-- Hoogle documentation, generated by Haddock -- See Hoogle, http://www.haskell.org/hoogle/ -- | A functional image processing library for Haskell. -- -- Friday provides functions to manipulate images in a functional way. -- The library is designed to be fast, generic and type-safe. -- -- Images can be represented in two representations: -- -- -- -- The library currently support four color spaces: RGB, RGBA, HSV and -- gray-scale images. -- -- Please read our README to get a detailed usage and some -- examples. @package friday @version 0.2.3.2 -- | Shapes are similar to what you could found in repa. -- Shape are used both for indexes and shapes. -- -- To create a shape/index, use the ix1, ix2, ix3 -- ... helpers : -- -- 
--   size = ix2 200 100
--
-- -- To pull values from a shape, use the Z and :. -- constructors : -- -- 
--   Z :. h :. w = size
--
module Vision.Primitive.Shape -- | Class of types that can be used as array shapes and indices. class Eq sh => Shape sh -- | Gets the number of dimensions in a shape. shapeRank :: Shape sh => sh -> Int -- | Gets the total number of elements in an array of this shape. shapeLength :: Shape sh => sh -> Int -- | Gives the first index of an array. shapeZero :: Shape sh => sh -- | Gives the successor of an index, given the shape of the array. shapeSucc :: Shape sh => sh -> sh -> sh -- | Convert an index into its equivalent flat, linear, row-major version. toLinearIndex :: Shape sh => sh -> sh -> Int -- | Inverse of toLinearIndex. fromLinearIndex :: Shape sh => sh -> Int -> sh -- | Return the ascending list of indexes for the given shape. shapeList :: Shape sh => sh -> [sh] -- | Check whether an index is within a given shape. inShape :: Shape sh => sh -> sh -> Bool -- | An index of dimension zero. data Z Z :: Z -- | Our index type, used for both shapes and indices. data tail :. head (:.) :: !tail -> !head -> (:.) tail head infixl 3 :. infixl 3 :. type DIM0 = Z type DIM1 = DIM0 :. Int type DIM2 = DIM1 :. Int type DIM3 = DIM2 :. Int type DIM4 = DIM3 :. Int type DIM5 = DIM4 :. Int type DIM6 = DIM5 :. Int type DIM7 = DIM6 :. Int type DIM8 = DIM7 :. Int type DIM9 = DIM8 :. Int -- | Helper 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. ix1 :: Int -> DIM1 ix2 :: Int -> Int -> DIM2 ix3 :: Int -> Int -> Int -> DIM3 ix4 :: Int -> Int -> Int -> Int -> DIM4 ix5 :: Int -> Int -> Int -> Int -> Int -> DIM5 ix6 :: Int -> Int -> Int -> Int -> Int -> Int -> DIM6 ix7 :: Int -> Int -> Int -> Int -> Int -> Int -> Int -> DIM7 ix8 :: Int -> Int -> Int -> Int -> Int -> Int -> Int -> Int -> DIM8 ix9 :: Int -> Int -> Int -> Int -> Int -> Int -> Int -> Int -> Int -> DIM9 instance GHC.Classes.Ord Vision.Primitive.Shape.Z instance GHC.Classes.Eq Vision.Primitive.Shape.Z instance GHC.Read.Read Vision.Primitive.Shape.Z instance GHC.Show.Show Vision.Primitive.Shape.Z instance (GHC.Classes.Ord tail, GHC.Classes.Ord head) => GHC.Classes.Ord (tail Vision.Primitive.Shape.:. head) instance (GHC.Classes.Eq tail, GHC.Classes.Eq head) => GHC.Classes.Eq (tail Vision.Primitive.Shape.:. head) instance (GHC.Read.Read tail, GHC.Read.Read head) => GHC.Read.Read (tail Vision.Primitive.Shape.:. head) instance (GHC.Show.Show tail, GHC.Show.Show head) => GHC.Show.Show (tail Vision.Primitive.Shape.:. head) instance (Data.Vector.Unboxed.Base.Unbox t, Data.Vector.Unboxed.Base.Unbox h) => Data.Vector.Generic.Mutable.Base.MVector Data.Vector.Unboxed.Base.MVector (t Vision.Primitive.Shape.:. h) instance (Data.Vector.Unboxed.Base.Unbox t, Data.Vector.Unboxed.Base.Unbox h) => Data.Vector.Generic.Base.Vector Data.Vector.Unboxed.Base.Vector (t Vision.Primitive.Shape.:. h) instance (Data.Vector.Unboxed.Base.Unbox t, Data.Vector.Unboxed.Base.Unbox h) => Data.Vector.Unboxed.Base.Unbox (t Vision.Primitive.Shape.:. h) instance Vision.Primitive.Shape.Shape sh => Vision.Primitive.Shape.Shape (sh Vision.Primitive.Shape.:. GHC.Types.Int) instance Foreign.Storable.Storable sh => Foreign.Storable.Storable (sh Vision.Primitive.Shape.:. GHC.Types.Int) instance Data.Vector.Generic.Mutable.Base.MVector Data.Vector.Unboxed.Base.MVector Vision.Primitive.Shape.Z instance Data.Vector.Generic.Base.Vector Data.Vector.Unboxed.Base.Vector Vision.Primitive.Shape.Z instance Data.Vector.Unboxed.Base.Unbox Vision.Primitive.Shape.Z instance Vision.Primitive.Shape.Shape Vision.Primitive.Shape.Z instance Foreign.Storable.Storable Vision.Primitive.Shape.Z module Vision.Primitive -- | Coordinates inside the image. -- -- Can be constructed using ix2. The first parameter is the y -- coordinate while the second is the x coordinate (i.e. ix2 y -- x). Image origin (ix2 0 0) is located in the -- upper left corner. type Point = DIM2 -- | Size of an object. -- -- Can be constructed using ix2. The first parameter is the height -- while the second is the width (i.e. ix2 h w). type Size = DIM2 data Rect Rect :: {-# UNPACK #-} !Int -> {-# UNPACK #-} !Int -> {-# UNPACK #-} !Int -> {-# UNPACK #-} !Int -> Rect [rX] :: Rect -> {-# UNPACK #-} !Int [rY] :: Rect -> {-# UNPACK #-} !Int [rWidth] :: Rect -> {-# UNPACK #-} !Int [rHeight] :: Rect -> {-# UNPACK #-} !Int -- | Rational coordinates used for interpolations. data RPoint RPoint :: {-# UNPACK #-} !RatioInt -> {-# UNPACK #-} !RatioInt -> RPoint [rpX] :: RPoint -> {-# UNPACK #-} !RatioInt [rpY] :: RPoint -> {-# UNPACK #-} !RatioInt instance GHC.Classes.Ord Vision.Primitive.Rect instance GHC.Classes.Eq Vision.Primitive.Rect instance GHC.Read.Read Vision.Primitive.Rect instance GHC.Show.Show Vision.Primitive.Rect instance GHC.Classes.Ord Vision.Primitive.RPoint instance GHC.Classes.Eq Vision.Primitive.RPoint instance GHC.Read.Read Vision.Primitive.RPoint instance GHC.Show.Show Vision.Primitive.RPoint module Vision.Image.Class -- | Determines the number of channels and the type of each pixel of the -- image and how images are represented. class Pixel p where { type PixelChannel p; } -- | Returns the number of channels of the pixel. -- -- Must not consume p (could be undefined). pixNChannels :: Pixel p => p -> Int pixIndex :: Pixel p => p -> Int -> PixelChannel p -- | Provides an abstraction for images which are not defined for each of -- their pixels. The interface is similar to Image except that -- indexing functions don't always return. -- -- Image origin (ix2 0 0) is located in the upper left -- corner. class Storable (ImagePixel i) => MaskedImage i where { type ImagePixel i; } shape :: MaskedImage i => i -> Size -- | Returns the pixel's value at 'Z :. y, :. x'. maskedIndex :: MaskedImage i => i -> Point -> Maybe (ImagePixel i) -- | Returns the pixel's value as if the image was a single dimension -- vector (row-major representation). maskedLinearIndex :: MaskedImage i => i -> Int -> Maybe (ImagePixel i) -- | Returns the non-masked values of the image. values :: MaskedImage i => i -> Vector (ImagePixel i) -- | Provides an abstraction over the internal representation of an image. -- -- Image origin (ix2 0 0) is located in the upper left -- corner. class MaskedImage i => Image i -- | Returns the pixel value at 'Z :. y :. x'. index :: Image i => i -> Point -> ImagePixel i -- | Returns the pixel value as if the image was a single dimension vector -- (row-major representation). linearIndex :: Image i => i -> Int -> ImagePixel i -- | Returns every pixel values as if the image was a single dimension -- vector (row-major representation). vector :: Image i => i -> Vector (ImagePixel i) type ImageChannel i = PixelChannel (ImagePixel i) -- | Provides ways to construct an image from a function. class FromFunction i where { type FromFunctionPixel i; } -- | Generates an image by calling the given function for each pixel of the -- constructed image. fromFunction :: FromFunction i => Size -> (Point -> FromFunctionPixel i) -> i -- | Generates an image by calling the last function for each pixel of the -- constructed image. -- -- The first function is called for each line, generating a line -- invariant value. -- -- This function is faster for some image representations as some -- recurring computation can be cached. fromFunctionLine :: FromFunction i => Size -> (Int -> a) -> (a -> Point -> FromFunctionPixel i) -> i -- | Generates an image by calling the last function for each pixel of the -- constructed image. -- -- The first function is called for each column, generating a column -- invariant value. -- -- This 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 fromFunction. fromFunctionCol :: (FromFunction i, Storable b) => Size -> (Int -> b) -> (b -> Point -> FromFunctionPixel i) -> i -- | Generates 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. -- -- This 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 fromFunctionLine. fromFunctionCached :: (FromFunction i, Storable b) => Size -> (Int -> a) -> (Int -> b) -> (a -> b -> Point -> FromFunctionPixel i) -> i -- | Defines a class for images on which a function can be applied. The -- class is different from Functor as there could be some -- constraints and transformations the pixel and image types. class (MaskedImage src, MaskedImage res) => FunctorImage src res map :: FunctorImage src res => (ImagePixel src -> ImagePixel res) -> src -> res -- | Alias of index. (!) :: Image i => i -> Point -> ImagePixel i -- | Alias of maskedIndex. (!?) :: MaskedImage i => i -> Point -> Maybe (ImagePixel i) -- | Returns the number of channels of an image. nChannels :: (Pixel (ImagePixel i), MaskedImage i) => i -> Int -- | Returns an undefined instance of a pixel of the image. This is -- sometime useful to satisfy the type checker as in a call to -- pixNChannels : -- -- 
--   nChannels img = pixNChannels (pixel img)
--
pixel :: MaskedImage i => i -> ImagePixel i instance Vision.Image.Class.Pixel GHC.Int.Int16 instance Vision.Image.Class.Pixel GHC.Int.Int32 instance Vision.Image.Class.Pixel GHC.Types.Int instance Vision.Image.Class.Pixel GHC.Word.Word8 instance Vision.Image.Class.Pixel GHC.Word.Word16 instance Vision.Image.Class.Pixel GHC.Word.Word32 instance Vision.Image.Class.Pixel GHC.Types.Word instance Vision.Image.Class.Pixel GHC.Types.Float instance Vision.Image.Class.Pixel GHC.Types.Double instance Vision.Image.Class.Pixel GHC.Types.Bool module Vision.Image.Type -- | Stores the image content in a Vector. data Manifest p Manifest :: !Size -> !Vector p -> Manifest p [manifestSize] :: Manifest p -> !Size [manifestVector] :: Manifest p -> !Vector p -- | A 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. data Delayed p Delayed :: !Size -> !Point -> p -> Delayed p [delayedSize] :: Delayed p -> !Size [delayedFun] :: Delayed p -> !Point -> p data DelayedMask p DelayedMask :: !Size -> !Point -> Maybe p -> DelayedMask p [delayedMaskSize] :: DelayedMask p -> !Size [delayedMaskFun] :: DelayedMask p -> !Point -> Maybe p -- | Delays an image in its delayed representation. delay :: Image i => i -> Delayed (ImagePixel i) -- | Computes the value of an image into a manifest representation. compute :: (Image i, Storable (ImagePixel i)) => i -> Manifest (ImagePixel i) -- | Forces an image to be in its delayed representation. Does nothing. delayed :: Delayed p -> Delayed p -- | Forces an image to be in its manifest representation. Does nothing. manifest :: Manifest p -> Manifest p instance (GHC.Show.Show p, Foreign.Storable.Storable p) => GHC.Show.Show (Vision.Image.Type.Manifest p) instance (Foreign.Storable.Storable p, GHC.Classes.Ord p) => GHC.Classes.Ord (Vision.Image.Type.Manifest p) instance (Foreign.Storable.Storable p, GHC.Classes.Eq p) => GHC.Classes.Eq (Vision.Image.Type.Manifest p) instance Foreign.Storable.Storable p => Vision.Image.Class.MaskedImage (Vision.Image.Type.DelayedMask p) instance Foreign.Storable.Storable p => Vision.Image.Class.FromFunction (Vision.Image.Type.DelayedMask p) instance (Vision.Image.Class.MaskedImage src, Foreign.Storable.Storable p) => Vision.Image.Class.FunctorImage src (Vision.Image.Type.DelayedMask p) instance Foreign.Storable.Storable p => Vision.Image.Class.MaskedImage (Vision.Image.Type.Delayed p) instance Foreign.Storable.Storable p => Vision.Image.Class.Image (Vision.Image.Type.Delayed p) instance Vision.Image.Class.FromFunction (Vision.Image.Type.Delayed p) instance (Vision.Image.Class.Image src, Foreign.Storable.Storable p) => Vision.Image.Class.FunctorImage src (Vision.Image.Type.Delayed p) instance (Foreign.Storable.Storable p1, Foreign.Storable.Storable p2, Data.Convertible.Base.Convertible p1 p2) => Data.Convertible.Base.Convertible (Vision.Image.Type.Delayed p1) (Vision.Image.Type.Delayed p2) instance (Foreign.Storable.Storable p1, Foreign.Storable.Storable p2, Data.Convertible.Base.Convertible p1 p2) => Data.Convertible.Base.Convertible (Vision.Image.Type.Delayed p1) (Vision.Image.Type.Manifest p2) instance (Foreign.Storable.Storable p1, Foreign.Storable.Storable p2, Data.Convertible.Base.Convertible p1 p2) => Data.Convertible.Base.Convertible (Vision.Image.Type.Manifest p1) (Vision.Image.Type.Delayed p2) instance Control.DeepSeq.NFData (Vision.Image.Type.Manifest p) instance Foreign.Storable.Storable p => Vision.Image.Class.MaskedImage (Vision.Image.Type.Manifest p) instance Foreign.Storable.Storable p => Vision.Image.Class.Image (Vision.Image.Type.Manifest p) instance Foreign.Storable.Storable p => Vision.Image.Class.FromFunction (Vision.Image.Type.Manifest p) instance (Vision.Image.Class.Image src, Foreign.Storable.Storable p) => Vision.Image.Class.FunctorImage src (Vision.Image.Type.Manifest p) instance (Foreign.Storable.Storable p1, Foreign.Storable.Storable p2, Data.Convertible.Base.Convertible p1 p2) => Data.Convertible.Base.Convertible (Vision.Image.Type.Manifest p1) (Vision.Image.Type.Manifest p2) -- | Contains an stateful image type which can be modified inside a -- ST monad. module Vision.Image.Mutable -- | Class for images which can be constructed from a mutable image. class Image (Freezed i) => MutableImage i where { -- | The type of the immutable version of the mutable image i. type Freezed i; } -- | mShape doesn't run in a monad as the size of a mutable image is -- constant. mShape :: MutableImage i => i s -> Size -- | Creates a new mutable image of the given size. Pixels are initialized -- with an unknown value. new :: (MutableImage i, PrimMonad m) => Size -> m (i (PrimState m)) -- | Creates a new mutable image of the given size and fill it with the -- given value. new' :: (MutableImage i, PrimMonad m) => Size -> ImagePixel (Freezed i) -> m (i (PrimState m)) -- | Returns the pixel value at Z :. y :. x. read :: (MutableImage i, PrimMonad m) => i (PrimState m) -> Point -> m (ImagePixel (Freezed i)) -- | Returns the pixel value as if the image was a single dimension vector -- (row-major representation). linearRead :: (MutableImage i, PrimMonad m) => i (PrimState m) -> Int -> m (ImagePixel (Freezed i)) -- | Overrides the value of the pixel at Z :. y :. x. write :: (MutableImage i, PrimMonad m) => i (PrimState m) -> Point -> ImagePixel (Freezed i) -> m () -- | Overrides the value of the pixel at the given index as if the image -- was a single dimension vector (row-major representation). linearWrite :: (MutableImage i, PrimMonad m) => i (PrimState m) -> Int -> ImagePixel (Freezed i) -> m () -- | Returns an immutable copy of the mutable image. freeze :: (MutableImage i, PrimMonad m) => i (PrimState m) -> m (Freezed i) -- | Returns the immutable version of the mutable image. The mutable image -- should not be modified thereafter. unsafeFreeze :: (MutableImage i, PrimMonad m) => i (PrimState m) -> m (Freezed i) -- | Returns a mutable copy of the immutable image. thaw :: (MutableImage i, PrimMonad m) => Freezed i -> m (i (PrimState m)) -- | Creates an immutable image from an ST action creating a mutable -- image. create :: MutableImage i => (forall s. ST s (i s)) -> Freezed i data MutableManifest p s MutableManifest :: !Size -> !MVector s p -> MutableManifest p s [mmSize] :: MutableManifest p s -> !Size [mmVector] :: MutableManifest p s -> !MVector s p instance Foreign.Storable.Storable p => Vision.Image.Mutable.MutableImage (Vision.Image.Mutable.MutableManifest p) module Vision.Image.Parallel -- | Parallel version of compute. -- -- Computes the value of an image into a manifest representation in -- parallel. -- -- The monad ensures that the image is fully evaluated before continuing. computeP :: (Monad m, Image i, Storable (ImagePixel i)) => i -> m (Manifest (ImagePixel i)) -- | Provides a way to estimate the value of a pixel at rational -- coordinates using a linear interpolation. module Vision.Image.Interpolate -- | Provides a way to apply the interpolation to every component of a -- pixel. class Interpolable p -- | Given a function which interpolates two points over a single channel, -- returns a function which interpolates two points over every channel of -- two pixels. interpol :: Interpolable p => (PixelChannel p -> PixelChannel p -> PixelChannel p) -> p -> p -> p -- | Uses 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 : -- -- 
--         x1       x2
--   
--   y1    a ------ b
--         -        -
--         -  p     -
--         -        -
--   y2    c ------ d
--
bilinearInterpol :: (Image i, Interpolable (ImagePixel i), Integral (ImageChannel i)) => i -> RPoint -> ImagePixel i instance Vision.Image.Interpolate.Interpolable GHC.Int.Int16 instance Vision.Image.Interpolate.Interpolable GHC.Int.Int32 instance Vision.Image.Interpolate.Interpolable GHC.Types.Int instance Vision.Image.Interpolate.Interpolable GHC.Word.Word8 instance Vision.Image.Interpolate.Interpolable GHC.Word.Word16 instance Vision.Image.Interpolate.Interpolable GHC.Word.Word32 instance Vision.Image.Interpolate.Interpolable GHC.Types.Word instance Vision.Image.Interpolate.Interpolable GHC.Types.Float instance Vision.Image.Interpolate.Interpolable GHC.Types.Double instance Vision.Image.Interpolate.Interpolable GHC.Types.Bool -- | Provides high level functions to do geometric transformations on -- images. -- -- Every transformation is been declared INLINABLE so new image -- types could be specialized. module Vision.Image.Transform -- | Defines the set of possible methods for pixel interpolations when -- looking for a pixel at floating point coordinates. data InterpolMethod -- | Selects the top left pixel (fastest). TruncateInteger :: InterpolMethod -- | Selects the nearest pixel (fast). NearestNeighbor :: InterpolMethod -- | Does a double linear interpolation over the four surrounding points -- (slow). Bilinear :: InterpolMethod -- | Maps the content of the image's rectangle in a new image. crop :: (Image i1, FromFunction i2, ImagePixel i1 ~ FromFunctionPixel i2) => Rect -> i1 -> i2 -- | Resizes the Image using the given interpolation method. resize :: (Image i1, Interpolable (ImagePixel i1), FromFunction i2, ImagePixel i1 ~ FromFunctionPixel i2, Integral (ImageChannel i1)) => InterpolMethod -> Size -> i1 -> i2 -- | Reverses the image horizontally. horizontalFlip :: (Image i1, FromFunction i2, ImagePixel i1 ~ FromFunctionPixel i2) => i1 -> i2 -- | Reverses the image vertically. verticalFlip :: (Image i1, FromFunction i2, ImagePixel i1 ~ FromFunctionPixel i2) => i1 -> i2 -- | Paints with a new value the pixels surrounding the given point of the -- image which have the same value as the starting point. floodFill :: (PrimMonad m, MutableImage i, Eq (ImagePixel (Freezed i))) => Point -> ImagePixel (Freezed i) -> i (PrimState m) -> m () module Vision.Image.RGBA.Type type RGBA = Manifest RGBAPixel data RGBAPixel RGBAPixel :: {-# UNPACK #-} !Word8 -> {-# UNPACK #-} !Word8 -> {-# UNPACK #-} !Word8 -> {-# UNPACK #-} !Word8 -> RGBAPixel [rgbaRed] :: RGBAPixel -> {-# UNPACK #-} !Word8 [rgbaGreen] :: RGBAPixel -> {-# UNPACK #-} !Word8 [rgbaBlue] :: RGBAPixel -> {-# UNPACK #-} !Word8 [rgbaAlpha] :: RGBAPixel -> {-# UNPACK #-} !Word8 type RGBADelayed = Delayed RGBAPixel instance GHC.Show.Show Vision.Image.RGBA.Type.RGBAPixel instance GHC.Classes.Eq Vision.Image.RGBA.Type.RGBAPixel instance Foreign.Storable.Storable Vision.Image.RGBA.Type.RGBAPixel instance Vision.Image.Class.Pixel Vision.Image.RGBA.Type.RGBAPixel instance Vision.Image.Interpolate.Interpolable Vision.Image.RGBA.Type.RGBAPixel module Vision.Image.RGB.Type type RGB = Manifest RGBPixel data RGBPixel RGBPixel :: {-# UNPACK #-} !Word8 -> {-# UNPACK #-} !Word8 -> {-# UNPACK #-} !Word8 -> RGBPixel [rgbRed] :: RGBPixel -> {-# UNPACK #-} !Word8 [rgbGreen] :: RGBPixel -> {-# UNPACK #-} !Word8 [rgbBlue] :: RGBPixel -> {-# UNPACK #-} !Word8 type RGBDelayed = Delayed RGBPixel instance GHC.Show.Show Vision.Image.RGB.Type.RGBPixel instance GHC.Classes.Eq Vision.Image.RGB.Type.RGBPixel instance Foreign.Storable.Storable Vision.Image.RGB.Type.RGBPixel instance Vision.Image.Class.Pixel Vision.Image.RGB.Type.RGBPixel instance Vision.Image.Interpolate.Interpolable Vision.Image.RGB.Type.RGBPixel module Vision.Image.HSV.Type -- | 24 bits (3 * 8 bits) HSV image. -- -- The Hue value is in [0..179], Saturation in [0..255] and Value in -- [0..255]. -- -- This 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. type HSV = Manifest HSVPixel data HSVPixel HSVPixel :: {-# UNPACK #-} !Word8 -> {-# UNPACK #-} !Word8 -> {-# UNPACK #-} !Word8 -> HSVPixel [hsvHue] :: HSVPixel -> {-# UNPACK #-} !Word8 [hsvSat] :: HSVPixel -> {-# UNPACK #-} !Word8 [hsvValue] :: HSVPixel -> {-# UNPACK #-} !Word8 type HSVDelayed = Delayed HSVPixel instance GHC.Show.Show Vision.Image.HSV.Type.HSVPixel instance GHC.Classes.Eq Vision.Image.HSV.Type.HSVPixel instance Foreign.Storable.Storable Vision.Image.HSV.Type.HSVPixel instance Vision.Image.Class.Pixel Vision.Image.HSV.Type.HSVPixel instance Vision.Image.Interpolate.Interpolable Vision.Image.HSV.Type.HSVPixel module Vision.Image.Grey.Type type Grey = Manifest GreyPixel newtype GreyPixel GreyPixel :: Word8 -> GreyPixel type GreyDelayed = Delayed GreyPixel instance Foreign.Storable.Storable Vision.Image.Grey.Type.GreyPixel instance GHC.Show.Show Vision.Image.Grey.Type.GreyPixel instance GHC.Read.Read Vision.Image.Grey.Type.GreyPixel instance GHC.Real.Real Vision.Image.Grey.Type.GreyPixel instance GHC.Classes.Ord Vision.Image.Grey.Type.GreyPixel instance GHC.Num.Num Vision.Image.Grey.Type.GreyPixel instance GHC.Real.Integral Vision.Image.Grey.Type.GreyPixel instance GHC.Classes.Eq Vision.Image.Grey.Type.GreyPixel instance GHC.Enum.Enum Vision.Image.Grey.Type.GreyPixel instance GHC.Enum.Bounded Vision.Image.Grey.Type.GreyPixel instance GHC.Bits.Bits Vision.Image.Grey.Type.GreyPixel instance Vision.Image.Class.Pixel Vision.Image.Grey.Type.GreyPixel instance Vision.Image.Interpolate.Interpolable Vision.Image.Grey.Type.GreyPixel module Vision.Image.Conversion -- | A typeclass that represents something that can be converted. A -- Convertible a b instance represents an a that can be -- converted to a b. class Convertible a b -- | Convert a to b, returning Right on success and Left -- on error. For a simpler interface, see convert. safeConvert :: Convertible a b => a -> ConvertResult b -- | Convert from one type of data to another. Raises an exception if there -- is an error with the conversion. For a function that does not raise an -- exception in that case, see safeConvert. convert :: Convertible a b => a -> b instance Data.Convertible.Base.Convertible Vision.Image.Grey.Type.GreyPixel Vision.Image.Grey.Type.GreyPixel instance Data.Convertible.Base.Convertible Vision.Image.HSV.Type.HSVPixel Vision.Image.Grey.Type.GreyPixel instance Data.Convertible.Base.Convertible Vision.Image.RGBA.Type.RGBAPixel Vision.Image.Grey.Type.GreyPixel instance Data.Convertible.Base.Convertible Vision.Image.RGB.Type.RGBPixel Vision.Image.Grey.Type.GreyPixel instance Data.Convertible.Base.Convertible Vision.Image.HSV.Type.HSVPixel Vision.Image.HSV.Type.HSVPixel instance Data.Convertible.Base.Convertible Vision.Image.Grey.Type.GreyPixel Vision.Image.HSV.Type.HSVPixel instance Data.Convertible.Base.Convertible Vision.Image.RGB.Type.RGBPixel Vision.Image.HSV.Type.HSVPixel instance Data.Convertible.Base.Convertible Vision.Image.RGBA.Type.RGBAPixel Vision.Image.HSV.Type.HSVPixel instance Data.Convertible.Base.Convertible Vision.Image.RGB.Type.RGBPixel Vision.Image.RGB.Type.RGBPixel instance Data.Convertible.Base.Convertible Vision.Image.Grey.Type.GreyPixel Vision.Image.RGB.Type.RGBPixel instance Data.Convertible.Base.Convertible Vision.Image.RGBA.Type.RGBAPixel Vision.Image.RGB.Type.RGBPixel instance Data.Convertible.Base.Convertible Vision.Image.HSV.Type.HSVPixel Vision.Image.RGB.Type.RGBPixel instance Data.Convertible.Base.Convertible Vision.Image.RGBA.Type.RGBAPixel Vision.Image.RGBA.Type.RGBAPixel instance Data.Convertible.Base.Convertible Vision.Image.Grey.Type.GreyPixel Vision.Image.RGBA.Type.RGBAPixel instance Data.Convertible.Base.Convertible Vision.Image.HSV.Type.HSVPixel Vision.Image.RGBA.Type.RGBAPixel instance Data.Convertible.Base.Convertible Vision.Image.RGB.Type.RGBPixel Vision.Image.RGBA.Type.RGBAPixel -- | Provides high level functions to define and apply filters on images. -- -- Filters are operations on images on which the surrounding of each -- processed pixel is considered according to a kernel. -- -- Please use Filter if you only want to apply filter to images. -- -- To apply a filter to an image, use the apply method: -- -- 
--   let -- Creates a filter which will blur the image. Uses a Double as
--       -- accumulator of the Gaussian kernel.
--       filter :: Blur GreyPixel Double GreyPixel
--       filter = gaussianBlur 2 Nothing
--   in apply filter img :: Grey
--
-- -- The radius argument of some filters is used to determine the -- kernel size. A radius as of 1 means a kernel of size 3, 2 a kernel of -- size 5 and so on. -- -- The acc type argument of some filters defines the type which -- will be used to store the accumulated value of the kernel (e.g. by -- setting acc to Double in the computation of a Gaussian -- blur, the kernel average will be computed using a Double). module Vision.Image.Filter.Internal -- | Provides an implementation to execute a type of filter. -- -- src is the original image, res the resulting image -- and f the filter. class Filterable src res f -- | Applies the given filter on the given image. apply :: Filterable src res f => f -> src -> res data Filter src kernel init fold acc res Filter :: !Size -> !KernelAnchor -> !kernel -> !Point -> src -> init -> !fold -> !Point -> src -> init -> acc -> res -> !BorderInterpolate src -> Filter src kernel init fold acc res [fKernelSize] :: Filter src kernel init fold acc res -> !Size [fKernelCenter] :: Filter src kernel init fold acc res -> !KernelAnchor -- | See Kernel and SeparableKernel. [fKernel] :: Filter src kernel init fold acc res -> !kernel -- | Computes a constant value for each pixel before applying the kernel. -- -- This value will be passed to fKernel functions and to the -- fPost function. For most filters, fInit will be _ -- _ -> (). [fInit] :: Filter src kernel init fold acc res -> !Point -> src -> init -- | Defines how the accumulated value is initialized. -- -- See FilterFold and FilterFold1. [fFold] :: Filter src kernel init fold acc res -> !fold -- | This function will be executed after the iteration of the kernel on a -- given point. -- -- Can be used to normalize the accumulated value, for example. [fPost] :: Filter src kernel init fold acc res -> !Point -> src -> init -> acc -> res [fInterpol] :: Filter src kernel init fold acc res -> !BorderInterpolate src -- | 2D filters which are initialized with a value. type BoxFilter src init acc res = Filter src (Kernel src init acc) init (FilterFold acc) acc res -- | 2D filters which are not initialized with a value. type BoxFilter1 src init res = Filter src (Kernel src init src) init FilterFold1 src res -- | Separable 2D filters which are initialized with a value. type SeparableFilter src init acc res = Filter src (SeparableKernel src init acc) init (FilterFold acc) acc res -- | Separable 2D filters which are not initialized with a value. type SeparableFilter1 src init res = Filter src (SeparableKernel src init src) init FilterFold1 src res -- | Defines how the center of the kernel will be determined. data KernelAnchor KernelAnchor :: !Point -> KernelAnchor KernelAnchorCenter :: KernelAnchor -- | A simple 2D kernel. -- -- The kernel function accepts the coordinates in the kernel, the value -- of the pixel at these coordinates (src), the current -- accumulated value and returns a new accumulated value. -- -- Non-separable filters computational complexity grows quadratically -- according to the size of the sides of the kernel. newtype Kernel src init acc Kernel :: (init -> Point -> src -> acc -> acc) -> Kernel src init acc -- | Some kernels can be factorized in two uni-dimensional kernels -- (horizontal and vertical). -- -- Separable filters computational complexity grows linearly according to -- the size of the sides of the kernel. -- -- See http://http://en.wikipedia.org/wiki/Separable_filter. data SeparableKernel src init acc SeparableKernel :: !init -> DIM1 -> src -> acc -> acc -> !init -> DIM1 -> acc -> acc -> acc -> SeparableKernel src init acc -- | Vertical (column) kernel. [skVertical] :: SeparableKernel src init acc -> !init -> DIM1 -> src -> acc -> acc -- | Horizontal (row) kernel. [skHorizontal] :: SeparableKernel src init acc -> !init -> DIM1 -> acc -> acc -> acc -- | Used 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. class (Image (SeparableFilterAccumulator src res acc), ImagePixel (SeparableFilterAccumulator src res acc) ~ acc, FromFunction (SeparableFilterAccumulator src res acc), FromFunctionPixel (SeparableFilterAccumulator src res acc) ~ acc) => SeparatelyFiltrable src res acc where { type SeparableFilterAccumulator src res acc; } -- | Uses the result of the provided function as the initial value of the -- kernel's accumulator, depending on the center coordinates in the -- image. -- -- For most filters, the function will always return the same value (i.e. -- defined as const 0), but this kind of initialization could be -- required for some filters. data FilterFold acc FilterFold :: (Point -> acc) -> FilterFold acc -- | 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. data FilterFold1 FilterFold1 :: FilterFold1 -- | Defines how image boundaries are extrapolated by the algorithms. -- -- '|' characters in examples are image borders. data BorderInterpolate a -- | Replicates the first and last pixels of the image. -- -- 
--   aaaaaa|abcdefgh|hhhhhhh
--
BorderReplicate :: BorderInterpolate a -- | Reflects the border of the image. -- -- 
--   fedcba|abcdefgh|hgfedcb
--
BorderReflect :: BorderInterpolate a -- | Considers that the last pixel of the image is before the first one. -- -- 
--   cdefgh|abcdefgh|abcdefg
--
BorderWrap :: BorderInterpolate a -- | Assigns a constant value to out of image pixels. -- -- 
--   iiiiii|abcdefgh|iiiiiii  with some specified 'i'
--
BorderConstant :: !a -> BorderInterpolate a -- | Given a method to compute the kernel anchor and the size of the -- kernel, returns the anchor of the kernel as coordinates. kernelAnchor :: KernelAnchor -> Size -> Point -- | Given a method of interpolation, the number of pixel in the dimension -- and an index in this dimension, returns either the index of the -- interpolated pixel or a constant value. borderInterpolate :: BorderInterpolate a -> Int -> Int -> Either Int a type Morphological pix = SeparableFilter1 pix () pix dilate :: Ord pix => Int -> Morphological pix erode :: Ord pix => Int -> Morphological pix type Blur src acc res = SeparableFilter src () acc res -- | Blurs the image by averaging the pixel inside the kernel. -- -- Considers using a type for acc with maxBound acc >= -- maxBound src * (kernel size)². blur :: (Integral src, Integral acc, Num res) => Int -> Blur src acc res -- | Blurs the image by averaging the pixel inside the kernel using a -- Gaussian function. -- -- See http://en.wikipedia.org/wiki/Gaussian_blur gaussianBlur :: (Integral src, Floating acc, RealFrac acc, Storable acc, Integral res) => Int -> Maybe acc -> Blur src acc res type Derivative src res = SeparableFilter src () res res data DerivativeType DerivativeX :: DerivativeType DerivativeY :: DerivativeType -- | Estimates the first derivative using the Scharr's 3x3 kernel. -- -- Convolves 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. scharr :: (Integral src, Integral res) => DerivativeType -> Derivative src res -- | Estimates the first derivative using a Sobel's kernel. -- -- Prefer scharr when radius equals 1 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. sobel :: (Integral src, Integral res, Storable res) => Int -> DerivativeType -> Derivative src res type Mean src acc res = SeparableFilter src () acc res -- | Computes the average of a kernel of the given size. -- -- This is similar to blur but with a rectangular kernel and a -- Fractional result. mean :: (Integral src, Integral acc, Fractional res) => Size -> SeparableFilter src () acc res instance (Vision.Image.Class.Image src, Vision.Image.Class.FromFunction res, src_pix GHC.Types.~ Vision.Image.Class.ImagePixel src, res_pix GHC.Types.~ Vision.Image.Class.FromFunctionPixel res) => Vision.Image.Filter.Internal.Filterable src res (Vision.Image.Filter.Internal.BoxFilter src_pix init acc res_pix) instance (Vision.Image.Class.Image src, Vision.Image.Class.FromFunction res, src_pix GHC.Types.~ Vision.Image.Class.ImagePixel src, res_pix GHC.Types.~ Vision.Image.Class.FromFunctionPixel res) => Vision.Image.Filter.Internal.Filterable src res (Vision.Image.Filter.Internal.BoxFilter1 src_pix init res_pix) instance (Vision.Image.Class.Image src, Vision.Image.Class.FromFunction res, src_pix GHC.Types.~ Vision.Image.Class.ImagePixel src, res_pix GHC.Types.~ Vision.Image.Class.FromFunctionPixel res, Vision.Image.Filter.Internal.SeparatelyFiltrable src res acc) => Vision.Image.Filter.Internal.Filterable src res (Vision.Image.Filter.Internal.SeparableFilter src_pix init acc res_pix) instance (Vision.Image.Class.Image src, Vision.Image.Class.FromFunction res, src_pix GHC.Types.~ Vision.Image.Class.ImagePixel src, res_pix GHC.Types.~ Vision.Image.Class.FromFunctionPixel res, Vision.Image.Filter.Internal.SeparatelyFiltrable src res src_pix) => Vision.Image.Filter.Internal.Filterable src res (Vision.Image.Filter.Internal.SeparableFilter1 src_pix init res_pix) instance Foreign.Storable.Storable acc => Vision.Image.Filter.Internal.SeparatelyFiltrable src (Vision.Image.Type.Manifest p) acc instance Foreign.Storable.Storable acc => Vision.Image.Filter.Internal.SeparatelyFiltrable src (Vision.Image.Type.Delayed p) acc -- | Provides high level filtering functions for images. -- -- Use Internal if you want to create new image filters. -- -- Filters are operations on images on which the surrounding of each -- processed pixel is considered according to a kernel. -- -- See http://en.wikipedia.org/wiki/Kernel_(image_processing) for -- details. -- -- The radius argument of some filters is used to determine the -- kernel size. A radius as of 1 means a kernel of size 3, 2 a kernel of -- size 5 and so on. -- -- Note: filters are currently not supported on multi-channel -- images (RGB, RGBA ...) are currently not supported. module Vision.Image.Filter -- | Provides an implementation to execute a type of filter. -- -- src is the original image, res the resulting image -- and f the filter. class Filterable src res f data Filter src kernel init fold acc res -- | Used 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. class (Image (SeparableFilterAccumulator src res acc), ImagePixel (SeparableFilterAccumulator src res acc) ~ acc, FromFunction (SeparableFilterAccumulator src res acc), FromFunctionPixel (SeparableFilterAccumulator src res acc) ~ acc) => SeparatelyFiltrable src res acc dilate :: (Image src, Ord (ImagePixel src), FromFunction res, FromFunctionPixel res ~ ImagePixel src, SeparatelyFiltrable src res (ImagePixel src)) => Int -> src -> res erode :: (Image src, Ord (ImagePixel src), FromFunction res, FromFunctionPixel res ~ ImagePixel src, SeparatelyFiltrable src res (ImagePixel src)) => Int -> src -> res -- | Blurs the image by averaging the pixel inside the kernel. -- -- Uses an Int32 as accumulator during the averaging operation. blur :: (Image src, Integral (ImagePixel src), FromFunction res, Num (FromFunctionPixel res), SeparatelyFiltrable src res Int32) => Int -> src -> res -- | Blurs the image by averaging the pixel inside the kernel using a -- Gaussian function. -- -- See http://en.wikipedia.org/wiki/Gaussian_blur gaussianBlur :: (Image src, Integral (ImagePixel src), FromFunction res, Integral (FromFunctionPixel res), Floating acc, RealFrac acc, Storable acc, SeparatelyFiltrable src res acc) => Int -> Maybe acc -> src -> res data DerivativeType DerivativeX :: DerivativeType DerivativeY :: DerivativeType -- | Estimates the first derivative using the Scharr's 3x3 kernel. -- -- Convolves 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
--
-- -- Uses an Int32 as accumulator during kernel application. scharr :: (Image src, Integral (ImagePixel src), FromFunction res, Integral (FromFunctionPixel res), Storable (FromFunctionPixel res), SeparatelyFiltrable src res (FromFunctionPixel res)) => DerivativeType -> src -> res -- | Estimates the first derivative using a Sobel's kernel. -- -- Prefer scharr when radius equals 1 as Scharr's kernel -- is more accurate and is implemented faster. -- -- Uses an Int32 as accumulator during kernel application. sobel :: (Image src, Integral (ImagePixel src), FromFunction res, Integral (FromFunctionPixel res), Storable (FromFunctionPixel res), SeparatelyFiltrable src res (FromFunctionPixel res)) => Int -> DerivativeType -> src -> res -- | Computes the average of a kernel of the given size. -- -- This is similar to blur but with a rectangular kernel and a -- Fractional result. -- -- Uses an Int32 as accumulator during the averaging operation. mean :: (Image src, Integral (ImagePixel src), FromFunction res, Fractional (FromFunctionPixel res), SeparatelyFiltrable src res Int32) => Size -> src -> res -- | Contains functions to compute and manipulate histograms as well as -- some images transformations which are histogram-based. -- -- Every polymorphic function is specialised for histograms of -- Int32, Double and Float. Other types can be -- specialized as every polymorphic function is declared -- INLINABLE. module Vision.Histogram data Histogram sh a Histogram :: !sh -> !Vector a -> Histogram sh a [shape] :: Histogram sh a -> !sh -- | Values of the histogram in row-major order. [vector] :: Histogram sh a -> !Vector a -- | Subclass of Shape which defines how to resize a shape so it -- will fit inside a resized histogram. class Shape sh => HistogramShape sh -- | Given a number of bins of an histogram, reduces an index so it will be -- mapped to a bin. toBin :: HistogramShape sh => sh -> sh -> sh -> sh -- | This class defines how many dimensions a histogram will have and what -- will be the default number of bins. class (Pixel p, Shape (PixelValueSpace p)) => ToHistogram p where { -- | Gives the value space of a pixel. Single-channel pixels will be -- DIM1 whereas three-channels pixels will be DIM3. This is -- used to determine the rank of the generated histogram. type PixelValueSpace p; } -- | Converts a pixel to an index. pixToIndex :: ToHistogram p => p -> PixelValueSpace p -- | Returns the maximum number of different values an index can take for -- each dimension of the histogram (aka. the maximum index returned by -- pixToIndex plus one). domainSize :: ToHistogram p => p -> PixelValueSpace p index :: (Shape sh, Storable a) => Histogram sh a -> sh -> a -- | Alias of index. (!) :: (Shape sh, Storable a) => Histogram sh a -> sh -> a -- | Returns the value at the index as if the histogram was a single -- dimension vector (row-major representation). linearIndex :: (Shape sh, Storable a) => Histogram sh a -> Int -> a map :: (Storable a, Storable b) => (a -> b) -> Histogram sh a -> Histogram sh b -- | Returns all index/value pairs from the histogram. assocs :: (Shape sh, Storable a) => Histogram sh a -> [(sh, a)] -- | Given the number of bins of an histogram and a given pixel, returns -- the corresponding bin. pixToBin :: (HistogramShape (PixelValueSpace p), ToHistogram p) => PixelValueSpace p -> p -> PixelValueSpace p -- | Computes an histogram from a (possibly) multi-channel image. -- -- If the size of the histogram is not given, there will be as many bins -- as the range of values of pixels of the original image (see -- domainSize). -- -- If the size of the histogram is specified, every bin of a given -- dimension will be of the same size (uniform histogram). histogram :: (MaskedImage i, ToHistogram (ImagePixel i), Storable a, Num a, HistogramShape (PixelValueSpace (ImagePixel i))) => Maybe (PixelValueSpace (ImagePixel i)) -> i -> Histogram (PixelValueSpace (ImagePixel i)) a -- | Similar to histogram but adds two dimensions for the y and -- x-coordinates of the sampled points. This way, the histogram will map -- different regions of the original image. -- -- For example, an RGB image will be mapped as Z -- :. red channel :. green channel :. blue channel -- :. y region :. x region. -- -- As there is no reason to create an histogram as large as the number of -- pixels of the image, a size is always needed. histogram2D :: (Image i, ToHistogram (ImagePixel i), Storable a, Num a, HistogramShape (PixelValueSpace (ImagePixel i))) => ((PixelValueSpace (ImagePixel i) :. Int) :. Int) -> i -> Histogram ((PixelValueSpace (ImagePixel i) :. Int) :. Int) a -- | Reduces a 2D histogram to its linear representation. See resize -- for a reduction of the number of bins of an histogram. -- -- 
--   histogram == reduce . histogram2D
--
reduce :: (HistogramShape sh, Storable a, Num a) => Histogram ((sh :. Int) :. Int) a -> Histogram sh a -- | Resizes an histogram to another index shape. See reduce for a -- reduction of the number of dimensions of an histogram. resize :: (HistogramShape sh, Storable a, Num a) => sh -> Histogram sh a -> Histogram sh a -- | Computes 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. cumulative :: (Storable a, Num a) => Histogram DIM1 a -> Histogram DIM1 a -- | Normalizes the histogram so that the sum of the histogram bins is -- equal to the given value (normalisation by the L1 norm). -- -- This is useful to compare two histograms which have been computed from -- images with a different number of pixels. normalize :: (Storable a, Real a, Storable b, Fractional b) => b -> Histogram sh a -> Histogram sh b -- | 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 H the cumulative of the histogram -- normalized over an L1 norm. -- -- See https://en.wikipedia.org/wiki/Histogram_equalization. equalizeImage :: (FunctorImage i i, Integral (ImagePixel i), ToHistogram (ImagePixel i), PixelValueSpace (ImagePixel i) ~ DIM1) => i -> i -- | Computes the Pearson's correlation coefficient between each -- corresponding bins of the two histograms. -- -- A value of 1 implies a perfect correlation, a value of -1 a perfect -- opposition and a value of 0 no correlation at all. -- -- 
--   compareCorrel = SUM  [ (H1(i) - µ(H1)) (H1(2) - µ(H2)) ]
--                     / (   SQRT [ SUM [ (H1(i) - µ(H1))^2 ] ]
--                         * SQRT [ SUM [ (H2(i) - µ(H2))^2 ] ] )
--
-- -- Where µ(H) is the average value of the histogram H. -- -- See -- http://en.wikipedia.org/wiki/Pearson_correlation_coefficient. compareCorrel :: (Shape sh, Storable a, Real a, Storable b, Eq b, Floating b) => Histogram sh a -> Histogram sh a -> b -- | Computes the Chi-squared distance between two histograms. -- -- A value of 0 indicates a perfect match. -- -- compareChi = SUM (d(i)) for each indice i of -- the histograms where d(i) = 2 * ((H1(i) - H2(i))^2 / (H1(i) + -- H2(i))). compareChi :: (Shape sh, Storable a, Real a, Storable b, Fractional b) => Histogram sh a -> Histogram sh a -> b -- | Computes the intersection of the two histograms. -- -- The higher the score is, the best the match is. -- -- compareIntersect = SUM (min(H1(i), H2(i)) for each -- indice i of the histograms. compareIntersect :: (Shape sh, Storable a, Num a, Ord a) => Histogram sh a -> Histogram sh a -> a -- | Computed the Earth mover's distance between two histograms. -- -- Current algorithm only supports histograms of one dimension. -- -- See https://en.wikipedia.org/wiki/Earth_mover's_distance. compareEMD :: (Num a, Storable a) => Histogram DIM1 a -> Histogram DIM1 a -> a instance (GHC.Show.Show sh, GHC.Show.Show a, Foreign.Storable.Storable a) => GHC.Show.Show (Vision.Histogram.Histogram sh a) instance (Foreign.Storable.Storable a, GHC.Classes.Ord sh, GHC.Classes.Ord a) => GHC.Classes.Ord (Vision.Histogram.Histogram sh a) instance (Foreign.Storable.Storable a, GHC.Classes.Eq sh, GHC.Classes.Eq a) => GHC.Classes.Eq (Vision.Histogram.Histogram sh a) instance Vision.Histogram.ToHistogram Vision.Image.Grey.Type.GreyPixel instance Vision.Histogram.ToHistogram Vision.Image.RGBA.Type.RGBAPixel instance Vision.Histogram.ToHistogram Vision.Image.RGB.Type.RGBPixel instance Vision.Histogram.ToHistogram Vision.Image.HSV.Type.HSVPixel instance Vision.Histogram.HistogramShape Vision.Primitive.Shape.Z instance Vision.Histogram.HistogramShape sh => Vision.Histogram.HistogramShape (sh Vision.Primitive.Shape.:. GHC.Types.Int) module Vision.Image.Threshold -- | Specifies what to do with pixels matching the threshold predicate. -- -- BinaryThreshold a b will replace matching pixels by -- a and non-matchings pixels by b. -- -- Truncate a will replace matching pixels by a. -- -- TruncateInv a will replace non-matching pixels by -- a. data ThresholdType src res [BinaryThreshold] :: res -> res -> ThresholdType src res [Truncate] :: src -> ThresholdType src src [TruncateInv] :: src -> ThresholdType src src -- | Given the thresholding method, a boolean indicating if the pixel match -- the thresholding condition and the pixel, returns the new pixel value. thresholdType :: ThresholdType src res -> Bool -> src -> res -- | Applies the given predicate and threshold policy on the image. threshold :: FunctorImage src res => (ImagePixel src -> Bool) -> ThresholdType (ImagePixel src) (ImagePixel res) -> src -> res -- | Defines how pixels of the kernel of the adaptive threshold will be -- weighted. -- -- With MeanKernel, pixels of the kernel have the same weight. -- -- With GaussianKernel sigma, pixels are weighted -- according to their distance from the thresholded pixel using a -- Gaussian function parametred by sigma. See -- gaussianBlur for details. data AdaptiveThresholdKernel acc [MeanKernel] :: Integral acc => AdaptiveThresholdKernel acc [GaussianKernel] :: (Floating acc, RealFrac acc) => Maybe acc -> AdaptiveThresholdKernel acc type AdaptiveThreshold src acc res = SeparableFilter src () acc res -- | Compares every pixel to its surrounding ones in the kernel of the -- given radius. adaptiveThreshold :: (Image src, Integral (ImagePixel src), Ord (ImagePixel src), FromFunction res, Integral (FromFunctionPixel res), Storable acc, SeparatelyFiltrable src res acc) => AdaptiveThresholdKernel acc -> Int -> ImagePixel src -> ThresholdType (ImagePixel src) (FromFunctionPixel res) -> src -> res -- | Creates an adaptive thresholding filter to be used with apply. -- -- Use adaptiveThreshold if you only want to apply the filter on -- the image. -- -- Compares every pixel to its surrounding ones in the kernel of the -- given radius. adaptiveThresholdFilter :: (Integral src, Ord src, Storable acc) => AdaptiveThresholdKernel acc -> Int -> src -> ThresholdType src res -> AdaptiveThreshold src acc res -- | Applies a clustering-based image thresholding using the Otsu's method. -- -- See https://en.wikipedia.org/wiki/Otsu's_method. otsu :: (HistogramShape (PixelValueSpace (ImagePixel src)), ToHistogram (ImagePixel src), FunctorImage src res, Ord (ImagePixel src), Num (ImagePixel src), Enum (ImagePixel src)) => ThresholdType (ImagePixel src) (ImagePixel res) -> src -> res -- | This 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 img
--
-- -- Let σA be the standard deviation of a fist window around a -- pixel and σB be the standard deviation of another window -- around the same pixel. Then the pixel will match the threshold if -- σB / σA >= beta, and will be thresholded according to the -- given ThresholdType. -- -- See -- http://www.academypublisher.com/jcp/vol04/no08/jcp0408771777.pdf. scw :: (Image src, Integral (ImagePixel src), FromFunction dst, Floating stdev, Fractional stdev, Ord stdev, Storable stdev) => Size -> Size -> stdev -> ThresholdType (ImagePixel src) (FromFunctionPixel dst) -> src -> dst -- | SPECIALIZE pragma declarations for RGBA images. module Vision.Image.RGBA.Specialize module Vision.Image.RGBA -- | SPECIALIZE pragma declarations for RGB images. module Vision.Image.RGB.Specialize module Vision.Image.RGB -- | SPECIALIZE pragma declarations for HSV images. module Vision.Image.HSV.Specialize module Vision.Image.HSV -- | SPECIALIZE pragma declarations for grey-scale images. module Vision.Image.Grey.Specialize module Vision.Image.Grey -- | Contour tracing of binary images (zero ~ background, nonzero ~ -- object). -- -- Terminology: -- -- A binary image is an image in which the pixel is boolean (represented -- here using Grey and zero or non-zero pixel values). -- -- All zero-value pixels are part of the "background". -- -- An object is an connected group of non-zero pixels. -- -- A Contour is a trace of an objects' outer or inner edges. Some -- objects are solid, having no inner contours (consider a filled circle, -- or letters such as h, s, k and l). -- Other objects have "holes", also known as inner contours. The letters -- a and e have one hole while the letter B -- has two. -- -- After obtaining a Contours structure (via the contours -- function) the raw traces (Contour type) can be used for further -- processing or the contours can be filtered by aspects of interest and -- selectively re-drawn (drawContour) , perhaps used to mask the -- original image. -- -- About Holes: -- -- In cases where there is only one hole it is uniquely recorded in the -- Contours structure. Objects with more than one hole record all -- inner contours in one vector making them hard to extract separately - -- this is due to the main data structure not being rich enough to record -- the holes separately. As of writing, this is not seen as an issue -- because the desired operation, drawContour, can still be achieved. -- Changing this behavior should be trivial if desired. -- -- Use: -- -- To use this library it is advised that you preprocess the image, -- including thresholding (ex: otsu on a grey scale image), to -- obtain a binary image then call: -- -- 
--   cs = contours img
--
-- -- The Contours structure can be accessed directly if desired. It -- includes an Map of all contours (numbered counting from 1) and -- a vector of the contour sizes (indexed by contour number, zero index -- is unused/zero). -- -- The algorithm implemented in this module follows the design laid out -- in 'A Linear-Time Component-Labeling Algorithm Using Contour Tracing -- Technique' [1]. -- -- module Vision.Image.Contour -- | Contours 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) data Contours Contours :: Map ContourId Contour -> !Vector Int -> Contours [contourOutlines] :: Contours -> Map ContourId Contour [contourSizes] :: Contours -> !Vector Int -- | Contours are identified by a numeric ID number. data ContourId -- | A 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 drawContour implementation. type OneContour = Vector ContourValue type ContourValue = (Point, Bool) data Contour Contour :: OneContour -> [OneContour] -> Contour [outerContour] :: Contour -> OneContour [innerContours] :: Contour -> [OneContour] -- | 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. type RowContour = Vector (Point, Point) -- | The meat of this module is the contours 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, Contours, contains enough information to -- determine the number of contours, their traces, the size in pixels -- (filled size and perimeter), number of holes, etc. contours :: (Image src, Num (ImagePixel src), Eq (ImagePixel src)) => src -> Contours allContourIds :: Contours -> [ContourId] lookupContour :: Contours -> ContourId -> Maybe Contour -- | 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. rowContour :: [ContourValue] -> RowContour contourSize :: Contours -> ContourId -> Int contourPerimeter :: Contours -> ContourId -> [Point] -- | Outline: Just draw the edge. -- -- OuterOutline: 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. data ContourDrawStyle OuterOutline :: ContourDrawStyle AllOutlines :: ContourDrawStyle Fill :: ContourDrawStyle FillWithHoles :: ContourDrawStyle -- | Draws 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. drawContour :: Contours -> Size -> ContourDrawStyle -> ContourId -> Grey -- | Draws many contours. See drawContour. drawContours :: Contours -> Size -> ContourDrawStyle -> [ContourId] -> Grey instance GHC.Show.Show Vision.Image.Contour.ContourId instance GHC.Num.Num Vision.Image.Contour.ContourId instance Foreign.Storable.Storable Vision.Image.Contour.ContourId instance GHC.Classes.Ord Vision.Image.Contour.ContourId instance GHC.Classes.Eq Vision.Image.Contour.ContourId instance GHC.Enum.Bounded Vision.Image.Contour.ContourDrawStyle instance GHC.Enum.Enum Vision.Image.Contour.ContourDrawStyle instance GHC.Read.Read Vision.Image.Contour.ContourDrawStyle instance GHC.Show.Show Vision.Image.Contour.ContourDrawStyle instance GHC.Classes.Ord Vision.Image.Contour.ContourDrawStyle instance GHC.Classes.Eq Vision.Image.Contour.ContourDrawStyle instance GHC.Classes.Eq Vision.Image.Contour.ContourType instance GHC.Show.Show Vision.Image.Contour.ContourPos instance GHC.Classes.Eq Vision.Image.Contour.ContourPos instance GHC.Enum.Bounded Vision.Image.Contour.ContourPos instance GHC.Enum.Enum Vision.Image.Contour.ContourPos -- | Images are manipulated by their Image and MaskedImage -- type-class instances. -- -- The Manifest representation uses an internal Vector to -- represent the image whereas the Delayed representation uses a -- function to generate pixels. Most transformation functions are generic -- to both representations in the way they apply to any type which -- implements the type-classes. -- -- The Delayed image should be used as intermediate -- representations of complex image transformations. -- -- Please refer to our README file for a detailed usage and -- examples. module Vision.Image module Vision.Detector.Edge -- | Detects edges using the Canny's algorithm. Edges are given the value -- maxBound while non-edges are given the value minBound. -- -- 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. -- -- The bidirectional derivative (gradient magnitude) is computed from -- x and y derivatives using sqrt(dx² + dy²). -- -- See http://en.wikipedia.org/wiki/Canny_edge_detector for -- details. -- -- This function is specialized for Grey images but is declared -- INLINABLE to be further specialized for new image types. canny :: (Image src, Integral (ImagePixel src), Bounded res, Eq res, Storable res) => Int -> Int32 -> Int32 -> src -> Manifest res