[;j,dZddlZddlZddlZddlmZddlmZm Z ddl m Z ddl m Z d Zee dd Zd Zd Zd Zy)z Adapted from "Contrast Limited Adaptive Histogram Equalization" by Karel Zuiderveld, Graphics Gems IV, Academic Press, 1994. http://tog.acm.org/resources/GraphicsGems/ Relicensed with permission of the author under the Modified BSD license. N)_supported_float_type) adapt_rgb hsv_value)rescale_intensity) img_as_uinti@c t|j}t|}tjt |dt dz fjtjt }|1t|jDcgc]}t|dzdc}}nQt|tjr|f|jz}n&t!||jk7rt#d||Dcgc] }t%|}}t'||||}|j|d}t |Scc}wcc}w)aContrast Limited Adaptive Histogram Equalization (CLAHE). An algorithm for local contrast enhancement, that uses histograms computed over different tile regions of the image. Local details can therefore be enhanced even in regions that are darker or lighter than most of the image. Parameters ---------- image : (M[, ...][, C]) ndarray Input image. kernel_size : int or array_like, optional Defines the shape of contextual regions used in the algorithm. If iterable is passed, it must have the same number of elements as ``image.ndim`` (without color channel). If integer, it is broadcasted to each `image` dimension. By default, ``kernel_size`` is 1/8 of ``image`` height by 1/8 of its width. clip_limit : float, optional Clipping limit, normalized between 0 and 1 (higher values give more contrast). nbins : int, optional Number of gray bins for histogram ("data range"). Returns ------- out : (M[, ...][, C]) ndarray Equalized image with float64 dtype. See Also -------- equalize_hist, rescale_intensity Notes ----- * For color images, the following steps are performed: - The image is converted to HSV color space - The CLAHE algorithm is run on the V (Value) channel - The image is converted back to RGB space and returned * For RGBA images, the original alpha channel is removed. .. versionchanged:: 0.17 The values returned by this function are slightly shifted upwards because of an internal change in rounding behavior. References ---------- .. [1] http://tog.acm.org/resources/GraphicsGems/ .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE rr) out_rangez"Incorrect value of `kernel_size`: F)copy)rdtyper nproundr NR_OF_GRAYastypemin_scalar_typetupleshapemax isinstancenumbersNumberndimlen ValueErrorint_clahe)image kernel_size clip_limitnbins float_dtypesks dG:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\skimage/exposure/_adapthist.pyequalize_adapthistr'sf( 4K  E HH&uJN8KL M T T :& EU[[A[Sa^[AB K 0"nuzz1 [ UZZ '=k]KLL#./;a3q6;K/ 5+z5 9E LL5L 1E U ##B 0s D88D=c |j}|j}|Dcgc]}|dz }}t||jDcgc]2\}}|||zz |zt t j |dz z4} }}t j|t|| D cgc] \} } | | g c} } d}dt|zz} t jtt jt} | | z} | |}t|j|Dcgc]\}}t ||z dz }}}t j||gjj}t jt jd|dzdt jd|dzdgj}t||Dcgc]\}}t|dz|dz||zz}}}|t|j!|}t j"||}|j}|j!t%j&|d f}t%j&|}|d kDr$t t j(||zdd }n|}t j*t j,d || }t j*t.d || }t1|dtdz |}|j!|d |dz}t j|t3|Dcgc]}ddgc}ddggzd}t|j|Dcgc]\}}t ||z }}}t j||gjj}t jt jd|dzdt jd|dzdgj}|j!|}t j"||}|j}t j |t%j&|t%j&|j|d f}t j4t|d d d Dcgc]}t j||z c}ddi}|D cgc]%} t j"| j'}} t7|D! cgc] \}!} d| z  }"}!} t j8|jt j:}#t7t j<dg|zD]\}$}%|tt|%|D&cgc]\}&}t|&|&|zc}}&}'|'j!t%j&|d f}'t j>|'|d }(t j&t7|%d d d D)&cgc]\})}&|"|g|&|)c}&})d}*|#|(|*zjA|#jz }#|#jA|}#|#j!|}#t jt jd|t j||dzgjj}+t j"|#|+}#|#j!|j}#tt|| |jD cgc]\} } }t| || z c}} } },|#|,}#|#Scc}wcc}}wcc} } wcc}}wcc}}wcc}wcc}}wcc}wcc} wcc} }!wcc}}&wcc}&})wcc}} } w)a.Contrast Limited Adaptive Histogram Equalization. Parameters ---------- image : (M[, ...]) ndarray Input image. kernel_size : int or N-tuple of int Defines the shape of contextual regions used in the algorithm. clip_limit : float Normalized clipping limit between 0 and 1 (higher values give more contrast). nbins : int Number of gray bins for histogram ("data range"). Returns ------- out : (M[, ...]) ndarray Equalized image. The number of "effective" graylevels in the output image is set by `nbins`; selecting a small value (e.g. 128) speeds up processing and still produces an output image of good quality. A clip limit of 0 or larger than or equal to 1 results in standard (non-contrast limited) AHE. rg@reflect)moder)rr)axesgN) minlength)r!)r,edgeindexingijaxis)!rrziprrrceilpadrarangerarrayTflattenslicerreshape transposemathprodclipapply_along_axisbincountclip_histogram map_histogramrangemeshgrid enumeratezerosfloat32ndindextake_along_axisr)-rr r!r"rrr%pad_start_per_dimr$pad_end_per_dimp_ip_fbin_sizelutns_histhist_blocks_shapehist_blocks_axis_ordern hist_slices hist_blockshist_block_assembled_shapekernel_elementsclimhist_ map_arrayns_proc blocks_shapeblocks_axis_orderblocksblocks_flattened_shapecoeffscdim inv_coeffsresultiedger.e edge_maps edge_mappedd edge_coeffsblocks_axis_rebuild_order unpad_slicess- r&rr^s2 ::D KKE *55Aa5 U[[11DAq QUa#bgga#g.//1 FF $'(9?$KL$KS#s$KL  E:&&H ))Jb&8&8&D ECHC JE+.ekk;*GH*G$!Qs1q5zA~*GGH';!78::BBDXX 1dQh "BIIa1$=> gi>Ag=VW=VTQ5aa!a%0=VKWk*+334EFK,,{1GHK!,!2!2%%tyy'92&>?Kii ,OC277:7DAB  r{{B u MD  ~r4D ID q*q./ BD <<25D9EA BDteDk:kq!fk:q!fXEFSI'*%++{&CD&Cdas1q5z&CGD88Wk2355==?L 1dQh "BIIa1$=> gi]]< (F \\&'8 9F#\\ ZZ7!3TYYv||DE?R5S T UF[[ +dd*;<*;Q ! q *;< =HLF28 8Abll1o%%'F 8&/&78&7FC!a%&7J8XXfll"** 5F qcDj!:; teT7AS$TASAU1a!e_AS$TUV %%tyy'92&>? ((FD gg4=d4R4j4I J4IDAqj& !! $Q '4I JA  ;,44V\\BB<]]5 !F^^2 3F " 1d RYYtTAX67!a \\&'@ AF ^^EKK (F ##4ou{{S S S! #q3w S L L !F MY6 MI X(;E= 88 %U K" sM \-7\2\8 \>"] ] ]] <*]6]1]%5]+ ]1 c||kD}||}|j|j|zz }|||<||jz}||z }||k}|||j|zz}||xx|z cc<tj||k\||k}||} || j| j|zz z }|||<|dkDr|} t |jD]c} ||k} t dtj | |z} | | d| } || d| | xxdz cc<|tj | z}|dkscn| |k(r |S|dkDr|S)aPerform clipping of the histogram and redistribution of bins. The histogram is clipped and the number of excess pixels is counted. Afterwards the excess pixels are equally redistributed across the whole histogram (providing the bin count is smaller than the cliplimit). Parameters ---------- hist : ndarray Histogram array. clip_limit : int Maximum allowed bin count. Returns ------- hist : ndarray Clipped histogram. rrN)sumsizer logical_andrDr count_nonzero)rZr! excess_maskexcessn_excessbin_incrupperlow_maskmid_maskmid prev_n_excessindex under_mask step_sizes r&rBrBs(#K + Fzz|fkkJ66H"D499$H  !Ee|H X##h..HNhN~~demTJ->?H x.C  CHHz111HDN Q, 499%E *JAr// ;xGHI#E$49$45J ! ! ": .! 3 . ((4 4H1}& H $  K Q, Kctj|djt}|||z |z z}||z }tj|d|||jt S)aCalculate the equalized lookup table (mapping). It does so by cumulating the input histogram. Histogram bins are assumed to be represented by the last array dimension. Parameters ---------- hist : ndarray Clipped histogram. min_val : int Minimum value for mapping. max_val : int Maximum value for mapping. n_pixels : int Number of pixels in the region. Returns ------- out : ndarray Mapped intensity LUT. r,r1N)a_mina_maxout)rcumsumrfloatr?r)rZmin_valmax_valn_pixelsrs r&rCrC"s\, ))Dr " ) )% 0CGg  ))C7NCGGCt74 ::c?r)Ng{Gz?)__doc__r=rnumpyr _shared.utilsrcolor.adapt_rgbrrexposurerutilr rr'rrBrCrr&rsT 12'   9C$C$LK\3lr