100 lines
3 KiB
Python
100 lines
3 KiB
Python
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import matplotlib.pyplot as plt
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import numpy as np
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from scipy import ndimage as ndi
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from skimage.io import imread
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from skimage.util import img_as_float
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from skimage.filters import gabor_kernel
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def compute_feats(image, kernels):
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feats = np.zeros((len(kernels), 2), dtype=np.double)
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for k, kernel in enumerate(kernels):
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filtered = ndi.convolve(image, kernel, mode='wrap')
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feats[k, 0] = filtered.mean()
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feats[k, 1] = filtered.var()
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return feats
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def match(feats, ref_feats):
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min_error = np.inf
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min_i = None
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for i in range(ref_feats.shape[0]):
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error = np.sum((feats - ref_feats[i, :])**2)
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if error < min_error:
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min_error = error
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min_i = i
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return min_i
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sigma = 10 # 1 - 10
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frequency = 0.35 # 0.05 - 0.35
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kernels = []
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for theta in range(4):
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theta = theta / 4. * np.pi
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kernel = np.real(gabor_kernel(frequency, theta=theta, n_stds=sigma))
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kernels.append(kernel)
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shrink = (slice(0, None, 3), slice(0, None, 3))
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disc = img_as_float(imread("discord-logo.png", as_gray=True))[shrink]
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feng = img_as_float(imread("Feng_wind.jpg", as_gray=True))[shrink]
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vazka = img_as_float(imread("vazka.png", as_gray=True))[shrink]
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image_names = ('Discord', 'Znak', 'Vážka')
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images = (disc, feng, vazka)
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# prepare reference features
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ref_feats = np.zeros((3, len(kernels), 2), dtype=np.double)
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ref_feats[0, :, :] = compute_feats(disc, kernels)
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ref_feats[1, :, :] = compute_feats(feng, kernels)
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ref_feats[2, :, :] = compute_feats(vazka, kernels)
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def power(image, kernel):
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# Normalize images for better comparison.
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image = (image - image.mean()) / image.std()
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return np.sqrt(ndi.convolve(image, np.real(kernel), mode='wrap')**2 +
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ndi.convolve(image, np.imag(kernel), mode='wrap')**2)
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# Plot a selection of the filter bank kernels and their responses.
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results = []
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kernel_params = []
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for theta in range(0, 10):
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theta = theta / np.pi
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kernel = gabor_kernel(frequency, theta=theta)
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params = f"Θ={18*(theta*np.pi):.0f}°"
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kernel_params.append(params)
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# Save kernel and the power image for each image
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results.append((kernel, [power(img, kernel) for img in images]))
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fig, axes = plt.subplots(nrows=len(results)+1, ncols=len(image_names)+1, figsize=(4, 9))
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plt.gray()
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fig.suptitle(f'Gaborovy filtry\n pro λ={frequency}, ϕ={0}, σ={sigma} a γ={1}.', fontsize=12)
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axes[0][0].axis('off')
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# Plot original images
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for label, img, ax in zip(image_names, images, axes[0][1:]):
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ax.imshow(img)
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ax.set_title(label, fontsize=9)
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ax.axis('off')
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for label, (kernel, powers), ax_row in zip(kernel_params, results, axes[1:]):
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# Plot Gabor kernel
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ax = ax_row[0]
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ax.imshow(np.real(kernel))
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ax.set_ylabel(label, fontsize=7)
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ax.set_xticks([])
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ax.set_yticks([])
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# Plot Gabor responses with the contrast normalized for each filter
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vmin = np.min(powers)
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vmax = np.max(powers)
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for patch, ax in zip(powers, ax_row[1:]):
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ax.imshow(patch, vmin=vmin, vmax=vmax)
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ax.axis('off')
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plt.show()
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