Find Color inside an shape C++ OpenCV - c++

Question

I have a code that find the countours of a video and track the shape that I choose. In this code I am searching for triangle and rectangle looking at 3 or 4 contours.
I need help in 2 questions:
1- Using this method, how can I detect circles?
2- How can I search the color ( the shape is done, so in my "if" I need to verify the color too, but how ? If I for example want to find a red triangle )
Thank you so much
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp""
#include "opencv2/imgproc/imgproc.hpp"
#include <opencv\cv.h>
#include <math.h>
using namespace cv;
using namespace std;
int iLastX = -1;
int iLastY = -1;
IplImage* imgTracking = 0;
int lastX1 = -1;
int lastY1 = -1;
int lastX2 = -1;
int lastY2 = -1;
void trackObject(IplImage* imgThresh){
CvSeq* contour; //hold the pointer to a contour
CvSeq* result; //hold sequence of points of a contour
CvMemStorage *storage = cvCreateMemStorage(0); //storage area for all contours
//finding all contours in the image
cvFindContours(imgThresh, storage, &contour, sizeof(CvContour), CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0, 0));
//iterating through each contour
while (contour)
{
//obtain a sequence of points of the countour, pointed by the variable 'countour'
result = cvApproxPoly(contour, sizeof(CvContour), storage, CV_POLY_APPROX_DP, cvContourPerimeter(contour)*0.02, 0);
//if there are 3 vertices in the contour and the area of the triangle is more than 100 pixels
if (result->total == 3 && fabs(cvContourArea(result, CV_WHOLE_SEQ))>100)
{
//iterating through each point
CvPoint *pt[3];
for (int i = 0; i < 3; i++){
pt[i] = (CvPoint*)cvGetSeqElem(result, i);
}
//drawing lines around the triangle
cvLine(imgTracking, *pt[0], *pt[1], cvScalar(255, 0, 0), 4);
cvLine(imgTracking, *pt[1], *pt[2], cvScalar(255, 0, 0), 4);
cvLine(imgTracking, *pt[2], *pt[0], cvScalar(255, 0, 0), 4);
}
else if (result->total == 4 && fabs(cvContourArea(result, CV_WHOLE_SEQ))>100)
{
//iterating through each point
CvPoint *pt[4];
for (int i = 0; i < 4; i++){
pt[i] = (CvPoint*)cvGetSeqElem(result, i);
}
//drawing lines around the quadrilateral
cvLine(imgTracking, *pt[0], *pt[1], cvScalar(0, 255, 0), 4);
cvLine(imgTracking, *pt[1], *pt[2], cvScalar(0, 255, 0), 4);
cvLine(imgTracking, *pt[2], *pt[3], cvScalar(0, 255, 0), 4);
cvLine(imgTracking, *pt[3], *pt[0], cvScalar(0, 255, 0), 4);
}
else if (CIRCLE???)
{
}
//obtain the next contour
contour = contour->h_next;
}
cvReleaseMemStorage(&storage);
}
int main(){
//load the video file to the memory
CvCapture *capture = cvCaptureFromAVI("F:/TCC/b2.avi");
if (!capture){
printf("Capture failure\n");
return -1;
}
IplImage* frame = 0;
frame = cvQueryFrame(capture);
if (!frame) return -1;
//create a blank image and assigned to 'imgTracking' which has the same size of original video
imgTracking = cvCreateImage(cvGetSize(frame), IPL_DEPTH_8U, 3);
cvZero(imgTracking); //covert the image, 'imgTracking' to black
cvNamedWindow("Video");
//iterate through each frames of the video
while (true){
cvSet(imgTracking, cvScalar(0, 0, 0));
frame = cvQueryFrame(capture);
if (!frame) break;
frame = cvCloneImage(frame);
//smooth the original image using Gaussian kernel
cvSmooth(frame, frame, CV_GAUSSIAN, 3, 3);
//converting the original image into grayscale
IplImage* imgGrayScale = cvCreateImage(cvGetSize(frame), 8, 1);
cvCvtColor(frame, imgGrayScale, CV_BGR2GRAY);
//thresholding the grayscale image to get better results
cvThreshold(imgGrayScale, imgGrayScale, 100, 255, CV_THRESH_BINARY_INV);
//track the possition of the ball
trackObject(imgGrayScale);
// Add the tracking image and the frame
cvAdd(frame, imgTracking, frame);
cvShowImage("Video", frame);
//Clean up used images
cvReleaseImage(&imgGrayScale);
cvReleaseImage(&frame);
//Wait 10mS
int c = cvWaitKey(10);
//If 'ESC' is pressed, break the loop
if ((char)c == 27) break;
}
cvDestroyAllWindows();
cvReleaseImage(&imgTracking);
cvReleaseCapture(&capture);
return 0;
}

Answer 1

Afaik, this will be a relatively complex project, not answerable in a single question, but here is my 2 cents. I'll try to sketch the big painting that comes to my mind right now and just hope it helps you somehow.
Basically, the process can be decoupled in:
Thresholding the image to get promising pixels out. As you say, you need to specify the color, so you can use that to set the threshold, i.e.: if you're looking for red objects, you can use a threshold that has a high R channel level and low on others.
Once you have promising figures out, you need a labeling algorithm to separate all figures. You can use this OpenCV addon library for that. This way, you will have all figures you found separated.
Now comes the hardest part imho. You can use a classifier to classify figures (there are a lot of docs on OpenCV classifiers out there, like this one). When you use a classifier for this task, you're comparing an input shape (in this case each figure you labeled previously) with several class of shapes (triangles and circles in your case), and decide which class fits better your input shape. The comparation is what the classifier does for you. Having told that, you need two things:
Define what properties you'll get from the shapes to be able to compare them. You can use Hu moments for that.
A dataset extracted from a training set of figures that you already know which class are. This is, you basically will get a bunch of well-known triangles, circles, squares and whatever shapes come to your mind, and get the properties at step 3.1 out of them.
This way, to check what class a figure fits on, you'll feed the classifier with the trained dataset with properties of well known classes, and the properties (i.e.: Hu moments) of an input shape (you got those from labeling).
Note that when using a classifier, if you detect a shape that you didn't provide data for at the training dataset, the classifier will try to fit it the better it can with the classes you did provide. For example, if you made the trained dataset for triangles and squares, when you provide a circle shape, the classifier will tell you it's a square or a triangle.
Sorry for not providing details, but I'm sure you can get info on the net on how to use a classifier, and check the OpenCV docs. Maybe also come later with more specific issues, but afaik, this is the thing I can tell.