Module-wrapper for FastCV hardware accelerated functions

Detailed Description

Classes
struct	cv::fastcv::ContourData
	Structure containing additional information about found contour. More...

Functions
void	cv::fastcv::bilateralFilter (InputArray _src, OutputArray _dst, int d, float sigmaColor, float sigmaSpace, int borderType=BORDER_DEFAULT)
	Applies Bilateral filter to an image considering d-pixel diameter of each pixel's neighborhood. This filter does not work inplace.
void	cv::fastcv::bilateralRecursive (cv::InputArray src, cv::OutputArray dst, float sigmaColor=0.03f, float sigmaSpace=0.1f)
	Recursive Bilateral Filtering.
void	cv::fastcv::clusterEuclidean (InputArray points, InputArray clusterCenters, OutputArray newClusterCenters, OutputArray clusterSizes, OutputArray clusterBindings, OutputArray clusterSumDists, int numPointsUsed=-1)
	Clusterizes N input points in D-dimensional space into K clusters.
void	cv::fastcv::FAST10 (InputArray src, InputArray mask, OutputArray coords, OutputArray scores, int barrier, int border, bool nmsEnabled)
	Extracts FAST corners and scores from the image based on the mask. The mask specifies pixels to be ignored by the detector.
void	cv::fastcv::FFT (InputArray src, OutputArray dst)
	Computes the 1D or 2D Fast Fourier Transform of a real valued matrix. For the 2D case, the width and height of the input and output matrix must be powers of 2. For the 1D case, the height of the matrices must be 1, while the width must be a power of 2.
void	cv::fastcv::fillConvexPoly (InputOutputArray img, InputArray pts, Scalar color)
	Draw convex polygon This function fills the interior of a convex polygon with the specified color.
void	cv::fastcv::houghLines (InputArray src, OutputArray lines, double threshold=0.25)
	Performs Hough Line detection.
void	cv::fastcv::IFFT (InputArray src, OutputArray dst)
	Computes the 1D or 2D Inverse Fast Fourier Transform of a complex valued matrix. For the 2D case, The width and height of the input and output matrix must be powers of 2. For the 1D case, the height of the matrices must be 1, while the width must be a power of 2.
void	cv::fastcv::matmuls8s32 (InputArray src1, InputArray src2, OutputArray dst)
	Matrix multiplication of two int8_t type matrices.
int	cv::fastcv::meanShift (InputArray src, Rect &rect, TermCriteria termCrit)
	Applies the meanshift procedure and obtains the final converged position. This function applies the meanshift procedure to an original image (usually a probability image) and obtains the final converged position. The converged position search will stop either it has reached the required accuracy or the maximum number of iterations.
cv::Moments	cv::fastcv::moments (InputArray _src, bool binary)
	Calculates all of the moments up to the third order of the image pixels' intensities The results are returned in the structure cv::Moments.
void	cv::fastcv::MSER (InputArray src, std::vector< std::vector< Point > > &contours, std::vector< cv::Rect > &boundingBoxes, std::vector< ContourData > &contourData, unsigned int numNeighbors=4, unsigned int delta=2, unsigned int minArea=30, unsigned int maxArea=14400, float maxVariation=0.15f, float minDiversity=0.2f)
	Runs MSER blob detector on the grayscale image.
void	cv::fastcv::MSER (InputArray src, std::vector< std::vector< Point > > &contours, std::vector< cv::Rect > &boundingBoxes, unsigned int numNeighbors=4, unsigned int delta=2, unsigned int minArea=30, unsigned int maxArea=14400, float maxVariation=0.15f, float minDiversity=0.2f)
	This is an overload for MSER() function.
void	cv::fastcv::MSER (InputArray src, std::vector< std::vector< Point > > &contours, unsigned int numNeighbors=4, unsigned int delta=2, unsigned int minArea=30, unsigned int maxArea=14400, float maxVariation=0.15f, float minDiversity=0.2f)
	This is an overload for MSER() function.
void	cv::fastcv::remap (InputArray src, OutputArray dst, InputArray map1, InputArray map2, int interpolation, int borderValue=0)
	Applies a generic geometrical transformation to a greyscale CV_8UC1 image.
void	cv::fastcv::remapRGBA (InputArray src, OutputArray dst, InputArray map1, InputArray map2, int interpolation)
	Applies a generic geometrical transformation to a 4-channel CV_8UC4 image with bilinear or nearest neighbor interpolation.
void	cv::fastcv::resizeDownBy2 (cv::InputArray _src, cv::OutputArray _dst)
	Down-scale the image by averaging each 2x2 pixel block.
void	cv::fastcv::resizeDownBy4 (cv::InputArray _src, cv::OutputArray _dst)
	Down-scale the image by averaging each 4x4 pixel block.
void	cv::fastcv::thresholdRange (InputArray src, OutputArray dst, uint8_t lowThresh, uint8_t highThresh, uint8_t trueValue, uint8_t falseValue)
	Binarizes a grayscale image based on a pair of threshold values. The binarized image will be in the two values selected by user.

Function Documentation

bilateralFilter()

void cv::fastcv::bilateralFilter	(	InputArray	_src,
		OutputArray	_dst,
		int	d,
		float	sigmaColor,
		float	sigmaSpace,
		int	borderType = BORDER_DEFAULT )

#include <opencv2/fastcv/bilateralFilter.hpp>

Applies Bilateral filter to an image considering d-pixel diameter of each pixel's neighborhood. This filter does not work inplace.

Parameters

_src	Intput image with type CV_8UC1
_dst	Destination image with same type as _src
d	kernel size (can be 5, 7 or 9)
sigmaColor	Filter sigma in the color space. Typical value is 50.0f. Increasing this value means increasing the influence of the neighboring pixels of more different color to the smoothing result.
sigmaSpace	Filter sigma in the coordinate space. Typical value is 1.0f. Increasing this value means increasing the influence of farther neighboring pixels within the kernel size distance to the smoothing result.
borderType	border mode used to extrapolate pixels outside of the image

bilateralRecursive()

void cv::fastcv::bilateralRecursive	(	cv::InputArray	src,
		cv::OutputArray	dst,
		float	sigmaColor = 0.03f,
		float	sigmaSpace = 0.1f )

#include <opencv2/fastcv/smooth.hpp>

Recursive Bilateral Filtering.

Different from traditional bilateral filtering, here the smoothing is actually performed in gradient domain. The algorithm claims that it's more efficient than the original bilateral filtering in both image quality and computation. See algorithm description in the paper Recursive Bilateral Filtering, ECCV2012 by Prof Yang Qingxiong

Parameters

src	Input image, should have one CV_8U channel
dst	Output array having one CV_8U channel
sigmaColor	Sigma in the color space, the bigger the value the more color difference is smoothed by the algorithm
sigmaSpace	Sigma in the coordinate space, the bigger the value the more distant pixels are smoothed

clusterEuclidean()

void cv::fastcv::clusterEuclidean	(	InputArray	points,
		InputArray	clusterCenters,
		OutputArray	newClusterCenters,
		OutputArray	clusterSizes,
		OutputArray	clusterBindings,
		OutputArray	clusterSumDists,
		int	numPointsUsed = -1 )

#include <opencv2/fastcv/cluster.hpp>

Clusterizes N input points in D-dimensional space into K clusters.

Parameters

points	Points array of type 8u, each row represets a point. Size is N rows by D columns, can be non-continuous.
clusterCenters	Initial cluster centers array of type 32f, each row represents a center. Size is K rows by D columns, can be non-continuous.
newClusterCenters	Resulting cluster centers array of type 32f, each row represents found center. Size is set to be K rows by D columns.
clusterSizes	Resulting cluster member counts array of type uint32, size is set to be 1 row by K columns.
clusterBindings	Resulting points indices array of type uint32, each index tells to which cluster the corresponding point belongs to. Size is set to be 1 row by numPointsUsed columns.
clusterSumDists	Resulting distance sums array of type 32f, each number is a sum of distances between each cluster center to its belonging points. Size is set to be 1 row by K columns
numPointsUsed	Number of points to clusterize starting from 0 to numPointsUsed-1 inclusively. Sets to N if negative.

FAST10()

void cv::fastcv::FAST10	(	InputArray	src,
		InputArray	mask,
		OutputArray	coords,
		OutputArray	scores,
		int	barrier,
		int	border,
		bool	nmsEnabled )

#include <opencv2/fastcv/fast10.hpp>

Extracts FAST corners and scores from the image based on the mask. The mask specifies pixels to be ignored by the detector.

Parameters

src	8-bit grayscale image
mask	Optional mask indicating which pixels should be omited from corner dection. Its size should be k times image width and height, where k = 1/2, 1/4 , 1/8 , 1, 2, 4 and 8 For more details see documentation to fcvCornerFast9InMaskScoreu8 function in FastCV
coords	Output array of CV_32S containing interleave x, y positions of detected corners
scores	Optional output array containing the scores of the detected corners. The score is the highest threshold that can still validate the detected corner. A higher score value indicates a stronger corner feature. For example, a corner of score 108 is stronger than a corner of score 50
barrier	FAST threshold. The threshold is used to compare difference between intensity value of the central pixel and pixels on a circle surrounding this pixel
border	Number for pixels to ignore from top,bottom,right,left of the image. Defaults to 4 if it's below 4
nmsEnabled	Enable non-maximum suppresion to prune weak key points

FFT()

void cv::fastcv::FFT	(	InputArray	src,
		OutputArray	dst )

#include <opencv2/fastcv/fft.hpp>

Computes the 1D or 2D Fast Fourier Transform of a real valued matrix. For the 2D case, the width and height of the input and output matrix must be powers of 2. For the 1D case, the height of the matrices must be 1, while the width must be a power of 2.

Parameters

src	Input array of CV_8UC1. The dimensions of the matrix must be powers of 2 for the 2D case, and in the 1D case, the height must be 1, while the width must be a power of 2.
dst	The computed FFT matrix of type CV_32FC2. The FFT Re and Im coefficients are stored in different channels. Hence the dimensions of the dst are (srcWidth, srcHeight)

fillConvexPoly()

void cv::fastcv::fillConvexPoly	(	InputOutputArray	img,
		InputArray	pts,
		Scalar	color )

#include <opencv2/fastcv/draw.hpp>

Draw convex polygon This function fills the interior of a convex polygon with the specified color.

Parameters

img	Image to draw on. Should have up to 4 8-bit channels
pts	Array of polygon points coordinates. Should contain N two-channel or 2*N one-channel 32-bit integer elements
color	Color of drawn polygon stored as B,G,R and A(if supported)

houghLines()

void cv::fastcv::houghLines	(	InputArray	src,
		OutputArray	lines,
		double	threshold = 0.25 )

#include <opencv2/fastcv/hough.hpp>

Performs Hough Line detection.

Parameters

src	Input 8-bit image containing binary contour. Width and step should be divisible by 8
lines	Output array containing detected lines in a form of (x1, y1, x2, y2) where all numbers are 32-bit floats
threshold	Controls the minimal length of a detected line. Value must be between 0.0 and 1.0 Values close to 1.0 reduces the number of detected lines. Values close to 0.0 detect more lines, but may be noisy. Recommended value is 0.25.

IFFT()

void cv::fastcv::IFFT	(	InputArray	src,
		OutputArray	dst )

#include <opencv2/fastcv/fft.hpp>

Computes the 1D or 2D Inverse Fast Fourier Transform of a complex valued matrix. For the 2D case, The width and height of the input and output matrix must be powers of 2. For the 1D case, the height of the matrices must be 1, while the width must be a power of 2.

Parameters

src	Input array of type CV_32FC2 containing FFT Re and Im coefficients stored in separate channels. The dimensions of the matrix must be powers of 2 for the 2D case, and in the 1D case, the height must be 1, while the width must be a power of 2.
dst	The computed IFFT matrix of type CV_8U. The matrix is real valued and has no imaginary components. Hence the dimensions of the dst are (srcWidth , srcHeight)

matmuls8s32()

void cv::fastcv::matmuls8s32	(	InputArray	src1,
		InputArray	src2,
		OutputArray	dst )

#include <opencv2/fastcv/arithm.hpp>

Matrix multiplication of two int8_t type matrices.

Parameters

src1	First source matrix of type CV_8S
src2	Second source matrix of type CV_8S
dst	Resulting matrix of type CV_32S

meanShift()

int cv::fastcv::meanShift	(	InputArray	src,
		Rect &	rect,
		TermCriteria	termCrit )

#include <opencv2/fastcv/shift.hpp>

Applies the meanshift procedure and obtains the final converged position. This function applies the meanshift procedure to an original image (usually a probability image) and obtains the final converged position. The converged position search will stop either it has reached the required accuracy or the maximum number of iterations.

Parameters

src	8-bit grayscale image which is usually a probability image computed based on object histogram
rect	Initial search window position which also returns the final converged window position
termCrit	The criteria used to finish the MeanShift which consists of two termination criteria: 1) epsilon: required accuracy; 2) max_iter: maximum number of iterations

Returns

Iteration number at which the loop stopped

moments()

cv::Moments cv::fastcv::moments	(	InputArray	_src,
		bool	binary )

#include <opencv2/fastcv/moments.hpp>

Calculates all of the moments up to the third order of the image pixels' intensities The results are returned in the structure cv::Moments.

Parameters

_src	Input image with type CV_8UC1, CV_32SC1, CV_32FC1
binary	If 1, binary image (0x00-black, oxff-white); if 0, grayscale image

MSER() [1/3]

void cv::fastcv::MSER	(	InputArray	src,
		std::vector< std::vector< Point > > &	contours,
		std::vector< cv::Rect > &	boundingBoxes,
		std::vector< ContourData > &	contourData,
		unsigned int	numNeighbors = 4,
		unsigned int	delta = 2,
		unsigned int	minArea = 30,
		unsigned int	maxArea = 14400,
		float	maxVariation = 0.15f,
		float	minDiversity = 0.2f )

#include <opencv2/fastcv/mser.hpp>

Runs MSER blob detector on the grayscale image.

Parameters

src	Source image of type CV_8UC1. Image width has to be greater than 50, and image height has to be greater than 5. Pixels at the image boundary are not processed. If boundary pixels are important for a particular application, please consider padding the input image with dummy pixels of one pixel wide.
contours	Array containing found contours
boundingBoxes	Array containing bounding boxes of found contours
contourData	Array containing additional information about found contours
numNeighbors	Number of neighbors in contours, can be 4 or 8
delta	Delta to be used in MSER algorithm (the difference in grayscale values within which the region is stable ). Typical value range [0.8 8], typical value 2
minArea	Minimum area (number of pixels) of a mser contour. Typical value range [10 50], typical value 30
maxArea	Maximum area (number of pixels) of a mser contour. Typical value 14400 or 0.25*width*height
maxVariation	Maximum variation in grayscale between 2 levels allowed. Typical value range [0.1 1.0], typical value 0.15
minDiversity	Minimum diversity in grayscale between 2 levels allowed. Typical value range [0.1 1.0], typical value 0.2

MSER() [2/3]

void cv::fastcv::MSER	(	InputArray	src,
		std::vector< std::vector< Point > > &	contours,
		std::vector< cv::Rect > &	boundingBoxes,
		unsigned int	numNeighbors = 4,
		unsigned int	delta = 2,
		unsigned int	minArea = 30,
		unsigned int	maxArea = 14400,
		float	maxVariation = 0.15f,
		float	minDiversity = 0.2f )

#include <opencv2/fastcv/mser.hpp>

This is an overload for MSER() function.

Parameters

src	Source image of type CV_8UC1. Image width has to be greater than 50, and image height has to be greater than 5. Pixels at the image boundary are not processed. If boundary pixels are important for a particular application, please consider padding the input image with dummy pixels of one pixel wide.
contours	Array containing found contours
boundingBoxes	Array containing bounding boxes of found contours
numNeighbors	Number of neighbors in contours, can be 4 or 8
delta	Delta to be used in MSER algorithm (the difference in grayscale values within which the region is stable ). Typical value range [0.8 8], typical value 2
minArea	Minimum area (number of pixels) of a mser contour. Typical value range [10 50], typical value 30
maxArea	Maximum area (number of pixels) of a mser contour. Typical value 14400 or 0.25*width*height
maxVariation	Maximum variation in grayscale between 2 levels allowed. Typical value range [0.1 1.0], typical value 0.15
minDiversity	Minimum diversity in grayscale between 2 levels allowed. Typical value range [0.1 1.0], typical value 0.2

MSER() [3/3]

void cv::fastcv::MSER	(	InputArray	src,
		std::vector< std::vector< Point > > &	contours,
		unsigned int	numNeighbors = 4,
		unsigned int	delta = 2,
		unsigned int	minArea = 30,
		unsigned int	maxArea = 14400,
		float	maxVariation = 0.15f,
		float	minDiversity = 0.2f )

#include <opencv2/fastcv/mser.hpp>

This is an overload for MSER() function.

Parameters

src	Source image of type CV_8UC1. Image width has to be greater than 50, and image height has to be greater than 5. Pixels at the image boundary are not processed. If boundary pixels are important for a particular application, please consider padding the input image with dummy pixels of one pixel wide.
contours	Array containing found contours
numNeighbors	Number of neighbors in contours, can be 4 or 8
delta	Delta to be used in MSER algorithm (the difference in grayscale values within which the region is stable ). Typical value range [0.8 8], typical value 2
minArea	Minimum area (number of pixels) of a mser contour. Typical value range [10 50], typical value 30
maxArea	Maximum area (number of pixels) of a mser contour. Typical value 14400 or 0.25*width*height
maxVariation	Maximum variation in grayscale between 2 levels allowed. Typical value range [0.1 1.0], typical value 0.15
minDiversity	Minimum diversity in grayscale between 2 levels allowed. Typical value range [0.1 1.0], typical value 0.2

remap()

void cv::fastcv::remap	(	InputArray	src,
		OutputArray	dst,
		InputArray	map1,
		InputArray	map2,
		int	interpolation,
		int	borderValue = 0 )

#include <opencv2/fastcv/remap.hpp>

Applies a generic geometrical transformation to a greyscale CV_8UC1 image.

Parameters

src	The first input image data, type CV_8UC1
dst	The output image data, type CV_8UC1
map1	Floating-point CV_32FC1 matrix with each element as the column coordinate of the mapped location in the source image
map2	Floating-point CV_32FC1 matrix with each element as the row coordinate of the mapped location in the source image.
interpolation	Only INTER_NEAREST and INTER_LINEAR interpolation is supported
borderValue	constant pixel value

remapRGBA()

void cv::fastcv::remapRGBA	(	InputArray	src,
		OutputArray	dst,
		InputArray	map1,
		InputArray	map2,
		int	interpolation )

#include <opencv2/fastcv/remap.hpp>

Applies a generic geometrical transformation to a 4-channel CV_8UC4 image with bilinear or nearest neighbor interpolation.

Parameters

src	The first input image data, type CV_8UC4
dst	The output image data, type CV_8UC4
map1	Floating-point CV_32FC1 matrix with each element as the column coordinate of the mapped location in the source image
map2	Floating-point CV_32FC1 matrix with each element as the row coordinate of the mapped location in the source image.
interpolation	Only INTER_NEAREST and INTER_LINEAR interpolation is supported

resizeDownBy2()

void cv::fastcv::resizeDownBy2	(	cv::InputArray	_src,
		cv::OutputArray	_dst )

#include <opencv2/fastcv/scale.hpp>

Down-scale the image by averaging each 2x2 pixel block.

Parameters

_src	The first input image data, type CV_8UC1, src height must be a multiple of 2
_dst	The output image data, type CV_8UC1

resizeDownBy4()

void cv::fastcv::resizeDownBy4	(	cv::InputArray	_src,
		cv::OutputArray	_dst )

#include <opencv2/fastcv/scale.hpp>

Down-scale the image by averaging each 4x4 pixel block.

Parameters

_src	The first input image data, type CV_8UC1, src height must be a multiple of 4
_dst	The output image data, type CV_8UC1

thresholdRange()

void cv::fastcv::thresholdRange	(	InputArray	src,
		OutputArray	dst,
		uint8_t	lowThresh,
		uint8_t	highThresh,
		uint8_t	trueValue,
		uint8_t	falseValue )

#include <opencv2/fastcv/thresh.hpp>

Binarizes a grayscale image based on a pair of threshold values. The binarized image will be in the two values selected by user.

Parameters

src	8-bit grayscale image
dst	Output image of the same size and type as input image, can be the same as input image
lowThresh	The lower threshold value for binarization
highThresh	The higher threshold value for binarization
trueValue	The value assigned to the destination pixel if the source is within the range inclusively defined by the pair of threshold values
falseValue	The value assigned to the destination pixel if the source is out of the range inclusively defined by the pair of threshold values