diff projective_transformation_points.py @ 4:aaac58d83043 draft

"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/projective_transformation_points/ commit 7391bb4256f49ec30cf38d9438f97e11ec25a115"

--- a/projective_transformation_points.py	Fri May 08 05:22:34 2020 -0400
+++ b/projective_transformation_points.py	Wed Dec 23 23:56:29 2020 +0000
@@ -42,15 +42,20 @@
     return tf_coords[:, ::-1]
  
 
-def transform(coords, warp_matrix, out):
-    roi_coords = np.array(pd.read_csv(coords, delimiter="\t"))
-    trans_matrix = np.array(pd.read_csv(warp_matrix, delimiter="\t", header=None))
+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')
     
     tol = 10
-    moving = np.zeros(np.max(roi_coords,axis=0)+tol, dtype=np.int8)
+    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] = 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
@@ -58,19 +63,24 @@
     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, mode='constant', cval=0)
-    idx_roi_coords_warped = np.where(moving_warped==1)
+    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)
     
     df = pd.DataFrame()
+    col_names = data.columns.tolist()
     df['x'] = idx_roi_coords_warped[0] + 1
     df['y'] = idx_roi_coords_warped[1] + 1
-    df.to_csv(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)
+    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 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)")
+    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)")
     args = parser.parse_args()
     transform(args.coords, args.warp_matrix, args.out)