props=[(\"writing-mode\", \"vertical-rl\"), \n",
    "                        ('transform', 'rotateZ(-90deg)'),\n",
    "                        ])]\n",
    ")\n",
    "df.to_csv(CELL_AREAS_FIG_FILENAME + '.csv')\n",
    "df.style.set_table_styles([dict(selector='th', props=[('text-align', 'right')])])\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10a7cf92-292f-4a54-b31d-c2f3c5471e89",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# show wound areas figure\n",
    "plt.figure()\n",
    "ax = plt.gca()\n",
    "for idn, a in areas.items():\n",
    "    ax.plot(times, a, '-o', label=idn)\n",
    "plt.title('Wound Area Over Time')\n",
    "plt.xlabel('Time (Minutes)')\n",
    "plt.ylabel('Area (μm²)')\n",
    "# plt.ylim(0, round(max(list(chain.from_iterable(list(areas.values()))))) * 1.2)\n",
    "plt.legend(loc='upper left')\n",
    "plt.yscale('log')\n",
    "plt.legend(handles=ax.get_legend_handles_labels()[0], title=\"Wound ID\",\n",
    "           bbox_to_anchor=(1, 1), loc=2, borderaxespad=0., edgecolor='black')\n",
    "plt.savefig(CELL_AREAS_FIG_FILENAME, dpi=DPI, bbox_inches=0)\n",
    "plt.close(plt.gcf())\n",
    "Image(CELL_AREAS_FIG_FILENAME, width=750)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "baabd0c0-128e-4c13-bfff-484c343ff29a",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 'Wound Area Over Time (Normalized to t=0) (%) (per cell ID)'\n",
    "norm_areas = {idn: list(np.array(a) / a[0] * 100) for idn, a in areas.items()}\n",
    "columns = list(areas.keys())\n",
    "columns = list(zip([k] * len(list(areas.keys())), columns))             \n",
    "columns = pd.MultiIndex.from_tuples(columns) \n",
    "\n",
    "rows = times\n",
    "rows = list(zip(['Time (Minutes)'] * len(times), rows))             \n",
    "rows = pd.MultiIndex.from_tuples(rows) \n",
    "\n",
    "df = pd.DataFrame({(k, idn): a for idn, a in norm_areas.items()}, index=rows, columns=columns)\n",
    "df.to_csv(CELL_AREAS_NORM_FIG_FILENAME + '.csv')\n",
    "df.style.set_table_styles([dict(selector='th', props=[('text-align', 'right')])])\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92bfd5fb-490c-443f-a05e-c1dd07e53c9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "ax = plt.gca()\n",
    "for idn, a in norm_areas.items():\n",
    "    ax.plot(times, a, '-o', label=idn)\n",
    "plt.title('Wound Area Over Time (Normalized to t=0)')\n",
    "plt.xlabel('Time (Minutes)')\n",
    "plt.ylabel('Relative Wound Area (%)')\n",
    "# plt.ylim(0, round(max(list(chain.from_iterable(list(areas.values()))))) * 1.2)\n",
    "plt.legend(handles=ax.get_legend_handles_labels()[0], title=\"Cell ID\",\n",
    "           bbox_to_anchor=(1, 1), loc=2, borderaxespad=0., edgecolor='black')\n",
    "plt.savefig(CELL_AREAS_NORM_FIG_FILENAME, dpi=DPI, bbox_inches=0)\n",
    "plt.close(plt.gcf())\n",
    "Image(CELL_AREAS_NORM_FIG_FILENAME, width=750)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9443b7cb-517b-49c4-9fae-a86989ab1df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "aucs = {('Wound ID', idn): np.trapz(a, dx=5) for idn, a in areas.items()}\n",
    "series = pd.Series(aucs, name='Wound Area Area Under the Curve (μm² Minutes)')\n",
    "series = series.to_frame()\n",
    "series.to_csv(AUC_CSV_FILENAME)\n",
    "series.style.set_table_styles([dict(selector='th', props=[('text-align', 'right')])])\n",
    "display(series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67efec86-e876-4c8f-8559-65fe2b6a08de",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "cmap = plt.cm.tab10\n",
    "colours = cmap(np.arange(len(areas)) % cmap.N)\n",
    "ax=pd.Series({idn: np.trapz(a, dx=5) for idn, a in areas.items()}).plot.bar(color=colours)\n",
    "ax.set_xlabel('Wound ID')\n",
    "ax.set_ylabel('Area Under the Curve (μm² Minutes)')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=0)\n",
    "ax.set_title('Wound Area Area Under the Curve')\n",
    "plt.savefig(AUC_FIG_FILENAME, dpi=DPI, bbox_inches=0)\n",
    "plt.close(plt.gcf())\n",
    "Image(AUC_FIG_FILENAME, width=750)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8566205-6943-4b35-bad0-67cc1eefa421",
   "metadata": {},
   "outputs": [],
   "source": [
    "aucs = {('Cell ID', idn): np.trapz(a, dx=5) for idn, a in norm_areas.items()}\n",
    "series = pd.Series(aucs, name='Wound Area Under the Curve (Normalized to t=0) (% Minutes)')\n",
    "series = series.to_frame()\n",
    "series.to_csv(AUC_NORM_CSV_FILENAME)\n",
    "series.style.set_table_styles([dict(selector='th', props=[('text-align', 'right')])])\n",
    "display(series)"
   ]
  },
  {
   "cell_type": "code",
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    "plt.figure()\n",
    "cmap = plt.cm.tab10\n",
    "colours = cmap(np.arange(len(areas)) % cmap.N)\n",
    "ax=pd.Series({idn: np.trapz(a, dx=5) for idn, a in norm_areas.items()}).plot.bar(color=colours)\n",
    "ax.set_xlabel('Cell ID')\n",
    "ax.set_ylabel('Area Under the Curve (% Minutes)')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=0)\n",
    "ax.set_title('Wound Area Under the Curve (Normalized to t=0)')\n",
    "plt.savefig(AUC_NORM_FIG_FILENAME, dpi=DPI, bbox_inches=0)\n",
    "plt.close(plt.gcf())\n",
    "Image(AUC_NORM_FIG_FILENAME, width=750)"
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