Mercurial > repos > imgteam > image_registration_affine

annotate image_registration_affine.py @ 0:c717e998453e draft

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"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit 79c2fd560fce8ded4d7f7fe97e876871794e2f9d"
author imgteam
date Wed, 30 Dec 2020 20:24:12 +0000
parents
children 704881eab59f
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c717e998453e "planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit 79c2fd560fce8ded4d7f7fe97e876871794e2f9d"
imgteam
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1 import skimage.io
c717e998453e "planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit 79c2fd560fce8ded4d7f7fe97e876871794e2f9d"
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2 from skimage.transform import ProjectiveTransform
c717e998453e "planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit 79c2fd560fce8ded4d7f7fe97e876871794e2f9d"
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3 from skimage.filters import gaussian
c717e998453e "planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit 79c2fd560fce8ded4d7f7fe97e876871794e2f9d"
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4 from scipy.ndimage import map_coordinates
c717e998453e "planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit 79c2fd560fce8ded4d7f7fe97e876871794e2f9d"
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5 from scipy.optimize import least_squares
c717e998453e "planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit 79c2fd560fce8ded4d7f7fe97e876871794e2f9d"
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6 import numpy as np
c717e998453e "planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit 79c2fd560fce8ded4d7f7fe97e876871794e2f9d"
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7 import pandas as pd
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8 import argparse
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12 def _stackcopy(a, b):
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13 if a.ndim == 3:
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14 a[:] = b[:, :, np.newaxis]
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15 else:
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16 a[:] = b
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20 def warp_coords_batch(coord_map, shape, dtype=np.float64, batch_size=1000000):
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21 rows, cols = shape[0], shape[1]
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22 coords_shape = [len(shape), rows, cols]
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23 if len(shape) == 3:
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24 coords_shape.append(shape[2])
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25 coords = np.empty(coords_shape, dtype=dtype)
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27 tf_coords = np.indices((cols, rows), dtype=dtype).reshape(2, -1).T
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29 for i in range(0, (tf_coords.shape[0]//batch_size+1)):
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30 tf_coords[batch_size*i:batch_size*(i+1)] = coord_map(tf_coords[batch_size*i:batch_size*(i+1)])
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31 tf_coords = tf_coords.T.reshape((-1, cols, rows)).swapaxes(1, 2)
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33 _stackcopy(coords[1, ...], tf_coords[0, ...])
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34 _stackcopy(coords[0, ...], tf_coords[1, ...])
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35 if len(shape) == 3:
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36 coords[2, ...] = range(shape[2])
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38 return coords
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42 def affine_registration(params,moving,fixed):
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43 tmat = np.eye(3)
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44 tmat[0,:] = params.take([0,1,2])
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45 tmat[1,:] = params.take([3,4,5])
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47 trans = ProjectiveTransform(matrix=tmat)
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48 warped_coords = warp_coords_batch(trans, fixed.shape)
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49 t = map_coordinates(moving, warped_coords, mode='reflect')
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51 eI = (t - fixed)**2
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52 return eI.flatten()
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56 def image_registration(fn_moving, fn_fixed, fn_out, smooth_sigma=1):
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57 moving = skimage.io.imread(fn_moving,as_gray=True)
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58 fixed = skimage.io.imread(fn_fixed,as_gray=True)
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60 moving = gaussian(moving, sigma=smooth_sigma)
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61 fixed = gaussian(fixed, sigma=smooth_sigma)
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63 x = np.array([1, 0, 0, 0, 1, 0],dtype='float64')
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64 result = least_squares(affine_registration, x, args=(moving,fixed))
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65
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66 tmat = np.eye(3)
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67 tmat[0,:] = result.x.take([0,1,2])
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68 tmat[1,:] = result.x.take([3,4,5])
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70 pd.DataFrame(tmat).to_csv(fn_out, header=None, index=False, sep="\t")
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74 if __name__ == "__main__":
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76 parser = argparse.ArgumentParser(description="Estimate the transformation matrix")
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77 parser.add_argument("fn_moving", help="Name of the moving image.png")
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78 parser.add_argument("fn_fixed", help="Name of the fixed image.png")
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79 parser.add_argument("fn_tmat", help="Name of output file to save the transformation matrix")
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80 args = parser.parse_args()
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82 image_registration(args.fn_moving, args.fn_fixed, args.fn_tmat)