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https://github.com/wassname/scikit-image.git
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157 lines
6.2 KiB
Python
157 lines
6.2 KiB
Python
import numpy as np
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from scipy.ndimage.filters import gaussian_filter
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from ..util import img_as_float
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from .util import _remove_border_keypoints
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from ._brief_cy import _brief_loop
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def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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sample_seed=1, variance=2, return_keypoints=False):
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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 : 2D 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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return_keypoints : bool
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If True, return the Q keypoints (after filtering out the border
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keypoints) about which the descriptors are extracted. Default is False.
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Returns
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-------
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descriptors : (Q, descriptor_size) ndarray of dtype bool
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2D ndarray of binary descriptors of size descriptor_size about Q
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keypoints after filtering out border keypoints with value at an index
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(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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keypoints : (Q, 2) ndarray
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Keypoints after removing out those that are near border.
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Returned only if return_keypoints is True.
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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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Examples
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--------
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>>> from skimage.feature.corner import *
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>>> from skimage.feature import brief, hamming_distance
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>>> square1 = np.zeros([10, 10])
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>>> square1[2:8, 2:8] = 1
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>>> square1
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array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
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>>> keypoints1 = corner_peaks(corner_harris(square1), min_distance=1)
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>>> keypoints1
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array([[2, 2],
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[2, 7],
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[7, 2],
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[7, 7]])
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>>> descriptors1 = brief(square1, keypoints1, patch_size = 5)
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>>> square2 = np.zeros([12, 12])
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>>> square2[3:9, 3:9] = 1
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>>> square2
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array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
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>>> keypoints2 = corner_peaks(corner_harris(square2), min_distance=1)
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>>> keypoints2
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array([[3, 3],
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[3, 8],
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[8, 3],
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[8, 8]])
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>>> descriptors2 = brief(square2, keypoints1, patch_size = 5)
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>>> hamming_distance(descriptors1, descriptors2)
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array([[ 0.02734375, 0.2890625 , 0.32421875, 0.6171875 ],
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[ 0.3359375 , 0.05078125, 0.6640625 , 0.37109375],
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[ 0.359375 , 0.64453125, 0.03125 , 0.33203125],
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[ 0.640625 , 0.40234375, 0.3828125 , 0.01953125]])
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"""
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np.random.seed(sample_seed)
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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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image = img_as_float(image)
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# Gaussian Low pass filtering to alleviate noise
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# sensitivity
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image = gaussian_filter(image, variance)
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image = np.ascontiguousarray(image)
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keypoints = np.array(keypoints + 0.5, dtype=np.intp, order='C')
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# Removing keypoints that are (patch_size / 2) distance from the image
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# border
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keypoints = _remove_border_keypoints(image, keypoints, patch_size / 2)
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descriptors = np.zeros((keypoints.shape[0], descriptor_size),
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dtype=bool, order='C')
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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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samples = (patch_size / 5) * np.random.randn(descriptor_size * 8)
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samples = np.array(samples, dtype=np.int32)
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samples = samples[(samples < (patch_size / 2))
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& (samples > - (patch_size - 1) / 2)]
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pos1 = samples[:descriptor_size * 2]
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pos1 = pos1.reshape(descriptor_size, 2)
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pos2 = samples[descriptor_size * 2:descriptor_size * 4]
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pos2 = pos2.reshape(descriptor_size, 2)
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else:
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samples = np.random.randint(-patch_size / 2, (patch_size / 2) + 1,
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(descriptor_size * 2, 2))
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pos1, pos2 = np.split(samples, 2)
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pos1 = np.ascontiguousarray(pos1)
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pos2 = np.ascontiguousarray(pos2)
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_brief_loop(image, descriptors.view(np.uint8), keypoints, pos1, pos2)
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if return_keypoints:
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return descriptors, keypoints
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else:
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return descriptors
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