mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-13 17:45:20 +08:00
Ankit Agrawal
99 lines
3.5 KiB
Python
99 lines
3.5 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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from scipy.spatial.distance import hamming
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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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keypoints = keypoints[(dist < keypoints[:, 0]) & (keypoints[:, 0] < width - dist) &
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(dist < keypoints[:, 1]) & (keypoints[:, 1] < height - dist)]
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return keypoints
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def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49, sample_seed=1):
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"""Extract BRIEF Descriptor about given keypoints for a given image.
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Parameters
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----------
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image : ndarray
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Input image.
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keypoints : (P, 2) ndarray
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Array of keypoint locations.
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descriptor_size : int
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Size of BRIEF descriptor about each keypoint. Sizes 128, 256 and 512
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preferred by the authors. Default is 256.
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mode : string
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Probability distribution for sampling location of decision pixel-pairs
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around keypoints. Default is 'normal' otherwise uniform.
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patch_size : int
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Length of the two dimensional square patch sampling region around
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the keypoints. Default is 49.
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sample_seed : int
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Seed for sampling the decision pixel-pairs. Default is 1.
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Returns
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-------
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descriptor : ndarray with dtype bool
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2D ndarray of dimensions (no_of_keypoints, descriptor_size) with value
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at an index (i, j) either being True or False representing the outcome
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of Intensity comparison about ith keypoint on jth decision pixel-pair.
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References
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----------
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.. [1] Michael Calonder, Vincent Lepetit, Christoph Strecha, and Pascal Fua
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"BRIEF : Binary robust independent elementary features",
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http://cvlabwww.epfl.ch/~lepetit/papers/calonder_eccv10.pdf
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"""
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if np.squeeze(image).ndim == 3:
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image = rgb2gray(image)
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# Removing keypoints that are (patch_size / 2) distance from the image border
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keypoints = np.array(keypoints + 0.5, dtype=np.intp)
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keypoints = _remove_border_keypoints(image, keypoints, patch_size / 2)
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descriptor = np.zeros((len(keypoints), descriptor_size), dtype=bool)
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# Gaussian Low pass filtering with variance 2 to alleviate noise sensitivity
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image = gaussian_filter(image, 2)
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# Sampling pairs of decision pixels in patch_size x patch_size window
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if mode == 'normal':
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np.random.seed(sample_seed)
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samples = np.round((patch_size / 5) * np.random.randn(descriptor_size * 8))
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samples = samples[(samples < (patch_size / 2)) & (samples > - (patch_size - 1) / 2)]
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first = (samples[: descriptor_size * 2]).reshape(descriptor_size, 2)
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second = (samples[descriptor_size * 2: descriptor_size * 4]).reshape(descriptor_size, 2)
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else:
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np.random.seed(sample_seed)
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samples = np.random.randint(-patch_size / 2, (patch_size / 2) + 1, (descriptor_size * 2, 2))
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first, second = np.split(samples, 2)
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# Intensity comparison tests for building the descriptor
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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):
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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] = True
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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] = hamming(descriptor_1[i][:], descriptor_2[j][:])
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return distance
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