comparison projective_transformation.py @ 2:1ffdb07020ee draft

"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/projective_transformation/ commit 40b2eeecc9e15be0b710f5e5195ac6f3b3b83c39"

1:974cf4357707

import skimage.io
from skimage.transform import ProjectiveTransform
from scipy.ndimage import map_coordinates
import numpy as np
import pandas as pd
import argparse 
import warnings
import shutil 


def _stackcopy(a, b):
    if a.ndim == 3:
        a[:] = b[:, :, np.newaxis]

        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 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)

    trans = ProjectiveTransform(matrix=warp_matrix)
    warped_coords = warp_coords_batch(trans, fixed_image.shape)
    t = map_coordinates(moving_image, warped_coords, mode='reflect')

    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        skimage.io.imsave(out, t)


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")
    args = parser.parse_args()
    transform(args.moving_image, args.fixed_image, args.warp_matrix, args.out)

2:1ffdb07020ee

"""
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 skimage.color
import skimage.io
import tifffile
from scipy.ndimage import map_coordinates
from skimage.transform import ProjectiveTransform


def _stackcopy(a, b):
    if a.ndim == 3:
        a[:] = b[:, :, np.newaxis]

        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 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

    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")
        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="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.warped_image)