mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-05 01:59:55 +08:00
Ankit Agrawal
393 lines
16 KiB
Python
393 lines
16 KiB
Python
import numpy as np
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from ..util import img_as_float
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from .util import _mask_border_keypoints
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from skimage.feature import (corner_fast, corner_orientations, corner_peaks,
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corner_harris)
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from skimage.transform import pyramid_gaussian
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def keypoints_orb(image, n_keypoints=200, fast_n=9, fast_threshold=0.20,
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harris_k=0.05, downscale_factor=np.sqrt(2), n_scales=5):
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"""Compute Oriented Fast keypoints.
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Parameters
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----------
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image : 2D ndarray
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Input grayscale image.
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n_keypoints : int
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Number of keypoints to be returned from this function. The function
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will return best `n_keypoints` if more than n_keypoints are detected
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based on the values of other parameters. If not, then all the detected
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keypoints are returned.
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fast_n : int
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The `n` parameter in `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 testpixel. 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
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The `threshold` parameter in `feature.corner_fast`. Threshold used to
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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
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The `k` parameter in `feature.corner_harris`. Sensitivity factor to
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separate corners from edges, typically in range `[0, 0.2]`. Small
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values of k result in detection of sharp corners.
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downscale_factor : float
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Downscale factor for the image pyramid.
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n_scales : int
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Number of scales from the bottom of the image pyramid to extract
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the features from.
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Returns
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-------
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keypoints : (N, 2) ndarray
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The oFAST keypoints.
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orientations : (N,) ndarray
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The orientations of the N extracted keypoints.
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scales : (N,) ndarray
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The scales of the N extracted keypoints.
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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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"""
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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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pyramid = list(pyramid_gaussian(image, n_scales - 1, downscale_factor))
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ofast_mask = np.array([[0, 0, 1, 1, 1, 0, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 0, 1, 1, 1, 0, 0]], dtype=np.uint8)
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keypoints_list = []
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orientations_list = []
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scales_list = []
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harris_measure_list = []
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for i in range(n_scales):
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harris_response = corner_harris(pyramid[i], method='k', k=harris_k)
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corners = corner_peaks(corner_fast(pyramid[i], fast_n, fast_threshold), min_distance=1)
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keypoints_list.append(corners)
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orientations_list.append(corner_orientations(pyramid[i], corners, ofast_mask))
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scales_list.append(i * np.ones((corners.shape[0]), dtype=np.intp))
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harris_measure_list.append(harris_response[corners[:, 0], corners[:, 1]])
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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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harris_measure = np.hstack(harris_measure_list)
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if keypoints.shape[0] < n_keypoints:
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return keypoints, orientations, scales
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else:
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best_indices = harris_measure.argsort()[::-1][:n_keypoints]
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return keypoints[best_indices], orientations[best_indices], scales[best_indices]
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def descriptor_orb(image, keypoints, keypoints_angle):
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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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mask = _mask_border_keypoints(image, keypoints, 13)
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keypoints = keypoints[mask]
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keypoints_angle = keypoints_angle[mask]
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pos1 = binary_tests[:, :2]
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pos2 = binary_tests[:, 2:]
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descriptors = np.zeros((keypoints.shape[0], 256), dtype=bool)
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for i in range(keypoints.shape[0]):
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angle = keypoints_angle[i]
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a = np.sin(angle * np.pi / 180.)
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b = np.cos(angle * np.pi / 180.)
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rotation_matrix = [[b, a], [-a, b]]
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steered_pos1 = pos1 * rotation_matrix
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steered_pos2 = pos2 * rotation_matrix
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for j in range(256):
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pr1 = steered_pos1[j][0]
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pc1 = steered_pos1[j][1]
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pr2 = steered_pos2[j][0]
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pc2 = steered_pos2[j][1]
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descriptors[i, j] = (image[pr1, pc1] < image[pr2, pc2])
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return descriptors
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# Learned 256 decision pairs for binary tests in rBRIEF. Taken from OpenCV.
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binary_tests = np.asarray([[8,-3, 9,5],
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[4,2, 7,-12],
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[-11,9, -8,2],
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[7,-12, 12,-13],
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[2,-13, 2,12],
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[1,-7, 1,6],
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[-2,-10, -2,-4],
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[-13,-13, -11,-8],
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[-13,-3, -12,-9],
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[10,4, 11,9],
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[-13,-8, -8,-9],
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[-11,7, -9,12],
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[7,7, 12,6],
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[-4,-5, -3,0],
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[-13,2, -12,-3],
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[-9,0, -7,5],
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[12,-6, 12,-1],
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[-3,6, -2,12],
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[-6,-13, -4,-8],
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[11,-13, 12,-8],
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[4,7, 5,1],
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[5,-3, 10,-3],
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[3,-7, 6,12],
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[-8,-7, -6,-2],
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[-2,11, -1,-10],
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[-13,12, -8,10],
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[-7,3, -5,-3],
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[-4,2, -3,7],
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[-10,-12, -6,11],
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[5,-12, 6,-7],
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[5,-6, 7,-1],
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[1,0, 4,-5],
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[9,11, 11,-13],
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[4,7, 4,12],
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[2,-1, 4,4],
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[-4,-12, -2,7],
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[-8,-5, -7,-10],
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[4,11, 9,12],
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[0,-8, 1,-13],
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[-13,-2, -8,2],
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[-3,-2, -2,3],
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[-6,9, -4,-9],
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[8,12, 10,7],
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[0,9, 1,3],
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[7,-5, 11,-10],
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[-13,-6, -11,0],
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[10,7, 12,1],
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[-6,-3, -6,12],
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[10,-9, 12,-4],
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[-13,8, -8,-12],
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[-13,0, -8,-4],
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[3,3, 7,8],
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[5,7, 10,-7],
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[-1,7, 1,-12],
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[3,-10, 5,6],
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[2,-4, 3,-10],
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[-13,0, -13,5],
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[-13,-7, -12,12],
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[-13,3, -11,8],
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[-7,12, -4,7],
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[6,-10, 12,8],
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[-9,-1, -7,-6],
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[-2,-5, 0,12],
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[-12,5, -7,5],
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[3,-10, 8,-13],
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[-7,-7, -4,5],
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[-3,-2, -1,-7],
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[2,9, 5,-11],
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[-11,-13, -5,-13],
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[-1,6, 0,-1],
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[5,-3, 5,2],
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[-4,-13, -4,12],
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[-9,-6, -9,6],
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[-12,-10, -8,-4],
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[10,2, 12,-3],
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[7,12, 12,12],
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[-7,-13, -6,5],
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[-4,9, -3,4],
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[7,-1, 12,2],
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[-7,6, -5,1],
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[-13,11, -12,5],
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[-3,7, -2,-6],
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[7,-8, 12,-7],
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[-13,-7, -11,-12],
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[1,-3, 12,12],
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[2,-6, 3,0],
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[-4,3, -2,-13],
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[-1,-13, 1,9],
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[7,1, 8,-6],
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[1,-1, 3,12],
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[9,1, 12,6],
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[-1,-9, -1,3],
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[-13,-13, -10,5],
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[7,7, 10,12],
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[12,-5, 12,9],
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[6,3, 7,11],
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[5,-13, 6,10],
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[2,-12, 2,3],
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[3,8, 4,-6],
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[2,6, 12,-13],
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[9,-12, 10,3],
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[-8,4, -7,9],
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[-11,12, -4,-6],
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[1,12, 2,-8],
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[6,-9, 7,-4],
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[2,3, 3,-2],
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[6,3, 11,0],
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[3,-3, 8,-8],
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[7,8, 9,3],
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[-11,-5, -6,-4],
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[-10,11, -5,10],
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[-5,-8, -3,12],
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[-10,5, -9,0],
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[8,-1, 12,-6],
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[4,-6, 6,-11],
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[-10,12, -8,7],
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[4,-2, 6,7],
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[-2,0, -2,12],
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[-5,-8, -5,2],
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[7,-6, 10,12],
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[-9,-13, -8,-8],
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[-5,-13, -5,-2],
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[8,-8, 9,-13],
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[-9,-11, -9,0],
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[1,-8, 1,-2],
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[7,-4, 9,1],
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[-2,1, -1,-4],
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[11,-6, 12,-11],
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[-12,-9, -6,4],
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[3,7, 7,12],
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[5,5, 10,8],
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[0,-4, 2,8],
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[-9,12, -5,-13],
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[0,7, 2,12],
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[-1,2, 1,7],
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[5,11, 7,-9],
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[3,5, 6,-8],
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[-13,-4, -8,9],
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[-5,9, -3,-3],
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[-4,-7, -3,-12],
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[6,5, 8,0],
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[-7,6, -6,12],
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[-13,6, -5,-2],
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[1,-10, 3,10],
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[4,1, 8,-4],
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[-2,-2, 2,-13],
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[2,-12, 12,12],
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[-2,-13, 0,-6],
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[4,1, 9,3],
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[-6,-10, -3,-5],
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[-3,-13, -1,1],
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[7,5, 12,-11],
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[4,-2, 5,-7],
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[-13,9, -9,-5],
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[7,1, 8,6],
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[7,-8, 7,6],
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[-7,-4, -7,1],
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[-8,11, -7,-8],
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[-13,6, -12,-8],
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[2,4, 3,9],
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[10,-5, 12,3],
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[-6,-5, -6,7],
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[8,-3, 9,-8],
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[2,-12, 2,8],
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[-11,-2, -10,3],
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[-12,-13, -7,-9],
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[-11,0, -10,-5],
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[5,-3, 11,8],
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[-2,-13, -1,12],
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[-1,-8, 0,9],
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[-13,-11, -12,-5],
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[-10,-2, -10,11],
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[-3,9, -2,-13],
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[2,-3, 3,2],
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[-9,-13, -4,0],
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[-4,6, -3,-10],
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[-4,12, -2,-7],
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[-6,-11, -4,9],
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[6,-3, 6,11],
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[-13,11, -5,5],
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[11,11, 12,6],
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[7,-5, 12,-2],
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[-1,12, 0,7],
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[-4,-8, -3,-2],
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[-7,1, -6,7],
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[-13,-12, -8,-13],
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[-7,-2, -6,-8],
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[-8,5, -6,-9],
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[-5,-1, -4,5],
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[-13,7, -8,10],
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[1,5, 5,-13],
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[1,0, 10,-13],
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[9,12, 10,-1],
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[5,-8, 10,-9],
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[-1,11, 1,-13],
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[-9,-3, -6,2],
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[-1,-10, 1,12],
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[-13,1, -8,-10],
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[8,-11, 10,-6],
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[2,-13, 3,-6],
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[7,-13, 12,-9],
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[-10,-10, -5,-7],
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[-10,-8, -8,-13],
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[4,-6, 8,5],
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[3,12, 8,-13],
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[-4,2, -3,-3],
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[5,-13, 10,-12],
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[4,-13, 5,-1],
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[-9,9, -4,3],
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[0,3, 3,-9],
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[-12,1, -6,1],
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[3,2, 4,-8],
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[-10,-10, -10,9],
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[8,-13, 12,12],
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[-8,-12, -6,-5],
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[2,2, 3,7],
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[10,6, 11,-8],
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[6,8, 8,-12],
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[-7,10, -6,5],
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[-3,-9, -3,9],
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[-1,-13, -1,5],
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[-3,-7, -3,4],
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[-8,-2, -8,3],
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[4,2, 12,12],
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[2,-5, 3,11],
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[6,-9, 11,-13],
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[3,-1, 7,12],
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[11,-1, 12,4],
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[-3,0, -3,6],
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[4,-11, 4,12],
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[2,-4, 2,1],
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[-10,-6, -8,1],
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[-13,7, -11,1],
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[-13,12, -11,-13],
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[6,0, 11,-13],
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[0,-1, 1,4],
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[-13,3, -9,-2],
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[-9,8, -6,-3],
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[-13,-6, -8,-2],
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[5,-9, 8,10],
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[2,7, 3,-9],
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[-1,-6, -1,-1],
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[9,5, 11,-2],
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[11,-3, 12,-8],
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[3,0, 3,5],
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[-1,4, 0,10],
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[3,-6, 4,5],
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[-13,0, -10,5],
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[5,8, 12,11],
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[8,9, 9,-6],
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[7,-4, 8,-12],
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[-10,4, -10,9],
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[7,3, 12,4],
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[9,-7, 10,-2],
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[7,0, 12,-2],
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[-1,-6, 0,-11]])
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