Mercurial > repos > imgteam > landmark_registration

comparison landmark_registration.py @ 2:4e089a0983b1 draft

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"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/landmark_registration/ commit 927b78d47c31714776ccdf3d16f26c3779298abb"
author imgteam
date Sun, 20 Feb 2022 15:46:58 +0000
parents b0503eec7bd6
children aee73493bf53
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1:b0503eec7bd6 2:4e089a0983b1
1 """
2 Copyright 2017-2022 Biomedical Computer Vision Group, Heidelberg University.
3
4 Distributed under the MIT license.
5 See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
6
7 """
8
9 import argparse
10
11 import numpy as np
12 import pandas as pd
13 from scipy.linalg import lstsq
1 from skimage.measure import ransac 14 from skimage.measure import ransac
2 from skimage.transform import AffineTransform 15 from skimage.transform import AffineTransform
3 import pandas as pd
4 import numpy as np
5 import argparse
6 16
7 def landmark_registration(points_file1, points_file2, out_file, residual_threshold=2, max_trials=100, delimiter="\t"):
8 points1 = pd.read_csv(points_file1, delimiter=delimiter)
9 points2 = pd.read_csv(points_file2, delimiter=delimiter)
10 17
11 src = np.concatenate([np.array(points1['x']).reshape([-1,1]), np.array(points1['y']).reshape([-1,1])], axis=-1) 18 def landmark_registration(pts_f1, pts_f2, out_f, res_th=None, max_ite=None, delimiter="\t"):
12 dst = np.concatenate([np.array(points2['x']).reshape([-1,1]), np.array(points2['y']).reshape([-1,1])], axis=-1)
13 19
14 model = AffineTransform() 20 points1 = pd.read_csv(pts_f1, delimiter=delimiter)
15 model_robust, inliers = ransac((src, dst), AffineTransform, min_samples=3, 21 points2 = pd.read_csv(pts_f2, delimiter=delimiter)
16 residual_threshold=residual_threshold, max_trials=max_trials) 22
17 pd.DataFrame(model_robust.params).to_csv(out_file, header=None, index=False, sep="\t") 23 src = np.concatenate([np.array(points1['x']).reshape([-1, 1]),
24 np.array(points1['y']).reshape([-1, 1])],
25 axis=-1)
26 dst = np.concatenate([np.array(points2['x']).reshape([-1, 1]),
27 np.array(points2['y']).reshape([-1, 1])],
28 axis=-1)
29
30 if res_th is not None and max_ite is not None:
31 model_robust, inliers = ransac((src, dst), AffineTransform, min_samples=3, residual_threshold=res_th, max_trials=max_ite)
32 pd.DataFrame(model_robust.params).to_csv(out_f, header=None, index=False, sep="\t")
33
34 else:
35 A = np.zeros((src.size, 6))
36 A[0:src.shape[0], [0, 1]] = src
37 A[0:src.shape[0], 2] = 1
38 A[src.shape[0]:, [3, 4]] = src
39 A[src.shape[0]:, 5] = 1
40 b = dst.T.flatten().astype('float64')
41 x = lstsq(A, b)
42
43 tmat = np.eye(3)
44 tmat[0, :] = x[0].take([0, 1, 2])
45 tmat[1, :] = x[0].take([3, 4, 5])
46 pd.DataFrame(tmat).to_csv(out_f, header=None, index=False, sep="\t")
47
18 48
19 if __name__ == "__main__": 49 if __name__ == "__main__":
20 parser = argparse.ArgumentParser(description="Estimate transformation from points") 50 parser = argparse.ArgumentParser(description="Estimate affine transformation matrix based on landmark coordinates")
21 parser.add_argument("points_file1", help="Paste path to src points") 51 parser.add_argument("fn_pts1", help="Coordinates of SRC landmarks (tsv file)")
22 parser.add_argument("points_file2", help="Paste path to dst points") 52 parser.add_argument("fn_pts2", help="Coordinates of DST landmarks (tsv file)")
23 parser.add_argument("warp_matrix", help="Paste path to warp_matrix.csv that should be used for transformation") 53 parser.add_argument("fn_tmat", help="Path the output (transformation matrix)")
24 parser.add_argument("--residual_threshold", dest="residual_threshold", help="Maximum distance for a data point to be classified as an inlier.", type=float, default=2) 54 parser.add_argument("--res_th", dest="res_th", type=float, help="Maximum distance for a data point to be classified as an inlier")
25 parser.add_argument("--max_trials", dest="max_trials", help="Maximum number of iterations for random sample selection.", type=int, default=100) 55 parser.add_argument("--max_ite", dest="max_ite", type=int, help="Maximum number of iterations for random sample selection")
26 args = parser.parse_args() 56 args = parser.parse_args()
27 landmark_registration(args.points_file1, args.points_file2, args.warp_matrix, residual_threshold=args.residual_threshold, max_trials=args.max_trials) 57
58 res_th = None
59 if args.res_th:
60 res_th = args.res_th
61 max_ite = None
62 if args.max_ite:
63 max_ite = args.max_ite
64
65 landmark_registration(args.fn_pts1, args.fn_pts2, args.fn_tmat, res_th=res_th, max_ite=max_ite)