mirror of
https://github.com/wassname/scikit-image.git
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emmanuelle
55f5103dd8
Modified travis_script.sh to account for the new structure of the gallery Added README.txt files in directories of gallery examples Fixed references to gallery images in user guide pages Fixed broken links
92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
"""
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=====================================
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Cross-Correlation (Phase Correlation)
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=====================================
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In this example, we use phase correlation to identify the relative shift
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between two similar-sized images.
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The ``register_translation`` function uses cross-correlation in Fourier space,
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optionally employing an upsampled matrix-multiplication DFT to achieve
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arbitrary subpixel precision. [1]_
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.. [1] Manuel Guizar-Sicairos, Samuel T. Thurman, and James R. Fienup,
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"Efficient subpixel image registration algorithms," Optics Letters 33,
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156-158 (2008).
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from skimage import data
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from skimage.feature import register_translation
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from skimage.feature.register_translation import _upsampled_dft
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from scipy.ndimage import fourier_shift
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image = data.camera()
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shift = (-2.4, 1.32)
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# (-2.4, 1.32) pixel offset relative to reference coin
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offset_image = fourier_shift(np.fft.fftn(image), shift)
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offset_image = np.fft.ifftn(offset_image)
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print("Known offset (y, x):")
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print(shift)
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# pixel precision first
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shift, error, diffphase = register_translation(image, offset_image)
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fig = plt.figure(figsize=(8, 3))
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ax1 = plt.subplot(1, 3, 1, adjustable='box-forced')
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ax2 = plt.subplot(1, 3, 2, sharex=ax1, sharey=ax1, adjustable='box-forced')
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ax3 = plt.subplot(1, 3, 3)
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ax1.imshow(image)
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ax1.set_axis_off()
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ax1.set_title('Reference image')
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ax2.imshow(offset_image.real)
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ax2.set_axis_off()
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ax2.set_title('Offset image')
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# View the output of a cross-correlation to show what the algorithm is
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# doing behind the scenes
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image_product = np.fft.fft2(image) * np.fft.fft2(offset_image).conj()
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cc_image = np.fft.fftshift(np.fft.ifft2(image_product))
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ax3.imshow(cc_image.real)
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ax3.set_axis_off()
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ax3.set_title("Cross-correlation")
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plt.show()
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print("Detected pixel offset (y, x):")
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print(shift)
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# subpixel precision
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shift, error, diffphase = register_translation(image, offset_image, 100)
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fig = plt.figure(figsize=(8, 3))
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ax1 = plt.subplot(1, 3, 1, adjustable='box-forced')
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ax2 = plt.subplot(1, 3, 2, sharex=ax1, sharey=ax1, adjustable='box-forced')
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ax3 = plt.subplot(1, 3, 3)
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ax1.imshow(image)
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ax1.set_axis_off()
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ax1.set_title('Reference image')
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ax2.imshow(offset_image.real)
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ax2.set_axis_off()
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ax2.set_title('Offset image')
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# Calculate the upsampled DFT, again to show what the algorithm is doing
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# behind the scenes. Constants correspond to calculated values in routine.
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# See source code for details.
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cc_image = _upsampled_dft(image_product, 150, 100, (shift*100)+75).conj()
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ax3.imshow(cc_image.real)
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ax3.set_axis_off()
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ax3.set_title("Supersampled XC sub-area")
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plt.show()
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print("Detected subpixel offset (y, x):")
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print(shift)
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