[;j0ddlZddlmZmZmZddlmZddlm Z ddl m Z m Z ddl mZdd lmZmZmZdd lmZgd Zgd Zgd ZgdZdZdZdZdZGddeZy)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$)rr%)rr)r!rr)r&r!c tj|jd|jd||z dzftj}|dk(r|j}tj j j|||jdz|||jdz|jdzf}t|}t||z dzD]?}||z}dd|zdzdzz } dd|dzzd |zzz } t|||dddd|f| | A|S|d k(r^t||z dzD]H}t||zdz \} } t||zdz \} }t|t| | | ||dddd|f<J|S|d k(rht||z dzD]T}tt ||zdz d}tt ||zdz d}t|t#|||dddd|f<V|S) Nrrdtypedob)strides?rrrr r )npzerosshapefloat64itemsizelib stride_tricks as_stridedrranger OCTAGON_OUTER_SHAPEOCTAGON_INNER_SHAPEr_octagon_kernel STAR_SHAPESTAR_FILTER_SHAPE _star_kernel)image min_scale max_scalemoderesponse item_size integral_imgin inner_weight outer_weightmonominims `G:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\skimage/feature/censure.py _filter_imagerUBs/xx QQY)>)BC2::H u}%% 66''22 HNN1--HNN1--q0AA3 &e, y9,q01AA A !a%!)!11L"q!t)a!e"34L <!Q'!2L, 2@ O  y9,q01A(Q):;FB(Q):;FB (BB0O PHQ1W 2 O y9,q01A,Y]Q->?BCA,Y]Q->?BCA ( Q0B CHQ1W 2 OcL|d|zzdzd|z|dzzz }|d|zzdzd|z|dzzz }d||z z }d|z }|d|zz|d|zzz dz}t||} tj|d|zz|d|zzf} t||| || || f<|| z||z| zz } | S)Nrrr4)r r5r6) rOrPrQrRouterinnerrNrMc outer_oct inner_octbfilters rTr@r@xs !b&[Q R26!2 2E !b&[Q R26!2 2E%%-(L;L q2v+"q2v+ &1,ABI"q2v+rAF{34I#BOIadAqbDjY&,*E)RRG NrVc||dzz|z |dzz }t|}tj|}t|||| || f<dtj||z z }dtj|z }||z||z|zz }|S)Nrr4)r r5 zeros_likesum)rSrLrZ outer_star inner_starrNrMr]s rTrCrCs AF QaAaJz*J!!WJq!tQrTz"&&j!89:L ++LZ'<,+F**TTG NrVc\t||d\}}}d|||zdz|||z|dzz zkD<y)Nrc)orderFrr ) feature_maskrDsigmaline_thresholdArrArcAccs rT_suppress_linesrls@$UE>MCcMRL#)!NcCi#q&6H$IIJrVc(eZdZdZ ddZdZy)CENSUREa 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]) c|j}|dvr td|dks |dks||z dkr td||_||_||_||_||_d|_d|_y)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 ValueErrorrErFrGnon_max_thresholdrh keypointsscales)selfrErFrGrrrhs rT__init__zCENSURE.__init__szz| 1 1NO O q=IMY-BQ-FU #" !2, rVc,t|d|j|jz }tjt |}t ||j|j|j}t|d|k(}t|d|k(}||z}d|||jk<td|D]:}t|dddd|f|d|j|zdz dz z|j<tj|dd|f\}} } tj|| g} | |jzdz} |jdk(r| |_| |_ytj$| j&d t( } |jd k(rnt|jdz|jD]G}t*|dz d dz dzt*|dz dz} | t-|j&| | | |k(zz} In|jd k(rxt|jdz|jD]R}t.t0|dz d t.t0|dz d dzz} | t-|j&| | | |k(zz} T| | |_| | |_y) zDetect CENSURE keypoints along with the corresponding scale. Parameters ---------- image : 2D ndarray Input image. r)rrrFrNg@.r2rr0r r )rrFrEr5ascontiguousarrayrrUrGrrrrr=rlrhnonzero column_stackrsrtr6r7boolr>rrArB)rurD num_scalesfilter_responseminimasmaximasrfrKrowscolsrtrscumulative_maskrZs rTdetectzCENSURE.detects0 ^^dnn4 $$%@%GH( 4>>4>>499  !)<O )<O( AF _t'='==>q*%A Q1W%dnnq(1,33##   & ZZ S!J,5F(GHdFOOT4L1 $..(1, 99 &DN DK ((9??1#5TB 99 !4>>A-t~~>(Q/2Q61>A-t~~>0Q7:; !21q5!9!!<=BC #9%++yRS#TaK$ ?#?3_- rVN)rrDoBg333333?r)__name__ __module__ __qualname____doc__rvrrVrTrnrns%Sn  4U.rVrn)numpyr5 scipy.ndimagerrr transformrcornerr morphologyr r censure_cyr feature.utilrrr _shared.utilsrr>r?rArBrUr@rCrlrnrrVrTrsmBB&$&) %   M   3l S E.oE.rV