# HG changeset patch
# User imgteam
# Date 1609359875 0
# Node ID e34222a620d4c111355d9ce1727ade020ccbde6f

"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit 79c2fd560fce8ded4d7f7fe97e876871794e2f9d"

diff -r 000000000000 -r e34222a620d4 image_registration_affine.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/image_registration_affine.py	Wed Dec 30 20:24:35 2020 +0000
@@ -0,0 +1,82 @@
+import skimage.io
+from skimage.transform import ProjectiveTransform
+from skimage.filters import gaussian
+from scipy.ndimage import map_coordinates
+from scipy.optimize import least_squares
+import numpy as np
+import pandas as pd
+import argparse
+
+
+
+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 affine_registration(params,moving,fixed):
+    tmat = np.eye(3)
+    tmat[0,:] = params.take([0,1,2])
+    tmat[1,:] = params.take([3,4,5])
+    
+    trans = ProjectiveTransform(matrix=tmat)
+    warped_coords = warp_coords_batch(trans, fixed.shape)
+    t = map_coordinates(moving, warped_coords, mode='reflect')
+    
+    eI = (t - fixed)**2
+    return eI.flatten()
+
+
+
+def image_registration(fn_moving, fn_fixed, fn_out, smooth_sigma=1):
+    moving = skimage.io.imread(fn_moving,as_gray=True)
+    fixed = skimage.io.imread(fn_fixed,as_gray=True)
+
+    moving = gaussian(moving, sigma=smooth_sigma)
+    fixed = gaussian(fixed, sigma=smooth_sigma)
+    
+    x = np.array([1, 0, 0, 0, 1, 0],dtype='float64')
+    result = least_squares(affine_registration, x, args=(moving,fixed))
+        
+    tmat = np.eye(3)
+    tmat[0,:] = result.x.take([0,1,2])
+    tmat[1,:] = result.x.take([3,4,5])
+    
+    pd.DataFrame(tmat).to_csv(fn_out, header=None, index=False, sep="\t")
+    
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(description="Estimate the transformation matrix")
+    parser.add_argument("fn_moving", help="Name of the moving image.png")
+    parser.add_argument("fn_fixed",  help="Name of the fixed image.png")
+    parser.add_argument("fn_tmat",   help="Name of output file to save the transformation matrix")
+    args = parser.parse_args()
+    
+    image_registration(args.fn_moving, args.fn_fixed, args.fn_tmat)
diff -r 000000000000 -r e34222a620d4 image_registration_affine.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/image_registration_affine.xml	Wed Dec 30 20:24:35 2020 +0000
@@ -0,0 +1,37 @@
+<tool id="ip_image_registration" name="Image Registration" version="0.0.1"> 
+    <description>based on intensity information</description>
+    <requirements>
+        <requirement type="package" version="0.14.2">scikit-image</requirement>
+        <requirement type="package" version="1.1.0">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>
+    <![CDATA[
+         python '$__tool_directory__/image_registration_affine.py'
+         '$fn_moving'
+         '$fn_fixed'
+         '$fn_tmat'
+    ]]>
+    </command>
+    <inputs>
+        <param name="fn_moving" type="data" format="png" label="Moving Image" />
+        <param name="fn_fixed"  type="data" format="png" label="Fixed Image" />
+    </inputs>
+    <outputs>
+       <data format="tabular" name="fn_tmat" />
+    </outputs>
+    <tests>
+      <test>
+        <param name="fn_moving" value="moving.png"/>
+        <param name="fn_fixed" value="fixed.png"/>
+        <output name="fn_tmat" value="tmat.tsv" ftype="tabular" compare="diff" lines_diff="6"/>
+      </test>
+    </tests>
+    <help>
+    **What it does**
+
+    This tool estimates the (affine) transformation matrix for registration of two images based on intensity information. 
+    </help>
+</tool>
diff -r 000000000000 -r e34222a620d4 test-data/fixed.png
Binary file test-data/fixed.png has changed
diff -r 000000000000 -r e34222a620d4 test-data/moving.png
Binary file test-data/moving.png has changed
diff -r 000000000000 -r e34222a620d4 test-data/tmat.tsv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/tmat.tsv	Wed Dec 30 20:24:35 2020 +0000
@@ -0,0 +1,3 @@
+0.8513326357669146	0.5366986237200791	-51.80508130298601
+-0.5191006904557887	0.8391654313316298	89.65319219211344
+0.0	0.0	1.0