opencv举例

进度条演示

#include "opencv2/imgproc/imgproc.hpp"#include "opencv2/highgui/highgui.hpp"#include <iostream>using namespace cv;using namespace std;Mat img;int threshval = 100;#define   FILE_PATH_TEST  "E:\\sunwork\\OpenCVTest\\TestImage\\stuff.jpg" void on_trackbar(int, void*){    Mat bw = threshval < 128 ? (img < threshval) : (img > threshval);    vector<vector<Point> > contours;    vector<Vec4i> hierarchy;    findContours( bw, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );    //namedWindow( "Image11", 1 ); //   imshow( "Image11", contours );    Mat dst = Mat::zeros(img.size(), CV_8UC3);    if( !contours.empty() && !hierarchy.empty() )    {        // iterate through all the top-level contours,        // draw each connected component with its own random color        int idx = 0;        for( ; idx >= 0; idx = hierarchy[idx][0] )        {            //Scalar color( (rand()&255), (rand()&255), (rand()&255) );//generate rand color             Scalar color( (255), (255), (255) );//generate rand color            drawContours( dst, contours, idx, color, CV_FILLED, 8, hierarchy );        }    }    imshow( "Connected Components", dst );}void help(){    cout << "\n This program demonstrates connected components and use of the trackbar\n"             "Usage: \n"             "  ./connected_components <image(stuff.jpg as default)>\n"             "The image is converted to grayscale and displayed, another image has a trackbar\n"             "that controls thresholding and thereby the extracted contours which are drawn in color\n";}int main( int argc, const char** argv ){    help();    string inputImage ="stuff.jpg";     img = imread(FILE_PATH_TEST, 0);//Read the gray image      if(img.empty())    {        cout << "Could not read input image file: " << inputImage << endl;        return -1;    }    namedWindow( "Image", 1 );    imshow( "Image", img );    namedWindow( "Connected Components", 1 );    createTrackbar( "Threshold", "Connected Components", &threshval, 255, on_trackbar );    on_trackbar(threshval, 0);    waitKey(60000);    return 0;}

进度条对图像像素点操作演示

//-----------------------------------【程序说明】----------------------------------------------  //  程序名称:：创建Trackbar&图像对比度、亮度值调整  //  VS2010  OpenCV：2.3.1  //  2017年1月17 日 Use by sunlinju //------------------------------------------------------------------------------------------------  #include <opencv2/core/core.hpp>  #include<opencv2/highgui/highgui.hpp>  #include"opencv2/imgproc/imgproc.hpp"  #include <iostream>  using namespace std;  using namespace cv;  static void ContrastAndBright(int, void *);  #define   FILE_PATH_TEST  "E:\\sunwork\\OpenCVTest\\TestImage\\TEST.jpg" int g_nContrastValue;int g_nBrightValue;  Mat g_srcImage,g_dstImage;  int main(  )  {        //set initial value       g_nContrastValue=80;         g_nBrightValue=80;         g_srcImage= imread( FILE_PATH_TEST);  //imread the image       if(!g_srcImage.data ) { printf("Imread image wrong！\n"); return false; }         g_dstImage= Mat::zeros( g_srcImage.size(), g_srcImage.type() );         namedWindow("WindowsName", 1);         createTrackbar("Contrast", "WindowsName",&g_nContrastValue,300,ContrastAndBright );         createTrackbar("Bright","WindowsName",&g_nBrightValue,200,ContrastAndBright );         //recall the function        ContrastAndBright(g_nContrastValue,0);         ContrastAndBright(g_nBrightValue,0);         //printf some help information        cout<<endl<<"\t[1]you can set the contrast and brightness;\n\n"                       <<"\t[2]Press q,quit the programe;\n"                       <<"\n\n\t\t\t\tby sun";         //Press q for quite    while(char(waitKey(1)) != 'q') {}         return 0;  }  //-----------------------------ContrastAndBright( )------------------------------------  //  Descripition:Convert the  Contrast and brightness//-------------------------------------------------------------------------------------static void ContrastAndBright(int, void *)  {         namedWindow("【Original】", 1);         //do g_dstImage(i,j) =a*g_srcImage(i,j) + b         for(int y = 0; y < g_srcImage.rows; y++ )         {                for(int x = 0; x < g_srcImage.cols; x++ )                {                       for(int c = 0; c < 3; c++ )                       {                              g_dstImage.at<Vec3b>(y,x)[c]= saturate_cast<uchar>( (g_nContrastValue*0.01)*(g_srcImage.at<Vec3b>(y,x)[c] ) + g_nBrightValue );                       }                }         }  //y是像素所在的行， x是像素所在的列， c是R、G、B（对应0、1、2）其中之一//运算结果可能超出像素取值范围（溢出），还可能是非整数（如果是浮点数的话），所以我们要用saturate_cast对//结果进行转换，以确保它为有效值//a也就是对比度，一般为了观察的效果，取值为0.0到3.0的浮点值，但是我们的轨迹条一般取值都会整数，所以在这//里我们可以，将其代表对比度值的nContrastValue参数设为0到300之间的整型，在最后的式子中乘以一个0.01，//这样就可以完成轨迹条中300个不同取值的变化       imshow("【Original】", g_srcImage);         imshow("WindowsName", g_dstImage);  }