view projective_transformation_points.py @ 0:470fd1c1c10c draft default tip

planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/projective_transformation_points/ commit 787ebcc8daa1834214bc92c201c921c704ef2d1f

+
−from skimage.transform import ProjectiveTransform
+
−import numpy as np
+
−import pandas as pd
+
−import argparse
+
−
+
−
+
−def warp_coords_batch(coord_map, coords, dtype=np.float64, batch_size=1000000):
+
−    tf_coords = coords.astype(np.float32)
+
−
+
−    for i in range(0, (tf_coords.shape[0]//batch_size+1)):
+
−        tf_coords[batch_size*i:batch_size*(i+1)] = coord_map(tf_coords[batch_size*i:batch_size*(i+1)])
+
−
+
−    return np.unique(np.round(tf_coords).astype(coords.dtype),axis=0)
+
−
+
−
+
−def transform(coords, warp_matrix, out):
+
−    indices = np.array(pd.read_csv(coords, delimiter="\t"))
+
−    a_matrix = np.array(pd.read_csv(warp_matrix, delimiter=",", header=None))
+
−    
+
−    trans = ProjectiveTransform(matrix=a_matrix)
+
−    warped_coords = warp_coords_batch(trans, indices)
+
−
+
−    df = pd.DataFrame()
+
−    df['x'] = warped_coords[:,0]
+
−    df['y'] = warped_coords[:,1]
+
−    df.to_csv(out, index = False, sep="\t")
+
−
+
−
+
−if __name__ == "__main__":
+
−    parser = argparse.ArgumentParser(description="Transform coordinates")
+
−    parser.add_argument("coords", help="Paste path to .csv with coordinates to transform (tab separated)")
+
−    parser.add_argument("warp_matrix", help="Paste path to .csv that should be used for transformation (, separated)")
+
−    parser.add_argument("out", help="Paste path to file in which transformed coords should be saved (tab separated)")
+
−    args = parser.parse_args()
+
−    transform(args.coords, args.warp_matrix, args.out)