Mercurial > repos > galaxyp > qupath_roi_splitter
changeset 0:b5e9cebb27e3 draft
planemo upload for repository hhttps://github.com/npinter/ROIsplitter commit cdf3e9652b10c7a0b179202129a797e32fd95909
| author | galaxyp |
|---|---|
| date | Tue, 25 Apr 2023 09:30:06 +0000 |
| parents | |
| children | 064b53fd3131 |
| files | qupath_roi_splitter.py qupath_roi_splitter.xml test-data/annotations_TMA_E-5.geojson test-data/annotations_TMA_F-5.geojson |
| diffstat | 4 files changed, 9998 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/qupath_roi_splitter.py Tue Apr 25 09:30:06 2023 +0000 @@ -0,0 +1,83 @@ +import argparse + +import cv2 +import geojson +import numpy as np +import pandas as pd + + +def draw_poly(input_df, input_img, col=(0, 0, 0)): + s = np.array(input_df) + output_img = cv2.fillPoly(input_img, pts=np.int32([s]), color=col) + return output_img + + +def split_qupath_roi(in_roi): + with open(in_roi) as file: + qupath_roi = geojson.load(file) + + # HE dimensions + dim_plt = [qupath_roi["dim"]["width"], qupath_roi["dim"]["height"]] + + tma_name = qupath_roi["name"] + cell_types = [ct.rsplit(" - ", 1)[-1] for ct in qupath_roi["featureNames"]] + + # create numpy array with white background + img = np.zeros((dim_plt[1], dim_plt[0], 3), dtype="uint8") + img.fill(255) + + for cell_type in cell_types: + for i, roi in enumerate(qupath_roi["features"]): + if roi["properties"]["classification"]["name"] == cell_type: + if len(roi["geometry"]["coordinates"]) == 1: + # Polygon w/o holes + img = draw_poly(roi["geometry"]["coordinates"][0], img) + else: + first_roi = True + for sub_roi in roi["geometry"]["coordinates"]: + # Polygon with holes + if not isinstance(sub_roi[0][0], list): + if first_roi: + img = draw_poly(sub_roi, img) + first_roi = False + else: + # holes in ROI + img = draw_poly(sub_roi, img, col=(255, 255, 255)) + else: + # MultiPolygon with holes + for sub_coord in sub_roi: + if first_roi: + img = draw_poly(sub_coord, img) + first_roi = False + else: + # holes in ROI + img = draw_poly(sub_coord, img, col=(255, 255, 255)) + + # get all black pixel + coords_arr = np.column_stack(np.where(img == (0, 0, 0))) + + # remove duplicated rows + coords_arr_xy = coords_arr[coords_arr[:, 2] == 0] + + # remove last column + coords_arr_xy = np.delete(coords_arr_xy, 2, axis=1) + + # to pandas and rename columns to x and y + coords_df = pd.DataFrame(coords_arr_xy, columns=['x', 'y']) + + # drop duplicates + coords_df = coords_df.drop_duplicates( + subset=['x', 'y'], + keep='last').reset_index(drop=True) + + coords_df.to_csv("{}_{}.txt".format(tma_name, cell_type), sep='\t', index=False) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Split ROI coordinates of QuPath TMA annotation by cell type (classfication)") + parser.add_argument("--qupath_roi", default=False, help="Input QuPath annotation (GeoJSON file)") + parser.add_argument('--version', action='version', version='%(prog)s 0.1.0') + args = parser.parse_args() + + if args.qupath_roi: + split_qupath_roi(args.qupath_roi)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/qupath_roi_splitter.xml Tue Apr 25 09:30:06 2023 +0000 @@ -0,0 +1,55 @@ +<tool id="qupath_roi_splitter" name="QuPath ROI Splitter" version="0.1.0"> + <description>Split ROI coordinates of QuPath TMA annotation by cell type (classification)</description> + <requirements> + <requirement type="package" version="3.0.1">geojson</requirement> + <requirement type="package" version="1.24.2">numpy</requirement> + <requirement type="package" version="4.7.0">opencv</requirement> + <requirement type="package" version="2.0.0">pandas</requirement> + </requirements> + <command detect_errors="exit_code"><![CDATA[ + #for $input in $input_collection + python3 '$__tool_directory__/qupath_roi_splitter.py' --qupath_roi '$input' && + #end for + mkdir out && + mv *.txt out/ + ]]></command> + <inputs> + <param name="input_collection" type="data_collection" format="geojson" label="Input QuPath annotation" help="Collection containing GeoJSON files"/> + </inputs> + <outputs> + <collection name="output_txts" type="list" label="${tool.name} on ${on_string}: ROI data"> + <discover_datasets pattern="__name_and_ext__" directory="out" visible="false" format="txt"/> + </collection> + </outputs> + <tests> + <test> + <param name="input_collection"> + <collection type="list"> + <element name="annotations_TMA_E-5.geojson" value="annotations_TMA_E-5.geojson" /> + <element name="annotations_TMA_F-5.geojson" value="annotations_TMA_F-5.geojson" /> + </collection> + </param> + <output_collection name="output_txts" type="list" count="4"> + <element name="F-5_Stroma"> + <assert_contents> + <has_text text="x"/> + <has_text text="y"/> + <has_text text="14240"/> + <has_text text="21008"/> + </assert_contents> + </element> + </output_collection> + </test> + </tests> + <help><![CDATA[ + **QuPath ROI Splitter** + + This tool extracts ROI coordinates of QuPath TMA annotation by cell type. + + Input: A collection containing GeoJSON files with QuPath annotation data. You need to run the QuPath TMA annotation script first which can be found at https://github.com/npinter/ROIsplitter. + + Output: A list of text files containing the ROI coordinates for each cell type. + + https://github.com/npinter/ROIsplitter + ]]></help> +</tool>
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