Mercurial > repos > imgteam > image_registration_affine

annotate image_registration_affine.py @ 2:16eedbaba295 draft default tip

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"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit ded1703bcd6e295b26201505edb58381991b265e"
author imgteam
date Mon, 28 Feb 2022 17:34:34 +0000
parents 704881eab59f
children
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704881eab59f "planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit e82400162e337b36c29d6e79fb2deb9871475397"
imgteam
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1 """
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2 Copyright 2021-2022 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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6
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7 """
704881eab59f "planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit e82400162e337b36c29d6e79fb2deb9871475397"
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8 import argparse
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9
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10 import numpy as np
704881eab59f "planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit e82400162e337b36c29d6e79fb2deb9871475397"
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11 import pandas as pd
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12 import skimage.io
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13 from scipy.ndimage import map_coordinates
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14 from scipy.optimize import least_squares
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15 from scipy.signal import convolve2d
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16 from skimage.color import rgb2gray, rgba2rgb
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17 from skimage.filters import gaussian
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18 from skimage.transform import ProjectiveTransform
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21 def _stackcopy(a, b):
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22 if a.ndim == 3:
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23 a[:] = b[:, :, np.newaxis]
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24 else:
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25 a[:] = b
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28 def warp_coords_batch(coord_map, shape, dtype=np.float64, batch_size=1000000):
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29 rows, cols = shape[0], shape[1]
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30 coords_shape = [len(shape), rows, cols]
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31 if len(shape) == 3:
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32 coords_shape.append(shape[2])
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33 coords = np.empty(coords_shape, dtype=dtype)
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35 tf_coords = np.indices((cols, rows), dtype=dtype).reshape(2, -1).T
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37 for i in range(0, (tf_coords.shape[0] // batch_size + 1)):
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38 tf_coords[batch_size * i:batch_size * (i + 1)] = coord_map(tf_coords[batch_size * i:batch_size * (i + 1)])
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39 tf_coords = tf_coords.T.reshape((-1, cols, rows)).swapaxes(1, 2)
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41 _stackcopy(coords[1, ...], tf_coords[0, ...])
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42 _stackcopy(coords[0, ...], tf_coords[1, ...])
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43 if len(shape) == 3:
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44 coords[2, ...] = range(shape[2])
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46 return coords
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48
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49 def affine_registration(params, moving, fixed, metric='mae'):
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50 tmat = np.eye(3)
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51 tmat[0, :] = params.take([0, 1, 2])
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52 tmat[1, :] = params.take([3, 4, 5])
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53
0
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54 trans = ProjectiveTransform(matrix=tmat)
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55 warped_coords = warp_coords_batch(trans, fixed.shape)
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56 t = map_coordinates(moving, warped_coords, mode='nearest')
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57 f = fixed
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58
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59 if metric == 'mse':
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60 err = (t - f) ** 2
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61
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62 elif metric == 'mae':
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63 err = (t - f)
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64
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65 elif metric == 'lcc':
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66 sum_filt = np.ones((9, 9))
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67 win_size = 81
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68
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69 t_sum = convolve2d(t, sum_filt, mode='same', boundary='symm')
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70 f_sum = convolve2d(f, sum_filt, mode='same', boundary='symm')
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71 t2_sum = convolve2d(t * t, sum_filt, mode='same', boundary='symm')
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72 f2_sum = convolve2d(f * f, sum_filt, mode='same', boundary='symm')
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73 tf_sum = convolve2d(t * f, sum_filt, mode='same', boundary='symm')
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74
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75 cross = tf_sum - f_sum * t_sum / win_size
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76 t_var = t2_sum - t_sum * t_sum / win_size
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77 f_var = f2_sum - f_sum * f_sum / win_size
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78 cc = cross * cross / (t_var * f_var + 1e-5)
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79 err = 1 - cc
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80
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81 return err.flatten()
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83
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84 def read_img_as_gray(fn):
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85 im = skimage.io.imread(fn)
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86 nDims = len(im.shape)
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87 assert nDims in [2, 3], 'this tool does not support multichannel images'
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88 if nDims == 3:
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89 assert im.shape[-1] in [3, 4], 'this tool does not support multichannel images'
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90 if im.shape[-1] == 4:
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91 im = rgba2rgb(im)
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92 im = rgb2gray(im)
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93 im = im.astype(float)
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94 im = im / np.max(im)
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95 return im
0
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96
1
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97
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98 def image_registration(fn_moving, fn_fixed, fn_out, smooth_sigma=3, metric='lcc'):
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99 moving = read_img_as_gray(fn_moving)
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100 fixed = read_img_as_gray(fn_fixed)
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101
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102 moving = gaussian(moving, sigma=smooth_sigma)
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103 fixed = gaussian(fixed, sigma=smooth_sigma)
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104
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105 x = np.array([1, 0, 0, 0, 1, 0], dtype='float64')
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106 result = least_squares(affine_registration, x, args=(moving, fixed, metric))
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107
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108 tmat = np.eye(3)
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109 tmat[0, :] = result.x.take([0, 1, 2])
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110 tmat[1, :] = result.x.take([3, 4, 5])
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111
2
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112 tmat = np.linalg.inv(tmat)
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113
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114 pd.DataFrame(tmat).to_csv(fn_out, header=None, index=False, sep="\t")
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115
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116
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117 if __name__ == "__main__":
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118
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119 parser = argparse.ArgumentParser(description="Estimate the transformation matrix")
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120 parser.add_argument("fn_moving", help="Path to the moving image")
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121 parser.add_argument("fn_fixed", help="Path to the fixed (reference) image")
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122 parser.add_argument("fn_tmat", help="Path to the output file for saving the transformation matrix")
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123 parser.add_argument("sigma", type=float, help="Sigma of Gaussian filter for smoothing input images")
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124 parser.add_argument("metric", help="Image similarity metric")
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125 args = parser.parse_args()
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126
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127 image_registration(args.fn_moving, args.fn_fixed, args.fn_tmat, args.sigma, args.metric)