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planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_feature_extraction/ commit b8e0b656d417db6e2ad0f187fc3c5afff0c3acd7
author imgteam
date Tue, 06 Jan 2026 09:25:17 +0000
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imgteam
parents: 4
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1 import giatools
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2 import numpy as np
54429bd1f4c6 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/2d_feature_extraction/ commit c3f4b766f03770f094fda6bda0a5882c0ebd4581
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3 import pandas as pd
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4 import scipy.ndimage as ndi
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5 import skimage.measure
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7 # Fail early if an optional backend is not available
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8 giatools.require_backend('omezarr')
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11 def surface(labels: np.ndarray, label: int) -> int:
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12 """
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13 Ad-hoc implementation for computation of the "perimeter" of an object in 3D (that is a surface).
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14 """
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15 assert labels.ndim == 3 # sanity check
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17 # Create 3-D structuring element with 4-connectivity
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18 selem = np.zeros((3, 3, 3), bool)
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19 for ijk in np.ndindex(*selem.shape):
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20 if (np.array(ijk) == 1).sum() >= 2:
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21 selem[*ijk] = True # noqa: E999
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22 assert selem.sum() == 7 # sanity check
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24 # Compute the area of the surface
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25 cc = (labels == label)
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26 cc_interior = ndi.binary_erosion(cc, selem)
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27 surface = np.logical_xor(cc, cc_interior)
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28 return surface.sum() # number of voxels on the surface of the object
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31 def compute_if_dask(obj):
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32 """
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33 Return the computed object or array if it is a Dask array or deferred computable Dask object.
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34 """
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35 return obj.compute() if hasattr(obj, 'compute') else obj
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37
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38 if __name__ == '__main__':
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39 tool = giatools.ToolBaseplate()
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40 tool.add_input_image('labels')
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41 tool.add_input_image('intensities', required=False)
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42 tool.parser.add_argument('--output', type=str)
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43 tool.parse_args()
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45 # Validate the input image
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46 try:
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47 label_image = tool.args.input_images['labels']
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48 if any(label_image.shape[label_image.axes.index(axis)] > 1 for axis in label_image.axes if axis not in 'XYZ'):
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49 raise ValueError(f'This tool is not applicable to images with {label_image.original_axes} axes.')
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51 # Extract the image features
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52 for section in tool.run('XYZ'): # the validation code above guarantees that we will have only a single iteration
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53 df = pd.DataFrame()
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54
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55 # Get the labels array and cast to `uint8` if it is `bool` (`skimage.measure.regionprops` refuses `bool` typed arrays)
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56 labels_section_data = section['labels'].data.squeeze()
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57 if np.issubdtype(labels_section_data.dtype, bool):
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58 print('Convert labels from bool to uint8')
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59 labels_section_data = labels_section_data.astype(np.uint8)
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61 # Some features currently cannot be computed from Dask arrays
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62 if any(
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63 feature_name in tool.args.params['features'] for feature_name in (
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64 'inertia_tensor_eigvals',
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65 'axis_major_length',
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66 'axis_minor_length',
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67 'eccentricity',
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68 'orientation',
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69 'moments_hu',
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70 )
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71 ):
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72 labels_section_data = compute_if_dask(labels_section_data)
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73
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74 # Compute the image features
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75 if 'intensities' in tool.args.input_images:
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76 regions = skimage.measure.regionprops(labels_section_data, intensity_image=section['intensities'].data.squeeze())
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77 else:
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78 regions = skimage.measure.regionprops(labels_section_data, intensity_image=None)
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79 df['it'] = np.arange(len(regions))
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80 for feature_name in tool.args.params['features']:
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81
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82 # Add the object label
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83 if feature_name == 'label':
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84 df['label'] = df['it'].map(lambda ait: regions[ait].label)
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85
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86 # Add the object perimeter/surface
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87 elif feature_name == 'perimeter' and labels_section_data.ndim == 3:
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88 df['perimeter'] = df['it'].map(
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89 lambda ait: surface(labels_section_data, regions[ait].label), # `skimage.measure.regionprops` cannot compute perimeters for 3-D data
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90 )
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91
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92 # Add the object centroid using separate columns for the different coordinates
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93 elif feature_name == 'centroid':
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94 for axis_idx, axis in enumerate(section['labels'].axes): # XYZ
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95 if section['labels'].shape[axis_idx] > 1:
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96 df[f'{feature_name}_{axis.lower()}'] = df['it'].map(
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97 lambda ait: getattr(regions[ait], feature_name)[axis_idx],
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98 )
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99
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100 # Skip features that are not available when processing 3-D images
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101 elif feature_name in ('eccentricity', 'moments_hu', 'orientation') and labels_section_data.ndim == 3:
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102 print(f'Skip feature that is not available for 3-D images: "{feature_name}"')
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103
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104 # Add another feature from `regions` that was computed via `skimage.measure.regionprops`
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105 else:
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106 try:
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107 df[feature_name] = df['it'].map(lambda ait: getattr(regions[ait], feature_name))
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108 except TypeError:
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109 raise ValueError(f'Unknown feature: "{feature_name}"')
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110
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111 # Resolve any remaining Dask objects to the actual values (e.g., when processing Zarrs)
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112 df = df.map(compute_if_dask)
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113
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114 # Convert lists/tuples/arrays to lists of plain Python numbers (e.g., float instead of np.float64)
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115 df = df.map(
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116 lambda obj: np.asarray(obj).tolist() if type(obj) in (list, tuple, np.ndarray) else obj,
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117 )
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118
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119 del df['it']
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120 df.to_csv(tool.args.raw_args.output, sep='\t', lineterminator='\n', index=False)
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121
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122 except ValueError as err:
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123 exit(err.args[0])