9i":SSKJr SSKrSSKJr SSKJr /SQr "SS5r \R"S 5\R"/S Q/S -5\R"\R"S 55\R"/S Q/S -5/r\"S 5Vs/sHn\R#S 5PM snrS \SSS2SS2S 4'S \S SS2S SS24'S \SS SS2SS24'S \S SS2/SQ/SQ4'S \SSS2/SQ/SQ4'S \S/SQSS2/SQ4'S \S/SQSS2/SQ4'S \S/SQ/SQSS24'S \S/SQ/SQSS24'SrSr\ "SS/5rSrSrSSS.SjrS&SjrS'SjrSS S S S 4S!jrS"S S#SS$4S%jrgs snf)()cycleN)ndimage)check_nD)morphological_chan_vese%morphological_geodesic_active_contourinverse_gaussian_gradientdisk_level_setcheckerboard_level_setc \rSrSrSrSrSrg)_fcyclec$[U5Ulg)z8Call functions from the iterable each time it is called.N)rfuncs)selfiterables `/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/segmentation/morphsnakes.py__init___fcycle.__init__s 8_ c<[UR5nU"U0UD6$N)nextr)rargskwargsfs r__call___fcycle.__call__s  $!&!!r)rN)__name__ __module__ __qualname____firstlineno__rr__static_attributes__rrr r s %"rr )rr )r%r%r%r&)rr&r)rr&rc~[R"U5S:Xa[nO,[R"U5S:Xa[nO [ S5e/nUHEnUR [ R"X5R[R55 MG [R"USS9RS5$)z SI operator.rr%8u has an invalid number of dimensions (should be 2 or 3)raxis) npndim_P2_P3 ValueErrorappendndibinary_erosionastypeint8stackmax)uPerosionsP_is rsup_infrA/s wwqzQ  q VWWH**1299"''BC 88H1 % ) )! ,,rc~[R"U5S:Xa[nO,[R"U5S:Xa[nO [ S5e/nUHEnUR [ R"X5R[R55 MG [R"USS9RS5$)z IS operator.rr%r.rr/) r1r2r3r4r5r6r7binary_dilationr9r:r;min)r=r> dilationsr@s rinf_suprF@s wwqzQ  q VWWI,,Q4;;BGGDE 88IA & * *1 --rc*[[U55$r)rArFr=s rrIRs wwqz"rc*[[U55$r)rFrArHs rrIrIRs ggaj.Arc[USS/5 [UR5[UR5:wa [S5eg)z8Check that shapes of `image` and `init_level_set` match.rr%zQThe dimensions of the initial level set do not match the dimensions of the image.N)rlenshaper5)imageinit_level_sets r _check_inputrPVsC UQF 5;;3~3344 1  5rc[U[5(a1US:Xa [U5nU$US:Xa [U5nU$[ S5eUnU$)zzAuxiliary function for initializing level sets with a string. If `init_level_set` is not a string, it is returned as is. checkerboarddiskz0`init_level_set` not in ['checkerboard', 'disk']) isinstancestrr r r5)rO image_shaperess r_init_level_setrXas] .#&& ^ +(5C J v % -C JRS S Jr)centerradiuscUc[SU55nUc[U5S-S- n[RUVs/sHn[ U5PM snnUR U- R nU[R "[R"US-S55- n[R"US:5nU$s snf)aMCreate a disk level set with binary values. Parameters ---------- image_shape : tuple of positive integers Shape of the image center : tuple of positive integers, optional Coordinates of the center of the disk given in (row, column). If not given, it defaults to the center of the image. radius : float, optional Radius of the disk. If not given, it is set to the 75% of the smallest image dimension. Returns ------- out : array with shape `image_shape` Binary level set of the disk with the given `radius` and `center`. See Also -------- checkerboard_level_set c3*# UH oS-v M g7f)rNr$).0is r !disk_level_set..s3{!Av{sg@g @rr) tuplerDr1mgridsliceTsqrtsumr:)rVrYrZr^gridphirWs rr r rs0~3{33 ~[!C'#- 88{3{!U1X{3 4D FFVO  D 2772664A+q12 2C ''#' C J 4sB<c[RUVs/sHn[U5PM snnX1-nUS-n[RR USS9n[R "U5nU$s snf)aiCreate a checkerboard level set with binary values. Parameters ---------- image_shape : tuple of positive integers Shape of the image. square_size : int, optional Size of the squares of the checkerboard. It defaults to 5. Returns ------- out : array with shape `image_shape` Binary level set of the checkerboard. See Also -------- disk_level_set r&rr/)r1rbrc bitwise_xorreducer:)rV square_sizer^rgrRrWs rr r si( 88{3{!U1X{3 4D  D !8D>>((A(6L '', C J4sA-ch[R"XSS9nS[R"SX--5- $)aInverse of gradient magnitude. Compute the magnitude of the gradients in the image and then inverts the result in the range [0, 1]. Flat areas are assigned values close to 1, while areas close to borders are assigned values close to 0. This function or a similar one defined by the user should be applied over the image as a preprocessing step before calling `morphological_geodesic_active_contour`. Parameters ---------- image : (M, N) or (L, M, N) array Grayscale image or volume. alpha : float, optional Controls the steepness of the inversion. A larger value will make the transition between the flat areas and border areas steeper in the resulting array. sigma : float, optional Standard deviation of the Gaussian filter applied over the image. Returns ------- gimage : (M, N) or (L, M, N) array Preprocessed image (or volume) suitable for `morphological_geodesic_active_contour`. nearest)modeg?)r7gaussian_gradient_magnituder1re)rNalphasigmagradnorms rr r s28..u)LH u//0 00rrRcgrr$xs rrIrIDrc[X R5n[X5 [R"US:5nU"U5 [ U5HnUSU- -R 5[SU- R 5S-5- n X-R 5[UR 5S-5- n [R"U5n [R"U 5R S5n XX - S--XPU - S--- -n SX}S:'SX}S:'[ U5Hn[U5nM U"U5 M U$)uD Morphological Active Contours without Edges (MorphACWE) Active contours without edges implemented with morphological operators. It can be used to segment objects in images and volumes without well defined borders. It is required that the inside of the object looks different on average than the outside (i.e., the inner area of the object should be darker or lighter than the outer area on average). Parameters ---------- image : (M, N) or (L, M, N) array Grayscale image or volume to be segmented. num_iter : uint Number of num_iter to run init_level_set : str, (M, N) array, or (L, M, N) array Initial level set. If an array is given, it will be binarized and used as the initial level set. If a string is given, it defines the method to generate a reasonable initial level set with the shape of the `image`. Accepted values are 'checkerboard' and 'disk'. See the documentation of `checkerboard_level_set` and `disk_level_set` respectively for details about how these level sets are created. smoothing : uint, optional Number of times the smoothing operator is applied per iteration. Reasonable values are around 1-4. Larger values lead to smoother segmentations. lambda1 : float, optional Weight parameter for the outer region. If `lambda1` is larger than `lambda2`, the outer region will contain a larger range of values than the inner region. lambda2 : float, optional Weight parameter for the inner region. If `lambda2` is larger than `lambda1`, the inner region will contain a larger range of values than the outer region. iter_callback : function, optional If given, this function is called once per iteration with the current level set as the only argument. This is useful for debugging or for plotting intermediate results during the evolution. Returns ------- out : (M, N) or (L, M, N) array Final segmentation (i.e., the final level set) See Also -------- disk_level_set, checkerboard_level_set Notes ----- This is a version of the Chan-Vese algorithm that uses morphological operators instead of solving a partial differential equation (PDE) for the evolution of the contour. The set of morphological operators used in this algorithm are proved to be infinitesimally equivalent to the Chan-Vese PDE (see [1]_). However, morphological operators are do not suffer from the numerical stability issues typically found in PDEs (it is not necessary to find the right time step for the evolution), and are computationally faster. The algorithm and its theoretical derivation are described in [1]_. References ---------- .. [1] A Morphological Approach to Curvature-based Evolution of Curves and Surfaces, Pablo Márquez-Neila, Luis Baumela, Luis Álvarez. In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2014, :DOI:`10.1109/TPAMI.2013.106` rr&g:0yE>r) rXrMrPr1r:rangerffloatgradientabs_curvop)rNnum_iterrO smoothinglambda1lambda2 iter_callbackr=_c0c1duabs_duauxs rrrs#Z%^[[AN' "#A! 8_q1uo " " $ua!e[[]T-A'B Bi__ quuw~!6 6[[^"5:!"33gPQ@Q6QQR' ' y!A A" a%( HrrSautocgrr$rus rrIrIIrwrcLUn[X'R5n[Xr5 US:Xa[R"US5n[R "S[ UR5-[RS9n[R"U5n US:waXt[R"U5- :n [R"US:5n U"U 5 [U5Hn US:a[R"X5n OUS:a[R"X5n US:waW W X'[R"U5n [R"U 5n[X5H unnXU-- n M SXS:'SXS:'[U5Hn [!U 5n M U"U 5 M U $)u Morphological Geodesic Active Contours (MorphGAC). Geodesic active contours implemented with morphological operators. It can be used to segment objects with visible but noisy, cluttered, broken borders. Parameters ---------- gimage : (M, N) or (L, M, N) array Preprocessed image or volume to be segmented. This is very rarely the original image. Instead, this is usually a preprocessed version of the original image that enhances and highlights the borders (or other structures) of the object to segment. :func:`morphological_geodesic_active_contour` will try to stop the contour evolution in areas where `gimage` is small. See :func:`inverse_gaussian_gradient` as an example function to perform this preprocessing. Note that the quality of :func:`morphological_geodesic_active_contour` might greatly depend on this preprocessing. num_iter : uint Number of num_iter to run. init_level_set : str, (M, N) array, or (L, M, N) array Initial level set. If an array is given, it will be binarized and used as the initial level set. If a string is given, it defines the method to generate a reasonable initial level set with the shape of the `image`. Accepted values are 'checkerboard' and 'disk'. See the documentation of `checkerboard_level_set` and `disk_level_set` respectively for details about how these level sets are created. smoothing : uint, optional Number of times the smoothing operator is applied per iteration. Reasonable values are around 1-4. Larger values lead to smoother segmentations. threshold : float, optional Areas of the image with a value smaller than this threshold will be considered borders. The evolution of the contour will stop in these areas. balloon : float, optional Balloon force to guide the contour in non-informative areas of the image, i.e., areas where the gradient of the image is too small to push the contour towards a border. A negative value will shrink the contour, while a positive value will expand the contour in these areas. Setting this to zero will disable the balloon force. iter_callback : function, optional If given, this function is called once per iteration with the current level set as the only argument. This is useful for debugging or for plotting intermediate results during the evolution. Returns ------- out : (M, N) or (L, M, N) array Final segmentation (i.e., the final level set) See Also -------- inverse_gaussian_gradient, disk_level_set, checkerboard_level_set Notes ----- This is a version of the Geodesic Active Contours (GAC) algorithm that uses morphological operators instead of solving partial differential equations (PDEs) for the evolution of the contour. The set of morphological operators used in this algorithm are proved to be infinitesimally equivalent to the GAC PDEs (see [1]_). However, morphological operators are do not suffer from the numerical stability issues typically found in PDEs (e.g., it is not necessary to find the right time step for the evolution), and are computationally faster. The algorithm and its theoretical derivation are described in [1]_. References ---------- .. [1] A Morphological Approach to Curvature-based Evolution of Curves and Surfaces, Pablo Márquez-Neila, Luis Baumela, Luis Álvarez. In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2014, :DOI:`10.1109/TPAMI.2013.106` r()r%)dtyperr&)rXrMrPr1 percentileonesrLr:r{r|ryr7rCr8 zeros_likezipr})gimager~rOr thresholdballoonrrN structuredimagethreshold_mask_balloonr=rrrel1el2s rrrBsml E$^[[AN'FMM%, s5;;//rww?I [[ F!|!&RVVG_)D!D "#A! 8_ Q;%%a3C q[$$Q2C a<(+,B(CA %mmE" [[^FHC 9 C(' ' y!A A" a+. Hr)r))gY@g@) itertoolsrnumpyr1scipyrr7 _shared.utilsr__all__r eyearrayflipudrot90r3ryzerosr4rArFr}rPrXr r r rr)r^s0rrs $ ""FF1IHHi[1_IIbffQiHHi[1_  %*!H-Hqrxx H-Aq!QwAq!QwAq!Qw"#Aq)Y"#Aq)Y"#Ay!Y"#Ay!Y"#Ay)Q"#Ay)Q-"." "$AB   "+/t"J>1F"  i ^   A .sF