# HG changeset patch # User bgruening # Date 1709244446 0 # Node ID 1e7334a517252933ab2e12c7b064ef9ac8f4c4b2 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/cellpose commit 06dd9637975e3b9d6d27a3d5a773c85e9a52baf2 diff -r 000000000000 -r 1e7334a51725 cellpose.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cellpose.xml Thu Feb 29 22:07:26 2024 +0000 @@ -0,0 +1,149 @@ + + a generalist algorithm for cell and nucleus segmentation + + 3.0.1 + 0 + + + + + + + + + biocontainers/cellpose:3.0.1_cv1 + + + + + echo "@VERSION@" + + + + + + + + + + + + + + + + + + + + + + + +

+ + + + + + + + + +

+ + + + + show_segmentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 10.1101/2020.02.02.931238 + + diff -r 000000000000 -r 1e7334a51725 cp_segmentation.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cp_segmentation.py Thu Feb 29 22:07:26 2024 +0000 @@ -0,0 +1,85 @@ +import argparse +import json +import os +import warnings + +import matplotlib.pyplot as plt +import numpy as np +import skimage.io +from cellpose import models, plot, transforms + + +def main(inputs, img_path, img_format, output_dir): + """ + Parameter + --------- + inputs : str + File path to galaxy tool parameter + img_path : str + File path for the input image + img_format : str + One of the ['ome.tiff', 'tiff', 'png', 'jpg'] + output_dir : str + Folder to save the outputs. + """ + warnings.simplefilter('ignore') + + with open(inputs, 'r') as param_handler: + params = json.load(param_handler) + + gpu = params['use_gpu'] + model_type = params['model_type'] + chan = params['chan'] + chan2 = params['chan2'] + chan_first = params['chan_first'] + if chan is None: + channels = None + else: + channels = [int(chan), int(chan2) if chan2 is not None else None] + + options = params['options'] + + img = skimage.io.imread(img_path) + + print(f"Image shape: {img.shape}") + # transpose to Ly x Lx x nchann and reshape based on channels + if img_format.endswith('tiff'): + img = np.transpose(img, (1, 2, 0)) + img = transforms.reshape(img, channels=channels, chan_first=chan_first) + + print(f"Image shape: {img.shape}") + model = models.Cellpose(gpu=gpu, model_type=model_type) + masks, flows, styles, diams = model.eval(img, channels=channels, **options) + + # save masks to tiff + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + skimage.io.imsave(os.path.join(output_dir, 'cp_masks.tif'), + masks.astype(np.uint16)) + + # make segmentation show # + if params['show_segmentation']: + img = skimage.io.imread(img_path) + # uniform image + if img_format.endswith('tiff'): + img = np.transpose(img, (1, 2, 0)) + img = transforms.reshape(img, channels=channels, chan_first=chan_first) + + maski = masks + flowi = flows[0] + fig = plt.figure(figsize=(12, 3)) + # can save images (set save_dir=None if not) + plot.show_segmentation(fig, img, maski, flowi, channels=channels) + fig.savefig(os.path.join(output_dir, 'segm_show.png'), dpi=300) + plt.close(fig) + + +if __name__ == '__main__': + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--inputs", dest="inputs", required=True) + aparser.add_argument("-p", "--img_path", dest="img_path") + aparser.add_argument("-f", "--img_format", dest="img_format") + aparser.add_argument("-O", "--output_dir", dest="output_dir") + args = aparser.parse_args() + + main(args.inputs, args.img_path, args.img_format, args.output_dir) diff -r 000000000000 -r 1e7334a51725 test-data/img02.png Binary file test-data/img02.png has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_masks_chan.tif Binary file test-data/img02_cp_masks_chan.tif has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_masks_cyto.tif Binary file test-data/img02_cp_masks_cyto.tif has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_masks_cyto2.tif Binary file test-data/img02_cp_masks_cyto2.tif has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_masks_cyto3.tif Binary file test-data/img02_cp_masks_cyto3.tif has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_masks_diameter.tif Binary file test-data/img02_cp_masks_diameter.tif has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_masks_gpu.tif Binary file test-data/img02_cp_masks_gpu.tif has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_masks_nuclei.tif Binary file test-data/img02_cp_masks_nuclei.tif has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_masks_rescale.tif Binary file test-data/img02_cp_masks_rescale.tif has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_segm_chan.png Binary file test-data/img02_cp_segm_chan.png has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_segm_cyto.png Binary file test-data/img02_cp_segm_cyto.png has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_segm_cyto2.png Binary file test-data/img02_cp_segm_cyto2.png has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_segm_cyto3.png Binary file test-data/img02_cp_segm_cyto3.png has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_segm_diameter.png Binary file test-data/img02_cp_segm_diameter.png has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_segm_gpu.png Binary file test-data/img02_cp_segm_gpu.png has changed diff -r 000000000000 -r 1e7334a51725 test-data/img02_cp_segm_nuclei.png Binary file test-data/img02_cp_segm_nuclei.png has changed