Mercurial > repos > galaxyp > qupath_roi_splitter

annotate qupath_roi_splitter.py @ 5:17c54a716a5b draft

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planemo upload for repository hhttps://github.com/npinter/ROIsplitter commit e3d24f3dc7a9812f8fa4447c9391e5bd3b5d4dc6
author galaxyp
date Tue, 30 Jul 2024 12:59:11 +0000
parents 9f136ebf73ac
children 6a8cf86fd3b7
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b5e9cebb27e3 planemo upload for repository hhttps://github.com/npinter/ROIsplitter commit cdf3e9652b10c7a0b179202129a797e32fd95909
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1 import argparse
b5e9cebb27e3 planemo upload for repository hhttps://github.com/npinter/ROIsplitter commit cdf3e9652b10c7a0b179202129a797e32fd95909
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3 import cv2
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4 import geojson
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5 import numpy as np
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6 import pandas as pd
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9 def collect_coords(input_coords, feature_index, coord_index=0):
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10 coords_with_index = []
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11 for coord in input_coords:
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12 coords_with_index.append((coord[0], coord[1], feature_index, coord_index))
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13 coord_index += 1
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14 return coords_with_index
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17 def collect_roi_coords(input_roi, feature_index):
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18 all_coords = []
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19 if len(input_roi["geometry"]["coordinates"]) == 1:
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20 # Polygon w/o holes
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21 all_coords.extend(collect_coords(input_roi["geometry"]["coordinates"][0], feature_index))
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22 else:
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23 coord_index = 0
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24 for sub_roi in input_roi["geometry"]["coordinates"]:
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25 if len(sub_roi) == 2:
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26 # Special case: LMD data
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27 all_coords.extend(collect_coords([sub_roi], feature_index, coord_index))
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28 coord_index += 1
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29 else:
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30 # Polygon with holes or MultiPolygon
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31 if not isinstance(sub_roi[0][0], list):
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32 all_coords.extend(collect_coords(sub_roi, feature_index, coord_index))
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33 coord_index += len(sub_roi)
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34 else:
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35 # MultiPolygon with holes
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36 for sub_coord in sub_roi:
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37 all_coords.extend(collect_coords(sub_coord, feature_index, coord_index))
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38 coord_index += len(sub_coord)
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39 return all_coords
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41
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42 def split_qupath_roi(in_roi):
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43 with open(in_roi) as file:
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44 qupath_roi = geojson.load(file)
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46 # HE dimensions
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47 dim_plt = [int(qupath_roi["dim"]["width"]), int(qupath_roi["dim"]["height"])]
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49 tma_name = qupath_roi["name"]
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50 cell_types = [ct.rsplit(" - ", 1)[-1] for ct in qupath_roi["featureNames"]]
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51
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52 coords_by_cell_type = {ct: [] for ct in cell_types}
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53 coords_by_cell_type['all'] = [] # For storing all coordinates if args.all is True
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55 for feature_index, roi in enumerate(qupath_roi["features"]):
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56 feature_coords = collect_roi_coords(roi, feature_index)
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57
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58 if args.all:
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59 coords_by_cell_type['all'].extend(feature_coords)
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60 elif "classification" in roi["properties"]:
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61 cell_type = roi["properties"]["classification"]["name"]
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62 if cell_type in cell_types:
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63 coords_by_cell_type[cell_type].extend(feature_coords)
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64
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65 for cell_type, coords in coords_by_cell_type.items():
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66 if coords:
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67 # Generate image (white background)
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68 img = np.ones((dim_plt[1], dim_plt[0]), dtype="uint8") * 255
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69
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70 # Convert to numpy array and ensure integer coordinates
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71 coords_arr = np.array(coords).astype(int)
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73 # Sort by feature_index first, then by coord_index
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74 coords_arr = coords_arr[np.lexsort((coords_arr[:, 3], coords_arr[:, 2]))]
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75
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76 # Get filled pixel coordinates
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77 if args.fill:
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78 filled_coords = np.column_stack(np.where(img == 0))
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79 all_coords = np.unique(np.vstack((coords_arr[:, :2], filled_coords[:, ::-1])), axis=0)
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80 else:
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81 all_coords = coords_arr[:, :2]
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82
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83 # Save all coordinates to CSV
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84 coords_df = pd.DataFrame(all_coords, columns=['x', 'y'], dtype=int)
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85 coords_df.to_csv("{}_{}.txt".format(tma_name, cell_type), sep='\t', index=False)
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86
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87 # Generate image for visualization if --img is specified
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88 if args.img:
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89 # Group coordinates by feature_index
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90 features = {}
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91 for x, y, feature_index, coord_index in coords_arr:
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92 if feature_index not in features:
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93 features[feature_index] = []
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94 features[feature_index].append((x, y))
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95
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96 # Draw each feature separately
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97 for feature_coords in features.values():
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98 pts = np.array(feature_coords, dtype=np.int32)
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99 if args.fill:
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100 cv2.fillPoly(img, [pts], color=0) # Black fill
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101 else:
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102 cv2.polylines(img, [pts], isClosed=True, color=0, thickness=1) # Black outline
0
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103
4
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104 cv2.imwrite("{}_{}.png".format(tma_name, cell_type), img)
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105
0
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106
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107 if __name__ == "__main__":
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108 parser = argparse.ArgumentParser(description="Split ROI coordinates of QuPath TMA annotation by cell type (classification)")
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109 parser.add_argument("--qupath_roi", default=False, help="Input QuPath annotation (GeoJSON file)")
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110 parser.add_argument("--fill", action="store_true", required=False, help="Fill pixels in ROIs (order of coordinates will be lost)")
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111 parser.add_argument('--version', action='version', version='%(prog)s 0.3.1')
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112 parser.add_argument("--all", action="store_true", required=False, help="Extracts all ROIs")
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113 parser.add_argument("--img", action="store_true", required=False, help="Generates image of ROIs")
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114 args = parser.parse_args()
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115
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116 if args.qupath_roi:
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117 split_qupath_roi(args.qupath_roi)