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158 lines
6.1 KiB
Python
158 lines
6.1 KiB
Python
"""
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Copyright (c) Contributors to the Open 3D Engine Project.
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For complete copyright and license terms please see the LICENSE at the root of this distribution.
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SPDX-License-Identifier: Apache-2.0 OR MIT
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Python implementation of Quaternion Structural Similarity by Amir Kolaman and Orly Yadid-Pecht as seen in
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IEEE Transaction on Image Processing Vol 21 No 4 April 2012.
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"""
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import imageio
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import numpy
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import os
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from scipy import ndimage
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def _quaternion_matrix_conj(q):
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q_out = numpy.zeros(q.shape)
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q_out[:, :, 0] = q[:, :, 0]
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q_out[:, :, 1] = -q[:, :, 1]
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q_out[:, :, 2] = -q[:, :, 2]
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q_out[:, :, 3] = -q[:, :, 3]
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return q_out
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def _quaternion_matrix_dot(q1, q2):
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return numpy.sqrt(numpy.sum(numpy.multiply(q1, q2), 2))
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def _quaternion_matrix_norm(q):
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return _quaternion_matrix_dot(q, q)
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def _quaternion_matrix_mult(q1, q2):
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"""q = (q1[0] * q2[0] - q1[1] * q2[1] - q1[2] * q2[2] - q1[3] * q2[3],
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q1[0] * q2[1] + q1[1] * q2[0] - q1[2] * q2[3] + q1[3] * q2[2],
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q1[0] * q2[2] + q1[1] * q2[3] + q1[2] * q2[0] - q1[3] * q2[1],
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q1[0] * q2[3] - q1[1] * q2[2] + q1[2] * q2[1] + q1[3] * q2[0])"""
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# add error checking for q1 and q2 being same size
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q = numpy.zeros(q1.shape)
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q[:, :, 0] = numpy.multiply(q1[:, :, 0], q2[:, :, 0]) - numpy.multiply(q1[:, :, 1], q2[:, :, 1]) \
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- numpy.multiply(q1[:, :, 2], q2[:, :, 2]) - numpy.multiply(q1[:, :, 3], q2[:, :, 3])
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q[:, :, 1] = numpy.multiply(q1[:, :, 0], q2[:, :, 1]) + numpy.multiply(q1[:, :, 1], q2[:, :, 0]) \
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- numpy.multiply(q1[:, :, 2], q2[:, :, 3]) + numpy.multiply(q1[:, :, 3], q2[:, :, 2])
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q[:, :, 2] = numpy.multiply(q1[:, :, 0], q2[:, :, 2]) + numpy.multiply(q1[:, :, 1], q2[:, :, 3]) \
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+ numpy.multiply(q1[:, :, 2], q2[:, :, 0]) - numpy.multiply(q1[:, :, 3], q2[:, :, 1])
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q[:, :, 3] = numpy.multiply(q1[:, :, 0], q2[:, :, 3]) - numpy.multiply(q1[:, :, 1], q2[:, :, 2]) \
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+ numpy.multiply(q1[:, :, 2], q2[:, :, 1]) + numpy.multiply(q1[:, :, 3], q2[:, :, 0])
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return q
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def _quaternion_matrix_div(q1, q2):
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q2_norm = _quaternion_matrix_norm(q2)
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q = _quaternion_matrix_mult(q1, _quaternion_matrix_conj(q2))
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return numpy.divide(q, numpy.dstack([q2_norm] * 4))
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def qssim(screenshot, goldenimage, channel_max=255, diff_path='.'):
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"""
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Returns the mean quaternion similarity index between two images.
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For images that are the same the expected result is 1.000.
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By default we are assuming a channel max of 255(8bit channels)
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There are a series of tuning parameters that are taken from the 2004 paper by Wang et al
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Image Quality Assesment: From Error Visibility to Structural Similarity.
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:param screenshot: Screenshot filename to test
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:param goldenimage: Golden image to test against.
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:param channel_max: Maximum channel value.
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:param diff_path: Target path where diff image should be stored.
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:return: Mean quaternion similarity from 0.00->1.00 (identical).
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"""
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# load image and copy it into a 4 channel array to treat rgb like quaternions
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img1 = imageio.imread(screenshot)
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img2 = imageio.imread(goldenimage)
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# Avoid later precision issues
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img1 = img1.astype(numpy.float64) / channel_max
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img2 = img2.astype(numpy.float64) / channel_max
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im1_size = img1.shape
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im2_size = img2.shape
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# check im1 and im2 are same size
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hue1 = numpy.zeros((im1_size[0], im1_size[1], im1_size[2] + 1))
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hue2 = numpy.zeros((im1_size[0], im1_size[1], im1_size[2] + 1))
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mu1 = numpy.zeros((im1_size[0], im1_size[1], im1_size[2] + 1))
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mu2 = numpy.zeros((im1_size[0], im1_size[1], im1_size[2] + 1))
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offset1 = numpy.zeros((im1_size[0], im1_size[1], im1_size[2] + 1))
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offset2 = numpy.zeros((im1_size[0], im1_size[1], im1_size[2] + 1))
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hue1[:, :, 1:4] = img1
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hue2[:, :, 1:4] = img2
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mu1[:, :, 1:4] = img1
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mu2[:, :, 1:4] = img2
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# Algorithm tuning parameters. Can me modified as needed.
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sigma = 1.5
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# These parameters are just to prevent divide by zero issues.
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L = 1
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K1 = 0.01
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K2 = 0.03
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C1 = (K1 * L) ** 2
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C2 = (K2 * L) ** 2
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offset1[:, :, 0] = C1
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offset2[:, :, 0] = C2
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# blur each color channel of both images
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mu1 = ndimage.filters.gaussian_filter1d(mu1, sigma, 0)
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mu1 = ndimage.filters.gaussian_filter1d(mu1, sigma, 1)
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mu2 = ndimage.filters.gaussian_filter1d(mu2, sigma, 0)
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mu2 = ndimage.filters.gaussian_filter1d(mu2, sigma, 1)
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mu1_sq = _quaternion_matrix_mult(mu1, _quaternion_matrix_conj(mu1))
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mu2_sq = _quaternion_matrix_mult(mu2, _quaternion_matrix_conj(mu2))
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mu12 = _quaternion_matrix_mult(mu1, _quaternion_matrix_conj(mu2))
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hue1_sq = _quaternion_matrix_mult(hue1 - mu1, _quaternion_matrix_conj(hue1 - mu1))
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hue2_sq = _quaternion_matrix_mult(hue2 - mu2, _quaternion_matrix_conj(hue2 - mu2))
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hue12 = _quaternion_matrix_mult(hue1 - mu1, _quaternion_matrix_conj(hue2 - mu2))
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sigma1 = ndimage.filters.gaussian_filter1d(hue1_sq, sigma, 0)
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sigma1 = ndimage.filters.gaussian_filter1d(sigma1, sigma, 1)
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sigma2 = ndimage.filters.gaussian_filter1d(hue2_sq, sigma, 0)
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sigma2 = ndimage.filters.gaussian_filter1d(sigma2, sigma, 1)
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sigma12 = ndimage.filters.gaussian_filter1d(hue12, sigma, 0)
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sigma12 = ndimage.filters.gaussian_filter1d(sigma12, sigma, 1)
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numerator1 = 2 * mu12 + offset1
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numerator2 = 2 * sigma12 + offset2
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denominator1 = mu1_sq + mu2_sq + offset1
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denominator2 = sigma1 + sigma2 + offset2
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part1 = _quaternion_matrix_norm(numerator1) / denominator1[:, :, 0]
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part2 = _quaternion_matrix_norm(numerator2) / denominator2[:, :, 0]
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qssim_map = numpy.multiply(part1, part2)
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extension = os.path.splitext(screenshot)[1]
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screenshot_name = os.path.basename(screenshot)
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diff_name = '.'.join(screenshot_name.split('.')[:-1]) + "_diff" + extension
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diff_full_path = os.path.join(diff_path, diff_name)
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imageio.imwrite(diff_full_path, (qssim_map * channel_max).astype(numpy.uint8))
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return ndimage.mean(numpy.abs(qssim_map))
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if __name__ == "__main__":
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""" If this is run by accident, inform user that this is a module, not a separate script. """
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print('qssim.py is not a standalone script.')
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