--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/overlay_moving_and_fixed_image.py	Mon Jan 07 05:37:21 2019 -0500
@@ -0,0 +1,73 @@
+import argparse
+from PIL import Image
+import skimage.io
+import skimage.color
+from skimage.transform import ProjectiveTransform
+from scipy.ndimage import map_coordinates
+import numpy as np
+import pandas as pd
+
+
+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):
+    trans = ProjectiveTransform(matrix=warp_matrix)
+    warped_coords = warp_coords_batch(trans, fixed_image.shape)
+    return map_coordinates(moving_image, warped_coords)
+
+
+def overlay(moving_image, fixed_image, factor, overlay_out_path):
+    moving_image = Image.fromarray(moving_image).convert("RGBA")
+    fixed_image = Image.fromarray(fixed_image).convert("RGBA")
+    overlay_out = Image.blend(moving_image, fixed_image, factor)
+    overlay_out.save(overlay_out_path, "PNG")
+
+
+if __name__=="__main__":
+    parser = argparse.ArgumentParser(description = "Overlay two images")
+    parser.add_argument("fixed_image", help = "Path to fixed image")
+    parser.add_argument("moving_image", help = "Path to moving image")
+    parser.add_argument("warp_matrix", help="Paste path to warp_matrix.csv that should be used for transformation")
+    parser.add_argument("--inverse_transform", dest='inverse_transform', action='store_true', help="Set if inverse transform should be visualized")
+    parser.add_argument("--factor", dest = "factor", help = "Enter the factor by which images should be blended, 1.0 returns a copy of second image", type = float, default = 0.5)
+    parser.add_argument("overlay_out", help = "Overlay output path")
+    args = parser.parse_args()
+
+    fixed_image = skimage.io.imread(args.fixed_image)
+    moving_image = skimage.io.imread(args.moving_image)
+
+    warp_matrix = pd.read_csv(args.warp_matrix, delimiter=",", header=None)
+    warp_matrix = np.array(warp_matrix)
+    if args.inverse_transform:
+        fixed_image = transform(fixed_image, moving_image, warp_matrix)
+    else:
+        warp_matrix = np.linalg.inv(warp_matrix)
+        moving_image = transform(moving_image, fixed_image, warp_matrix)
+
+    overlay(moving_image, fixed_image, args.factor, args.overlay_out)