Image processing

Collaboration diagram for Image processing:

Modules
	Image Filtering
	Functions and classes described in this section are used to perform various linear or non-linear filtering operations on 2D images (represented as Mat's).
	Geometric Image Transformations
	The functions in this section perform various geometrical transformations of 2D images.
	Miscellaneous Image Transformations
	Drawing Functions
	Drawing functions work with matrices/images of arbitrary depth.
	ColorMaps in OpenCV
	The human perception isn't built for observing fine changes in grayscale images.
	Planar Subdivision
	The Subdiv2D class described in this section is used to perform various planar subdivision on a set of 2D points (represented as vector of Point2f).
	Histograms
	Structural Analysis and Shape Descriptors
	Motion Analysis and Object Tracking
	Feature Detection
	Object Detection
	C API
	Hardware Acceleration Layer

Classes
class	cv::CLAHE
class	cv::GeneralizedHough
	finds arbitrary template in the grayscale image using Generalized Hough Transform More...
class	cv::GeneralizedHoughBallard
	Ballard, D.H. More...
class	cv::GeneralizedHoughGuil
	Guil, N., González-Linares, J.M. More...

Enumerations
enum	cv::HoughModes {   cv::HOUGH_STANDARD = 0,   cv::HOUGH_PROBABILISTIC = 1,   cv::HOUGH_MULTI_SCALE = 2,   cv::HOUGH_GRADIENT = 3 }
	Variants of a Hough transform. More...

Functions
void	cv::blendLinear (InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst)
	Performs linear blending of two images. More...
Ptr< CLAHE >	cv::createCLAHE (double clipLimit=40.0, Size tileGridSize=Size(8, 8))
Ptr< GeneralizedHoughBallard >	cv::createGeneralizedHoughBallard ()
	Ballard, D.H. More...
Ptr< GeneralizedHoughGuil >	cv::createGeneralizedHoughGuil ()
	Guil, N., González-Linares, J.M. More...
void	cv::demosaicing (InputArray _src, OutputArray _dst, int code, int dcn=0)

Detailed Description

Enumeration Type Documentation

enum cv::HoughModes

#include <imgproc/include/opencv2/imgproc.hpp>

Variants of a Hough transform.

Enumerator
HOUGH_STANDARD	classical or standard Hough transform. Every line is represented by two floating-point numbers \((\rho, \theta)\) , where \(\rho\) is a distance between (0,0) point and the line, and \(\theta\) is the angle between x-axis and the normal to the line. Thus, the matrix must be (the created sequence will be) of CV_32FC2 type
HOUGH_PROBABILISTIC	probabilistic Hough transform (more efficient in case if the picture contains a few long linear segments). It returns line segments rather than the whole line. Each segment is represented by starting and ending points, and the matrix must be (the created sequence will be) of the CV_32SC4 type.
HOUGH_MULTI_SCALE	multi-scale variant of the classical Hough transform. The lines are encoded the same way as HOUGH_STANDARD.
HOUGH_GRADIENT	basically 21HT, described in [93]

Function Documentation

void cv::blendLinear	(	InputArray	src1,
		InputArray	src2,
		InputArray	weights1,
		InputArray	weights2,
		OutputArray	dst
	)

#include <imgproc/include/opencv2/imgproc.hpp>

Performs linear blending of two images.

Ptr<CLAHE> cv::createCLAHE	(	double	clipLimit = 40.0,
		Size	tileGridSize = Size(8, 8)
	)

#include <imgproc/include/opencv2/imgproc.hpp>

Ptr<GeneralizedHoughBallard> cv::createGeneralizedHoughBallard

(

)

#include <imgproc/include/opencv2/imgproc.hpp>

Ballard, D.H.

(1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122. Detects position only without traslation and rotation

Ptr<GeneralizedHoughGuil> cv::createGeneralizedHoughGuil

(

)

#include <imgproc/include/opencv2/imgproc.hpp>

Guil, N., González-Linares, J.M.

and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038. Detects position, traslation and rotation

void cv::demosaicing	(	InputArray	_src,
		OutputArray	_dst,
		int	code,
		int	dcn = 0
	)

#include <imgproc/include/opencv2/imgproc.hpp>

Referenced by cv::morphologyDefaultBorderValue().

Here is the caller graph for this function: