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48 lines
2.2 KiB
Python
48 lines
2.2 KiB
Python
import numpy as np
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from scipy.ndimage.filters import maximum_filter, minimum_filter
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from skimage.transform import integral_image
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import time
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def _get_filtered_image(image, n, mode='DoB'):
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if mode == 'DoB':
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inner_wt = (1.0 / (2*n + 1)**2)
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outer_wt = (1.0 / (12*n**2 + 4*n))
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integral_img = integral_image(image)
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filtered_image = np.zeros(image.shape)
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# TODO : Outsource to Cython
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start = time.time()
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for i in range(2 * n, image.shape[0] - 2 * n):
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for j in range(2 * n, image.shape[1] - 2 * n):
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inner = integral_img[i + n, j + n] + integral_img[i - n, j - n] - integral_img[i + n, j - n] - integral_img[i - n, j + n]
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outer = integral_img[i + 2 * n, j + 2 * n] + integral_img[i - 2 * n, j - 2 * n] - integral_img[i + 2 * n, j - 2 * n] - integral_img[i - 2 * n, j + 2 * n]
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filtered_image[i, j] = outer_wt * outer - (inner_wt + outer_wt) * inner
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print time.time() - start
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return filtered_image
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def censure_keypoints(image, mode='DoB', threshold=0.1):
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# TODO : Decide mode for convolve function
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# TODO : Decide number of scales. Image-size dependent?
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image = np.squeeze(image)
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if image.ndim != 2:
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raise ValueError("Only 2-D gray-scale images supported.")
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# Generating all the scales
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scale1 = _get_filtered_image(image, 1, mode)
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scale2 = _get_filtered_image(image, 2, mode)
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scale3 = _get_filtered_image(image, 3, mode)
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scale4 = _get_filtered_image(image, 4, mode)
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scale5 = _get_filtered_image(image, 5, mode)
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scale6 = _get_filtered_image(image, 6, mode)
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scale7 = _get_filtered_image(image, 7, mode)
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# Stacking all the scales in the 3rd dimension
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scales = np.dstack((scale1, scale2, scale3, scale4, scale5, scale6, scale7))
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# Suppressing points that are neither minima or maxima in their 3 x 3 x 3
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# neighbourhood to zero
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minimas = (minimum_filter(scales, (3, 3, 3)) == scales).astype(int) * scales
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maximas = (maximum_filter(scales, (3, 3, 3)) == scales).astype(int) * scales
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# Suppressing minimas and maximas weaker than threshold
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minimas[np.abs(minimas) < threshold] = 0
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maximas[np.abs(maximas) < threshold] = 0
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response = maximas + np.abs(minimas)
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return response
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