void help()
{
- cout <<
- "\nA program using OCL module pyramid scaling, Canny, dilate functions; cpu contours, contour simpification and\n"
- "memory storage (it's got it all folks) to find\n"
- "squares in a list of images pic1-6.png\n"
- "Returns sequence of squares detected on the image.\n"
- "the sequence is stored in the specified memory storage\n"
- "Call:\n"
- "./squares\n"
- "Using OpenCV version %s\n" << CV_VERSION << "\n" << endl;
+ cout <<
+ "\nA program using OCL module pyramid scaling, Canny, dilate functions, threshold, split; cpu contours, contour simpification and\n"
+ "memory storage (it's got it all folks) to find\n"
+ "squares in a list of images pic1-6.png\n"
+ "Returns sequence of squares detected on the image.\n"
+ "the sequence is stored in the specified memory storage\n"
+ "Call:\n"
+ "./squares\n"
+ "Using OpenCV version %s\n" << CV_VERSION << "\n" << endl;
}
void findSquares( const Mat& image, vector<vector<Point> >& squares )
{
squares.clear();
-
- Mat pyr, timg, gray0(image.size(), CV_8U), gray;
- cv::ocl::oclMat pyr_ocl, timg_ocl, gray0_ocl(gray0), gray_ocl;
+
+ Mat gray;
+ cv::ocl::oclMat pyr_ocl, timg_ocl, gray0_ocl, gray_ocl;
// down-scale and upscale the image to filter out the noise
ocl::pyrDown(ocl::oclMat(image), pyr_ocl);
ocl::pyrUp(pyr_ocl, timg_ocl);
- timg = Mat(timg_ocl);
vector<vector<Point> > contours;
-
+ vector<cv::ocl::oclMat> gray0s;
+ ocl::split(timg_ocl, gray0s); // split 3 channels into a vector of oclMat
// find squares in every color plane of the image
for( int c = 0; c < 3; c++ )
{
- int ch[] = {c, 0};
- mixChannels(&timg, 1, &gray0, 1, ch, 1);
-
+ gray0_ocl = gray0s[c];
// try several threshold levels
for( int l = 0; l < N; l++ )
{
cv::ocl::Canny(gray0_ocl, gray_ocl, 0, thresh, 5);
// dilate canny output to remove potential
// holes between edge segments
- ocl::dilate(gray0_ocl, gray_ocl, Mat(), Point(-1,-1));
+ ocl::dilate(gray_ocl, gray_ocl, Mat(), Point(-1,-1));
gray = Mat(gray_ocl);
}
else
{
// apply threshold if l!=0:
// tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
- gray = gray0 >= (l+1)*255/N;
+ cv::ocl::threshold(gray0_ocl, gray_ocl, (l+1)*255/N, 255, THRESH_BINARY);
+ gray = gray_ocl;
}
// find contours and store them all as a list
findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
vector<Point> approx;
-
+
// test each contour
for( size_t i = 0; i < contours.size(); i++ )
{
// approximate contour with accuracy proportional
// to the contour perimeter
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);
-
+
// square contours should have 4 vertices after approximation
// relatively large area (to filter out noisy contours)
// and be convex.
help();
namedWindow( wndname, 1 );
vector<vector<Point> > squares;
-
+
for( int i = 0; names[i] != 0; i++ )
{
Mat image = imread(names[i], 1);
cout << "Couldn't load " << names[i] << endl;
continue;
}
-
+
findSquares(image, squares);
drawSquares(image, squares);