使用OpenCV检测视频中的特定(定性)颜色

图像处理不是精确的科学，解决方案取决于输入数据…

• 如果您使用数字创建的图像，您可以在过滤图像上使用cvCountNonZero计算正像素
• 如果您的图像是真实世界的捕获，那么任何给定的图像都有可能在您的滤镜颜色中至少有一些像素。在局部滑动窗口中计算像素可能效果更好，但这肯定不是唯一的方法。例如，获取坐标(0,0)到(100,100)之间的区域，并计算非零的数量。如果超过某个数字，假设你得到的是正数。否则，继续进行重叠窗口(0,50)到(100,150)…

您可以循环遍历图像的行和颜色，如果红色值大于某个阈值，则可以将另一帧中的相应像素设置为白色。我也没有把它写在下面的代码中，但是你也可以设置绿色和蓝色的阈值。因为如果你要找的对象是红色的，那么它在绿色和蓝色记录中的位置就会比较低。

#include <opencv/cv.h>
#include <opencv/highgui.h>
//#include <opencv/imgproc.h>
#include <iostream>
using namespace std;
//This function threshold the HSV image and create a binary image
IplImage* GetThresholdedImage(IplImage* imgHSV){       
   IplImage* imgThresh=cvCreateImage(cvGetSize(imgHSV),IPL_DEPTH_8U, 1);
   IplImage* imgThresh2=cvCreateImage(cvGetSize(imgHSV),IPL_DEPTH_8U, 1);
   IplImage* imgThresh3=cvCreateImage(cvGetSize(imgHSV),IPL_DEPTH_8U, 1);
   cvInRangeS(imgHSV, cvScalar(170,160,60), cvScalar(180,256,256), imgThresh2);
   cvInRangeS(imgHSV, cvScalar(0,160,60), cvScalar(22,256,256), imgThresh3);
   cvOr(imgThresh2, imgThresh3, imgThresh);
   cvInRangeS(imgHSV, cvScalar(75,160,60), cvScalar(130,256,256), imgThresh3);
   cvOr(imgThresh, imgThresh3, imgThresh);
   return imgThresh;
} 
int main(){
  CvCapture* capture =0;       
  capture = cvCaptureFromCAM(0);
  if(!capture){
printf("Capture failuren");
return -1;
  }
  IplImage * frame = 0;
  cvNamedWindow("Video"); 
  cvNamedWindow("Ball");

  //iterate through each frames of the video     
  while(true){
        frame = cvQueryFrame(capture);           
        if(!frame) break;
        frame=cvCloneImage(frame); 
        //cvSmooth(frame, frame, CV_GAUSSIAN,3,3); //smooth the original image using Gaussian kernel
        //IplImage* imgHSV = cvCreateImage(cvGetSize(frame), IPL_DEPTH_8U, 3);
        //cvCvtColor(frame, imgHSV, CV_BGR2HSV); //Change the color format from BGR to HSV
        //IplImage* imgThresh = GetThresholdedImage(imgHSV);
        //IplImage* imgThresh=cvCreateImage(cvGetSize(imgHSV),IPL_DEPTH_8U, 1);
        //cvCvtColor(imgHSV, imgThresh, CV_HSV2BGR);
        //cvSmooth(imgThresh, imgThresh, CV_GAUSSIAN,3,3); //smooth the binary image using Gaussian kernel
        //cvShowImage("Ball", imgThresh);           
        int sum = 150;
        for(int i=0; i< frame->height; i++){
          for(int j=0; j< frame->width; j++){
              int blue = frame->imageData[frame->widthStep*i + frame->nChannels* j + 0];
              int green = frame->imageData[frame->widthStep*i + frame->nChannels* j + 1];
              int red = frame->imageData[frame->widthStep*i + frame->nChannels* j + 2];
              //cout << red << " " << green << " " << blue << endl;
              if ( red + 128 >= sum)
              {
                if (blue < 30)
                {
                  if (green < 30)
                  {
                frame->imageData[frame->widthStep*i + frame->nChannels* j + 0] = 255;
                frame->imageData[frame->widthStep*i + frame->nChannels* j + 1] = 255;
                frame->imageData[frame->widthStep*i + frame->nChannels* j + 2] = 255;
              } 
            }
              }
            }
          } 
          cvShowImage("Video", frame);

                    if (sum > 1) { cout << "Detected"; }
                    else { cout << "Not Detected"; }
        //Clean up used images
        //cvReleaseImage(&imgHSV);
        //cvReleaseImage(&imgThresh);            
        cvReleaseImage(&frame);
        //Wait 50mS
        int c = cvWaitKey(10);
        //If 'ESC' is pressed, break the loop
        if((char)c==27 ) break;      
  }
  cvDestroyAllWindows() ;
  cvReleaseCapture(&capture);     
  return 0;
}

除了一些小改动外，magic的代码工作正常…这将检测红色。

#include <opencv/cv.h>
#include <opencv/highgui.h>
#include <iostream>
using namespace std;
int main(){
  CvCapture* capture =0;       
  capture = cvCaptureFromCAM(0);
  if(!capture){
   printf("Capture failuren");
   return -1;
  }
  IplImage * frame = 0;
  cvNamedWindow("Video"); 
  //iterate through each frames of the video     
  while(true){
        frame = cvQueryFrame(capture);           
        if(!frame) break;
        frame=cvCloneImage(frame);         
        int sum = 0;
        for(int i=0; i< frame->height; i++){
          for(int j=0; j< frame->width; j++){
              int blue = frame->imageData[frame->widthStep*i + frame->nChannels* j + 0];
              int green = frame->imageData[frame->widthStep*i + frame->nChannels* j + 1];
              int red = frame->imageData[frame->widthStep*i + frame->nChannels* j + 2];
              //cout << red << " " << green << " " << blue << endl;
              if ( red + 128 >= 250)
              {
                if (blue < 50)
                {
                  if (green < 50)
                  {
                  frame->imageData[frame->widthStep*i + frame->nChannels* j + 0] = 255;
                  frame->imageData[frame->widthStep*i + frame->nChannels* j + 1] = 255;
                  frame->imageData[frame->widthStep*i + frame->nChannels* j + 2] = 255;
                  sum = 1
                  } 
                }
              }
            }
          } 
        cvShowImage("Video", frame);
        if (sum > 0) { cout << "Detected"; }
        else { cout << "Not Detected"; }
        cvReleaseImage(&frame);
        //Wait 50mS
        int c = cvWaitKey(10);
        //If 'ESC' is pressed, break the loop
        if((char)c==27 ) break;      
  }
  cvDestroyAllWindows() ;
  cvReleaseCapture(&capture);     
  return 0;
}