9i.VSrSSKrSSKJr SrSSSSSS.SjrS S jrS S jr SS jr g)z4Utility functions used in the morphology subpackage.N)ndimagecUcSn[R"U5(a[R"X5nO6[R"U[ 5nUR U:wa [S5eUc^[URVs/sH oDS-S:HPM sn5(a [S5e[R"UR5S-nX24$s snf)aConvert any valid connectivity to a footprint and offset. Parameters ---------- image_dim : int The number of dimensions of the input image. connectivity : int, array, or None The neighborhood connectivity. An integer is interpreted as in ``scipy.ndimage.generate_binary_structure``, as the maximum number of orthogonal steps to reach a neighbor. An array is directly interpreted as a footprint and its shape is validated against the input image shape. ``None`` is interpreted as a connectivity of 1. offset : tuple of int, or None The coordinates of the center of the footprint. Returns ------- c_connectivity : array of bool The footprint (structuring element) corresponding to the input `connectivity`. offset : array of int The offset corresponding to the center of the footprint. Raises ------ ValueError: If the image dimension and the connectivity or offset dimensions don't match. z,Connectivity dimension must be same as imagerz2Connectivity array must have an unambiguous center) npisscalarndigenerate_binary_structurearrayboolndim ValueErroranyshape) image_dim connectivityoffsetc_connectivityxs X/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/morphology/_util.py_validate_connectivityrs<  {{<  66yO,5   ) +KL L ~ N$8$89$8qA $89 : :TU U.../14  !! :sCrC) footprintrcenterspacingorderc[U5nUc[R"XbS9nUc[SUR55nUR Us=:Xa[U5:Xd O [ S5e[R"[[R"U5U5VVs/sH upxXx- PM snnSS9n US:XaU SS2SSS24n USSS2nOUS:wa [ S 5eUS SS -n [R"U SSS25SSS2n X-RS S9n Uc[R"U5nX-n [R"[R"U S -S S95n U [R"U S S9n[R "U S S9n[#S[XR555(a7[R$"USS9unn[R "US S9nUUnUUnUS SnUS SnX4$s snnf)aCompute offsets to neighboring pixels in raveled coordinate space. This function also returns the corresponding distances from the center pixel given a spacing (assumed to be 1 along each axis by default). Parameters ---------- image_shape : tuple of int The shape of the image for which the offsets are being computed. footprint : array of bool The footprint of the neighborhood, expressed as an n-dimensional array of 1s and 0s. If provided, the connectivity argument is ignored. connectivity : {1, ..., ndim} The square connectivity of the neighborhood: the number of orthogonal steps allowed to consider a pixel a neighbor. See `scipy.ndimage.generate_binary_structure`. Ignored if footprint is provided. center : tuple of int Tuple of indices to the center of the footprint. If not provided, it is assumed to be the center of the footprint, either provided or generated by the connectivity argument. spacing : tuple of float The spacing between pixels/voxels along each axis. order : 'C' or 'F' The ordering of the array, either C or Fortran ordering. Returns ------- raveled_offsets : ndarray Linear offsets to a samples neighbors in the raveled image, sorted by their distance from the center. distances : ndarray The pixel distances corresponding to each offset. Notes ----- This function will return values even if `image_shape` contains a dimension length that is smaller than `footprint`. Examples -------- >>> off, d = _raveled_offsets_and_distances( ... (4, 5), footprint=np.ones((4, 3)), center=(1, 1) ... ) >>> off array([-5, -1, 1, 5, -6, -4, 4, 6, 10, 9, 11]) >>> d[0] 1.0 >>> d[-1] # distance from (1, 1) to (3, 2) 2.236... N)rankrc3*# UH oS-v M g7f)rN.0ss r 1_raveled_offsets_and_distances..xs7!AvzQnumber of dimensions in image shape, footprint and itscenter index does not match)axisFrzorder must be 'C' or 'F'rrrstable)kindc3.# UH upX:v M g7f)Nr )r"rys rr$r%s ?>TQ15>sT) return_index)lenr r tuplerr rrstackzipnonzerocumprodsumonessqrtargsortsortrunique) image_shaperrrrrr idxcoffsets ravel_factorsraveled_offsetsweighted_offsets distancessorted_raveled_offsetssorted_distances_indicess r_raveled_offsets_and_distancesrH8s x { D11tW  ~7y77 >>T 0S[ 0 *  hh!$RZZ %:F!CD!Cvs#'!CD2G |!TrT'"!$B$' #344 Od*MJJ}TrT23DbD9M.333;O''$-(/2; ???YY5DI 7'''1!7!@+G44AB7'+ ! 33G Es"H c [XX#S9SnU$)aTCompute offsets to a samples neighbors if the image would be raveled. Parameters ---------- image_shape : tuple The shape of the image for which the offsets are computed. footprint : ndarray The footprint (structuring element) determining the neighborhood expressed as an n-D array of 1's and 0's. center : tuple Tuple of indices to the center of `footprint`. order : {"C", "F"}, optional Whether the image described by `image_shape` is in row-major (C-style) or column-major (Fortran-style) order. Returns ------- raveled_offsets : ndarray Linear offsets to a samples neighbors in the raveled image, sorted by their distance from the center. Notes ----- This function will return values even if `image_shape` contains a dimension length that is smaller than `footprint`. Examples -------- >>> _offsets_to_raveled_neighbors((4, 5), np.ones((4, 3)), (1, 1)) array([-5, -1, 1, 5, -6, -4, 4, 6, 10, 9, 11]) >>> _offsets_to_raveled_neighbors((2, 3, 2), np.ones((3, 3, 3)), (1, 1, 1)) array([-6, -2, -1, 1, 2, 6, -8, -7, -5, -4, -3, 3, 4, 5, 7, 8, -9, 9]) )rrrr)rH)r<rrrrAs r_offsets_to_raveled_neighborsrJs$F5 O clUcUcUn[R"X!5nU$[R"U[S9nUR U:wa [ S5eU(a,[SUR55(a [ S5e[SUR55(a [ S5eU$)aoValidate or create a footprint (structuring element). Depending on the values of `connectivity` and `footprint` this function either creates a new footprint (`footprint` is None) using `connectivity` or validates the given footprint (`footprint` is not None). Parameters ---------- footprint : ndarray The footprint (structuring) element used to determine the neighborhood of each evaluated pixel (``True`` denotes a connected pixel). It must be a boolean array and have the same number of dimensions as `image`. If neither `footprint` nor `connectivity` are given, all adjacent pixels are considered as part of the neighborhood. connectivity : int A number used to determine the neighborhood of each evaluated pixel. Adjacent pixels whose squared distance from the center is less than or equal to `connectivity` are considered neighbors. Ignored if `footprint` is not None. ndim : int Number of dimensions `footprint` ought to have. enforce_adjacency : bool A boolean that determines whether footprint must only specify direct neighbors. Returns ------- footprint : ndarray Validated or new footprint specifying the neighborhood. Examples -------- >>> _resolve_neighborhood(None, 1, 2) array([[False, True, False], [ True, True, True], [False, True, False]]) >>> _resolve_neighborhood(None, None, 3).shape (3, 3, 3) )dtypez7number of dimensions in image and footprint do notmatchc3*# UH oS:gv M g7f)Nr r!s rr$(_resolve_neighborhood..s$E_!V_r&z$dimension size in footprint is not 3c30# UH oS-S:gv M g7f)rrNr r!s rr$rPs7!a%1*sz/footprint size must be odd along all dimensions) r r rasarrayr r rrr)rrr enforce_adjacencys r_resolve_neighborhoodrTsP  L11$E  JJy5 >>T !L  $EY__$E!E!ECD D 7y7 7 7NO O rKc.[R"U5(aX"44UR-nO$[U5UR:wa [ S5e[ U5Hup4[U5S:wa [ S5eUupVXVs=:XaS:XaO OM4XVs=:XaS:XaO O[ S54U-S-S-nXU'M^US:a$[ S54U-[ SU54-S-nXU'US:dM[ S54U-[ U*S54-S-nXU'M g) a Set edge values along all axes to a constant value. Parameters ---------- image : ndarray The array to modify inplace. value : scalar The value to use. Should be compatible with `image`'s dtype. border_width : int or sequence of tuples A sequence with one 2-tuple per axis where the first and second values are the width of the border at the start and end of the axis, respectively. If an int is provided, a uniform border width along all axes is used. Examples -------- >>> image = np.zeros((4, 5), dtype=int) >>> _set_border_values(image, 1) >>> image array([[1, 1, 1, 1, 1], [1, 0, 0, 0, 1], [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]]) >>> image = np.zeros((8, 8), dtype=int) >>> _set_border_values(image, 1, border_width=((1, 1), (2, 3))) >>> image array([[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 0, 0, 0, 1, 1, 1], [1, 1, 0, 0, 0, 1, 1, 1], [1, 1, 0, 0, 0, 1, 1, 1], [1, 1, 0, 0, 0, 1, 1, 1], [1, 1, 0, 0, 0, 1, 1, 1], [1, 1, 0, 0, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]]) z.length of `border_width` must match image.ndimrz2each sequence in `border_width` must have length 2rrN))rr').)rrr r0r enumerateslice)imagevalue border_widthr(npadw_startw_endsls r_set_border_valuesr_sH {{<  %46C \ ejj (IJJ -  t9>TU U  q   " "+$&3frcs=: ."h   l4^'T:z:rK