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planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/superdsm/ commit c86a1b93cb7732f7331a981d13465653cc1a2790
author imgteam
date Wed, 24 Apr 2024 08:13:34 +0000
parents dc5f72f6b1e9
children
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1 """
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2 Copyright 2023 Leonid Kostrykin, Biomedical Computer Vision Group, Heidelberg University.
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3
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4 Distributed under the MIT license.
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5 See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
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7 """
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8
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9 import argparse
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10 import csv
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11 import imghdr
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12 import os
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13 import pathlib
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14 import shutil
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15 import tempfile
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18 hyperparameters = [
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19 ('AF_scale', float),
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20 ('c2f-region-analysis/min_atom_radius', float),
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21 ('c2f-region-analysis/min_norm_energy_improvement', float),
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22 ('c2f-region-analysis/max_atom_norm_energy', float),
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23 ('c2f-region-analysis/max_cluster_marker_irregularity', float),
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24 ('dsm/alpha', float),
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25 ('dsm/AF_alpha', float),
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26 ('global-energy-minimization/pruning', str),
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27 ('global-energy-minimization/beta', float),
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28 ('global-energy-minimization/AF_beta', float),
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29 ('postprocess/mask_max_distance', int),
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30 ('postprocess/mask_stdamp', float),
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31 ('postprocess/max_norm_energy', float),
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32 ('postprocess/min_contrast', float),
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33 ('postprocess/min_object_radius', float),
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34 ]
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35
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36
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37 def get_param_name(key):
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38 return key.replace('/', '_').replace('-', '_')
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39
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40
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41 def create_config(args):
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42 cfg = superdsm.config.Config()
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43 for key, _ in hyperparameters:
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44 value = getattr(args, get_param_name(key))
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45 if value is not None:
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46 cfg[key] = value
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47 return cfg
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48
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49
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50 def flatten_dict(d, sep='/'):
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51 result = {}
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52 for key, val in d.items():
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53 if isinstance(val, dict):
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54 for sub_key, sub_val in flatten_dict(val, sep=sep).items():
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55 result[f'{key}{sep}{sub_key}'] = sub_val
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56 else:
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57 result[key] = val
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58 return result
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59
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60
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61 if __name__ == "__main__":
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62 parser = argparse.ArgumentParser(description='Segmentation of cell nuclei in 2-D fluorescence microscopy images')
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63 parser.add_argument('image', type=str, help='Path to the input image')
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64 parser.add_argument('slots', type=int)
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65 parser.add_argument('--do-masks', type=str, default=None, help='Path to the file containing the segmentation masks')
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66 parser.add_argument('--do-cfg', type=str, default=None, help='Path to the file containing the configuration')
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67 parser.add_argument('--do-overlay', type=str, default=None, help='Path to the file containing the overlay of the segmentation results')
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68 parser.add_argument('--do-overlay-border', type=int)
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69 for key, ptype in hyperparameters:
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70 parser.add_argument('--' + get_param_name(key), type=ptype, default=None)
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71 args = parser.parse_args()
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72
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73 if args.slots >= 2:
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74 num_threads_per_process = 2
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75 num_processes = args.slots // num_threads_per_process
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76 else:
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77 num_threads_per_process = 1
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78 num_processes = 1
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79
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80 os.environ['MKL_NUM_THREADS'] = str(num_threads_per_process)
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81 os.environ['OPENBLAS_NUM_THREADS'] = str(num_threads_per_process)
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82
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83 import giatools.io
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84 import ray
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85 import superdsm.automation
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86 import superdsm.io
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87 import superdsm.render
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88
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89 ray.init(num_cpus=num_processes, log_to_driver=True)
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90
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91 with tempfile.TemporaryDirectory() as tmpdirname:
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92 tmpdir = pathlib.Path(tmpdirname)
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93 img_ext = imghdr.what(args.image)
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94 img_filepath = tmpdir / f'input.{img_ext}'
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95 shutil.copy(str(args.image), img_filepath)
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96
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97 pipeline = superdsm.pipeline.create_default_pipeline()
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98 cfg = create_config(args)
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99 img = giatools.io.imread(img_filepath, impl=superdsm.io.imread)
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100
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101 # Create configuration if it is required:
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102 if args.do_cfg or args.do_overlay or args.do_masks:
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103 cfg, _ = superdsm.automation.create_config(pipeline, cfg, img)
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104
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105 # Perform segmentation if it is required:
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106 if args.do_overlay or args.do_masks:
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107 print('Performing segmentation')
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108 data, cfg, _ = pipeline.process_image(img, cfg)
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109
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110 # Write configuration used for segmentation, or the automatically created one, otherwise:
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111 if args.do_cfg:
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112 print(f'Writing config to: {args.do_cfg}')
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113 with open(args.do_cfg, 'w') as fp:
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114 tsv_out = csv.writer(fp, delimiter='\t')
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115 tsv_out.writerow(['Hyperparameter', 'Value'])
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116 rows = sorted(flatten_dict(cfg.entries).items(), key=lambda item: item[0])
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117 for key, value in rows:
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118 tsv_out.writerow([key, value])
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119
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120 # Write the overlay image:
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121 if args.do_overlay:
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122 print(f'Writing overlay to: {args.do_overlay}')
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123 overlay = superdsm.render.render_result_over_image(data, border_width=args.do_overlay_border, normalize_img=False)
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124 superdsm.io.imwrite(args.do_overlay, overlay)
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125
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126 # Write the label map:
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127 if args.do_masks:
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128 print(f'Writing masks to: {args.do_masks}')
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129 masks = superdsm.render.rasterize_labels(data)
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130 superdsm.io.imwrite(args.do_masks, masks)