bool MarkerDetector::findMarkers(const BGRAVideoFrame& frame, std::vector<Marker>& detectedMarkers)
{
cv::Mat bgraMat(frame.height, frame.width, CV_8UC3, frame.data, frame.stride);
// Convert the image to grayscale
prepareImage(bgraMat, m_grayscaleImage);
// Make it binary
performThreshold(m_grayscaleImage, m_thresholdImg);
// Detect contours
findContours(m_thresholdImg, m_contours, m_grayscaleImage.cols / 20);
// Find closed contours that can be approximated with 4 points
findMarkerCandidates(m_contours, detectedMarkers);
// Find is them are markers
detectMarkers(m_grayscaleImage, detectedMarkers);
// Calcualte their poses
estimatePosition(detectedMarkers);
//sort by id
std::sort(detectedMarkers.begin(), detectedMarkers.end());
return false;
}
// The workflow of the marker detection routine is the following:
// 1. convert the input image to grayscale;
// 2. perform a binary threshold operation;
// 3. detect contours;
// 4. search for possible markers;
// 5. detect and decode markers;
// 6. estimate marker 3D pose.
void MarkerDetector::processFrame(const cv::Mat& frame)
{
// ...
std::vector<Marker> markers;
//cv::Mat bgraMat( frame.rows, frame.cols, CV_8UC4 );
//cv::cvtColor( frame, bgraMat, CV_BGR2BGRA );
// Convert the image to grayscale.
// See: http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html#cvtcolor
// The conversion to grayscale is necessary because markers usually contain only black and white blocks.
// So it is much easier to operate with them using grayscale images.
//cv::cvtColor( bgraMat, grayscaleImage, CV_BGRA2GRAY );
cv::cvtColor(frame, grayscaleImage, CV_BGR2GRAY);
// Make it binary.
// See: http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html#adaptivethreshold
// The binarization operation will transform each pixel of our image to black (0 intensity) or white (255 intensity).
// This step is required to find contours.
cv::threshold(grayscaleImage, thresholdImg, 127.0, 255.0, cv::THRESH_BINARY_INV);
#ifdef _DEBUG
cv::imshow("THR", thresholdImg);
#endif
// Detect contours.
MarkerDetector::findContours(thresholdImg, contours, grayscaleImage.cols / 5);
#ifdef _DEBUG
cv::Mat contoursImage(thresholdImg.size(), CV_8UC1);
contoursImage = cv::Scalar(0);
// See: http://docs.opencv.org/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html#drawcontours
cv::drawContours(contoursImage, contours, -1, cv::Scalar(255), 2, CV_AA);
cv::imshow("CNT", contoursImage);
#endif
// Find closed contours that can be approximated with 4 points.
findMarkerCandidates(contours, markers);
// Find if there are markers.
detectMarkers(grayscaleImage, markers);
// Calcualte their poses.
estimatePosition(markers);
// Sort by id.
// See: http://www.cplusplus.com/reference/algorithm/sort/
std::sort(markers.begin(), markers.end());
// ...
transformations.clear();
ids.clear();
for (size_t i = 0; i < markers.size(); i++) {
transformations.push_back(markers[i].transformation);
ids.push_back(markers[i].id);
}
}
void ofxAruco::detectBoard(ofPixels & pixels){
detectMarkers(pixels);
boardProbability = boardDetector.detect(markers,boardConfig,board,camParams,markerSize);
}