diff projective_transformation.py @ 0:17f5d0c3f8a3 draft

planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/projective_transformation/ commit c3f4b766f03770f094fda6bda0a5882c0ebd4581

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/projective_transformation.py	Sat Feb 09 14:44:40 2019 -0500
@@ -0,0 +1,61 @@
+import skimage.io
+from skimage.transform import ProjectiveTransform
+from scipy.ndimage import map_coordinates
+import numpy as np
+import pandas as pd
+import argparse
+import warnings
+import shutil
+
+
+def _stackcopy(a, b):
+    if a.ndim == 3:
+        a[:] = b[:, :, np.newaxis]
+    else:
+        a[:] = b
+
+
+def warp_coords_batch(coord_map, shape, dtype=np.float64, batch_size=1000000):
+    rows, cols = shape[0], shape[1]
+    coords_shape = [len(shape), rows, cols]
+    if len(shape) == 3:
+        coords_shape.append(shape[2])
+    coords = np.empty(coords_shape, dtype=dtype)
+
+    tf_coords = np.indices((cols, rows), dtype=dtype).reshape(2, -1).T
+
+    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)])
+    tf_coords = tf_coords.T.reshape((-1, cols, rows)).swapaxes(1, 2)
+
+    _stackcopy(coords[1, ...], tf_coords[0, ...])
+    _stackcopy(coords[0, ...], tf_coords[1, ...])
+    if len(shape) == 3:
+        coords[2, ...] = range(shape[2])
+
+    return coords
+
+
+def transform(moving_image, fixed_image, warp_matrix, out):
+    moving_image = skimage.io.imread(moving_image)
+    fixed_image = skimage.io.imread(fixed_image)
+    warp_matrix = pd.read_csv(warp_matrix, delimiter="\t", header=None)
+    warp_matrix = np.array(warp_matrix)
+
+    trans = ProjectiveTransform(matrix=warp_matrix)
+    warped_coords = warp_coords_batch(trans, fixed_image.shape)
+    t = map_coordinates(moving_image, warped_coords, mode='reflect')
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        skimage.io.imsave(out, t)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Transform the image")
+    parser.add_argument("fixed_image", help="Paste path to image.png that should be transformed")
+    parser.add_argument("moving_image", help="Paste path to fixed image.png")
+    parser.add_argument("warp_matrix", help="Paste path to warp_matrix.csv that should be used for transformation")
+    parser.add_argument("out", help="Paste path to file in which transformed image should be saved")
+    args = parser.parse_args()
+    transform(args.moving_image, args.fixed_image, args.warp_matrix, args.out)