diff projective_transformation_points.py @ 5:3a686b6aa7fc draft

"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/projective_transformation_points/ commit c4424f5dd4110c92ae39815ebfe2ea8c1010be9c"

--- a/projective_transformation_points.py	Wed Dec 23 23:56:29 2020 +0000
+++ b/projective_transformation_points.py	Wed Jan 26 15:25:38 2022 +0000
@@ -1,8 +1,17 @@
-from skimage.transform import ProjectiveTransform
-from scipy.ndimage import map_coordinates
+"""
+Copyright 2019-2022 Biomedical Computer Vision Group, Heidelberg University.
+
+Distributed under the MIT license.
+See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
+
+"""
+
+import argparse
+
 import numpy as np
 import pandas as pd
-import argparse
+from scipy.ndimage import map_coordinates
+from skimage.transform import ProjectiveTransform
 
 
 def _stackcopy(a, b):
@@ -21,8 +30,8 @@
 
     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)])
+    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, ...])
@@ -36,51 +45,50 @@
 def warp_coords_batch(coord_map, coords, dtype=np.float64, batch_size=1000000):
     tf_coords = coords.astype(np.float32)[:, ::-1]
 
-    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)])
+    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 tf_coords[:, ::-1]
- 
+
 
 def transform(fn_roi_coords, fn_warp_matrix, fn_out):
     data = pd.read_csv(fn_roi_coords, delimiter="\t")
     all_data = np.array(data)
-    
-    nrows = all_data.shape[0]
-    ncols = all_data.shape[1]
-    roi_coords = all_data.take([0,1],axis=1).astype('int64')
-    
+
+    nrows, ncols = all_data.shape[0:2]
+    roi_coords = all_data.take([0, 1], axis=1).astype('int64')
+
     tol = 10
-    moving = np.zeros(np.max(roi_coords,axis=0)+tol, dtype=np.uint32)
-    idx_roi_coords = (roi_coords[:,0]-1) * moving.shape[1] + roi_coords[:,1] - 1
-    moving.flat[idx_roi_coords] = np.transpose(np.arange(nrows)+1)
-    
+    moving = np.zeros(np.max(roi_coords, axis=0) + tol, dtype=np.uint32)
+    idx_roi_coords = (roi_coords[:, 0] - 1) * moving.shape[1] + roi_coords[:, 1] - 1
+    moving.flat[idx_roi_coords] = np.transpose(np.arange(nrows) + 1)
+
     trans_matrix = np.array(pd.read_csv(fn_warp_matrix, delimiter="\t", header=None))
     transP = ProjectiveTransform(matrix=trans_matrix)
     roi_coords_warped_direct = warp_coords_batch(transP, roi_coords)
-    shape_fixed = np.round(np.max(roi_coords_warped_direct,axis=0)).astype(roi_coords.dtype)+tol
-    
+    shape_fixed = np.round(np.max(roi_coords_warped_direct, axis=0)).astype(roi_coords.dtype) + tol
+
     transI = ProjectiveTransform(matrix=np.linalg.inv(trans_matrix))
     img_coords_warped = warp_img_coords_batch(transI, shape_fixed)
-    
+
     moving_warped = map_coordinates(moving, img_coords_warped, order=0, mode='constant', cval=0)
-    idx_roi_coords_warped = np.where(moving_warped>0)
-    roi_annots_warped = moving_warped.compress((moving_warped>0).flat)
-    
+    idx_roi_coords_warped = np.where(moving_warped > 0)
+    roi_annots_warped = moving_warped.compress((moving_warped > 0).flat)
+
     df = pd.DataFrame()
     col_names = data.columns.tolist()
     df['x'] = idx_roi_coords_warped[0] + 1
     df['y'] = idx_roi_coords_warped[1] + 1
-    if ncols>2:
-        for i in range(2,ncols):
-            df[col_names[i]] = all_data[:,i].take(roi_annots_warped)
-    df.to_csv(fn_out, index = False, sep="\t")
+    if ncols > 2:
+        for i in range(2, ncols):
+            df[col_names[i]] = all_data[:, i].take(roi_annots_warped - 1)
+    df.to_csv(fn_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 (and labels) to transform (tab separated)")
-    parser.add_argument("warp_matrix", help="Paste path to .csv that should be used for transformation (tab separated)")
-    parser.add_argument("out", help="Paste path to file in which transformed coords (and labels) should be saved (tab separated)")
+    parser = argparse.ArgumentParser(description="Transform ROIs defined by pixel (point) coordinates")
+    parser.add_argument("coords", help="Path to the TSV file of the coordinates (and labels) to be transformed")
+    parser.add_argument("tmat", help="Path to the TSV file of the transformation matrix")
+    parser.add_argument("out", help="Path to the TSV file of the transformed coordinates (and labels)")
     args = parser.parse_args()
-    transform(args.coords, args.warp_matrix, args.out)
+    transform(args.coords, args.tmat, args.out)