| Previous changeset 1:974cf4357707 (2019-12-18) Next changeset 3:be9a815e2240 (2022-01-20) |
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Commit message:
"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/projective_transformation/ commit 40b2eeecc9e15be0b710f5e5195ac6f3b3b83c39" |
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modified:
projective_transformation.py projective_transformation.xml |
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| diff -r 974cf4357707 -r 1ffdb07020ee projective_transformation.py --- a/projective_transformation.py Wed Dec 18 05:03:09 2019 -0500 +++ b/projective_transformation.py Fri Jan 14 03:07:08 2022 +0000 |
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| @@ -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) |
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| diff -r 974cf4357707 -r 1ffdb07020ee projective_transformation.xml --- a/projective_transformation.xml Wed Dec 18 05:03:09 2019 -0500 +++ b/projective_transformation.xml Fri Jan 14 03:07:08 2022 +0000 |
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| @@ -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> |