使用opencv识别颜色序列

Question

What could be the possible machine vision solution for correct color recognition using opencv?

I must check if the color sequence of the connector bellow is correct. Is it better to use color regonition technique or pattern match technique? Is there any better approach to solve this?

In the image bellow is connector with colored wires, how to check correct sequence of wires?

Answer 1

I suggest doing following steps (with simple code ilustration):

1. converting to L a b color space;

   https://en.wikipedia.org/wiki/Lab_color_space/

   cv::cvtColor(img,img,CV_BGR2Lab);

2. take subimage which contains only wires

   img = img(cv::Rect(x,y,width,height)); // detect wires

3. compute mean values for each column and get 1D vector of values

    std::vector<cv::Vec3f> aggregatedVector; for(int i=0;i<img.cols;i++) { cv::Vec3f sum = cv::Vec3f(0,0,0); for(int j=0;j<img.rows;j++) { sum[0]+= img.at<Vecb>(j,i)[0]); sum[1]+= img.at<Vecb>(j,i)[1]; sum[2]+= img.at<Vecb>(j,i)[2]; } sum = sum/img.rows; aggregatedVector.push_back(sum); } 

4. extract uniform fields using, for example gradient and get vector with 20 values

    std::vector<Vec3f> fields cv::Vec3f mean = 0; int counter =0; for(int i=0;i<aggregatedVector.size();i++) { mean+= aggregatedVector[i]; if(cv::norm(aggregatedVector[i+1] - aggregatedVector[i]) > /*thresh here */ { fields.push_back(mean/(double)counter); mean = cv::Vec3f(0,0,0); counter=0; } counter++ } 

5. compute vector of color distances between calculated vector and reference

     double totalError = 0; for(int i=0;i<fields.size();i++) { totalError+= cv::mean(reference[i]-fields[i]); } 

   Then you can make decision based on error vector values. Have fun!