Mercurial > repos > bgruening > cellpose

comparison cp_segmentation.py @ 0:1e7334a51725 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/cellpose commit 06dd9637975e3b9d6d27a3d5a773c85e9a52baf2
author bgruening
date Thu, 29 Feb 2024 22:07:26 +0000
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children 32153c43126c
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-1:000000000000 0:1e7334a51725
1 import argparse
2 import json
3 import os
4 import warnings
5
6 import matplotlib.pyplot as plt
7 import numpy as np
8 import skimage.io
9 from cellpose import models, plot, transforms
10
11
12 def main(inputs, img_path, img_format, output_dir):
13 """
14 Parameter
15 ---------
16 inputs : str
17 File path to galaxy tool parameter
18 img_path : str
19 File path for the input image
20 img_format : str
21 One of the ['ome.tiff', 'tiff', 'png', 'jpg']
22 output_dir : str
23 Folder to save the outputs.
24 """
25 warnings.simplefilter('ignore')
26
27 with open(inputs, 'r') as param_handler:
28 params = json.load(param_handler)
29
30 gpu = params['use_gpu']
31 model_type = params['model_type']
32 chan = params['chan']
33 chan2 = params['chan2']
34 chan_first = params['chan_first']
35 if chan is None:
36 channels = None
37 else:
38 channels = [int(chan), int(chan2) if chan2 is not None else None]
39
40 options = params['options']
41
42 img = skimage.io.imread(img_path)
43
44 print(f"Image shape: {img.shape}")
45 # transpose to Ly x Lx x nchann and reshape based on channels
46 if img_format.endswith('tiff'):
47 img = np.transpose(img, (1, 2, 0))
48 img = transforms.reshape(img, channels=channels, chan_first=chan_first)
49
50 print(f"Image shape: {img.shape}")
51 model = models.Cellpose(gpu=gpu, model_type=model_type)
52 masks, flows, styles, diams = model.eval(img, channels=channels, **options)
53
54 # save masks to tiff
55 with warnings.catch_warnings():
56 warnings.simplefilter("ignore")
57 skimage.io.imsave(os.path.join(output_dir, 'cp_masks.tif'),
58 masks.astype(np.uint16))
59
60 # make segmentation show #
61 if params['show_segmentation']:
62 img = skimage.io.imread(img_path)
63 # uniform image
64 if img_format.endswith('tiff'):
65 img = np.transpose(img, (1, 2, 0))
66 img = transforms.reshape(img, channels=channels, chan_first=chan_first)
67
68 maski = masks
69 flowi = flows[0]
70 fig = plt.figure(figsize=(12, 3))
71 # can save images (set save_dir=None if not)
72 plot.show_segmentation(fig, img, maski, flowi, channels=channels)
73 fig.savefig(os.path.join(output_dir, 'segm_show.png'), dpi=300)
74 plt.close(fig)
75
76
77 if __name__ == '__main__':
78 aparser = argparse.ArgumentParser()
79 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
80 aparser.add_argument("-p", "--img_path", dest="img_path")
81 aparser.add_argument("-f", "--img_format", dest="img_format")
82 aparser.add_argument("-O", "--output_dir", dest="output_dir")
83 args = aparser.parse_args()
84
85 main(args.inputs, args.img_path, args.img_format, args.output_dir)