Mercurial > repos > imgteam > projective_transformation

changeset 2:1ffdb07020ee draft

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"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/projective_transformation/ commit 40b2eeecc9e15be0b710f5e5195ac6f3b3b83c39"
author imgteam
date Fri, 14 Jan 2022 03:07:08 +0000
parents 974cf4357707
children be9a815e2240
files projective_transformation.py projective_transformation.xml
diffstat 2 files changed, 87 insertions(+), 36 deletions(-) [+]
line wrap: on
line diff
--- a/projective_transformation.py	Wed Dec 18 05:03:09 2019 -0500
+++ b/projective_transformation.py	Fri Jan 14 03:07:08 2022 +0000
@@ -1,11 +1,22 @@
-import skimage.io
-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 imghdr
+import warnings
+
 import numpy as np
 import pandas as pd
-import argparse 
-import warnings
-import shutil 
+import skimage.color
+import skimage.io
+import tifffile
+from scipy.ndimage import map_coordinates
+from skimage.transform import ProjectiveTransform
 
 
 def _stackcopy(a, b):
@@ -24,8 +35,8 @@
 
     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, ...])
@@ -36,26 +47,65 @@
     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)
+def transform(moving_fn, fixed_fn, warp_mat, output_fn):
+
+    moving = skimage.io.imread(moving_fn)
+    extension = imghdr.what(moving_fn)
+    nDims = len(moving.shape)
+    assert nDims in [2, 3, 4, 5, 6], 'this tool only supports up to 6 dimensions'
+
+    if moving.shape[-1] in [3, 4] and nDims > 2:
+        isRGB = True
+        moving = np.transpose(moving, (nDims - 1,) + tuple(_ for _ in range(nDims - 1)))
+    else:
+        isRGB = False
+
+    if nDims > 3 or (nDims == 3 and not isRGB):
+        isMulCh = True
+    else:
+        isMulCh = False
 
-    trans = ProjectiveTransform(matrix=warp_matrix)
-    warped_coords = warp_coords_batch(trans, fixed_image.shape)
-    t = map_coordinates(moving_image, warped_coords, mode='reflect')
+    fixed = skimage.io.imread(fixed_fn)
+    if fixed.shape[-1] in [3, 4] and len(fixed.shape) > 2:
+        hw_fixed = fixed.shape[-3:-1]
+    else:
+        hw_fixed = fixed.shape[-2:]
+
+    if isRGB or isMulCh:
+        shapeCh = moving.shape[0:-2]
+        nCh = np.prod(shapeCh)
+        moving = np.reshape(moving, (nCh,) + moving.shape[-2:])
+        warped_moving = np.zeros((nCh,) + hw_fixed, dtype=moving.dtype)
+
+    warp_mat = pd.read_csv(warp_mat, delimiter="\t", header=None)
+    warp_mat = np.array(warp_mat)
+    assert warp_mat.shape[0] in [3], 'only 2D image transformaton is supported'
+
+    trans = ProjectiveTransform(matrix=warp_mat)
+    warped_coords = warp_coords_batch(trans, hw_fixed)
+
+    if isMulCh or isRGB:
+        for i in range(nCh):
+            warped_moving[i, ...] = map_coordinates(moving[i, ...], warped_coords, cval=0)
+        warped_moving = np.reshape(warped_moving, shapeCh + warped_moving.shape[-2:])
+        if isRGB:
+            warped_moving = np.transpose(warped_moving, tuple(_ for _ in range(1, nDims)) + (0,))
+    else:
+        warped_moving = map_coordinates(moving, warped_coords, cval=0)
 
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        skimage.io.imsave(out, t)
+        if isMulCh:
+            tifffile.imwrite(output_fn + '.tif', warped_moving, imagej=True, metadata={'mode': 'composite'})
+        else:
+            skimage.io.imsave(output_fn + '.' + extension, warped_moving)
 
 
 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")
+    parser.add_argument("fixed_image", help="Path to the fixed image")
+    parser.add_argument("moving_image", help="Path to the moving image (to be transformed)")
+    parser.add_argument("warp_matrix", help="Path to the transformation matrix")
+    parser.add_argument("warped_image", help="Path to the output (transfirmed moving image)")
     args = parser.parse_args()
-    transform(args.moving_image, args.fixed_image, args.warp_matrix, args.out)
+    transform(args.moving_image, args.fixed_image, args.warp_matrix, args.warped_image)
--- a/projective_transformation.xml	Wed Dec 18 05:03:09 2019 -0500
+++ b/projective_transformation.xml	Fri Jan 14 03:07:08 2022 +0000
@@ -1,11 +1,11 @@
-<tool id="ip_projective_transformation" name="Projective Transformation" version="0.0.4"> 
-    <description>Projective Transformation</description>
+<tool id="ip_projective_transformation" name="Projective Transformation" version="0.1.0" profile="20.05"> 
+    <description>2D Projective Transformation</description>
     <requirements>
-        <requirement type="package" version="0.14.2">scikit-image</requirement>
-        <requirement type="package" version="0.23.4">pandas</requirement>
-        <requirement type="package" version="1.15.4">numpy</requirement>
-        <requirement type="package" version="1.1.0">scipy</requirement>
-        <requirement type="package" version="0.15.1">tifffile</requirement>
+        <requirement type="package" version="0.18.1">scikit-image</requirement>
+        <requirement type="package" version="1.2.4">pandas</requirement>
+        <requirement type="package" version="1.20.2">numpy</requirement>
+        <requirement type="package" version="1.6.2">scipy</requirement>
+        <requirement type="package" version="2020.10.1">tifffile</requirement>
     </requirements>
     <command detect_errors="aggressive">
     <![CDATA[
@@ -13,29 +13,30 @@
          '$fixed_image'
          '$moving_image'
          '$warp_matrix'
-         ./out.png
+         ./out
     ]]>
     </command>
     <inputs>
-        <param name="moving_image" type="data" format="png" label="Moving Image" />
-        <param name="fixed_image" type="data" format="png" label="Fixed Image" />
-        <param name="warp_matrix" type= "data" format="tabular" label="Warp Matrix" />
+        <param name="moving_image" type="data" format="tiff,png,jpg" label="Moving image (to be transformed)" />
+        <param name="fixed_image" type="data" format="tiff,png,jpg" label="Fixed image (the reference)" />
+        <param name="warp_matrix" type="data" format="tabular" label="Transformation matrix" />
     </inputs>
     <outputs>
-       <data format="png" name="out" from_work_dir="out.png" />
+        <data format_source="moving_image" name="warped_image" from_work_dir="out" />
     </outputs>
     <tests>
       <test>
         <param name="fixed_image" value="sample1.png"/>
         <param name="moving_image" value="sample2.png"/>
         <param name="warp_matrix" value="warp_matrix.tsv"/>
-        <output name="out" value="yam.png" ftype="png" compare="sim_size"/>
+        <output name="warped_image" value="yam.png" ftype="png" compare="sim_size"/>
       </test>
     </tests>
     <help>
     **What it does**
 
-    This tool performs a projective transformation of the input (moving) image so that it fits the fixed image.
+    This tool performs a projective transformation of the moving image so that it fits the fixed image. 
+    Multi-channel and RGB images are supported.
     </help>
     <citations>
         <citation type="doi">10.1016/j.jbiotec.2017.07.019</citation>