mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 20:07:56 +08:00
Johannes Schönberger
230 lines
8.3 KiB
Python
230 lines
8.3 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 _mask_border_keypoints, pairwise_hamming_distance
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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):
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"""**Experimental function**.
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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 in the format (row, col).
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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. From a square window with
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length patch_size, pixel pairs are sampled using the `mode` parameter
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to build the descriptors using intensity comparison. The value of
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`sample_seed` should be the same for the images to be matched while
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building the descriptors. Default is 1.
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variance : float
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Variance of the Gaussian Low Pass filter applied on the image to
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alleviate noise sensitivity. Default is 2.
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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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Location i.e. (row, col) of keypoints after removing out those that
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are near border.
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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 import corner_peaks, corner_harris, \\
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.. pairwise_hamming_distance, brief, match_keypoints_brief
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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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>> 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, 5],
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[5, 2],
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[5, 5]])
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>> descriptors1, keypoints1 = brief(square1, keypoints1, patch_size=5)
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>> keypoints1
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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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>> 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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>> keypoints2 = corner_peaks(corner_harris(square2), min_distance=1)
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>> keypoints2
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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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>> descriptors2, keypoints2 = brief(square2, keypoints2, patch_size=5)
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>> keypoints2
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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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>> pairwise_hamming_distance(descriptors1, descriptors2)
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array([[ 0.03125 , 0.3203125, 0.3671875, 0.6171875],
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[ 0.3203125, 0.03125 , 0.640625 , 0.375 ],
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[ 0.375 , 0.6328125, 0.0390625, 0.328125 ],
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[ 0.625 , 0.3671875, 0.34375 , 0.0234375]])
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>> match_keypoints_brief(keypoints1, descriptors1,
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.. keypoints2, descriptors2)
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array([[[ 2, 2],
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[ 2, 2]],
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[[ 2, 5],
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[ 2, 6]],
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[[ 5, 2],
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[ 6, 2]],
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[[ 5, 5],
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[ 6, 6]]])
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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 within (patch_size / 2) distance from the
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# image border
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keypoints = keypoints[_mask_border_keypoints(image, keypoints, patch_size // 2)]
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keypoints = np.ascontiguousarray(keypoints)
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descriptors = np.zeros((keypoints.shape[0], descriptor_size), dtype=bool,
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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 / 5.0) * 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 - 2) // 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) // 2,
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(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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return descriptors, keypoints
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def match_keypoints_brief(keypoints1, descriptors1, keypoints2,
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descriptors2, threshold=0.15):
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"""**Experimental function**.
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Match keypoints described using BRIEF descriptors in one image to
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those in second image.
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Parameters
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----------
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keypoints1 : (M, 2) ndarray
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M Keypoints from the first image described using skimage.feature.brief
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descriptors1 : (M, P) ndarray
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BRIEF descriptors of size P about M keypoints in the first image.
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keypoints2 : (N, 2) ndarray
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N Keypoints from the second image described using skimage.feature.brief
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descriptors2 : (N, P) ndarray
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BRIEF descriptors of size P about N keypoints in the second image.
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threshold : float in range [0, 1]
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Maximum allowable hamming distance between descriptors of two keypoints
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in separate images to be regarded as a match. Default is 0.15.
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Returns
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-------
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match_keypoints_brief : (Q, 2, 2) ndarray
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Location of Q matched keypoint pairs from two images.
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"""
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if (keypoints1.shape[0] != descriptors1.shape[0]
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or keypoints2.shape[0] != descriptors2.shape[0]):
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raise ValueError("The number of keypoints and number of described "
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"keypoints do not match. Make the optional parameter "
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"return_keypoints True to get described keypoints.")
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if descriptors1.shape[1] != descriptors2.shape[1]:
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raise ValueError("Descriptor sizes for matching keypoints in both "
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"the images should be equal.")
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# Get hamming distances between keeypoints1 and keypoints2
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distance = pairwise_hamming_distance(descriptors1, descriptors2)
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temp = distance > threshold
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row_check = np.any(~temp, axis=1)
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matched_keypoints2 = keypoints2[np.argmin(distance, axis=1)]
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matched_keypoint_pairs = np.zeros((np.sum(row_check), 2, 2), dtype=np.intp)
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matched_keypoint_pairs[:, 0, :] = keypoints1[row_check]
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matched_keypoint_pairs[:, 1, :] = matched_keypoints2[row_check]
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return matched_keypoint_pairs
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