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