I have this image of a pin header, and I need to detect if there are bent pins in the header using OpenCV.
What is the best way to do this!? I have made the following so far. But I'm not sure if this is the correct way of doing it.
I use Canny edge detection and then HoughLinesP to check for lines.
for (const auto &entry: fs::directory_iterator(SAMPLES)) {
try {
// Load src image
src = imread(entry.path(), IMREAD_COLOR);
cvtColor(src, gray, COLOR_BGR2GRAY);
// blur(gray, blurMat, Size(3, 3));
cropped = gray(Range(150,230), Range(0, gray.cols));
// Edge detection
Canny(cropped, detected_edges, thres1, thres2, 3);
imshow("cropped", cropped);
imshow("src", src);
imshow("detected_edges", detected_edges);
// Probabilistic Line Transform
vector<Vec4i> linesP; // will hold the results of the detection
HoughLinesP(detected_edges, linesP, 1, CV_PI/180, 20, 50, 10 ); // runs the actual detection
// Draw the lines
for( size_t i = 0; i < linesP.size(); i )
{
Vec4i l = linesP[i];
line( src, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0,0,255), 3, LINE_AA);
}
imshow(entry.path().filename(), src);
waitKey();
} catch (const std::exception &e) {
cout << e.what() << endl;
}
}
The output looks like this then:
So in theory I only have to check if the angle of a line is greater than some threshold. But Maybe I got it all wrong. I like to hear from you.
CodePudding user response：
Have a look at cv::findContours.
You should be able to extract the pins with that, maybe binarize first with cv::threshold(). Then using center-of-mass and the moment-of-area for the contours found, you can describe the position and angle of the pins. Or just using the bounding rectangle might even be enough.
CodePudding user response：
Here is my approach with code and results.
Steps to produce:
(preprocessing)
- Apply median to decrease noise in the image
- Apply threshold to get a clear image
Here is the result of these 2 steps:
(there is a server error but i will add image later)
- Check each row and get the sticks according to the thickness threshold.
Result image of this step:
(there is a server error but i will add image later)
- Get mid point's y axis values of each stick and hold in an array.
- Calculate the standard deviation of each array and choose the ones which are higher.
Here is the code:
#include <string>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn_superres.hpp>
#include <numeric>
#include <boost/accumulators/accumulators.hpp>
#include <boost/accumulators/statistics/stats.hpp>
#include <boost/accumulators/statistics/mean.hpp>
#include <boost/accumulators/statistics/moment.hpp>
using namespace boost::accumulators;
using namespace std;
using namespace boost;
double stddev(std::vector<int> const & func)
{
double mean = std::accumulate(func.begin(), func.end(), 0.0) / func.size();
double sq_sum = std::inner_product(func.begin(), func.end(), func.begin(), 0.0,
[](double const & x, double const & y) { return x y; },
[mean](double const & x, double const & y) { return (x - mean)*(y - mean); });
return std::sqrt(sq_sum / func.size());
}
int main(){
cv::Mat img = cv::imread("/home/yns/Downloads/aaa.jpg",cv::IMREAD_GRAYSCALE);
cv::namedWindow("input",0);
cv::namedWindow("output",0);
cv::namedWindow("output2",0);
cv::imshow("input",img);
cv::Mat out;
cv::medianBlur(img,img,3);
cv::threshold(img,out,80,255,cv::THRESH_BINARY);
cv::Mat out2;
cv::cvtColor(out,out2,cv::COLOR_GRAY2BGR);
cv::imshow("output",out);
int start = 0;
int cnt = 0;
int refX = 0;
int thresholdThickness = 5;
int orderNum = 1;
std::vector<int> yAxis_1;
std::vector<int> yAxis_2;
std::vector<int> yAxis_3;
std::vector<int> yAxis_4;
std::vector<int> yAxis_5;
std::vector<int> yAxis_6;
std::vector<int> yAxis_7;
std::vector<int> yAxis_8;
std::vector<int> yAxis_9;
std::vector<int> yAxis_10;
std::vector<int> yAxis_11;
std::vector<int> yAxis_12;
int annen = 0;
for(int i=0; i<out.rows; i )
{
orderNum = 1;
annen = 0;
for(int j=0; j<out.cols; j )
{
if(out.at<uchar>(cv::Point(j,i))==255 && start != 1)
{
start = 1;
refX = j;
cnt = 0;
}
else if (out.at<uchar>(cv::Point(j,i))==255)
{
cnt ;
}
else if (out.at<uchar>(cv::Point(j,i))==0 && start == 1 && cnt>thresholdThickness) {
cv::circle(out2,cv::Point((j refX)/2,i),1,cv::Scalar(0,0,255),cv::FILLED);
start = 0;
annen ;
if(orderNum == 1)
yAxis_1.push_back(j);
if(orderNum == 2)
yAxis_2.push_back(j);
if(orderNum == 3)
yAxis_3.push_back(j);
if(orderNum == 4)
yAxis_4.push_back(j);
if(orderNum == 5)
yAxis_5.push_back(j);
if(orderNum == 6)
yAxis_6.push_back(j);
if(orderNum == 7)
yAxis_7.push_back(j);
if(orderNum == 8)
yAxis_8.push_back(j);
if(orderNum == 9)
yAxis_9.push_back(j);
if(orderNum == 10)
yAxis_10.push_back(j);
if(orderNum == 11)
yAxis_11.push_back(j);
if(orderNum == 12)
yAxis_12.push_back(j);
orderNum ;
}
else if (out.at<uchar>(cv::Point(j,i))==0 && start == 1)
{
start = 0;
}
}
}
std::cout<<stddev(yAxis_1)<<std::endl;
std::cout<<stddev(yAxis_2)<<std::endl;
std::cout<<stddev(yAxis_3)<<std::endl;
std::cout<<stddev(yAxis_4)<<std::endl;
std::cout<<stddev(yAxis_5)<<std::endl;
std::cout<<stddev(yAxis_6)<<std::endl;
std::cout<<stddev(yAxis_7)<<std::endl;
std::cout<<stddev(yAxis_8)<<std::endl;
std::cout<<stddev(yAxis_9)<<std::endl;
std::cout<<stddev(yAxis_10)<<std::endl;
std::cout<<stddev(yAxis_11)<<std::endl;
std::cout<<stddev(yAxis_12)<<std::endl;
float average = accumulate( yAxis_6.begin(), yAxis_6.end(), 0.0)/yAxis_6.size();
float average2 = accumulate( yAxis_7.begin(), yAxis_7.end(), 0.0)/yAxis_7.size();
cv::circle(out2,cv::Point(average,out2.rows/2),25,cv::Scalar(0,255,255),5);
cv::circle(out2,cv::Point(average2,out2.rows/2),25,cv::Scalar(0,255,255),5);
cv::imshow("output2",out2);
cv::waitKey(0);
return 0;
}