9i8 SSKJr SSKrSSKJr SSKJr SSK J r Sr SSjr Sr S rS rS SSS \R SS\R \R 4 S jrS S S\R 4Sjrg))warnN)measure)ensure_spacingc2[R"U5nXn[R"U*SS9n[R"U5Un[R"U5(a [ U5nOSnUS:a [ XSXHS9n[U5U:aUSUnU$)z0 Return the highest intensity peak coordinates. stable)kindN)spacingp_normmax_out)npnonzeroargsort transposeisfiniteintrlen) imagemask num_peaks min_distancer coord intensities idx_maxsortr s T/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/feature/peak.py_get_high_intensity_peaksr s JJt E,K**k\9K LL  ,E {{9i.a   5zIjy! LclURS:XdURS:XaX:$[R"XSS9nX:HnUc[R"U5O[R"XS5nU(a7SUSS&Ub/[R "U[R "U55nSXW'XPU:-nU$)zB Return the mask containing all peak candidates above thresholds. r nearest) footprintmodeNFT)sizendimaximum_filterrall logical_xorbinary_opening)rr! thresholdr image_maxoutimage_is_trivial isolated_pxs r_get_peak_maskr.%s~~ejjAo  ""5INI  C'+lrvvc{sy8IA  ..s/A/A$/GHK#C 9 C Jrc[U5HNup#US:XaM SU[S54U-[SU54-'SU[S54U-[U*S54-'MP U$)zSet label border values to 0.rN) enumerateslice)label border_widthiwidths r_exclude_borderr6=spl+ A: <=uT{nq E$$6#889=>uT{nq E5&$$7#99: , LrcnUbUOUR5nUb[X2UR5-5nU$)zKReturn the threshold value according to an absolute and a relative value. )minmax)r threshold_abs threshold_relr)s r_get_thresholdr<Hs7 "/!:  I  599;#>? rc[U[5(aU(aUOS4UR-nU$[U[5(a#US:a [ S5eU4UR-nU$[U[ 5(ab[ U5UR:wa [ S5eUH4n[U[5(d [ S5eUS:dM+[ S5e UnU$[S5e)zCReturn border_width values relative to a min_distance if requested.rz+`exclude_border` cannot be a negative valuezP`exclude_border` should have the same length as the dimensionality of the image.zD`exclude_border`, when expressed as a tuple, must only contain ints.z,`exclude_border` can not be a negative valueze`exclude_border` must be bool, int, or tuple with the same length as the dimensionality of the image.) isinstanceboolndimr ValueErrortupler TypeError)rrexclude_borderr3excludes r_get_excluded_border_widthrFUs.$''(6 A?%**L 4 3 NC ( ( A JK K&(5::5 , + NE * * ~ %** ,/ &Ggs++ ${ !OPP&&    9  rr Tc FUbURS:XaUS:a[S[SS9 [XU5n [ XU5n Uc0SU-S-n [ R "U 4UR-[S9nO[ R"U5nUc&[XU 5n [X5n [X XQU 5nU$[UR[SS9U 5n[ R"UR [ R"5(a+[ R$"UR 5R&nO*[ R("UR 5R&n/n[+[,R."U55HunnUcM UUUS-:HnUUR15nUU[ R2"U5'[UXkU5n [UXX5n[+U5H!unnUSS2U4==UR4- ss'M# UR7U5 M U(a[ R8"U5nO[ R:"S [S9n[=U5U:a?[ R>"U[S9nS U[AURB5'[UUXQU 5nU$) aFind peaks in an image as coordinate list. Peaks are the local maxima in a region of `2 * min_distance + 1` (i.e. peaks are separated by at least `min_distance`). If both `threshold_abs` and `threshold_rel` are provided, the maximum of the two is chosen as the minimum intensity threshold of peaks. .. versionchanged:: 0.18 Prior to version 0.18, peaks of the same height within a radius of `min_distance` were all returned, but this could cause unexpected behaviour. From 0.18 onwards, an arbitrary peak within the region is returned. See issue gh-2592. Parameters ---------- image : ndarray Input image. min_distance : int, optional The minimal allowed distance separating peaks. To find the maximum number of peaks, use `min_distance=1`. threshold_abs : float or None, optional Minimum intensity of peaks. By default, the absolute threshold is the minimum intensity of the image. threshold_rel : float or None, optional Minimum intensity of peaks, calculated as ``max(image) * threshold_rel``. exclude_border : int, tuple of ints, or bool, optional If positive integer, `exclude_border` excludes peaks from within `exclude_border`-pixels of the border of the image. If tuple of non-negative ints, the length of the tuple must match the input array's dimensionality. Each element of the tuple will exclude peaks from within `exclude_border`-pixels of the border of the image along that dimension. If True, takes the `min_distance` parameter as value. If zero or False, peaks are identified regardless of their distance from the border. num_peaks : int, optional Maximum number of peaks. When the number of peaks exceeds `num_peaks`, return `num_peaks` peaks based on highest peak intensity. footprint : ndarray of bools, optional If provided, `footprint == 1` represents the local region within which to search for peaks at every point in `image`. labels : ndarray of ints, optional If provided, each unique region `labels == value` represents a unique region to search for peaks. Zero is reserved for background. num_peaks_per_label : int, optional Maximum number of peaks for each label. p_norm : float Which Minkowski p-norm to use. Should be in the range [1, inf]. A finite large p may cause a ValueError if overflow can occur. ``inf`` corresponds to the Chebyshev distance and 2 to the Euclidean distance. Returns ------- output : ndarray The coordinates of the peaks. Notes ----- The peak local maximum function returns the coordinates of local peaks (maxima) in an image. Internally, a maximum filter is used for finding local maxima. This operation dilates the original image. After comparison of the dilated and original images, this function returns the coordinates of the peaks where the dilated image equals the original image. See also -------- skimage.feature.corner_peaks Examples -------- >>> img1 = np.zeros((7, 7)) >>> img1[3, 4] = 1 >>> img1[3, 2] = 1.5 >>> img1 array([[0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 1.5, 0. , 1. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. ]]) >>> peak_local_max(img1, min_distance=1) array([[3, 2], [3, 4]]) >>> peak_local_max(img1, min_distance=2) array([[3, 2]]) >>> img2 = np.zeros((20, 20, 20)) >>> img2[10, 10, 10] = 1 >>> img2[15, 15, 15] = 1 >>> peak_idx = peak_local_max(img2, exclude_border=0) >>> peak_idx array([[10, 10, 10], [15, 15, 15]]) >>> peak_mask = np.zeros_like(img2, dtype=bool) >>> peak_mask[tuple(peak_idx.T)] = True >>> np.argwhere(peak_mask) array([[10, 10, 10], [15, 15, 15]]) Nr zmWhen min_distance < 1, peak_local_max acts as finding image > max(threshold_abs, threshold_rel * max(image)).r) stackleveldtypesafe)casting)rrT)"r#rRuntimeWarningrFr<ronesr@r?asarrayr.r6rastyper issubdtyperJfloatingfinfor8iinfor0r$ find_objectscopy logical_notstartappendvstackemptyr zeros_likerBT)rrr:r;rDrr!labelsnum_peaks_per_labelr r3r)r#r coordinates_labelsbg_vallabels_peak_coord label_idxroi label_mask img_objectidxsr+s rpeak_local_maxrjvsUn Y^^q0lQ6F  F   .e>RLu]CI<!#GGTGejj0= JJy)  ~e :t20 & f ]"&--V-"DlS ==bkk 2 2XXekk*..FXXekk*..F'(8(8(ABNIs{  A 5Js*J5;Jr~~j1 2!*iJOD3D|K $C.QAsF#qww.#)  $ $[ 1+C. ))$56K((65K { i '--T2C(,Ckmm$ %3sIVK rcUR5nURupgUcS[R"U5-nSU-S-nSU-S-n [R "XXSSSS9n [R "XSSSS9n XZ:Hn X[-nXS:n [ R"U 5n [ R"X5n[USS 9SSS 2n[R"UVs/sH#n[R"UR5PM% sn[S 9n/n/n/n[RU*US-2U*US-24unnUHunnU UU4nUU:dMUU-nUU-n[R"US:UU:5nUUnUUnUS:nUUU- UU'UU==U- ss'UU:nUUU- UU'UU==U-ss'SU UU4'UR!U5 UR!U5 UR!U5 M [R"U5n[R"U5n[R"U5nU[#U5:a.[R$"U5SSS 2SUnUUnUUnUUnUUU4$s snf) a'Return peaks with non-maximum suppression. Identifies most prominent features separated by certain distances. Non-maximum suppression with different sizes is applied separately in the first and second dimension of the image to identify peaks. Parameters ---------- image : (M, N) ndarray Input image. min_xdistance : int Minimum distance separating features in the x dimension. min_ydistance : int Minimum distance separating features in the y dimension. threshold : float Minimum intensity of peaks. Default is `0.5 * max(image)`. num_peaks : int Maximum number of peaks. When the number of peaks exceeds `num_peaks`, return `num_peaks` coordinates based on peak intensity. Returns ------- intensity, xcoords, ycoords : tuple of array Peak intensity values, x and y indices. 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