mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-10 11:17:17 +08:00
Ankit Agrawal
61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
# TODO Normal sampling from image patch of size 49 x 49
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# TODO Tests, example, doc
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import numpy as np
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from skimage.color import rgb2gray
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from scipy.ndimage.filters import gaussian_filter
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KERNEL_SIZE = (9, 9)
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PATCH_SIZE = (49, 49)
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def _remove_border_keypoints(image, keypoints, dist):
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width = image.shape[0]
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height = image.shape[1]
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for i, j in keypoints:
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if i > width - dist[0] or i < dist[0] or j < dist[1] or j > height - dist[0]:
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keypoints.remove((i, j))
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return keypoints
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def brief(image, keypoints, descriptor_size=32, mode='uniform'):
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if np.squeeze(image).ndim == 3:
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image = rgb2gray(image)
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keypoints = _remove_border_keypoints(image, keypoints, (PATCH_SIZE[0] / 2, PATCH_SIZE[1] / 2))
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descriptor = np.zeros((len(keypoints), descriptor_size * 8), dtype=int)
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image = gaussian_filter(image, 2)
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if mode == 'uniform':
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np.random.seed(1)
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first = np.random.randint(-PATCH_SIZE / 2, (PATCH_SIZE / 2) + 1, (descriptor_size * 8, 2))
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np.random.seed(2)
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second = np.random.randint(-PATCH_SIZE / 2, (PATCH_SIZE / 2) + 1, (descriptor_size * 8, 2))
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else:
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#TODO mode='normal'
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pass
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for i in range(len(keypoints)):
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set_1 = first + keypoints[i]
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set_2 = second + keypoints[i]
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for j in range(descriptor_size * 8):
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if image[set_1[j, 0]][set_1[j, 1]] < image[set_2[j, 0]][set_2[j, 0]]:
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descriptor[i][j] = 1
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else:
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descriptor[i][j] = 0
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return descriptor
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def hamming_distance(descriptor_1, descriptor_2):
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distance = np.zeros((len(descriptor_1), len(descriptor_2)), dtype=int)
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for i in range(len(descriptor_1)):
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for j in range(len(descriptor_2)):
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distance[i, j] = sum(np.bitwise_xor(descriptor_1[i][:], descriptor_2[j][:]))
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return distance / descriptor_1.shape[1]
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