Question

所以：我有这个OpenCV程序，它从相机捕获视频，并在两个窗口上显示。一个没有颜色检测;另一个突出显示某些特定颜色（如红色）。

我需要的是一种方法来确定图像在特定时间是否包含该特定颜色。现在，第一个窗口只是一个常规视频输出。第二个窗口全部为黑色，直到看到与我指定的颜色匹配的对象，这使得对象在第二个窗口中显示为白色。

我想知道何时检测到它然后输出'检测到'或'未检测到'。

我该怎么做？我想我会迭代修改后的图像的宽度和高度，然后检查，但我不知道如何做到这一点。任何帮助表示赞赏 - 我一直试图找到这个问题的答案，现在几天没有运气。我检查了StackOverflow，但它没有提供我需要的东西。谢谢！

CODE：

#include <opencv/cv.h>
#include <opencv/highgui.h>

//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 failure\n");
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);

        cvSmooth(imgThresh, imgThresh, CV_GAUSSIAN,3,3); //smooth the binary image using Gaussian kernel

        cvShowImage("Ball", imgThresh);           
        cvShowImage("Video", frame);
        int sum = 0;
        for (int i = 0; i < imgThresh->width; i++) {
            for (int j = 0; j < imgThresh->height; j++) {
                // WHAT DO I NEED HERE TO CALCULATE CERTAIN COLOR
            }
        }
                    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;
}

Answer 1

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片，则可以使用过滤后的图片上的cvCountNonZero计算正像素
如果您的图像是真实世界的图像，那么任何给定的图像都有可能在滤镜的颜色中至少有一些像素。计算局部滑动窗口中的像素可能会更好，但这肯定不是唯一的方法。例如，抓住坐标（0,0）到（100,100）之间的区域，并计算非零的数量。如果超过一定数量，假设你有积极的一面。否则，继续重叠窗口（0,50）到（100,150）......

Answer 2

您可以遍历图像的行和列，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。我也没有在下面的代码中写它，但你也可以设置绿色和蓝色的阈值。因为如果您要查找的对象是红色，则绿色和蓝色记录中的对象会很低。

#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 failure\n");
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;
}

Answer 3

好maggick的代码除了一些细微的变化之外都有效......这将检测到红色。

#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 failure\n");
   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;
}

使用OpenCV检测视频中的特定（定性）颜色

3 个答案: