9i.SSKrSSKJrJrJr SSKJr SSKJ r SSK J r J r SSK Jr SS KJrJrJr SS KJr /S Qr/S Qr/S Qr/SQrSrSrSrSr"SS\5rg)N)maximum_filterminimum_filterconvolve)integral_imagestructure_tensor)octagonstar)_censure_dob_loop)FeatureDetector_prepare_grayscale_input_2D_mask_border_keypoints)check_nD) rr)r) rr) r) )r r )r)r ) )rrrr)rrrr)rr)rr)rr)rrr#r)rrrrr#rr -.@Z) )rrr")rrr)rr)r%rr$)rr%)rr)r!rr)r&r!c v[R"URSURSX!- S-4[RS9nUS:XaURnXTRS-UXTRS-URS-4Ul[ U5n[X!- S-5H=nX-nSSU-S-S-- n SSUS--SU--- n [XUSS2SS2U4X5 M? U$US :XaY[X!- S-5HCn[X-S- up[X-S- up[U[XX55USS2SS2U4'ME U$US :Xaf[X!- S-5HRn[[X-S- Sn[[X-S- Sn[U[X55USS2SS2U4'MT U$) Nrrdtypedob?rrrr r )npzerosshapefloat64itemsizestridesrranger OCTAGON_OUTER_SHAPEOCTAGON_INNER_SHAPEr_octagon_kernel STAR_SHAPESTAR_FILTER_SHAPE _star_kernel)image min_scale max_scalemoderesponse item_size integral_imgin inner_weight outer_weightmonominims W/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/feature/censure.py _filter_imagerRBsxx QQ)>)BC2::H u}%% q) )  q) )HNN1,= =  &e, y,q01A A !a%!)!11L"q!t)a!e"34L !Q'!2L 2@ O  y,q01A():;FB():;FB (0O PHQ1W 2 O y,q01A,Y]Q->?BCA,Y]Q->?BCA ( Q0B CHQ1W 2 Oc@USU--S-SU-US--- nUSU--S-SU-US--- nSXE- - nSU- nUSU--USU--- S-n[X5n [R"USU--USU--45n [X#5XU*2X*24'Xi-Xg-U -- n U $)Nrrr3)r r4r5) rLrMrNrOouterinnerrKrJc outer_oct inner_octbfilters rQr=r=us !b&[Q R26!2 2E !b&[Q R26!2 2E%-(L;L q2v+"q2v+ &1,AI"q2v+rAF{34I#BOIdAbDj&,*E)RRG NrScXS--U- US-- n[U5n[R"U5n[U5XBU*2X"*24'S[R"X4- 5- nS[R"U5- nXS-XV-U-- nU$)Nrr3)r r4 zeros_likesum)rPrIrW outer_star inner_starrKrJrZs rQr@r@s F QaAaJz*J!!WJ!tQrTz"&&!89:L ++L'<+F**TTG NrScN[XSS9upEnSXU-S-X4U-US-- -:'g)Nrc)orderFrr ) feature_maskrAsigmaline_thresholdArrArcAccs rQ_suppress_linesris8$U>MCcMRL)!NCi#q&6H$IIJrSc2\rSrSrSrSSjrSrSrg)CENSUREam CENSURE keypoint detector. min_scale : int, optional Minimum scale to extract keypoints from. max_scale : int, optional Maximum scale to extract keypoints from. The keypoints will be extracted from all the scales except the first and the last i.e. from the scales in the range [min_scale + 1, max_scale - 1]. The filter sizes for different scales is such that the two adjacent scales comprise of an octave. mode : {'DoB', 'Octagon', 'STAR'}, optional Type of bi-level filter used to get the scales of the input image. Possible values are 'DoB', 'Octagon' and 'STAR'. The three modes represent the shape of the bi-level filters i.e. box(square), octagon and star respectively. For instance, a bi-level octagon filter consists of a smaller inner octagon and a larger outer octagon with the filter weights being uniformly negative in both the inner octagon while uniformly positive in the difference region. Use STAR and Octagon for better features and DoB for better performance. non_max_threshold : float, optional Threshold value used to suppress maximas and minimas with a weak magnitude response obtained after Non-Maximal Suppression. line_threshold : float, optional Threshold for rejecting interest points which have ratio of principal curvatures greater than this value. Attributes ---------- keypoints : (N, 2) array Keypoint coordinates as ``(row, col)``. scales : (N,) array Corresponding scales. References ---------- .. [1] Motilal Agrawal, Kurt Konolige and Morten Rufus Blas "CENSURE: Center Surround Extremas for Realtime Feature Detection and Matching", https://link.springer.com/chapter/10.1007/978-3-540-88693-8_8 :DOI:`10.1007/978-3-540-88693-8_8` .. [2] Adam Schmidt, Marek Kraft, Michal Fularz and Zuzanna Domagala "Comparative Assessment of Point Feature Detectors and Descriptors in the Context of Robot Navigation" http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-268aaf28-0faf-4872-a4df-7e2e61cb364c/c/Schmidt_comparative.pdf :DOI:`10.1.1.465.1117` Examples -------- >>> from skimage.data import astronaut >>> from skimage.color import rgb2gray >>> from skimage.feature import CENSURE >>> img = rgb2gray(astronaut()[100:300, 100:300]) >>> censure = CENSURE() >>> censure.detect(img) >>> censure.keypoints array([[ 4, 148], [ 12, 73], [ 21, 176], [ 91, 22], [ 93, 56], [ 94, 22], [ 95, 54], [100, 51], [103, 51], [106, 67], [108, 15], [117, 20], [122, 60], [125, 37], [129, 37], [133, 76], [145, 44], [146, 94], [150, 114], [153, 33], [154, 156], [155, 151], [184, 63]]) >>> censure.scales array([2, 6, 6, 2, 4, 3, 2, 3, 2, 6, 3, 2, 2, 3, 2, 2, 2, 3, 2, 2, 4, 2, 2]) cUR5nUS;a [S5eUS:dUS:dX!- S:a [S5eXlX lX0lX@lXPlSUlSUlg)N)r2r r z/`mode` must be one of 'DoB', 'Octagon', 'STAR'.rrz@The scales must be >= 1 and the number of scales should be >= 3.) lower ValueErrorrBrCrDnon_max_thresholdre keypointsscales)selfrBrCrDrpres rQ__init__CENSURE.__init__svzz| 1 1NO O q=IMY-BQ-FU #" !2, rSc$[US5 URUR- n[R"[ U55n[ XRURUR5n[US5U:Hn[US5U:HnXE-nSXcUR:'[SU5H;n[USS2SS2U4USURU-S- S- -UR5 M= [R"USSU245upn [R"X/5n XR-S-n URS:Xa XlXlg[R$"U R&S [(S 9n URS :Xan[URS-UR5HFn[*US- S S- S-[*US- S-n U [-UR&X5X:H--n MH OURS :Xax[URS-UR5HQn[.[0US- S [.[0US- S S--n U [-UR&X5X:H--n MS XUlXUlg) zyDetect CENSURE keypoints along with the corresponding scale. Parameters ---------- image : 2D ndarray Input image. r)rrrFrNg@.r2rr0r r )rrCrBr4ascontiguousarrayrrRrDrrrpr:rirenonzero column_stackrqrrr5r6boolr;rr>r?)rsrA num_scalesfilter_responseminimasmaximasrcrHrowscolsrrrqcumulative_maskrWs rQdetectCENSURE.detects0 ^^dnn4 $$%@%GH( >>4>>499  !)<O )<O( AF t'='==>q*%A Q1W%dnnq(1,33##   & ZZ S!J,5F(GHFOOTL1 ..(1, 99 &N K ((9??1#5TB 99 !4>>A-t~~>(Q/2Q61>A-t~~>0Q7:; !21q5!9!!<=BC #9%++y#TK$ ?#3- rS)rqrerCrBrDrprrN)rrDoBg333333?r)__name__ __module__ __qualname____firstlineno____doc__rtr__static_attributes__rSrQrkrks%Sn  4U.rSrk)numpyr4 scipy.ndimagerrr transformrcornerr morphologyr r censure_cyr feature.utilrrr _shared.utilsrr;r<r>r?rRr=r@rirkrrSrQrsmBB&$&) %   M   0f S E.oE.rS