annotate landmark_registration_ls.py @ 1:69db8c7d4244 draft default tip

"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/landmark_registration_ls/ commit 927b78d47c31714776ccdf3d16f26c3779298abb"

import argparse

import numpy as np
import pandas as pd
from scipy.linalg import lstsq


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)