diff landmark_registration_ls.py @ 0:2f36165c49fb draft

"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/landmark_registration_ls/ commit abd8b0b42f6a700d61c7b042dbf5e5a291b148a3"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/landmark_registration_ls.py	Tue Dec 29 12:10:53 2020 +0000
@@ -0,0 +1,43 @@
+from scipy.linalg import lstsq
+import pandas as pd
+import numpy as np
+import argparse
+
+def landmark_registration_ls(pts_f1, pts_f2, out_f, delimiter="\t"):
+    
+    points1 = pd.read_csv(pts_f1, delimiter=delimiter)
+    points2 = pd.read_csv(pts_f2, delimiter=delimiter)
+
+    src = np.concatenate([np.array(points1['x']).reshape([-1,1]), 
+                          np.array(points1['y']).reshape([-1,1])], 
+                         axis=-1)
+    dst = np.concatenate([np.array(points2['x']).reshape([-1,1]), 
+                          np.array(points2['y']).reshape([-1,1])], 
+                         axis=-1)
+
+    A = np.zeros((src.size,6))
+    A[0:src.shape[0],[0,1]] = src
+    A[0:src.shape[0],2] = 1
+    A[src.shape[0]:,[3,4]] = src
+    A[src.shape[0]:,5] = 1
+    b = dst.T.flatten().astype('float64')
+    x = lstsq(A,b)
+    
+    tmat = np.eye(3)
+    tmat[0,:] = x[0].take([0,1,2])
+    tmat[1,:] = x[0].take([3,4,5])
+    pd.DataFrame(tmat).to_csv(out_f, header=None, index=False, sep="\t")
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(description="Estimate transformation from points using least squares")
+    
+    parser.add_argument("fn_pts1", help="File name src points")
+    parser.add_argument("fn_pts2", help="File name dst points")
+    parser.add_argument("fn_tmat", help="File name transformation matrix")
+    args = parser.parse_args()
+    
+    landmark_registration_ls(args.fn_pts1, args.fn_pts2, args.fn_tmat)
+
+