Mercurial > repos > imgteam > landmark_registration_ls

comparison landmark_registration_ls.py @ 0:2f36165c49fb draft

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"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/landmark_registration_ls/ commit abd8b0b42f6a700d61c7b042dbf5e5a291b148a3"
author imgteam
date Tue, 29 Dec 2020 12:10:53 +0000
parents
children 69db8c7d4244
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-1:000000000000 0:2f36165c49fb
1 from scipy.linalg import lstsq
2 import pandas as pd
3 import numpy as np
4 import argparse
5
6 def landmark_registration_ls(pts_f1, pts_f2, out_f, delimiter="\t"):
7
8 points1 = pd.read_csv(pts_f1, delimiter=delimiter)
9 points2 = pd.read_csv(pts_f2, delimiter=delimiter)
10
11 src = np.concatenate([np.array(points1['x']).reshape([-1,1]),
12 np.array(points1['y']).reshape([-1,1])],
13 axis=-1)
14 dst = np.concatenate([np.array(points2['x']).reshape([-1,1]),
15 np.array(points2['y']).reshape([-1,1])],
16 axis=-1)
17
18 A = np.zeros((src.size,6))
19 A[0:src.shape[0],[0,1]] = src
20 A[0:src.shape[0],2] = 1
21 A[src.shape[0]:,[3,4]] = src
22 A[src.shape[0]:,5] = 1
23 b = dst.T.flatten().astype('float64')
24 x = lstsq(A,b)
25
26 tmat = np.eye(3)
27 tmat[0,:] = x[0].take([0,1,2])
28 tmat[1,:] = x[0].take([3,4,5])
29 pd.DataFrame(tmat).to_csv(out_f, header=None, index=False, sep="\t")
30
31
32 if __name__ == "__main__":
33
34 parser = argparse.ArgumentParser(description="Estimate transformation from points using least squares")
35
36 parser.add_argument("fn_pts1", help="File name src points")
37 parser.add_argument("fn_pts2", help="File name dst points")
38 parser.add_argument("fn_tmat", help="File name transformation matrix")
39 args = parser.parse_args()
40
41 landmark_registration_ls(args.fn_pts1, args.fn_pts2, args.fn_tmat)
42
43