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annotate cp_segmentation.py @ 3:c793edde4284 draft default tip

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/cellpose commit 3f2ba60f101c923896ca95ed62981fcbb0a5ced3
author bgruening
date Fri, 12 Dec 2025 12:36:21 +0000
parents e5370bb71633
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4ddb0af5a806 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/cellpose commit e80ca9b0e2e6f7ae94371170d0a672f46f2d9c3c
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1 import argparse
4ddb0af5a806 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/cellpose commit e80ca9b0e2e6f7ae94371170d0a672f46f2d9c3c
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2 import json
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3 import os
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4 import warnings
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6 # Make sure, that `MKL_NUM_THREADS` is set to 1, to ensure reproducibility:
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7 # https://forum.image.sc/t/reproducibility-how-we-spent-years-building-containers-and-then-mkl-decided-to-screw-our-results/109599
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8 if str(os.environ['MKL_NUM_THREADS']) != '1':
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9 warnings.warn('MKL_NUM_THREADS must be set to 1 to ensure reproducibility, and will be adjusted accordingly for now.')
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10 os.environ['MKL_NUM_THREADS'] = '1'
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12 # Load the remaining packages *after* adjusting `MKL_NUM_THREADS` (this likely necessary for it to take effect)
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13 import matplotlib.pyplot as plt
4ddb0af5a806 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/cellpose commit e80ca9b0e2e6f7ae94371170d0a672f46f2d9c3c
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14 import numpy as np
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15 import skimage.io
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16 import torch
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17 from cellpose import models, plot
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19 # Apply PyTorch guidelines for reproducibility
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20 torch.backends.cudnn.benchmark = True
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21 torch.backends.cudnn.deterministic = True
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22 torch.manual_seed(0)
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25 def main(inputs, img_path, output_dir):
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26 """
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27 Parameter
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28 ---------
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29 inputs : str
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30 File path to galaxy tool parameter
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31 img_path : str
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32 File path for the input image
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33 output_dir : str
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34 Folder to save the outputs.
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35 """
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36 warnings.simplefilter('ignore')
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37 np.random.seed(42)
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38 with open(inputs, 'r') as param_handler:
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39 params = json.load(param_handler)
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41 gpu = params['use_gpu']
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42 model_type = params['model_type']
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43 options = params['options']
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44 img = skimage.io.imread(img_path)
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46 print(f"Image shape: {img.shape}")
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48 model = models.Cellpose(gpu=gpu, model_type=model_type)
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49 masks, flows, styles, diams = model.eval(img, channels=[0, 0], **options)
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51 # save masks to tiff
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52 with warnings.catch_warnings():
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53 warnings.simplefilter("ignore")
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54 skimage.io.imsave(os.path.join(output_dir, 'cp_masks.tif'),
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55 masks.astype(np.uint16))
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57 # make segmentation show #
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58 if params['show_segmentation']:
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59 img = skimage.io.imread(img_path)
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61 maski = masks
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62 flowi = flows[0]
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63 fig = plt.figure(figsize=(8, 2))
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64 # can save images (set save_dir=None if not)
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65 plot.show_segmentation(fig, img, maski, flowi, channels=[0, 0])
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66 fig.savefig(os.path.join(output_dir, 'segm_show.png'), dpi=300)
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67 plt.close(fig)
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70 if __name__ == '__main__':
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71 aparser = argparse.ArgumentParser()
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72 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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73 aparser.add_argument("-p", "--img_path", dest="img_path")
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74 aparser.add_argument("-O", "--output_dir", dest="output_dir")
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75 args = aparser.parse_args()
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76
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77 main(args.inputs, args.img_path, args.output_dir)