OpenCV 系列 1
设置鼠标callback setMouseCallback()
//cpp调用c interface
void cv::setMouseCallback( const String& windowName, MouseCallback onMouse, void* param)
{
CV_TRACE_FUNCTION();
cvSetMouseCallback(windowName.c_str(), onMouse, param);
}
void On_MouseHandle()
{
//处理 鼠标按下事件
//处理 鼠标松开事件
}
mat 操作
Mat A,C;// 仅仅创建信息头部分
A= imread("1.jpg",CV_LOAD_IMAGE_COLOR);//这里为矩阵开辟内存
Mat B(A);//拷贝构造
C=A; //赋值构造
Mat D(A,Rect(10,10,100,100));//使用矩形界定
Mat E=A(Range:all(),Range(1,3));//用行和列 来界定
Mat F = A.clone();
Mat G;
A.copyTo(G);
方法1： 使用Mat()构造函数
Mat M( 2, 2, CV_8UC3, Scalar(0,0,255));
cout << "M=" <<endl; <<" " << M<<endl <<endl;
CV_8UC3:
- 8 位
- UC 带符号
- C 通道数量(channel)
方法2：在C/C++ 中通过构造函数进行初始化
int sz[3] ={2,2,2};
Mat L(3,sz,CV_8UC, Scalar::all(0));
创建一个超过2维的矩阵，
指定维度数，然后传递一个指向一个数组的指针，这个数组包含每个维度的尺寸。
方法3： 为已存在的IplImage指针创建信息头
IplImage* img = cvLoadImage("1.jpg", 1);
Mat mtx(img); // IplImage* 转换为Mat
方法4： Create()
M.create( 4,4, CV_8UC(2));
//M.create(rows, cols, CV_MAKETYPE(depth, 3));
cout << "M="<< endl<<" "<<M<<endl<<endl;
方法5： 采用Matlab式的初始化方式
zeros(), ones(),eyes()
使用以下方式指定储存和数据类型
Mat E =Mat::eye(4,4,CV_64F);
cout<<"E= "<<endl<<" "<<E<<endl<<endl;
Mat O = Mat::ones(2,2,CV_32F);
cout<<"O= "<<endl<<" "<<O<<endl<<endl;
Mat Z = Mat::zeros(3,3,CV_8UC1);
cout<<"Z= "<<endl<<" "<<Z<<endl<<endl;
方法6： 对于小矩阵，使用都好分隔 初始化函数
Mat C =(Mat_<double>(3,3)) <<0,-1,0,-1,5,-1,0,-1,0);
cout<<"C= "<<endl<<" "<<C<<endl<<endl;
显示：C = [0，-1, 0;
-1,5,-1;
0,-1,0]
方法7： 为已存在的对象穿件新信息头
Mat RowClone = C.row(1).clone();
cout<<"RowClone= "<<endl<<" "<<RowClone<<endl<<endl;
显示：RowClone = [-1,5,-1]
example： image.cpp
#include <stdio.h>
#include <iostream>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/core/utility.hpp>
using namespace cv; // all the new API is put into "cv" namespace. Export its content
using namespace std;
static void help()
{
cout <<
"\nThis program shows how to use cv::Mat and IplImages converting back and forth.\n"
"It shows reading of images, converting to planes and merging back, color conversion\n"
"and also iterating through pixels.\n"
"Call:\n"
"./image [image-name Default: ../data/lena.jpg]\n" << endl;
}
// enable/disable use of mixed API in the code below. C/C++ interface混用
#define DEMO_MIXED_API_USE 1
#ifdef DEMO_MIXED_API_USE
# include <opencv2/highgui/highgui_c.h>
# include <opencv2/imgcodecs/imgcodecs_c.h>
#endif
int main( int argc, char** argv )
{
cv::CommandLineParser parser(argc, argv, "{help h | |}{@image|../data/lena.jpg|}");
if (parser.has("help"))
{
help();
return 0;
}
string imagename = parser.get<string>("@image");
#if DEMO_MIXED_API_USE
//! [iplimage]
Ptr<IplImage> iplimg(cvLoadImage(imagename.c_str())); // Ptr<T> is safe ref-counting pointer class //安全类型，有指针计数
if(!iplimg)
{
fprintf(stderr, "Can not load image %s\n", imagename.c_str());
return -1;
}
Mat img = cv::cvarrToMat(iplimg); // cv::Mat replaces the CvMat and IplImage, but it's easy to convert // IplImage 转换CvMat
// between the old and the new data structures (by default, only the header
// is converted, while the data is shared)
//! [iplimage]
#else
Mat img = imread(imagename); // the newer cvLoadImage alternative, MATLAB-style function
if(img.empty())
{
fprintf(stderr, "Can not load image %s\n", imagename.c_str());
return -1;
}
#endif
if( img.empty() ) // check if the image has been loaded properly
return -1;
Mat img_yuv;
cvtColor(img, img_yuv, COLOR_BGR2YCrCb); // convert image to YUV color space. The output image will be created automatically
vector<Mat> planes; // Vector is template vector class, similar to STL's vector. It can store matrices too.
split(img_yuv, planes); // split the image into separate color planes
#if 1
// method 1. process Y plane using an iterator
MatIterator_<uchar> it = planes[0].begin<uchar>(), it_end = planes[0].end<uchar>();
for(; it != it_end; ++it)
{
double v = *it*1.7 + rand()%21-10;
*it = saturate_cast<uchar>(v*v/255.);
}
// method 2. process the first chroma plane using pre-stored row pointer.
// method 3. process the second chroma plane using individual element access
for( int y = 0; y < img_yuv.rows; y++ )
{
uchar* Uptr = planes[1].ptr<uchar>(y);
for( int x = 0; x < img_yuv.cols; x++ )
{
Uptr[x] = saturate_cast<uchar>((Uptr[x]-128)/2 + 128);
uchar& Vxy = planes[2].at<uchar>(y, x);
Vxy = saturate_cast<uchar>((Vxy-128)/2 + 128);
}
}
#else
Mat noise(img.size(), CV_8U); // another Mat constructor; allocates a matrix of the specified size and type
randn(noise, Scalar::all(128), Scalar::all(20)); // fills the matrix with normally distributed random values;
// there is also randu() for uniformly distributed random number generation
GaussianBlur(noise, noise, Size(3, 3), 0.5, 0.5); // blur the noise a bit, kernel size is 3x3 and both sigma's are set to 0.5
const double brightness_gain = 0;
const double contrast_gain = 1.7;
#if DEMO_MIXED_API_USE
// it's easy to pass the new matrices to the functions that only work with IplImage or CvMat:
// step 1) - convert the headers, data will not be copied
IplImage cv_planes_0 = planes[0], cv_noise = noise;
// step 2) call the function; do not forget unary "&" to form pointers
cvAddWeighted(&cv_planes_0, contrast_gain, &cv_noise, 1, -128 + brightness_gain, &cv_planes_0);
#else
addWeighted(planes[0], contrast_gain, noise, 1, -128 + brightness_gain, planes[0]);
#endif
const double color_scale = 0.5;
// Mat::convertTo() replaces cvConvertScale. One must explicitly specify the output matrix type (we keep it intact - planes[1].type())
planes[1].convertTo(planes[1], planes[1].type(), color_scale, 128*(1-color_scale));
// alternative form of cv::convertScale if we know the datatype at compile time ("uchar" here).
// This expression will not create any temporary arrays and should be almost as fast as the above variant
planes[2] = Mat_<uchar>(planes[2]*color_scale + 128*(1-color_scale));
// Mat::mul replaces cvMul(). Again, no temporary arrays are created in case of simple expressions.
planes[0] = planes[0].mul(planes[0], 1./255);
#endif
// now merge the results back
merge(planes, img_yuv);
// and produce the output RGB image
cvtColor(img_yuv, img, COLOR_YCrCb2BGR);
// this is counterpart for cvNamedWindow
namedWindow("image with grain", WINDOW_AUTOSIZE);
#if DEMO_MIXED_API_USE
// this is to demonstrate that img and iplimg really share the data - the result of the above
// processing is stored in img and thus in iplimg too.
cvShowImage("image with grain", iplimg);
#else
imshow("image with grain", img);
#endif
waitKey();
return 0;
// all the memory will automatically be released by Vector<>, Mat and Ptr<> destructors.
}
