a6d94BddlZddlmZmZmZmZddlmZm Z m Z m Z ddl m Z ddlmZddlmZejd Zgd Zed d D]@Zeeee eeedzD]Zded ezd ezf<AGddeeZdS)N)FeatureDetectorDescriptorExtractor_mask_border_keypoints_prepare_grayscale_input_2D) corner_fastcorner_orientations corner_peaks corner_harris)pyramid_gaussian)check_nD) _orb_loop)r)rrrrr r ircDeZdZdZ ddZd Zd Zd Zd Zd Z dZ dS)ORBaOriented 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. 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)``. References ---------- .. [1] 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 -------- >>> from skimage.feature import ORB, match_descriptors >>> img1 = np.zeros((100, 100)) >>> img2 = np.zeros_like(img1) >>> rng = np.random.default_rng(19481137) # do not copy this value >>> square = rng.random((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, 4], [4, 3]]) >>> detector_extractor1.keypoints[matches[:, 0]] array([[59. , 59. ], [40. , 40. ], [57. , 40. ], [46. , 58. ], [58.8, 58.8]]) >>> detector_extractor2.keypoints[matches[:, 1]] array([[72., 72.], [53., 53.], [70., 53.], [59., 71.], [72., 72.]]) 333333?rr{Gz?{Gz?c||_||_||_||_||_||_d|_d|_d|_d|_ d|_ dS)N) downscalen_scales n_keypointsfast_nfast_thresholdharris_k keypointsscales responses orientations descriptors)selfr#r$r%r&r'r(s 3lib/python3.11/site-packages/skimage/feature/orb.py__init__z ORB.__init__us[#  & ,    czt|}tt||jdz |jdS)Nr) channel_axis)rlistr r$r#)r.images r/_build_pyramidzORB._build_pyramidsK+E22$UDMA,=%)^$HHHII Ir1c|j}t||j|j}t |d}t |dkrAt jd|t jd|t jd|fSt|j |d}||}t||t}t|d |j }||dddf|dddff}|||fS) Nr) min_distancer)rrdtype)rrdistancek)methodr=)r:rr&r'r lennpzerosrshaper OFAST_MASKr r() r. octave_imager: fast_responser)maskr,harris_responser+s r/_detect_octavezORB._detect_octaves"#L$+$($799  Q??? y>>Q  HV5111HU%000HU%0002 2&l&8)/1333dO *<+577 ( S*.-999#IaaadOYqqq!t_$DE , 11r1ct|d||}g}g}g}g}tt|D]}t j||}t j|jdkrn||\} } } | | |j |zz| | | t j | j d|j |z|j | | t j|} t j|} t j|} t j|} | j d|jkr| |_| |_| |_| |_dS| dddd|j} | | |_| | |_| | |_| | |_dS)zDetect oriented FAST keypoints along with the corresponding scale. Parameters ---------- image : 2D array Input image. rrr9N)r r6ranger?r@ascontiguousarraysqueezendimrHappendr#fullrBr:vstackhstackr%r)r*r,r+argsort)r.r5pyramidkeypoints_listorientations_list scales_listresponses_listoctaverDr)r,r+r* best_indicess r/detectz ORB.detects %%e,, CLL)) - -F/@@Lz,'',q00151D1D22 .I|Y  ! !)dn.F"F G G G  $ $\ 2 2 2   rw"DNf$<"( * * * + + +  ! !) , , , ,In-- y!233 ;''In-- ?1  0 0 0&DN DK ,D &DNNN%,,..ttt45Fd6F5FGL&|4DN .DK ,\ :D &|4DNNNr1ct|j|d}tj||tjdd}tj||dd}t |||}||fS)Nr;CF)r:ordercopy)r_r`)rrBr@arrayintpr)r.rDr)r,rFr-s r/_extract_octavezORB._extract_octaves%l&8)/1333HYt_BG3"'))) x T 2#%*,,,  iFF D  r1ct|d||}g}g}tj|tj|jz tj}tt|D]} || k} tj | dkr{tj || } || } | |j| zz} || } | | | | \}}| || |tj |t|_tj||_dS)aExtract 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. rrN)r r6r@logr#astyperbrKr?sumrLrcrOrQviewboolr-rRmask_)r.r5r)r*r,rTdescriptors_list mask_listoctavesrY octave_maskrDoctave_keypointsoctave_orientationsr-rFs r/extractz ORB.extractsa( %%e,, 6&>>BF4>$:$::BB27KKCLL)) ' 'F"V+Kvk""Q&&!3GFODD #,[#9  DNf$<< &2;&?#$($8$89I9L%N%N! T!'' 444  &&&9%566;;DAAYy)) r1cXt|d||}g}g}g}g}g}tt|D]}t j||} t j| jdkrn]|| \} } } t| dkrY| | | | | t j dt| | | | \} }| ||j |zz}| || | || | || |j |zt j|jdtjz| | t|dkrt#dt j|} t j|} t j|}t j|} t j|t} | jd|jkr%| |_||_| |_| |_| |_dS| dddd|j}| ||_|||_| ||_| ||_| ||_dS)zDetect 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. rr)rr9znORB found no features. Try passing in an image containing greater intensity contrasts between adjacent pixels.NrJ)r r6rKr?r@rLrMrNrHrOrArircr#onesrBrb RuntimeErrorrQrRrhr%r)r*r,r+r-rS)r.r5rTrUrXrWrVrkrYrDr)r,r+r-rFscaled_keypointsr*rZs r/detect_and_extractzORB.detect_and_extracts* %%e,, CLL)) 1 1F/@@Lz,'',q00151D1D22 .I|Y9~~""%%i000%%i000 ''(F(F(FGGG $ 4 4\95A!C!C K )61II   ! !"2 3 3 3  ! !)D/ 2 2 2  $ $\$%7 8 8 8   t~7!w'7'=a'@PPP Q R R R  # #K 0 0 0 0 {  q GHH HIn-- In-- ;''y!233 i 01166t<< ?1  0 0 0&DN DK ,D &DN*D   %,,..ttt45Fd6F5FGL&|4DN .DK ,\ :D &|4DN*<8D   r1N)rrrrr r!) __name__ __module__ __qualname____doc__r0r6rHr[rcrqrwr1r/rrs[[z01;?    III 2222454545l ! ! !3*3*3*jJ9J9J9J9J9r1r)numpyr@ feature.utilrrrrfeaturerr r r transformr _shared.utilsr orb_cyrrArC OFAST_UMAXrKiabsjrr|r1r/rs999999999999&&&&&&&&&&&&(((((($$$$$$RXh   I I I sB''A UJss1vv&& 33q66(:Q(> ? ?''%& 2626>""'N9N9N9N9N9/.N9N9N9N9N9r1