нУЁh&~╒tЫ° !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~─│┌┐└┘├┤┬┴┼▀▄█▌▐░▒▓⌠■∙√≈≤≥ ⌡°²·÷═║╒ёє╔ії╗╘╙╚╛ґў╞╟╠ ╡ Ё Є ╣ І Ї ╦ ╧ ╨ ╩ ╪ Ґ Ў © ю а б ц д е ф гхийклмнопярстужвьызшэщчъ Ю А Б Ц Д Е Ф Г Х И Й К Л М Н О ПЯРСТУЖВЬЫЗШЭЩЧЪ─│┌┐└┘├┤┬┴┼▀▄█▌▐░▒▓⌠■∙√≈≤≥ ⌡°²·÷═║╒ёє╔ії╗╘╙╚╛ґў╞╟╠╡ЁЄ╣ІЇ╦╧╨╩╪ҐЎ©юабцдефгхийклмнопярстужвьызшэщчъЮАБЦДЕФГХИЙКЛМНОПЯРСТУЖВЬЫЗШЭЩЧЪ─│┌┐└┘├┤┬┴┼▀▄█▌▐░▒▓⌠■∙√≈≤≥ ⌡ Safe-InferredабцделызK friday1Our index type, used for both shapes and indices.fridayAn index of dimension zero.fridayGives the successor of an index, given the shape of the array.fridayеConvert an index into its equivalent flat, linear, row-major version.fridayInverse of .friday9Return the ascending list of indexes for the given shape.friday/Check whether an index is within a given shape.fridayHelper for index construction./Use this instead of explicit constructors like (Z :. (x :: Int)). The this is sometimes needed to ensure that x is constrained to be in Int.fridayShape of the array.fridayIndex.fridayShape of the array.fridayIndex into the array.fridayShape of the array.friday!Index into linear representation.fridayShape of the array.fridayIndex to check for. !" !" 33 Safe-Inferred░5friday-Rational coordinates used for interpolations.?fridaySize of an object.Can be constructed using и. The first parameter is the height while the second is the width (i.e. ix2 h w).@fridayCoordinates inside the image.Can be constructed using ж. The first parameter is the y coordinate while the second is the x coordinate (i.e. y x). Image origin ( 0 0') is located in the upper left corner., !"56789:;<=>?@@?9:;<=>5678 Safe-InferredбделзIfridayэDefines a class for images on which a function can be applied. The class is different from °с as there could be some constraints and transformations the pixel and image types.Kfriday4Provides ways to construct an image from a function.MfridayзGenerates an image by calling the given function for each pixel of the constructed image.NfridayыGenerates an image by calling the last function for each pixel of the constructed image.оThe first function is called for each line, generating a line invariant value.ДThis function is faster for some image representations as some recurring computation can be cached.OfridayыGenerates an image by calling the last function for each pixel of the constructed image.сThe first function is called for each column, generating a column invariant value.АThis function *can* be faster for some image representations as some recurring computations can be cached. However, it may requires a vector allocation for these values. If the column invariant is cheap to compute, prefer M.PfridayыGenerates an image by calling the last function for each pixel of the constructed image.┬The two first functions are called for each line and for each column, respectively, generating common line and column invariant values.шThis function is faster for some image representations as some recurring computation can be cached. However, it may requires a vector allocation for column values. If the column invariant is cheap to compute, prefer N.QfridayеProvides an abstraction over the internal representation of an image.Image origin (ix2 0 0&) is located in the upper left corner.Rfriday)Returns the pixel value at 'Z :. y :. x'.SfridayБReturns the pixel value as if the image was a single dimension vector (row-major representation).TfridayЕReturns every pixel values as if the image was a single dimension vector (row-major representation).VfridayПProvides an abstraction for images which are not defined for each of their pixels. The interface is similar to Q5 except that indexing functions don't always return.Image origin (ix2 0 0&) is located in the upper left corner.Yfriday,Returns the pixel's value at 'Z :. y, :. x'.ZfridayДReturns the pixel's value as if the image was a single dimension vector (row-major representation).[friday+Returns the non-masked values of the image.\fridayЙDetermines the number of channels and the type of each pixel of the image and how images are represented.^friday,Returns the number of channels of the pixel.Must not consume p (could be ²).`friday Alias of Y.afriday Alias of R.bfriday+Returns the number of channels of an image.cfridayReturns an ²Х instance of a pixel of the image. This is sometime useful to satisfy the type checker as in a call to ^ :(nChannels img = pixNChannels (pixel img)Pfriday Line functionfridayColumn functionfridayPixel functionIJKLMNOPQTRSUVWXYZ[\]^_`abc\]^_VWXYZ[QTRSUKLMNOPIJa`bc Safe-InferredабцделзSrfridayбA delayed image is an image which is constructed using a function.≤Usually, a delayed image maps each of its pixels over another image. Delayed images are useful by avoiding intermediate images in a transformation pipeline of images or by avoiding the computation of the whole resulting image when only a portion of its pixels will be accessed.vfridayStores the image content in a ·.zfriday.Delays an image in its delayed representation.{friday>Computes the value of an image into a manifest representation.|fridayбForces an image to be in its delayed representation. Does nothing.}fridayцForces an image to be in its manifest representation. Does nothing.nopqrstuvwxyz{|}vwxyrstunopqz{|} Safe-Inferredблвзэ#. ∙friday?Class for images which can be constructed from a mutable image.√friday7The type of the immutable version of the mutable image i.≈friday≈д doesn't run in a monad as the size of a mutable image is constant.≤fridayщCreates a new mutable image of the given size. Pixels are initialized with an unknown value.≥fridayпCreates a new mutable image of the given size and fill it with the given value. fridayReturns the pixel value at Z :. y :. x.⌡fridayБReturns the pixel value as if the image was a single dimension vector (row-major representation).°friday$Overrides the value of the pixel at Z :. y :. x.²fridayЧOverrides the value of the pixel at the given index as if the image was a single dimension vector (row-major representation).·friday/Returns an immutable copy of the mutable image.÷fridayИReturns the immutable version of the mutable image. The mutable image should not be modified thereafter.═friday.Returns a mutable copy of the immutable image.║friday#Creates an immutable image from an ÷! action creating a mutable image.▒▓⌠■∙√·═÷≤ °≥⌡²≈║∙√·═÷≤ °≥⌡²≈║▒▓⌠■ Safe-Inferredб$fёfridayParallel version of compute.йComputes the value of an image into a manifest representation in parallel.фThe monad ensures that the image is fully evaluated before continuing.ёё Safe-Inferredб&оєfridayхProvides a way to apply the interpolation to every component of a pixel.╔friday Given a function which interpolates two points over a single channel, returns a function which interpolates two points over every channel of two pixels.іfridayзUses a bilinear interpolation to find the value of the pixel at the rational coordinates.(Estimates the value of a rational point p using a, b, c and d :Х x1 x2 y1 a ------ b - - - p - - - y2 c ------ d є╔іє╔і Safe-Inferredблз)© ╠fridayУDefines the set of possible methods for pixel interpolations when looking for a pixel at floating point coordinates.╡friday%Selects the top left pixel (fastest).Ёfriday!Selects the nearest pixel (fast).ЄfridayлDoes a double linear interpolation over the four surrounding points (slow).╣friday9Maps the content of the image's rectangle in a new image.ІfridayResizes the Q& using the given interpolation method.Їfriday Reverses the image horizontally.╦fridayReverses the image vertically.╧fridayЩPaints with a new value the pixels surrounding the given point of the image which have the same value as the starting point. ╠╡ЁЄ╣ІЇ╦╧ ╠╡ЁЄ╣ІЇ╦╧ Safe-Inferred %&лыз*╨╩╪ҐЎ©юа╩╪ҐЎ©юа╨ Safe-Inferred %&лыз*gгхийклмхийклмг Safe-Inferred %&лыз,тfriday24 bits (3 * 8 bits) HSV image.кThe Hue value is in [0..179], Saturation in [0..255] and Value in [0..255].БThis image type is more respectful to human eye perception of colors and can be converted (using convert) from RGB images.Uses (http://en.wikipedia.org/wiki/HSL_and_HSV' equations to convert from and to RGB.стужвьытужвьыс Safe-Inferredляз,jъЮАБЮАБъ Safe-Inferredде,Ш═fridayгConverts the colors to greyscale using the human eye colors perception. Safe-Inferred)*абцделтзэA"┤fridayюDefines how image boundaries are extrapolated by the algorithms.'|'* characters in examples are image borders.┬friday2Replicates the first and last pixels of the image.aaaaaa|abcdefgh|hhhhhhh┴friday!Reflects the border of the image.fedcba|abcdefgh|hgfedcb┼fridayцConsiders that the last pixel of the image is before the first one.cdefgh|abcdefgh|abcdefg▀friday0Assigns a constant value to out of image pixels.0iiiiii|abcdefgh|iiiiiii with some specified 'i'▄friday√Uses the first pixel in the kernel as initial value. The kernel must not be empty and the accumulator type must be the same as the source pixel type.?This kind of initialization is needed by morphological filters.▌friday█Uses the result of the provided function as the initial value of the kernel's accumulator, depending on the center coordinates in the image.сFor most filters, the function will always return the same value (i.e. defined as const 0г), but this kind of initialization could be required for some filters.░fridayшUsed to determine the type of the accumulator image used when computing separable filters.src and res? are respectively the source and the result image types while acc& is the pixel type of the accumulator.▓fridayыSome kernels can be factorized in two uni-dimensional kernels (horizontal and vertical).ЛSeparable filters computational complexity grows linearly according to the size of the sides of the kernel.See 4http://http://en.wikipedia.org/wiki/Separable_filter.■fridayVertical (column) kernel.∙fridayHorizontal (row) kernel.√fridayA simple 2D kernel.ИThe kernel function accepts the coordinates in the kernel, the value of the pixel at these coordinates (srcф), the current accumulated value and returns a new accumulated value.УNon-separable filters computational complexity grows quadratically according to the size of the sides of the kernel.≤friday8Defines how the center of the kernel will be determined.⌡friday ().╔friday1Defines how the accumulated value is initialized.See ▌ and ▄.іfridayсThis function will be executed after the iteration of the kernel on a given point.= maxBound src * (kernel size)╡.╞fridayтBlurs the image by averaging the pixel inside the kernel using a Gaussian function.See *http://en.wikipedia.org/wiki/Gaussian_blur╟friday=Estimates the first derivative using the Scharr's 3x3 kernel.4Convolves the following kernel for the X derivative:$ -3 0 3 -10 0 10 -3 0 3 %And this kernel for the Y derivative:$ -3 -10 -3 0 0 0 3 10 3 *Considers using a signed integer type for res with !maxBound res >= 16 * maxBound src.╠friday6Estimates the first derivative using a Sobel's kernel.Prefer ╟ when radius equals 1ю as Scharr's kernel is more accurate and is implemented faster.*Considers using a signed integer type for res% which is significantly larger than src, especially for large kernels.╡friday3Computes the average of a kernel of the given size.This is similar to ў% but with a rectangular kernel and a ║ result.╣fridayбSeparable filters initialized using the first pixel of the kernel.Іfriday1Separable filters initialized with a given value.Їfriday for a reduction of the number of dimensions of an histogram.тfridayхComputes the cumulative histogram of another single dimension histogram.C(i) = SUM H(j) for each j in [0..i] where C# is the cumulative histogram, and H the original histogram.уfridayРNormalizes the histogram so that the sum of the histogram bins is equal to the given value (normalisation by the L1 norm).ЯThis is useful to compare two histograms which have been computed from images with a different number of pixels.жfriday=Equalizes a single channel image by equalising its histogram. The algorithm equalizes the brightness and increases the contrast of the image by mapping each pixel values to the value at the index of the cumulative L1-normalized histogram :N(x, y) = H(I(x, y)) where N is the equalized image, I is the image and H4 the cumulative of the histogram normalized over an L1 norm.See 4https://en.wikipedia.org/wiki/Histogram_equalization.вfriday Computes the !Pearson's correlation coefficient8 between each corresponding bins of the two histograms.ВA value of 1 implies a perfect correlation, a value of -1 a perfect opposition and a value of 0 no correlation at all.вё = SUM [ (H1(i) - ╣(H1)) (H1(2) - ╣(H2)) ] / ( SQRT [ SUM [ (H1(i) - ╣(H1))^2 ] ] * SQRT [ SUM [ (H2(i) - ╣(H2))^2 ] ] )Where ╣(H)' is the average value of the histogram H.See Applies the given predicate and threshold policy on the image.НfridayпCompares every pixel to its surrounding ones in the kernel of the given radius.Оfriday8Creates an adaptive thresholding filter to be used with ╘.Use Н3 if you only want to apply the filter on the image.пCompares every pixel to its surrounding ones in the kernel of the given radius.ПfridayфApplies a clustering-based image thresholding using the Otsu's method.See +https://en.wikipedia.org/wiki/Otsu's_method.ЯfridayІThis is a sliding concentric window filter (SCW) that uses the ratio of the standard deviations of two sliding windows centered on a same point to detect regions of interest (ROI)..scw sizeWindowA sizeWindowB beta thresType imgLet цA? be the standard deviation of a fist window around a pixel and цBЯ be the standard deviation of another window around the same pixel. Then the pixel will match the threshold if цB / цA >= beta2, and will be thresholded according to the given Х.See юhttp://www.academypublisher.com/jcp/vol04/no08/jcp0408771777.pdf.ёfridayАGiven a mean image and an original image, computes the variance of the kernel of the given size.еaverage [ (origPix - mean)^2 | origPix <- kernel pixels on original ].НfridayKernel radius.fridayшMinimum difference between the pixel and the kernel average. The pixel is thresholded if &pixel_value - kernel_mean > difference3 where difference is this number. Can be negative.ОfridayKernel radius.fridayшMinimum difference between the pixel and the kernel average. The pixel is thresholded if &pixel_value - kernel_mean > difference3 where difference is this number. Can be negative.ДЕФГХИЙКЛМНОПЯХИЙКЛМЕФГДНОПЯ Safe-Inferrede╔ Safe-Inferredeк╨╩╪аю©ҐЎ Safe-Inferredf Safe-Inferredf'гхимлйк Safe-Inferredf[ Safe-Inferredf│стуыьжв Safe-Inferredf╣ Safe-InferredfшъЮАБ Safe-Inferred абцяэДo* РfridayOutline: Just draw the edge.тOuterOutline: Outline the outer contours only, no hole contours AllOutlines: Draw all contours Fill: Draw the object but fill it in, ignoring holes. FillWithHoles: Draw the object and do not fill in the holes.Вfriday╣RowContour is a method of expressing contours by, for each row, recording the start of an object and the end (due to reaching the other side or a hole/inner contour) for each row.ЩfridayИA contour is described by the points on the perimeter and a boolean indicating if that point is "terminal" (next pixel to the right is background iff the point is terminal). The terminal information allows for a slightly simpler ┤ implementation.Чfriday/Contours are identified by a numeric ID number.ЪfridayПContours of an image include: * A map from contour number to outer points and negative contour number of inner contour points. * A vector of sizes for each contour for domain [1..size contourOutlines] (the zero index is meaningless)┤fridayЇDraws a given contour. The size specified must be large enough to include the coordinate originally occupied by the contour being drawn, no cropping or other transformation is done.┬fridayDraws many contours. See ┤.єfridayрDraws rows, throwing an exception when the size is too small for the coordinates.┴friday▄Given a vector including outer (and optionally inner) contour points, make 'row contour' from which is easier to transform back into a binary image. By not including the inner contour points the row will be filled, making traces of objects with holes appear solid.┼fridayThe meat of this module is the ┼╣ function, which extracts the contours (outer and inner outlines) of a binary image. Zero-valued pixels are the background and non-zero are active/objects to trace. The output, Ъ≤, contains enough information to determine the number of contours, their traces, the size in pixels (filled size and perimeter), number of holes, etc.РСТУЖВЬЫЗШЭЩЧЪ─│┌┐└┘├┤┬┴┼Ъ─│┌ЧЩЭЬЫЗШВ┼┐├┴┘└РСТУЖ┤┬ Safe-InferredoЄ┘IJKPONLMLQSTRUV[ZYWXW\_]^]`abcnqoprustvywxz{|}▒■▓⌠∙≈²⌡≥° ≤÷═√·√║ёє╔і╠Є╡Ё╣ІЇ╦╧╨╩╪аю©ҐЎгхимлйкстуыьжвъЮАБ│┌┐░÷╗╧╨╩╪ҐЎ©ДЕФГХКИЙЛМНОПЯ Safe-InferredбДtЯ╔friday|іfriday∙@їfriday/╗friday\⌡fridayфDetects edges using the Canny's algorithm. Edges are given the value ╘% while non-edges are given the value ╙.÷This implementation doesn't perform any noise erasing (as blurring) before edge detection. Noisy images might need to be pre-processed using a Gaussian blur.цThe bidirectional derivative (gradient magnitude) is computed from x and y derivatives using sqrt(dx╡ + dy╡).See 0http://en.wikipedia.org/wiki/Canny_edge_detector for details.!This function is specialized for Ю images but is declared INLINABLE0 to be further specialized for new image types.⌡fridayRadius of the Sobel's filter.fridayІLow threshold. Pixels for which the bidirectional derivative is greater than this value and which are connected to another pixel which is part of an edge will be part of this edge.fridayРHigh threshold. Pixels for which the bidirectional derivative is greater than this value will be part of an edge.⌡⌡╚ !"#$%&'()*+,,--./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRRSTUUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~─│┌┐└┘├┤┬┴┴┼▀▄▄█▌▐▐░▒▓⌠■∙√≈≤≥ ⌡°²·÷═║╒ёє╔ії╗╘╘╙╚╛ґў╞╟╠╡ЁЄ╣ІЇ╦╧╨╩╪ҐЎ©юабцдефг х и й к л м н о п я р с с т у ж в ь ы з ш эщчъъЮАБЦДЕФГХИЙЙКЛМНОПЯР С Т У У Ж В Ь Ы З Ш Э Щ Ч Ъ ─ │ ┌┐└┘├┤┬┴┼▀▄█▌▐░▒▓⌠■∙√≈≤≥ ⌡°²·÷÷══║╒ёёє╔ііїї╗╘╙╚╛ґґў╞╟╠╡ЁЄ╣ІЇ╦╧╨╩╪ҐЎ©юабцде╧╨╩╪ҐЎ©фгхийкллsom|neмнопямрстужвьызшэщчъЮАБЦДЕФГХИЙКЛМНОПЯРСТУЖЖВЬЫЗШЭЭЩЧЪ─│┌┐└┘├┤┬┴┼▀▄█▌▐░▒▓⌠■∙√≈≤≥ ≤⌡°²·÷≤═║╒≤ёє≤╔ії╗╘╙╚╛≤ґў≤ґ╞╟%friday-0.2.3.2-8On5VvNOMcCDg1mEiVpYRlVision.Image.ConversionVision.Primitive.ShapeVision.PrimitiveVision.Image.ClassVision.Image.TypeVision.Image.MutableVision.Image.ParallelVision.Image.InterpolateVision.Image.TransformVision.Image.RGBA.TypeVision.Image.RGB.TypeVision.Image.HSV.TypeVision.Image.Grey.TypeVision.Image.Filter.InternalVision.Image.FilterVision.HistogramVision.Image.ThresholdVision.Image.ContourVision.Detector.EdgeVision.Image.RGBA.SpecializeVision.Image.RGBAVision.Image.RGB.SpecializeVision.Image.RGBVision.Image.HSV.SpecializeVision.Image.HSVVision.Image.Grey.SpecializeVision.Image.GreyVision.Image*convertible-1.1.1.1-Joy6hn0SiOmKsvCIwOlOVoData.Convertible.BaseconvertsafeConvertConvertibleDIM9DIM8DIM7DIM6DIM5DIM4DIM3DIM2DIM1DIM0:.ZShape shapeRankshapeLength shapeZero shapeSucc toLinearIndexfromLinearIndex shapeListinShapeix1ix2ix3ix4ix5ix6ix7ix8ix9$fStorableZ$fShapeZ$fUnboxZ$fVectorVectorZ$fMVectorMVectorZ$fStorable:. $fShape:. $fUnbox:.$fVectorVector:.$fMVectorMVector:.$fShow:.$fRead:.$fEq:.$fOrd:.$fShowZ$fReadZ$fEqZ$fOrdZRPointrpXrpYRectrXrYrWidthrHeightSizePoint$fShowRPoint$fReadRPoint $fEqRPoint$fOrdRPoint $fShowRect $fReadRect$fEqRect $fOrdRectFunctorImagemapFromFunctionFromFunctionPixelfromFunctionfromFunctionLinefromFunctionColfromFunctionCachedImageindexlinearIndexvectorImageChannelMaskedImage ImagePixelshapemaskedIndexmaskedLinearIndexvaluesPixelPixelChannelpixNChannelspixIndex!?! nChannelspixel$fPixelBool $fPixelDouble$fPixelFloat$fPixelWord $fPixelWord32 $fPixelWord16$fPixelWord8 $fPixelInt$fPixelInt32$fPixelInt16DelayedMaskdelayedMaskSizedelayedMaskFunDelayeddelayedSize delayedFunManifestmanifestSizemanifestVectordelaycomputedelayedmanifest$fConvertibleManifestManifest$fFunctorImagesrcManifest$fFromFunctionManifest$fImageManifest$fMaskedImageManifest$fNFDataManifest$fConvertibleManifestDelayed$fConvertibleDelayedManifest$fConvertibleDelayedDelayed$fFunctorImagesrcDelayed$fFromFunctionDelayed$fImageDelayed$fMaskedImageDelayed$fFunctorImagesrcDelayedMask$fFromFunctionDelayedMask$fMaskedImageDelayedMask$fEqManifest $fOrdManifest$fShowManifestMutableManifestmmSizemmVectorMutableImageFreezedmShapenewnew'read linearReadwritelinearWritefreezeunsafeFreezethawcreate$fMutableImageMutableManifestcomputePInterpolableinterpolbilinearInterpol$fInterpolableBool$fInterpolableDouble$fInterpolableFloat$fInterpolableWord$fInterpolableWord32$fInterpolableWord16$fInterpolableWord8$fInterpolableInt$fInterpolableInt32$fInterpolableInt16InterpolMethodTruncateIntegerNearestNeighborBilinearcropresizehorizontalFlipverticalFlip floodFillRGBADelayedRGBA RGBAPixelrgbaRed rgbaGreenrgbaBlue rgbaAlpha$fInterpolableRGBAPixel$fPixelRGBAPixel$fStorableRGBAPixel $fEqRGBAPixel$fShowRGBAPixel RGBDelayedRGBRGBPixelrgbRedrgbGreenrgbBlue$fInterpolableRGBPixel$fPixelRGBPixel$fStorableRGBPixel$fEqRGBPixel$fShowRGBPixel HSVDelayedHSVHSVPixelhsvHuehsvSathsvValue$fInterpolableHSVPixel$fPixelHSVPixel$fStorableHSVPixel$fEqHSVPixel$fShowHSVPixelGreyDelayedGrey GreyPixel$fInterpolableGreyPixel$fPixelGreyPixel$fBitsGreyPixel$fBoundedGreyPixel$fEnumGreyPixel $fEqGreyPixel$fIntegralGreyPixel$fNumGreyPixel$fOrdGreyPixel$fRealGreyPixel$fReadGreyPixel$fShowGreyPixel$fStorableGreyPixel$fConvertibleRGBPixelRGBAPixel$fConvertibleHSVPixelRGBAPixel$fConvertibleGreyPixelRGBAPixel$fConvertibleRGBAPixelRGBAPixel$fConvertibleHSVPixelRGBPixel$fConvertibleRGBAPixelRGBPixel$fConvertibleGreyPixelRGBPixel$fConvertibleRGBPixelRGBPixel$fConvertibleRGBAPixelHSVPixel$fConvertibleRGBPixelHSVPixel$fConvertibleGreyPixelHSVPixel$fConvertibleHSVPixelHSVPixel$fConvertibleRGBPixelGreyPixel$fConvertibleRGBAPixelGreyPixel$fConvertibleHSVPixelGreyPixel$fConvertibleGreyPixelGreyPixelMeanDerivativeTypeDerivativeXDerivativeY DerivativeBlur MorphologicalBorderInterpolateBorderReplicate BorderReflect BorderWrapBorderConstantFilterFold1 FilterFoldSeparatelyFiltrableSeparableFilterAccumulatorSeparableKernel skVerticalskHorizontalKernelKernelAnchorKernelAnchorCenterSeparableFilter1SeparableFilter BoxFilter1 BoxFilterFilterfKernelSize fKernelCenterfKernelfInitfFoldfPost fInterpol FilterableapplykernelAnchorborderInterpolatedilateerodeblurgaussianBlurscharrsobelmean"$fSeparatelyFiltrablesrcDelayedacc#$fSeparatelyFiltrablesrcManifestacc$fFilterablesrcresFilter$fFilterablesrcresFilter0$fFilterablesrcresFilter1$fFilterablesrcresFilter2ToHistogramPixelValueSpace pixToIndex domainSizeHistogramShapetoBin HistogramassocspixToBin histogramhistogram2Dreduce cumulative normalize equalizeImage compareCorrel compareChicompareIntersect compareEMD$fHistogramShape:.$fHistogramShapeZ$fToHistogramHSVPixel$fToHistogramRGBPixel$fToHistogramRGBAPixel$fToHistogramGreyPixel $fEqHistogram$fOrdHistogram$fShowHistogramAdaptiveThresholdAdaptiveThresholdKernel MeanKernelGaussianKernel ThresholdTypeBinaryThresholdTruncateTruncateInv thresholdType thresholdadaptiveThresholdadaptiveThresholdFilterotsuscwContourDrawStyleOuterOutlineAllOutlinesFill FillWithHoles RowContourContourouterContour innerContoursContourValue OneContour ContourIdContourscontourOutlinescontourSizes allContourIdscontourPerimetercontourSize lookupContourdrawContourdrawContours rowContourcontours$fEnumContourPos$fBoundedContourPos$fEqContourPos$fShowContourPos$fEqContourType$fEqContourDrawStyle$fOrdContourDrawStyle$fShowContourDrawStyle$fReadContourDrawStyle$fEnumContourDrawStyle$fBoundedContourDrawStyle $fEqContourId$fOrdContourId$fStorableContourId$fNumContourId$fShowContourIdcannybaseGHC.BaseFunctorGHC.Err undefined&vector-0.13.0.0-JKrBPPZBIK2JBM2KZEUb7ZData.Vector.StorableVectorGHC.STST rgbToGreyGHC.Real FractionalGHC.IntInt32variancedrawRows NorthSouthWestEastNorthEastSouthWestNorthWestSouthEastGHC.EnummaxBoundminBound