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tidy formatting
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@PatBall1
PatBall1 committed Oct 1, 2023
1 parent bd73935 commit b2a906a
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Showing 3 changed files with 42 additions and 41 deletions.
69 changes: 34 additions & 35 deletions detectree2/models/outputs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@
from rasterio.crs import CRS
from shapely.affinity import scale
from shapely.geometry import Polygon, box, shape
from shapely.ops import orient, unary_union
from shapely.ops import orient


def polygon_from_mask(masked_arr):
Expand All @@ -39,9 +39,9 @@ def polygon_from_mask(masked_arr):
if contour.size >= 10:
contour = contour.flatten().tolist()
# Ensure the polygon is closed (get rid of fiona warning?)
if contour[:2] != contour[-2:]: # if not closed
#continue # better to skip?
contour.extend(contour[:2]) # small artifacts due to this?
if contour[:2] != contour[-2:]: # if not closed
# continue # better to skip?
contour.extend(contour[:2]) # small artifacts due to this?
segmentation.append(contour)

[x, y, w, h] = cv2.boundingRect(masked_arr)
Expand Down Expand Up @@ -143,7 +143,7 @@ def to_eval_geojson(directory=None): # noqa:N803
json.dump(geofile, dest)


def project_to_geojson(tiles_path, pred_fold=None, output_fold=None, Multi_class = False): # noqa:N803
def project_to_geojson(tiles_path, pred_fold=None, output_fold=None, multi_class: bool = False): # noqa:N803
"""Projects json predictions back in geographic space.
Takes a json and changes it to a geojson so it can overlay with orthomosaic. Another copy is produced to overlay
Expand Down Expand Up @@ -190,7 +190,7 @@ def project_to_geojson(tiles_path, pred_fold=None, output_fold=None, Multi_class

# json file is formated as a list of segmentation polygons so cycle through each one
for crown_data in datajson:
if Multi_class == True:
if multi_class:
category = crown_data["category_id"]
# print(category)
crown = crown_data["segmentation"]
Expand All @@ -205,10 +205,10 @@ def project_to_geojson(tiles_path, pred_fold=None, output_fold=None, Multi_class

crown_coords_array = np.array(crown_coords).reshape(-1, 2)
x_coords, y_coords = rasterio.transform.xy(transform=raster_transform,
rows=crown_coords_array[:, 1],
cols=crown_coords_array[:, 0])
rows=crown_coords_array[:, 1],
cols=crown_coords_array[:, 0])
moved_coords = list(zip(x_coords, y_coords))
if Multi_class == False:
if multi_class:
geofile["features"].append({
"type": "Feature",
"properties": {
Expand All @@ -219,7 +219,7 @@ def project_to_geojson(tiles_path, pred_fold=None, output_fold=None, Multi_class
"coordinates": [moved_coords],
},
})
if Multi_class == True:
if multi_class:
geofile["features"].append({
"type": "Feature",
"properties": {
Expand Down Expand Up @@ -260,7 +260,8 @@ def box_filter(filename, shift: int = 0):
shift: Number of meters to shift the size of the bounding box in by. This is to avoid edge crowns.
Returns:
gpd.GeoDataFrame: A GeoDataFrame containing the bounding box."""
gpd.GeoDataFrame: A GeoDataFrame containing the bounding box.
"""
minx, miny, width, buffer, crs = filename_geoinfo(filename)
bounding_box = box_make(minx, miny, width, buffer, crs, shift)
return bounding_box
Expand Down Expand Up @@ -314,7 +315,7 @@ def stitch_crowns(folder: str, shift: int = 1):
if idx % 50 == 0:
print(f"Stitching file {idx} of {total_files}: {file}")

crowns_tile = gpd.read_file(file) # This throws a huge amount of warnings fiona closed ring detected
crowns_tile = gpd.read_file(file) # This throws a huge amount of warnings fiona closed ring detected

geo = box_filter(file, shift)

Expand Down Expand Up @@ -409,42 +410,43 @@ def clean_crowns(crowns: gpd.GeoDataFrame,

return crowns_out.reset_index(drop=True)


def post_clean(unclean_df: gpd.GeoDataFrame,
clean_df: gpd.GeoDataFrame,
iou_threshold: float = 0.3,
field: str = "Confidence_score") -> gpd.GeoDataFrame:
"""Fill in the gaps left by clean_crowns.
Args:
unclean_df (gpd.GeoDataFrame): Unclean crowns.
clean_df (gpd.GeoDataFrame): Clean crowns.
iou_threshold (float, optional): IoU threshold that determines whether
iou_threshold (float, optional): IoU threshold that determines whether predictions are considered overlapping.
crowns are overlapping. Defaults to 0.3.
"""
# Spatial join between unclean and clean dataframes using the new syntax
joined_df = gpd.sjoin(unclean_df, clean_df, how='inner', predicate='intersects')
joined_df = gpd.sjoin(unclean_df, clean_df, how="inner", predicate="intersects")

to_remove = []
for idx, row in joined_df.iterrows():
# Using the default suffix 'left' for columns from the unclean_df and 'right' for columns from the clean_df
unclean_shape = unclean_df.loc[idx, 'geometry']
clean_shape = clean_df.loc[row['index_right'], 'geometry']
unclean_shape = unclean_df.loc[idx, "geometry"]
clean_shape = clean_df.loc[row["index_right"], "geometry"]

unclean_shape = unclean_shape.buffer(0)
clean_shape = clean_shape.buffer(0)

intersection_area = unclean_shape.intersection(clean_shape).area
union_area = unclean_shape.union(clean_shape).area
iou = intersection_area / union_area

if iou > iou_threshold:
to_remove.append(idx)

reduced_unclean_df = unclean_df.drop(index=to_remove)

# Concatenate the reduced unclean dataframe with the clean dataframe
result_df = pd.concat([clean_df, reduced_unclean_df], ignore_index=True)

result_df.reset_index(drop=True, inplace=True)

reclean_df = clean_crowns(result_df, iou_threshold=iou_threshold, field=field)
Expand All @@ -453,24 +455,18 @@ def post_clean(unclean_df: gpd.GeoDataFrame,


def load_geopandas_dataframes(folder):
# Get a list of all .gpkg filenames in the folder
"""Load all GeoPackage files in a folder into a list of GeoDataFrames."""
all_files = glob.glob(f"{folder}/*.gpkg")
filenames = [f for f in all_files if re.match(f"{folder}/crowns_\d+\.gpkg", f)]
filenames = [f for f in all_files if re.match(f"{folder}/crowns_\d+\.gpkg", f)] # noqa:N605

# Load each file into a GeoDataFrame and add it to a list
geopandas_dataframes = [gpd.read_file(filename) for filename in filenames]

return geopandas_dataframes


# Function to normalize and average polygons, considering weights
#def normalize_polygon(polygon, num_points):
# total_perimeter = polygon.length
# distance_between_points = total_perimeter / num_points
# points = [polygon.boundary.interpolate(i * distance_between_points) for i in range(num_points)]
# return Polygon(points)

def normalize_polygon(polygon, num_points):
"""Normalize a polygon to a set number of points."""
# Orient polygon to ensure consistent vertex order (counterclockwise)
polygon = orient(polygon, sign=1.0)

Expand All @@ -495,6 +491,7 @@ def normalize_polygon(polygon, num_points):


def average_polygons(polygons, weights=None, num_points=300):
"""Average a set of polygons."""
normalized_polygons = [normalize_polygon(poly, num_points) for poly in polygons]

avg_polygon_points = []
Expand Down Expand Up @@ -526,7 +523,8 @@ def average_polygons(polygons, weights=None, num_points=300):
return avg_polygon_scaled


def combine_and_average_polygons(gdfs, iou = 0.9):
def combine_and_average_polygons(gdfs, iou=0.9):
"""Combine and average polygons."""
# Combine all dataframes into one
combined_gdf = gpd.GeoDataFrame(pd.concat(gdfs, ignore_index=True))
combined_gdf["Confidence_score"] = pd.to_numeric(combined_gdf["Confidence_score"], errors="coerce")
Expand Down Expand Up @@ -586,14 +584,15 @@ def combine_and_average_polygons(gdfs, iou = 0.9):
new_gdf["combined_counts"] = combined_counts
if "Confidence_score" in combined_gdf.columns:
new_gdf["summed_confidences"] = summed_confidences

# Set crs
new_gdf.set_crs(gdfs[0].crs, inplace=True)

return new_gdf


def clean_predictions(directory, iou_threshold=0.7):
"""Clean predictions prior to accuracy assessment."""
pred_fold = directory
entries = os.listdir(pred_fold)

Expand Down Expand Up @@ -630,7 +629,7 @@ def clean_predictions(directory, iou_threshold=0.7):


def clean_outputs(crowns: gpd.GeoDataFrame, iou_threshold=0.7):
"""Clean predictions prior to accuracy assessment
"""Clean predictions prior to accuracy assessment.
Outputs can contain highly overlapping crowns including in the buffer region.
This function removes crowns with a high degree of overlap with others but a
Expand Down
3 changes: 1 addition & 2 deletions detectree2/models/predict.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@
"""
import json
import os
import random
from pathlib import Path

import cv2
Expand Down Expand Up @@ -68,7 +67,7 @@ def predict_on_data(
evaluations = instances_to_coco_json(outputs["instances"].to("cpu"), d["file_name"])
with open(output_file, "w") as dest:
json.dump(evaluations, dest)

if i % 50 == 0:
print(f"Predicted {i} files of {total_files}")

Expand Down
11 changes: 7 additions & 4 deletions detectree2/preprocessing/tiling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,7 +77,10 @@ def tile_data(
crs = crs.to_epsg()
tilename = Path(data.name).stem

total_tiles = int(((data.bounds[2] - data.bounds[0]) / tile_width) * ((data.bounds[3] - data.bounds[1]) / tile_height))
total_tiles = int(
((data.bounds[2] - data.bounds[0]) / tile_width) *
((data.bounds[3] - data.bounds[1]) / tile_height)
)

tile_count = 0
print(f"Tiling to {total_tiles} total tiles")
Expand All @@ -86,7 +89,7 @@ def tile_data(
tile_width, int):
for miny in np.arange(data.bounds[1], data.bounds[3] - tile_height,
tile_height, int):

tile_count += 1
# Naming conventions
out_path_root = out_path / f"{tilename}_{minx}_{miny}_{tile_width}_{buffer}_{crs}"
Expand Down Expand Up @@ -178,7 +181,7 @@ def tile_data(
)
if tile_count % 50 == 0:
print(f"Processed {tile_count} tiles of {total_tiles} tiles")

print("Tiling complete")


Expand Down Expand Up @@ -379,7 +382,7 @@ def tile_data_train( # noqa: C901
except ValueError:
print("Cannot write empty DataFrame to file.")
continue

print("Tiling complete")


Expand Down

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