Mercurial > repos > imgteam > 2d_simple_filter

annotate filter.py @ 4:5ab62693dca5 draft default tip

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planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
author imgteam
date Sat, 20 Dec 2025 18:28:21 +0000
parents b2d9c92bc431
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b2d9c92bc431 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit a6fd77be465068f709a71d377900da99becf94d8
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diff changeset
1 import argparse
b2d9c92bc431 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit a6fd77be465068f709a71d377900da99becf94d8
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2 import json
b2d9c92bc431 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit a6fd77be465068f709a71d377900da99becf94d8
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3 from typing import (
b2d9c92bc431 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit a6fd77be465068f709a71d377900da99becf94d8
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4 Any,
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5 Callable,
b2d9c92bc431 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit a6fd77be465068f709a71d377900da99becf94d8
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6 )
b2d9c92bc431 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit a6fd77be465068f709a71d377900da99becf94d8
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7
b2d9c92bc431 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit a6fd77be465068f709a71d377900da99becf94d8
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8 import giatools
b2d9c92bc431 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit a6fd77be465068f709a71d377900da99becf94d8
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9 import numpy as np
b2d9c92bc431 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit a6fd77be465068f709a71d377900da99becf94d8
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10 import scipy.ndimage as ndi
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12
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13 def image_astype(image: giatools.Image, dtype: np.dtype) -> giatools.Image:
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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14 if np.issubdtype(image.data.dtype, dtype):
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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15 return image # no conversion needed
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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16 else:
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17 return giatools.Image(
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18 data=image.data.astype(dtype),
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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19 axes=image.axes,
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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20 original_axes=image.original_axes,
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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21 metadata=image.metadata,
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22 )
2
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24
4
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25 def get_anisotropy(image: giatools.Image, axes: str) -> tuple[float, ...] | None:
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26 """
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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27 Get the anisotropy of the image pixels/voxels along the given `axes`.
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28
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29 TODO: Migrate to `giatools.Image.get_anisotropy` with `giatools >=0.7`
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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30 """
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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31
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32 # Determine the pixel/voxel size
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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33 voxel_size = list()
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34 for axis in axes:
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35 match axis:
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36 case 'X':
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37 if image.metadata.pixel_size is None:
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38 return None # unknown size
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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39 else:
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40 voxel_size.append(image.metadata.pixel_size[0])
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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41 case 'Y':
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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42 if image.metadata.pixel_size is None:
5ab62693dca5 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_simple_filter/ commit c9cc62c508da3804872d105800993746c36cca48
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43 return None # unknown size
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44 else:
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45 voxel_size.append(image.metadata.pixel_size[1])
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46 case 'Z':
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47 if image.metadata.z_spacing is None:
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48 return None # unknown size
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49 else:
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50 voxel_size.append(image.metadata.z_spacing)
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51
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52 # Check for unknown size and compute anisotropy
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53 if any(abs(s) < 1e-8 for s in voxel_size):
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54 print('Unknown pixel/voxel size')
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55 return None
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56 else:
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57 denom = pow(np.prod(voxel_size), 1 / len(voxel_size)) # geometric mean
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58 anisotropy = tuple(np.divide(voxel_size, denom).tolist())
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59 print(f'Anisotropy of {axes} pixels/voxels:', anisotropy)
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60 return anisotropy
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61
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62
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63 def get_anisotropic_size(image: giatools.Image, axes: str, size: int) -> tuple[int, ...] | int:
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64 if (anisotropy := get_anisotropy(image, axes)) is not None:
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65 return tuple(
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66 np.divide(size, anisotropy).round().clip(1, np.inf).astype(int).tolist(),
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67 )
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68 else:
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69 return size
2
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70
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71
4
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72 class Filters:
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73
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74 @staticmethod
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75 def gaussian(
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76 image: giatools.Image,
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77 sigma: float,
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78 anisotropic: bool,
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79 axes: str,
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80 order: int = 0,
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81 direction: int | None = None,
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82 **kwargs: Any,
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83 ) -> giatools.Image:
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84 if direction is None:
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85 _order = 0
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86 elif order >= 1:
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87 _order = [0] * len(axes)
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88 _order[direction] = order
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89 _order = tuple(_order)
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90 if anisotropic and (anisotropy := get_anisotropy(image, axes)) is not None:
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91 _sigma = tuple(np.divide(sigma, anisotropy).tolist())
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92 else:
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93 _sigma = sigma
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94 return apply_nd_filter(
2
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95 ndi.gaussian_filter,
4
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96 image,
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97 sigma=_sigma,
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98 order=_order,
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99 axes=axes,
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100 **kwargs,
2
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101 )
4
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102
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103 @staticmethod
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104 def uniform(image: giatools.Image, size: int, anisotropic: bool, axes: str, **kwargs: Any) -> giatools.Image:
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105 _size = get_anisotropic_size(image, axes, size) if anisotropic else size
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106 return apply_nd_filter(
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107 ndi.uniform_filter,
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108 image,
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109 size=_size,
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110 axes=axes,
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111 **kwargs,
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112 )
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113
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114 @staticmethod
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115 def median(image: giatools.Image, size: int, anisotropic: bool, axes: str, **kwargs: Any) -> giatools.Image:
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116 _size = get_anisotropic_size(image, axes, size) if anisotropic else size
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117 return apply_nd_filter(
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118 ndi.median_filter,
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119 image,
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120 size=_size,
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121 axes=axes,
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122 **kwargs,
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123 )
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124
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125 @staticmethod
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126 def prewitt(image: giatools.Image, direction: int, **kwargs: Any) -> giatools.Image:
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127 return apply_nd_filter(
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128 ndi.prewitt,
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129 image,
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130 axis=direction,
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131 **kwargs,
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132 )
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133
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134 @staticmethod
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135 def sobel(image: giatools.Image, direction: int, **kwargs: Any) -> giatools.Image:
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136 return apply_nd_filter(
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137 ndi.sobel,
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138 image,
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139 axis=direction,
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140 **kwargs,
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141 )
2
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142
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143
4
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144 def apply_nd_filter(
2
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145 filter_impl: Callable[[np.ndarray, Any, ...], np.ndarray],
4
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146 image: giatools.Image,
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147 axes: str,
2
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148 **kwargs: Any,
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149 ) -> giatools.Image:
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150 """
4
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151 Apply the filter to the 2-D/3-D, potentially multi-frame and multi-channel image.
2
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152 """
4
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153 print(
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154 'Applying filter:',
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155 filter_impl.__name__,
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156 'with',
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157 ', '.join(
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158 f'{key}={repr(value)}' for key, value in (kwargs | dict(axes=axes)).items()
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159 if not isinstance(value, np.ndarray)
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160 ),
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161 )
2
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162 result_data = None
4
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163 for section_sel, section_arr in image.iterate_jointly(axes):
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164 assert len(axes) == section_arr.ndim and section_arr.ndim in (2, 3) # sanity check, always True
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165
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166 # Define the section using the requested axes layout (compatible with `kwargs`)
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167 joint_axes_original_order = ''.join(filter(lambda axis: axis in axes, image.axes))
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168 section = giatools.Image(section_arr, joint_axes_original_order).reorder_axes_like(axes)
2
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169
4
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170 # Perform 2-D or 3-D filtering
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171 section_result = giatools.Image(
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172 filter_impl(section.data, **kwargs),
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173 axes, # axes layout compatible with `kwargs`
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174 ).reorder_axes_like(
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175 joint_axes_original_order, # axes order compatible to the input `image`
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176 )
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177
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178 # Update the result image for the current section
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179 if result_data is None:
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180 result_data = np.empty(image.data.shape, section_result.data.dtype)
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181 result_data[section_sel] = section_result.data
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182
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183 # Return results
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184 return giatools.Image(result_data, image.axes, image.metadata)
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185
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186
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187 if __name__ == "__main__":
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188 parser = argparse.ArgumentParser()
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189 parser.add_argument('input', type=str, help='Input image filepath')
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190 parser.add_argument('output', type=str, help='Output image filepath (TIFF)')
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191 parser.add_argument('params', type=str)
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192 args = parser.parse_args()
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193
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194 # Read the config file
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195 with open(args.params) as cfgf:
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196 cfg = json.load(cfgf)
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197 cfg.setdefault('axes', 'YX')
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198
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199 # Read the input image
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200 image = giatools.Image.read(args.input)
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201 print('Input image shape:', image.data.shape)
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202 print('Input image axes:', image.axes)
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203 print('Input image dtype:', image.data.dtype)
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204
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205 # Convert the image to the explicitly requested `dtype`, or the same as the input image
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206 convert_to = getattr(np, cfg.pop('dtype', str(image.data.dtype)))
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207 if np.issubdtype(image.data.dtype, convert_to):
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208 convert_to = image.data.dtype # use the input image `dtype` if `convert_to` is a superset
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209 elif convert_to == np.floating:
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210 convert_to = np.float64 # use `float64` if conversion to *any* float is *required*
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211
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212 # If the input image is `float16` or conversion to `float16` is requested, ...
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213 if convert_to == np.float16:
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214 image = image_astype(image, np.float32) # ...convert to `float32` as an intermediate
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215 else:
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216 image = image_astype(image, convert_to) # ...otherwise, convert now
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217
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218 # Perform filtering
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219 filter_type = cfg.pop('filter_type')
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220 filter_impl = getattr(Filters, filter_type)
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221 result = filter_impl(image, **cfg)
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222
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223 # Apply `dtype` conversion
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224 result = image_astype(result, convert_to)
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225
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226 # Write the result
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227 result = result.normalize_axes_like(
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228 image.original_axes,
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229 )
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230 print('Output image shape:', result.data.shape)
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231 print('Output image axes:', result.axes)
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232 print('Output image dtype:', result.data.dtype)
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233 result.write(args.output, backend='tifffile')