Mercurial > repos > imgteam > image_registration_affine
changeset 0:e34222a620d4 draft
"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/image_registration_affine/ commit 79c2fd560fce8ded4d7f7fe97e876871794e2f9d"
| author | imgteam |
|---|---|
| date | Wed, 30 Dec 2020 20:24:35 +0000 |
| parents | |
| children | fa769715b6b0 |
| files | image_registration_affine.py image_registration_affine.xml test-data/fixed.png test-data/moving.png test-data/tmat.tsv |
| diffstat | 5 files changed, 122 insertions(+), 0 deletions(-) [+] |
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--- /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)
--- /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>