186 lines
7 KiB
Python
186 lines
7 KiB
Python
import numpy as np
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from scipy.ndimage import gaussian_filter
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from .util import (DescriptorExtractor, _mask_border_keypoints,
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_prepare_grayscale_input_2D)
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from .brief_cy import _brief_loop
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from .._shared.utils import check_nD
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class BRIEF(DescriptorExtractor):
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"""BRIEF binary descriptor extractor.
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BRIEF (Binary Robust Independent Elementary Features) is an efficient
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feature point descriptor. It is highly discriminative even when using
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relatively few bits and is computed using simple intensity difference
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tests.
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For each keypoint, intensity comparisons are carried out for a specifically
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distributed number N of pixel-pairs resulting in a binary descriptor of
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length N. For binary descriptors the Hamming distance can be used for
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feature matching, which leads to lower computational cost in comparison to
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the L2 norm.
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Parameters
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----------
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descriptor_size : int, optional
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Size of BRIEF descriptor for each keypoint. Sizes 128, 256 and 512
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recommended by the authors. Default is 256.
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patch_size : int, optional
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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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mode : {'normal', 'uniform'}, optional
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Probability distribution for sampling location of decision pixel-pairs
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around keypoints.
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sample_seed : int, optional
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Seed for the random sampling of the decision pixel-pairs. From a square
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window with length `patch_size`, pixel pairs are sampled using the
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`mode` parameter to build the descriptors using intensity comparison.
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The value of `sample_seed` must be the same for the images to be
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matched while building the descriptors.
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sigma : float, optional
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Standard deviation of the Gaussian low-pass filter applied to the image
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to alleviate noise sensitivity, which is strongly recommended to obtain
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discriminative and good descriptors.
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Attributes
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----------
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descriptors : (Q, `descriptor_size`) array of dtype bool
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2D ndarray of binary descriptors of size `descriptor_size` for Q
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keypoints after filtering out border keypoints with value at an
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index ``(i, j)`` either being ``True`` or ``False`` representing
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the outcome of the intensity comparison for i-th keypoint on j-th
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decision pixel-pair. It is ``Q == np.sum(mask)``.
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mask : (N, ) array of dtype bool
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Mask indicating whether a keypoint has been filtered out
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(``False``) or is described in the `descriptors` array (``True``).
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Examples
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--------
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>>> from skimage.feature import (corner_harris, corner_peaks, BRIEF,
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... match_descriptors)
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>>> import numpy as np
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>>> square1 = np.zeros((8, 8), dtype=np.int32)
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>>> square1[2:6, 2:6] = 1
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>>> square1
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array([[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)
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>>> square2 = np.zeros((9, 9), dtype=np.int32)
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>>> square2[2:7, 2:7] = 1
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>>> square2
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array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)
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>>> keypoints1 = corner_peaks(corner_harris(square1), min_distance=1,
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... threshold_rel=0)
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>>> keypoints2 = corner_peaks(corner_harris(square2), min_distance=1,
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... threshold_rel=0)
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>>> extractor = BRIEF(patch_size=5)
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>>> extractor.extract(square1, keypoints1)
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>>> descriptors1 = extractor.descriptors
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>>> extractor.extract(square2, keypoints2)
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>>> descriptors2 = extractor.descriptors
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>>> matches = match_descriptors(descriptors1, descriptors2)
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>>> matches
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array([[0, 0],
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[1, 1],
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[2, 2],
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[3, 3]])
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>>> keypoints1[matches[:, 0]]
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array([[2, 2],
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[2, 5],
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[5, 2],
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[5, 5]])
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>>> keypoints2[matches[:, 1]]
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array([[2, 2],
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[2, 6],
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[6, 2],
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[6, 6]])
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"""
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def __init__(self, descriptor_size=256, patch_size=49,
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mode='normal', sigma=1, sample_seed=1):
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mode = mode.lower()
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if mode not in ('normal', 'uniform'):
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raise ValueError("`mode` must be 'normal' or 'uniform'.")
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self.descriptor_size = descriptor_size
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self.patch_size = patch_size
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self.mode = mode
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self.sigma = sigma
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self.sample_seed = sample_seed
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self.descriptors = None
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self.mask = None
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def extract(self, image, keypoints):
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"""Extract BRIEF binary descriptors for given keypoints in image.
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Parameters
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----------
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image : 2D array
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Input image.
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keypoints : (N, 2) array
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Keypoint coordinates as ``(row, col)``.
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"""
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check_nD(image, 2)
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random = np.random.RandomState()
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random.seed(self.sample_seed)
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image = _prepare_grayscale_input_2D(image)
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# Gaussian low-pass filtering to alleviate noise sensitivity
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image = np.ascontiguousarray(gaussian_filter(image, self.sigma))
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# Sampling pairs of decision pixels in patch_size x patch_size window
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desc_size = self.descriptor_size
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patch_size = self.patch_size
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if self.mode == 'normal':
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samples = (patch_size / 5.0) * random.randn(desc_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 - 2) // 2)]
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pos1 = samples[:desc_size * 2].reshape(desc_size, 2)
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pos2 = samples[desc_size * 2:desc_size * 4].reshape(desc_size, 2)
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elif self.mode == 'uniform':
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samples = random.randint(-(patch_size - 2) // 2,
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(patch_size // 2) + 1,
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(desc_size * 2, 2))
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samples = np.array(samples, dtype=np.int32)
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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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# Removing keypoints that are within (patch_size / 2) distance from the
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# image border
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self.mask = _mask_border_keypoints(image.shape, keypoints,
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patch_size // 2)
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keypoints = np.array(keypoints[self.mask, :], dtype=np.intp,
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order='C', copy=False)
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self.descriptors = np.zeros((keypoints.shape[0], desc_size),
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dtype=bool, order='C')
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_brief_loop(image, self.descriptors.view(np.uint8), keypoints,
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pos1, pos2)
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