68 lines
2.6 KiB
Python
68 lines
2.6 KiB
Python
"""
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==============================================
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Band-pass filtering by Difference of Gaussians
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==============================================
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Band-pass filters attenuate signal frequencies outside of a range (band) of
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interest. In image analysis, they can be used to denoise images while
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at the same time reducing low-frequency artifacts such a uneven illumination.
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Band-pass filters can be used to find image features such as blobs and edges.
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One method for applying band-pass filters to images is to subtract an image
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blurred with a Gaussian kernel from a less-blurred image. This example shows
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two applications of the Difference of Gaussians approach for band-pass
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filtering.
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"""
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######################################################################
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# Denoise image and reduce shadows
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# ================================
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import matplotlib.pyplot as plt
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import numpy as np
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from skimage.data import gravel
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from skimage.filters import difference_of_gaussians, window
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from scipy.fftpack import fftn, fftshift
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image = gravel()
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wimage = image * window('hann', image.shape) # window image to improve FFT
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filtered_image = difference_of_gaussians(image, 1, 12)
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filtered_wimage = filtered_image * window('hann', image.shape)
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im_f_mag = fftshift(np.abs(fftn(wimage)))
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fim_f_mag = fftshift(np.abs(fftn(filtered_wimage)))
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fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8, 8))
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ax[0, 0].imshow(image, cmap='gray')
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ax[0, 0].set_title('Original Image')
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ax[0, 1].imshow(np.log(im_f_mag), cmap='magma')
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ax[0, 1].set_title('Original FFT Magnitude (log)')
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ax[1, 0].imshow(filtered_image, cmap='gray')
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ax[1, 0].set_title('Filtered Image')
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ax[1, 1].imshow(np.log(fim_f_mag), cmap='magma')
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ax[1, 1].set_title('Filtered FFT Magnitude (log)')
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plt.show()
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######################################################################
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# Enhance edges in an image
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# =========================
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from skimage.data import camera
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image = camera()
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wimage = image * window('hann', image.shape) # window image to improve FFT
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filtered_image = difference_of_gaussians(image, 1.5)
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filtered_wimage = filtered_image * window('hann', image.shape)
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im_f_mag = fftshift(np.abs(fftn(wimage)))
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fim_f_mag = fftshift(np.abs(fftn(filtered_wimage)))
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fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8, 8))
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ax[0, 0].imshow(image, cmap='gray')
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ax[0, 0].set_title('Original Image')
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ax[0, 1].imshow(np.log(im_f_mag), cmap='magma')
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ax[0, 1].set_title('Original FFT Magnitude (log)')
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ax[1, 0].imshow(filtered_image, cmap='gray')
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ax[1, 0].set_title('Filtered Image')
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ax[1, 1].imshow(np.log(fim_f_mag), cmap='magma')
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ax[1, 1].set_title('Filtered FFT Magnitude (log)')
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plt.show()
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