In my code, I'm trying to threshold an image to Yellow hues and then select and color those Yellow circles. What am I missing in my code? Because although I see the image after Gaussian to avoid false circle detection, the HoughCircles isn't picking and outlining the Yellow circles? Please let me know what information I can share to debug. /* * YellowCircleDetect.cpp * * Created on: Jun 13, 2019 * Author: techsri */
include "YellowcircleDetect.h"
using namespace cv; using namespace std;
// we'll be using the HSV colour space, instead of the more common RGB colour space. In HSV, each "tint" of colour is assigned a particular number (the Hue). //The "amount" of colour is assigned another number (the Saturation) and the brightness of the colour is assigned another number (the Intensity or Value). //This gives us the advantage of having a single number (hue) for the yellow circle.
IplImage* get_thresholded_image(IplImage* img) { // Convert the image into an HSV image IplImage* imgHSV = cvCreateImage(cvGetSize(img), 8, 3); cvCvtColor(img, imgHSV, CV_BGR2HSV); IplImage* imgThreshed = cvCreateImage(cvGetSize(img), 8, 1); // cvInRangeS(imgHSV, cvScalar(20, 100, 100), cvScalar(30, 255, 255), imgThreshed);
cvReleaseImage(&imgHSV);
return imgThreshed;
}
/** @function main */
int main(int argc, char** argv) { Mat src;
/// Read the image
const char* filename = argc >=2 ? argv[1] : "./data/computer-vision-test-image.png";
// Loads an image
src = imread( filename, IMREAD_COLOR );
// Check if image is loaded fine
if(src.empty()){
printf(" Error opening image\n");
printf(" Program Arguments: [image_name -- default %s] \n", filename);
return -1;
}
//౩ Channels conversion of mat to IpIImage.
IplImage* ptr_image = cvCreateImage(cvSize(src.cols, src.rows), 8, 3);
IplImage ipltemp = src;
cvCopy(&ipltemp, ptr_image);
//Threshold to yellow hue. Yellow becomes white. Rest becomes blackened.
IplImage* img_yellow_thresh = get_thresholded_image(ptr_image);
//Convert the thresholded image to an cv::Mat structure for use in HoughCircles function.
cv::Mat src_gray = cv::cvarrToMat(img_yellow_thresh);
// Reduce the noise so we avoid false circle detection
GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 );
cvShowImage("Noise reduced grey Image",img_yellow_thresh);
std::vector<cv::Vec3f> circles;
// Apply the Hough Transform to find just the circles
//gray: Input image (grayscale).
//HOUGH_GRADIENT: Define the detection method. Currently this is the only one available in OpenCV.
//dp = 1: The inverse ratio of resolution.
//min_dist = gray.rows/16: Minimum distance between detected centers.
//param_1 = 200: Upper threshold for the internal Canny edge detector.
//param_2 = 100*: Threshold for center detection.
//min_radius = 0: Minimum radius to be detected. If unknown, put zero as default.
//max_radius = 0: Maximum radius to be detected. If unknown, put zero asstll default.
HoughCircles(src_gray, circles, CV_HOUGH_GRADIENT, 1, src_gray.rows/16, 200, 100, 0, 0 );
imshow("Noise reduced grey Image",src_gray);
//![draw]
for( size_t i = 0; i < circles.size(); i++ )
{
Vec3i c = circles[i];
Point center = Point(c[0], c[1]);
// circle center
circle( src, center, 1, Scalar(0,100,100), 3, LINE_AA);
// circle outline
int radius = c[2];
circle( src, center, radius, Scalar(255,0,255), 3, LINE_AA);
}
//![draw]
//![display]
imshow("detected circles", src);
waitKey();
//![display]
return 0;
}
