learning opencv 3

Basic Data Structure of opencv Cvpoint: the point in the image. Cvpoint2d32f: point in two-dimensional space Cvpoint3d32f: Point in 3D space These are struct and are not classes in the C ++ language. Therefore, their constructor is a simple inline function. 1 typedef struct CvPoint 2 { 3 int x; 4 int y; 5 } 6 CvPoint; 7 8 9 CV_INLINE CvPoint cvPoint( int x, int y )10 {11 CvPoint p;12 13

Author: gnuhpcSource: http://www.cnblogs.com/gnuhpc/ # Include "CV. H "# include" cxcore. H "# include" highgui. H "# include int main (INT argc, char ** argv) {cvpoint center; // defines a two-dimensional coordinate point double scale =-3; int I, J; iplimage * image = argc = 2? Cvloadimage (argv [1], cv_load_image_color): 0; // open an image if (! Image) Return-1; center = cvpoint (image-> width/2, image-> height/2); // construct the point of the Tw

Machine Vision Learning Notes (8)--Bouguet stereo correction based on OPENCV In Machine Vision Learning notes (7)--based on OPENCV's binocular camera calibration, we have calculated the Matrix R and T, which describes the relationship of two {camera} coordinate systems, and the stereo correction is mainly the function of these two parameters. Binocular camera

, and so on of the tracked object. In many cases, non-linear and non-Gaussian models of object motion and similarity can obtain an unmanageable filtering distribution. Particle Filter overcomes this difficulty by re-representing this distribution as a group of weighted values, or by calling it a particle. Each particle represents a possible system status instance. In other words, each particle describes a possible location of the tracked object. A particle set contains the most likely location o

It is said that in the later version of opencv, the condensation Algorithm for CV of particle filtering has been removed. The previously learned condensation algorithm cannot be developed in C ++ or only in C, (a previous article about particle filter used to achieve mouse tracking ). To use particle filter tracking, we can use http://web.engr.oregonstate.edu /~ The particle filter code provided by Hess/particle filter has many variants. Rob Hess shou

We all say that GPU Cuda is very Nb-hard, so the next step is to run the program through GPU acceleration. This week, we have been configuring the Cuda environment of opencv. Today we finally ended up failing because the graphics card of the lab machine does not support Cuda... Sorry, a week !!! Cuda-supported GPU: http://developer.nvidia.com/cuda-gpus Although the failure eventually failed, there are still some gains. Now I will sum up it to rem

Document directory 1) load the cascade classifier 2) read Video Streams 3) use this classifier for each frame 4) display the target The target detection method supported by opencv is the classifier training based on the Haar feature of the sample to obtain the cascade boosted classification ). Note: In addition to haar features, the new C ++ interface can also use the HSV features. First, we will in

following four values:Gcd_bgd (= 0), background;Gcd_fgd (= 1), foreground;Gcd_pr_bgd (= 2), possible background;Gcd_pr_fgd (= 3), possible prospects.If gcd_bgd or gcd_fgd is not manually marked, only gcd_pr_bgd or gcd_pr_fgd are displayed;Rect: used to limit the image range to be split. Only the image part in the rectangle window is processed;Bgdmodel-background model. If it is null, the function automatically creates a bgdmodel. bgdmodel must be a s

computationally large, because each text to be classified is calculated from its distance to all known samples in order to obtain its K nearest neighbor points. At present, the common solution is to pre-edit the known sample points in advance to remove the small sample of the role of classification. In addition, there is a reverse KNN method, which can reduce the computational complexity of KNN algorithm and improve the efficiency of classification.This algorithm is suitable for the automatic c

In the last lecture on opencv Study Notes (), we compared the speed of reading and writing mat data among ordinary young people, young artists, and violent young people, I thought it was my own method, and I felt that it was not systematic enough. I sorted out my ideas this time, referred to the document, and summarized the methods that I could think of. I hope it would be helpful to everyone.1. Access a single pixel value The most common method is i

// Adapted by the example of leanring opencv by mikewolf // 2008-11-26 # include "stdafx. H "# include Cvhistogram * Hist, * hist1; {int hist_size [] = {h_bins, s_bins, v_bins}; float h_ranges [] = {0,180 }; // hue is [0,180] float s_ranges [] = {0,255}; float v_ranges [] = {0,255}; float * ranges [] = {h_ranges, s_ranges, v_ranges }; hist = cvcreatehist (3, hist_size, cv_hist_array, ranges, 1); hist1 = cv

Example: Detection of contours according to the slider parameters, re-detection when the sliding bar changes Effect Diagram: /** our Example 8-2 are drawn from the OpenCV package. Here we create a window with an image in it. A trackbar sets a simple threshold, and the contours in the thresholded im-age is drawn. The image is updated whenever the trackbar is adjusted. Example 8-2. Finding contours based on a trackbar ' s location;

author: gnuhpc Source: http://www.cnblogs.com/gnuhpc/ # Include "highgui. H "# include" CV. H "# include In opencv, the defined macros are generally used to take elements: The Macro of the element with the mat structure is cv_mat_elem, which is the most convenient to obtain the 2D matrix. The macro of the iplimage element is cv_image_elem, for example, the gray value of an image (x, y) point: Uchar * PTR = cv_iamge_elem (image, uchar, I, j *

3. Opencv 1.0 has a problem with storing video files, and 2.0 has solved this problem.

which the filter times 4 is interpolated. So the output image is 4 times times the size of the input image. (Hunnish: The principle is not clear, yet to be explored) The implementation example is entitled: #include "StdAfx.h" #include "highgui.h" #include "cv.h" //superdont //blog.csdn.net/superdont int main (int argc,char * * argv) { Iplimage * src = cvloadimage ("guagua.bmp"); Iplimage * result1 = Cvcreateimage ( cvsize (SRC->WIDTH/2, SRC->HEIGHT/2), src->depth, src

Http://www.cnblogs.com/tiandsp/archive/2013/04/20/3032860.html Three kinds of common edge detection operators. #include "cv.h" #include "highgui.h" using namespace CV; int main (int argc, char* argv[]) { Mat src = imread ("misaka.jpg"); Mat DST; Input image //output image //input Image Color channel number //x direction order number //y Direction order Sobel (Src,dst,src.depth (), n); Imwrite ("Sobel.jpg", DST);

); Cvnamedwindow ("threshold", 1); Cvshowimage ("threshold", threshold);Morphological Changes Iplconvkernel * kernal;iplimage * morph = cvcreateimage (cvgetsize (Threshold), threshold->depth, 1);//Custom 1x3 The nucleus of the X-directional swelling corrosion kernal = Cvcreatestructuringelementex (3, 1, 1, 0, cv_shape_rect); Cvdilate (threshold, Morph, kernal, 2); //x Expansion Unicom Digital Cverode (morph, Morph, kernal, 4); x corrosion

Sobel Edge Detection (derivative operation)Main function Sobel ()1#include 2#include 3#include string>4 5 using namespacestd;6 using namespaceCV;7 8 voidShowimg (Const stringwin_name,ConstMat img)9 {Ten Namedwindow (win_name,cv_window_autosize); One imshow (win_name,img); A } - - intMainintargcChar**argv) the { - if((argc-2)!=0) - return-1; -Mat Src=imread (argv[1]); + if(Src.empty ()) - return-2; +Gaussianblur (Src,src,size (

Start today with the official Image Processing section.Image filtering:This paper mainly introduces the usage of four kinds of filter functions.Example code:1#include 2#include 3#include string>4 5 #pragmaComment (linker, "/subsystem:\" windows\ "/entry:\" Maincrtstartup\ "")6 7 intMax_kernel_length = to;8 9 using namespacestd;Ten using namespaceCV; One A voidShow (std::stringname,mat img) - { - Namedwindow (name, cv_window_autosize); the imshow (Nam

elements used to add straight squares can be extracted from the same location as the corresponding input image.4,Cvapi (cvhistogram*) cvcreatehist (int dims, int* sizes, int type, float** Ranges Cv_default (NULL), int uniform cv_default (1));The 3rd function shows that we want to calculate the histogram, we must have a histogram pointer, then we have to first use Cvcreathist () to create a histogram pointer;The first parameter: