orb

ORB

class skimage.feature.ORB(downscale=1.2, n_scales=8, n_keypoints=500, fast_n=9, fast_threshold=0.08, harris_k=0.04) [source]

Bases: skimage.feature.util.FeatureDetector, skimage.feature.util.DescriptorExtractor

Oriented FAST and rotated BRIEF feature detector and binary descriptor extractor.

Parameters:

n_keypoints : int, optional Number of keypoints to be returned. The function will return the best n_keypoints according to the Harris corner response if more than n_keypoints are detected. If not, then all the detected keypoints are returned. fast_n : int, optional The n parameter in skimage.feature.corner_fast. Minimum number of consecutive pixels out of 16 pixels on the circle that should all be either brighter or darker w.r.t test-pixel. A point c on the circle is darker w.r.t test pixel p if Ic < Ip - threshold and brighter if Ic > Ip + threshold. Also stands for the n in FAST-n corner detector. fast_threshold : float, optional The threshold parameter in feature.corner_fast. Threshold used to decide whether the pixels on the circle are brighter, darker or similar w.r.t. the test pixel. Decrease the threshold when more corners are desired and vice-versa. harris_k : float, optional The k parameter in skimage.feature.corner_harris. Sensitivity factor to separate corners from edges, typically in range [0, 0.2]. Small values of k result in detection of sharp corners. downscale : float, optional Downscale factor for the image pyramid. Default value 1.2 is chosen so that there are more dense scales which enable robust scale invariance for a subsequent feature description. n_scales : int, optional Maximum number of scales from the bottom of the image pyramid to extract the features from.

References

[R160]

Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary Bradski “ORB: An efficient alternative to SIFT and SURF” http://www.vision.cs.chubu.ac.jp/CV-R/pdf/Rublee_iccv2011.pdf

Examples

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>>> from skimage.feature import ORB, match_descriptors >>> img1 = np.zeros((100, 100)) >>> img2 = np.zeros_like(img1) >>> np.random.seed(1) >>> square = np.random.rand(20, 20) >>> img1[40:60, 40:60] = square >>> img2[53:73, 53:73] = square >>> detector_extractor1 = ORB(n_keypoints=5) >>> detector_extractor2 = ORB(n_keypoints=5) >>> detector_extractor1.detect_and_extract(img1) >>> detector_extractor2.detect_and_extract(img2) >>> matches = match_descriptors(detector_extractor1.descriptors, ...                             detector_extractor2.descriptors) >>> matches array([[0, 0],        [1, 1],        [2, 2],        [3, 3],        [4, 4]]) >>> detector_extractor1.keypoints[matches[:, 0]] array([[ 42.,  40.],        [ 47.,  58.],        [ 44.,  40.],        [ 59.,  42.],        [ 45.,  44.]]) >>> detector_extractor2.keypoints[matches[:, 1]] array([[ 55.,  53.],        [ 60.,  71.],        [ 57.,  53.],        [ 72.,  55.],        [ 58.,  57.]])

Attributes

keypoints	((N, 2) array) Keypoint coordinates as (row, col).
scales	((N, ) array) Corresponding scales.
orientations	((N, ) array) Corresponding orientations in radians.
responses	((N, ) array) Corresponding Harris corner responses.
descriptors	((Q, descriptor_size) array of dtype bool) 2D array 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).

__init__(downscale=1.2, n_scales=8, n_keypoints=500, fast_n=9, fast_threshold=0.08, harris_k=0.04) [source]

detect(image) [source]

Detect oriented FAST keypoints along with the corresponding scale.

Parameters:	image : 2D array Input image.

detect_and_extract(image) [source]

Detect oriented FAST keypoints and extract rBRIEF descriptors.

Note that this is faster than first calling detect and then extract.

Parameters:	image : 2D array Input image.

extract(image, keypoints, scales, orientations) [source]

Extract rBRIEF binary descriptors for given keypoints in image.

Note that the keypoints must be extracted using the same downscale and n_scales parameters. Additionally, if you want to extract both keypoints and descriptors you should use the faster detect_and_extract.

Parameters:	image : 2D array Input image. keypoints : (N, 2) array Keypoint coordinates as (row, col). scales : (N, ) array Corresponding scales. orientations : (N, ) array Corresponding orientations in radians.