Mercurial > repos > bgruening > cellpose
comparison cp_segmentation.py @ 0:1e7334a51725 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/cellpose commit 06dd9637975e3b9d6d27a3d5a773c85e9a52baf2
author | bgruening |
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date | Thu, 29 Feb 2024 22:07:26 +0000 |
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-1:000000000000 | 0:1e7334a51725 |
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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) |