Update Vision documentation.

dmjio · dmjio · commit 0fc23d81c3ff · 2019-10-26T20:42:27.000-04:00
diff --git a/src/ArrayFire/BLAS.hs b/src/ArrayFire/BLAS.hs
@@ -12,13 +12,13 @@
 --
 -- @
 -- main :: IO ()
--- main = 'print' ('matmul' x y xProp yProp)
+-- main = print (matmul x y xProp yProp)
 --  where
---     x,y :: 'Array' 'Double'
---     x = 'matrix' (2,3) [1..]
---     y = 'matrix' (3,2) [1..]
+--     x,y :: Array Double
+--     x = matrix (2,3) [1..]
+--     y = matrix (3,2) [1..]
 --
---     xProp, yProp :: 'MatProp'
+--     xProp, yProp :: MatProp
 --     xProp = None
 --     yProp = None
 -- @
diff --git a/src/ArrayFire/LAPACK.hs b/src/ArrayFire/LAPACK.hs
@@ -263,6 +263,7 @@ norm arr (fromNormType -> a) b c =
 -- | Is LAPACK available
 --
 -- [ArrayFire Docs](http://arrayfire.org/docs/group__lapack__helper__func__available.htm)
+--
 isLAPACKAvailable
   :: Bool
   -- ^ Returns if LAPACK is available
diff --git a/src/ArrayFire/Vision.hs b/src/ArrayFire/Vision.hs
@@ -28,6 +28,8 @@ import ArrayFire.Internal.Types
 
 -- | FAST feature detectors
 --
+-- [ArrayFire Docs](http://arrayfire.org/docs/group__cv__func__fast.htm)
+--
 -- A circle of radius 3 pixels, translating into a total of 16 pixels, is checked for sequential segments of pixels much brighter or much darker than the central one.
 -- For a pixel p to be considered a feature, there must exist a sequential segment of arc_length pixels in the circle around it such that all are greather than (p + thr) or smaller than (p - thr).
 -- After all features in the image are detected, if nonmax is true, the non-maximal suppression is applied, checking all detected features and the features detected in its 8-neighborhood and discard it if its score is non maximal.
@@ -54,13 +56,25 @@ fast (Array fptr) thr (fromIntegral -> arc) (fromIntegral . fromEnum -> non) rat
          Features <$>
            newForeignPtr af_release_features feat
 
+-- | Harris corner detection
+--
+-- [ArrayFire Docs](http://arrayfire.org/docs/group__cv__func__harris.htm)
+--
+-- Harris corner detector.
+--
 harris
   :: Array a
+  -- ^ array containing a grayscale image (color images are not supported)
   -> Int
+  -- ^ maximum number of corners to keep, only retains those with highest Harris responses
   -> Float
+  -- ^ minimum response in order for a corner to be retained, only used if max_corners = 0
   -> Float
+  -- ^ the standard deviation of a circular window (its dimensions will be calculated according to the standard deviation), the covariation matrix will be calculated to a circular neighborhood of this standard deviation (only used when block_size == 0, must be >= 0.5f and <= 5.0f)
   -> Int
+  -- ^ square window size, the covariation matrix will be calculated to a square neighborhood of this size (must be >= 3 and <= 31)
   -> Float
+  -- ^ struct containing arrays for x and y coordinates and score (Harris response), while arrays orientation and size are set to 0 and 1, respectively, because Harris does not compute that information
   -> Features
 harris (Array fptr) (fromIntegral -> maxc) minresp sigma (fromIntegral -> bs) thr
   = unsafePerformIO . mask_ . withForeignPtr fptr $ \aptr ->
@@ -70,14 +84,27 @@ harris (Array fptr) (fromIntegral -> maxc) minresp sigma (fromIntegral -> bs) th
          Features <$>
            newForeignPtr af_release_features feat
 
+-- | ORB Feature descriptor
+--
+-- [ArrayFire Docs](http://arrayfire.org/docs/group__cv__func__orb.htm)
+--
+-- Extract ORB descriptors from FAST features that hold higher Harris responses. FAST does not compute orientation, thus, orientation of features is calculated using the intensity centroid. As FAST is also not multi-scale enabled, a multi-scale pyramid is calculated by downsampling the input image multiple times followed by FAST feature detection on each scale.
+-- 
 orb
   :: Array a
+  -- ^ 'Array' containing a grayscale image (color images are not supported)
   -> Float
+  -- ^ FAST threshold for which a pixel of the circle around the central pixel is considered to be brighter or darker
   -> Int
+  -- ^ maximum number of features to hold (will only keep the max_feat features with higher Harris responses)
   -> Float
+  -- ^ factor to downsample the input image, meaning that each level will hold prior level dimensions divided by scl_fctr
   -> Int
+  -- ^ number of levels to be computed for the image pyramid
   -> Bool
+  -- ^ blur image with a Gaussian filter with sigma=2 before computing descriptors to increase robustness against noise if true
   -> (Features, Array a)
+  -- ^ 'Features' struct composed of arrays for x and y coordinates, score, orientation and size of selected features
 orb (Array fptr) thr (fromIntegral -> feat) scl (fromIntegral -> levels) (fromIntegral . fromEnum -> blur)
   = unsafePerformIO . mask_ . withForeignPtr fptr $ \inptr ->
       do (feature, arr) <-
@@ -89,16 +116,32 @@ orb (Array fptr) thr (fromIntegral -> feat) scl (fromIntegral -> levels) (fromIn
          array <- Array <$> newForeignPtr af_release_array_finalizer arr
          pure (feats, array)
 
+-- | SIFT feature detector and descriptor extractor.
+--
+-- [ArrayFire Docs](http://arrayfire.org/docs/group__cv__func__sift.htm)
+--
+-- C Interface for SIFT feature detector and descriptor.
+-- 
 sift
   :: Array a
+  -- ^ Array containing a grayscale image (color images are not supported)
   -> Int
+  -- ^ number of layers per octave, the number of octaves is computed automatically according to the input image dimensions, the original SIFT paper suggests 3
   -> Float
+  -- ^ threshold used to filter out features that have low contrast, the original SIFT paper suggests 0.04
   -> Float
+  -- ^ threshold used to filter out features that are too edge-like, the original SIFT paper suggests 10.0
   -> Float
+  -- ^ the sigma value used to filter the input image at the first octave, the original SIFT paper suggests 1.6
   -> Bool
+  -- ^ if true, the input image dimensions will be doubled and the doubled image will be used for the first octave
   -> Float
+  -- ^ the inverse of the difference between the minimum and maximum grayscale intensity value, e.g.: if the ranges are 0-256, the proper intensity_scale value is 1/256, if the ranges are 0-1, the proper intensity-scale value is 1/1
   -> Float
+  -- ^ maximum ratio of features to detect, the maximum number of features is calculated by feature_ratio * in.elements(). The maximum number of features is not based on the score, instead, features detected after the limit is reached are discarded
   -> (Features, Array a)
+  -- ^ Features object composed of arrays for x and y coordinates, score, orientation and size of selected features
+  -- Nx128 array containing extracted descriptors, where N is the number of features found by SIFT
 sift (Array fptr) (fromIntegral -> a) b c d (fromIntegral . fromEnum -> e) f g
   = unsafePerformIO . mask_ . withForeignPtr fptr $ \inptr ->
       do (feat, arr) <-
@@ -110,16 +153,32 @@ sift (Array fptr) (fromIntegral -> a) b c d (fromIntegral . fromEnum -> e) f g
          array <- Array <$> newForeignPtr af_release_array_finalizer arr
          pure (feats, array)
 
+-- | SIFT feature detector and descriptor extractor.
+--
+-- [ArrayFire Docs](http://arrayfire.org/docs/group__cv__func__gloh.htm)
+--
+-- C Interface for SIFT feature detector and descriptor.
+-- 
 gloh
   :: Array a
+  -- ^ 'Array' containing a grayscale image (color images are not supported)
   -> Int
+  -- ^ number of layers per octave, the number of octaves is computed automatically according to the input image dimensions, the original SIFT paper suggests 3
   -> Float
+  -- ^ threshold used to filter out features that have low contrast, the original SIFT paper suggests 0.04
   -> Float
+  -- ^ threshold used to filter out features that are too edge-like, the original SIFT paper suggests 10.0
   -> Float
+  -- ^ the sigma value used to filter the input image at the first octave, the original SIFT paper suggests 1.6
   -> Bool
+  -- ^ if true, the input image dimensions will be doubled and the doubled image will be used for the first octave
   -> Float
+  -- ^ the inverse of the difference between the minimum and maximum grayscale intensity value, e.g.: if the ranges are 0-256, the proper intensity_scale value is 1/256, if the ranges are 0-1, the proper intensity-scale value is 1/1
   -> Float
+  -- ^ maximum ratio of features to detect, the maximum number of features is calculated by feature_ratio * in.elements(). The maximum number of features is not based on the score, instead, features detected after the limit is reached are discarded
   -> (Features, Array a)
+  -- ^ 'Features' object composed of arrays for x and y coordinates, score, orientation and size of selected features
+  -- ^ Nx272 array containing extracted GLOH descriptors, where N is the number of features found by SIFT
 gloh (Array fptr) (fromIntegral -> a) b c d (fromIntegral . fromEnum -> e) f g
   = unsafePerformIO . mask_ . withForeignPtr fptr $ \inptr ->
       do (feat, arr) <-
@@ -131,50 +190,74 @@ gloh (Array fptr) (fromIntegral -> a) b c d (fromIntegral . fromEnum -> e) f g
          array <- Array <$> newForeignPtr af_release_array_finalizer arr
          pure (feats, array)
 
+-- | Hamming Matcher
+--
+-- [ArrayFire Docs](http://arrayfire.org/docs/group__cv__func__hamming__matcher.htm)
+--
+-- Calculates Hamming distances between two 2-dimensional arrays containing features, one of the arrays containing the training data and the other the query data. One of the dimensions of the both arrays must be equal among them, identifying the length of each feature. The other dimension indicates the total number of features in each of the training and query arrays. Two 1-dimensional arrays are created as results, one containg the smallest N distances of the query array and another containing the indices of these distances in the training array. The resulting 1-dimensional arrays have length equal to the number of features contained in the query array.
+-- 
 hammingMatcher
  :: Array a
+ -- ^ is the 'Array' containing the data to be queried
  -> Array a
+ -- ^ is the 'Array' containing the data used as training data
  -> Int
+ -- ^ indicates the dimension to analyze for distance (the dimension indicated here must be of equal length for both query and train arrays)
  -> Int
+ -- ^ is the number of smallest distances to return (currently, only 1 is supported)
  -> (Array a, Array a)
+ -- ^ is an array of MxN size, where M is equal to the number of query features and N is equal to n_dist. The value at position IxJ indicates the index of the Jth smallest distance to the Ith query value in the train data array. the index of the Ith smallest distance of the Mth query.
+ -- is an array of MxN size, where M is equal to the number of query features and N is equal to n_dist. The value at position IxJ indicates the Hamming distance of the Jth smallest distance to the Ith query value in the train data array.
 hammingMatcher a b (fromIntegral -> x) (fromIntegral -> y)
   = op2p2 a b (\p c d e -> af_hamming_matcher p c d e x y)
 
+-- | Nearest Neighbor
+--
+-- [ArrayFire Docs](http://arrayfire.org/docs/group__cv__func__nearest__neighbour.htm)
+--
+-- Calculates nearest distances between two 2-dimensional arrays containing features based on the type of distance computation chosen. Currently, AF_SAD (sum of absolute differences), AF_SSD (sum of squared differences) and AF_SHD (hamming distance) are supported. One of the arrays containing the training data and the other the query data. One of the dimensions of the both arrays must be equal among them, identifying the length of each feature. The other dimension indicates the total number of features in each of the training and query arrays. Two 1-dimensional arrays are created as results, one containg the smallest N distances of the query array and another containing the indices of these distances in the training array. The resulting 1-dimensional arrays have length equal to the number of features contained in the query array.
+-- 
 nearestNeighbor
  :: Array a
+ -- ^ is the array containing the data to be queried
  -> Array a
+ -- ^ is the array containing the data used as training data
  -> Int
+ -- ^ indicates the dimension to analyze for distance (the dimension indicated here must be of equal length for both query and train arrays)
  -> Int
+ -- ^ is the number of smallest distances to return (currently, only values <= 256 are supported)
  -> MatchType
+ -- ^ is the distance computation type. Currently AF_SAD (sum of absolute differences), AF_SSD (sum of squared differences), and AF_SHD (hamming distances) are supported.
  -> (Array a, Array a)
+ -- ^ is an array of MxN size, where M is equal to the number of query features and N is equal to n_dist. The value at position IxJ indicates the index of the Jth smallest distance to the Ith query value in the train data array. the index of the Ith smallest distance of the Mth query.
+ -- is an array of MxN size, where M is equal to the number of query features and N is equal to n_dist. The value at position IxJ indicates the distance of the Jth smallest distance to the Ith query value in the train data array based on the dist_type chosen.
 nearestNeighbor a b (fromIntegral -> x) (fromIntegral -> y) (fromMatchType -> match)
   = op2p2 a b (\p c d e -> af_nearest_neighbour p c d e x y match)
 
+-- | Nearest Neighbor
+--
+-- [ArrayFire Docs](http://arrayfire.org/docs/group__cv__func__match__template.htm)
+--
+-- C Interface for image template matching.
+-- 
 matchTemplate
  :: Array a
+ -- ^ is an 'Array' with image data
  -> Array a
+ -- ^ is the template we are looking for in the image
  -> MatchType
+ -- ^ is metric that should be used to calculate the disparity between window in the image and the template image. It can be one of the values defined by the enum af_match_type
  -> Array a
+ -- ^ will have disparity values for the window starting at corresponding pixel position
 matchTemplate a b (fromMatchType -> match)
   = op2 a b (\p c d -> af_match_template p c d match)
 
 -- | SUSAN corner detector.
 --
--- SUSAN is an acronym standing for Smallest Univalue Segment Assimilating Nucleus. This method places a circular disc over the pixel to be tested (a.k.a nucleus) to compute the corner measure of that corresponding pixel. The region covered by the circular disc is M, and a pixel in this region is represented by m   M where m  0 is the nucleus. Every pixel in the region is compared to the nucleus using the following comparison function:
---
--- c(m  )=e ((I(m  ) I(m  0))/t)6
--- where t is radius of the region, I is the brightness of the pixel.
---
--- Response of SUSAN operator is given by the following equation:
+-- [ArrayFire Docs](http://arrayfire.org/docs/group__cv__func__susan.htm)
 --
--- R(M)={g n(M)if n(M)<g0otherwise,
--- where n(M)= m   Mc(m  ), g is named the geometric threshold and n is the number of pixels in the mask which are within t of the nucleus.
---
--- Importance of the parameters, t and g is explained below:
---
--- t determines how similar points have to be to the nucleusbefore they are considered to be a part of the univalue segment
+-- SUSAN is an acronym standing for Smallest Univalue Segment Assimilating Nucleus. This method places a circular disc over the pixel to be tested (a.k.a nucleus) to compute the corner measure of that corresponding pixel. The region covered by the circular disc is M, and a pixel in this region is represented by m   M where m  0 is the nucleus. Every pixel in the region is compared to the nucleus using the following comparison function:
 --
--- g determines the minimum size of the univalue segment. For a large enough g, SUSAN operator becomes an edge dectector.
 susan
   :: Array a
   -- ^ is input grayscale/intensity image
@@ -199,6 +282,8 @@ susan (Array fptr) (fromIntegral -> a) b c d (fromIntegral -> e)
 
 -- | Difference of Gaussians.
 --
+-- [ArrayFire Docs](http://arrayfire.org/docs/group__cv__func__dog.htm)
+--
 -- Given an image, this function computes two different versions of smoothed input image using the difference smoothing parameters and subtracts one from the other and returns the result.
 --
 dog
@@ -215,20 +300,10 @@ dog a (fromIntegral -> x) (fromIntegral -> y) =
 
 -- | Homography Estimation.
 --
--- Homography estimation find a perspective transform between two sets of 2D points.
--- Currently, two methods are supported for the estimation, RANSAC (RANdom SAmple Consensus)
--- and LMedS (Least Median of Squares). Both methods work by randomly selecting a subset of 4 points
--- of the set of source points, computing the eigenvectors of that set and finding the perspective transform.
--- The process is repeated several times, a maximum of times given by the value passed to the iterations arguments
--- for RANSAC (for the CPU backend, usually less than that, depending on the quality of the dataset,
--- but for CUDA and OpenCL backends the transformation will be computed exactly the amount of times passed via
--- the iterations parameter), the returned value is the one that matches the best number of inliers, which are
--- all of the points that fall within a maximum L2 distance from the value passed to the inlier_thr argument.
--- For the LMedS case, the number of iterations is currently hardcoded to meet the following equation:
+-- [ArrayFire Docs](http://arrayfire.org/docs/group__cv__func__homography.htm)
 --
--- m=log(1−P)log[1−(1−ϵ)p],
+-- Homography estimation find a perspective transform between two sets of 2D points.
 --
--- where P=0.99, ϵ=40% and p=4.
 homography
  :: forall a . AFType a
  => Array a
@@ -250,6 +325,8 @@ homography
  -> Int
   -- ^ maximum number of iterations when htype is AF_HOMOGRAPHY_RANSAC and backend is CPU, if backend is CUDA or OpenCL, iterations is the total number of iterations, an iteration is a selection of 4 random points for which the homography is estimated and evaluated for number of inliers.
  -> (Int, Array a)
+ -- ^ is a 3x3 array containing the estimated homography.
+ -- is the number of inliers that the homography was estimated to comprise, in the case that htype is AF_HOMOGRAPHY_RANSAC, a higher inlier_thr value will increase the estimated inliers. Note that if the number of inliers is too low, it is likely that a bad homography will be returned.
 homography
   (Array a)
   (Array b)
