Updated the description of tool "projective_transformation_points" (#32)

* Updated tool "projective_transformation_points": bug fix, new description, new test data

* Updated license info

LICENSE.md

@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (c) 2016 
+Copyright (c) 2016-2022 Biomedical Computer Vision Group, Heidelberg University 
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

tools/projective_transformation_points/.shed.yml

@@ -1,7 +1,7 @@
 categories:
   - Imaging
-description: Projective transformation - Points
-long_description: This tool performs a projective transformation of input coordinates with a warp matrix.
+description: Projective transformation of ROIs defined by pixel (point) coordinates
+long_description: This tool performs a projective transformation of regions of interest (ROIs) defined by pixel (point) coordinates based on a given transformation matrix.
 name: projective_transformation_points
 owner: imgteam
 homepage_url: https://github.com/bmcv

tools/projective_transformation_points/projective_transformation_points.py

@@ -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 @@ def warp_img_coords_batch(coord_map, shape, dtype=np.float64, batch_size=1000000
 
     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_img_coords_batch(coord_map, shape, dtype=np.float64, batch_size=1000000
 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)

tools/projective_transformation_points/projective_transformation_points.xml

@@ -1,38 +1,45 @@
-<tool id="ip_projective_transformation_points" name="Projective Transformation" version="0.1.0">
-    <description>of input points</description>
+<tool id="ip_projective_transformation_points" name="Projective Transformation" license="MIT" version="0.1.1" profile="20.05">
+    <description>of ROIs defined by pixel (point) coordinates</description>
     <requirements>
         <requirement type="package" version="0.14.2">scikit-image</requirement>
         <requirement type="package" version="1.2.1">scipy</requirement>
         <requirement type="package" version="0.23.4">pandas</requirement>
         <requirement type="package" version="1.15.2">numpy</requirement>
         <requirement type="package" version="0.15.1">tifffile</requirement>
     </requirements>
-    <command>
+    <command detect_errors="aggressive">
     <![CDATA[
         python '$__tool_directory__/projective_transformation_points.py'
-            '$moving_points'
-            '$warp_matrix'
-            '$out'
+            '$input'
+            '$tmat'
+            '$output'
     ]]>
     </command>
     <inputs>
-        <param name="moving_points" type="data" format="tabular" label="Moving Points" />
-        <param name="warp_matrix" type= "data" format="tabular" label="Warp Matrix" />
+        <param name="input" type="data" format="tabular" label="ROIs (pixel coordinates) to be transformed (TSV file)" />
+        <param name="tmat" type= "data" format="tabular" label="Transformation matrix (TSV file)" />
     </inputs>
     <outputs>
-       <data format="tabular" name="out" />
+       <data format="tabular" name="output" />
     </outputs> 
     <tests>
       <test>
-        <param name="moving_points" value="points_tsv.tsv" />
-        <param name="warp_matrix" value="warp_matrix.tsv" />
-        <output name="out" value="out.tsv" ftype="tabular" />
+        <param name="input" value="in.tabular" />
+        <param name="tmat" value="tmat.tabular" />
+        <output name="output" value="out.tabular" ftype="tabular" compare="diff" />
       </test>
     </tests>
     <help>
     **What it does**
 
-    This tool performs a projective transformation of the input (moving) points (with/without labels).
+    This tool performs a projective transformation of regions of interest (ROIs) defined by pixel (point) coordinates with/without labels.
+
+    About the format of point coordinates (and labels) in the input TSV table: 
+    1st column: x-coordinate; 
+    2nd column: y-coordinate; 
+    (optional) more column(s): point label(s) (numerical or categorical).
+    The top row of the table will be regarded as column headers.
+
     </help>
     <citations>
         <citation type="doi">10.1016/j.jbiotec.2017.07.019</citation>

tools/projective_transformation_points/test-data/in.tabular

@@ -0,0 +1,10 @@
+x	y	tissue_type	ROI_ID
+11	49	tumor	1
+11	50	stroma	1
+11	51	tumor	2
+11	52	tumor	2
+12	49	tumor	1
+12	50	stroma	1
+12	51	tumor	2
+12	52	tumor	2
+13	49	tumor	1

tools/projective_transformation_points/test-data/out.tabular

@@ -0,0 +1,10 @@
+x	y	tissue_type	ROI_ID
+111	99	tumor	1
+111	100	stroma	1
+111	101	tumor	2
+111	102	tumor	2
+112	99	tumor	1
+112	100	stroma	1
+112	101	tumor	2
+112	102	tumor	2
+113	99	tumor	1

tools/projective_transformation_points/test-data/tmat.tabular

@@ -0,0 +1,3 @@
+1.0	0.0	50
+0.0	1.0	100
+0.0	0.0	1.0