forked from 170010011/fr
349 lines
13 KiB
Python
349 lines
13 KiB
Python
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import numpy as np
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from ..feature.util import (FeatureDetector, DescriptorExtractor,
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_mask_border_keypoints,
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_prepare_grayscale_input_2D)
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from ..feature import (corner_fast, corner_orientations, corner_peaks,
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corner_harris)
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from ..transform import pyramid_gaussian
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from .._shared.utils import check_nD
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from .orb_cy import _orb_loop
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OFAST_MASK = np.zeros((31, 31))
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OFAST_UMAX = [15, 15, 15, 15, 14, 14, 14, 13, 13, 12, 11, 10, 9, 8, 6, 3]
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for i in range(-15, 16):
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for j in range(-OFAST_UMAX[abs(i)], OFAST_UMAX[abs(i)] + 1):
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OFAST_MASK[15 + j, 15 + i] = 1
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class ORB(FeatureDetector, DescriptorExtractor):
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"""Oriented FAST and rotated BRIEF feature detector and binary descriptor
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extractor.
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Parameters
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----------
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n_keypoints : int, optional
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Number of keypoints to be returned. The function will return the best
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`n_keypoints` according to the Harris corner response if more than
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`n_keypoints` are detected. If not, then all the detected keypoints
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are returned.
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fast_n : int, optional
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The `n` parameter in `skimage.feature.corner_fast`. Minimum number of
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consecutive pixels out of 16 pixels on the circle that should all be
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either brighter or darker w.r.t test-pixel. A point c on the circle is
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darker w.r.t test pixel p if ``Ic < Ip - threshold`` and brighter if
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``Ic > Ip + threshold``. Also stands for the n in ``FAST-n`` corner
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detector.
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fast_threshold : float, optional
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The ``threshold`` parameter in ``feature.corner_fast``. Threshold used
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to decide whether the pixels on the circle are brighter, darker or
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similar w.r.t. the test pixel. Decrease the threshold when more
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corners are desired and vice-versa.
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harris_k : float, optional
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The `k` parameter in `skimage.feature.corner_harris`. Sensitivity
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factor to separate corners from edges, typically in range ``[0, 0.2]``.
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Small values of `k` result in detection of sharp corners.
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downscale : float, optional
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Downscale factor for the image pyramid. Default value 1.2 is chosen so
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that there are more dense scales which enable robust scale invariance
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for a subsequent feature description.
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n_scales : int, optional
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Maximum number of scales from the bottom of the image pyramid to
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extract the features from.
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Attributes
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----------
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keypoints : (N, 2) array
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Keypoint coordinates as ``(row, col)``.
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scales : (N, ) array
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Corresponding scales.
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orientations : (N, ) array
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Corresponding orientations in radians.
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responses : (N, ) array
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Corresponding Harris corner responses.
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descriptors : (Q, `descriptor_size`) array of dtype bool
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2D array 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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References
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----------
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.. [1] Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary Bradski
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"ORB: An efficient alternative to SIFT and SURF"
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http://www.vision.cs.chubu.ac.jp/CV-R/pdf/Rublee_iccv2011.pdf
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Examples
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--------
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>>> from skimage.feature import ORB, match_descriptors
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>>> img1 = np.zeros((100, 100))
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>>> img2 = np.zeros_like(img1)
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>>> np.random.seed(1)
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>>> square = np.random.rand(20, 20)
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>>> img1[40:60, 40:60] = square
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>>> img2[53:73, 53:73] = square
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>>> detector_extractor1 = ORB(n_keypoints=5)
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>>> detector_extractor2 = ORB(n_keypoints=5)
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>>> detector_extractor1.detect_and_extract(img1)
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>>> detector_extractor2.detect_and_extract(img2)
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>>> matches = match_descriptors(detector_extractor1.descriptors,
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... detector_extractor2.descriptors)
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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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[4, 4]])
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>>> detector_extractor1.keypoints[matches[:, 0]]
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array([[42., 40.],
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[47., 58.],
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[44., 40.],
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[59., 42.],
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[45., 44.]])
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>>> detector_extractor2.keypoints[matches[:, 1]]
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array([[55., 53.],
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[60., 71.],
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[57., 53.],
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[72., 55.],
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[58., 57.]])
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"""
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def __init__(self, downscale=1.2, n_scales=8,
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n_keypoints=500, fast_n=9, fast_threshold=0.08,
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harris_k=0.04):
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self.downscale = downscale
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self.n_scales = n_scales
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self.n_keypoints = n_keypoints
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self.fast_n = fast_n
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self.fast_threshold = fast_threshold
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self.harris_k = harris_k
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self.keypoints = None
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self.scales = None
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self.responses = None
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self.orientations = None
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self.descriptors = None
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def _build_pyramid(self, image):
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image = _prepare_grayscale_input_2D(image)
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return list(pyramid_gaussian(image, self.n_scales - 1,
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self.downscale, multichannel=False))
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def _detect_octave(self, octave_image):
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dtype = octave_image.dtype
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# Extract keypoints for current octave
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fast_response = corner_fast(octave_image, self.fast_n,
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self.fast_threshold)
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keypoints = corner_peaks(fast_response, min_distance=1)
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if len(keypoints) == 0:
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return (np.zeros((0, 2), dtype=dtype),
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np.zeros((0, ), dtype=dtype),
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np.zeros((0, ), dtype=dtype))
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mask = _mask_border_keypoints(octave_image.shape, keypoints,
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distance=16)
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keypoints = keypoints[mask]
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orientations = corner_orientations(octave_image, keypoints,
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OFAST_MASK)
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harris_response = corner_harris(octave_image, method='k',
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k=self.harris_k)
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responses = harris_response[keypoints[:, 0], keypoints[:, 1]]
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return keypoints, orientations, responses
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def detect(self, image):
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"""Detect oriented FAST keypoints along with the corresponding scale.
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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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"""
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check_nD(image, 2)
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pyramid = self._build_pyramid(image)
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keypoints_list = []
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orientations_list = []
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scales_list = []
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responses_list = []
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for octave in range(len(pyramid)):
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octave_image = np.ascontiguousarray(pyramid[octave])
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keypoints, orientations, responses = self._detect_octave(
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octave_image)
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keypoints_list.append(keypoints * self.downscale ** octave)
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orientations_list.append(orientations)
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scales_list.append(np.full(
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keypoints.shape[0], self.downscale ** octave,
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dtype=octave_image.dtype))
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responses_list.append(responses)
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keypoints = np.vstack(keypoints_list)
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orientations = np.hstack(orientations_list)
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scales = np.hstack(scales_list)
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responses = np.hstack(responses_list)
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if keypoints.shape[0] < self.n_keypoints:
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self.keypoints = keypoints
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self.scales = scales
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self.orientations = orientations
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self.responses = responses
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else:
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# Choose best n_keypoints according to Harris corner response
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best_indices = responses.argsort()[::-1][:self.n_keypoints]
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self.keypoints = keypoints[best_indices]
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self.scales = scales[best_indices]
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self.orientations = orientations[best_indices]
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self.responses = responses[best_indices]
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def _extract_octave(self, octave_image, keypoints, orientations):
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mask = _mask_border_keypoints(octave_image.shape, keypoints,
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distance=20)
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keypoints = np.array(keypoints[mask], dtype=np.intp, order='C',
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copy=False)
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orientations = np.array(orientations[mask], order='C',
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copy=False)
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descriptors = _orb_loop(octave_image, keypoints, orientations)
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return descriptors, mask
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def extract(self, image, keypoints, scales, orientations):
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"""Extract rBRIEF binary descriptors for given keypoints in image.
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Note that the keypoints must be extracted using the same `downscale`
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and `n_scales` parameters. Additionally, if you want to extract both
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keypoints and descriptors you should use the faster
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`detect_and_extract`.
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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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scales : (N, ) array
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Corresponding scales.
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orientations : (N, ) array
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Corresponding orientations in radians.
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"""
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check_nD(image, 2)
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pyramid = self._build_pyramid(image)
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descriptors_list = []
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mask_list = []
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# Determine octaves from scales
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octaves = (np.log(scales) / np.log(self.downscale)).astype(np.intp)
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for octave in range(len(pyramid)):
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# Mask for all keypoints in current octave
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octave_mask = octaves == octave
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if np.sum(octave_mask) > 0:
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octave_image = np.ascontiguousarray(pyramid[octave])
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octave_keypoints = keypoints[octave_mask]
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octave_keypoints /= self.downscale ** octave
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octave_orientations = orientations[octave_mask]
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descriptors, mask = self._extract_octave(octave_image,
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octave_keypoints,
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octave_orientations)
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descriptors_list.append(descriptors)
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mask_list.append(mask)
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self.descriptors = np.vstack(descriptors_list).view(bool)
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self.mask_ = np.hstack(mask_list)
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def detect_and_extract(self, image):
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"""Detect oriented FAST keypoints and extract rBRIEF descriptors.
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Note that this is faster than first calling `detect` and then
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`extract`.
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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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"""
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check_nD(image, 2)
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pyramid = self._build_pyramid(image)
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keypoints_list = []
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responses_list = []
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scales_list = []
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orientations_list = []
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descriptors_list = []
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for octave in range(len(pyramid)):
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octave_image = np.ascontiguousarray(pyramid[octave])
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keypoints, orientations, responses = self._detect_octave(
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octave_image)
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if len(keypoints) == 0:
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keypoints_list.append(keypoints)
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responses_list.append(responses)
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descriptors_list.append(np.zeros((0, 256), dtype=bool))
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continue
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descriptors, mask = self._extract_octave(octave_image, keypoints,
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orientations)
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scaled_keypoints = keypoints[mask] * self.downscale ** octave
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keypoints_list.append(scaled_keypoints)
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responses_list.append(responses[mask])
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orientations_list.append(orientations[mask])
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scales_list.append(self.downscale ** octave *
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np.ones(scaled_keypoints.shape[0], dtype=np.intp))
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descriptors_list.append(descriptors)
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if len(scales_list) == 0:
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raise RuntimeError(
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"ORB found no features. Try passing in an image containing "
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"greater intensity contrasts between adjacent pixels.")
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keypoints = np.vstack(keypoints_list)
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responses = np.hstack(responses_list)
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scales = np.hstack(scales_list)
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orientations = np.hstack(orientations_list)
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descriptors = np.vstack(descriptors_list).view(bool)
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if keypoints.shape[0] < self.n_keypoints:
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self.keypoints = keypoints
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self.scales = scales
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self.orientations = orientations
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self.responses = responses
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self.descriptors = descriptors
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else:
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# Choose best n_keypoints according to Harris corner response
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best_indices = responses.argsort()[::-1][:self.n_keypoints]
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self.keypoints = keypoints[best_indices]
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self.scales = scales[best_indices]
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self.orientations = orientations[best_indices]
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self.responses = responses[best_indices]
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self.descriptors = descriptors[best_indices]
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