185 lines
6.9 KiB
Python
185 lines
6.9 KiB
Python
|
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)
|
||
|
|
>>> keypoints2 = corner_peaks(corner_harris(square2), min_distance=1)
|
||
|
|
>>> 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)
|