特徵檢測之特徵提取（Detect）
// Detect keypoints in image using the traditional Shi-Thomasi detector
void detKeypointsShiTomasi(vector<cv::KeyPoint> &keypoints, cv::Mat &img, bool bVis)
{
// compute detector parameters based on image size
int blockSize = 4; // size of an average block for computing a derivative covariation matrix over each pixel neighborhood
double maxOverlap = 0.0; // max. permissible overlap between two features in %
double minDistance = (1.0 - maxOverlap) * blockSize;
int maxCorners = img.rows * img.cols / max(1.0, minDistance); // max. num. of keypoints
double qualityLevel = 0.01; // minimal accepted quality of image corners
double k = 0.04;
// Apply corner detection
double t = (double)cv::getTickCount();
vector<cv::Point2f> corners;
cv::goodFeaturesToTrack(img, corners, maxCorners, qualityLevel, minDistance, cv::Mat(), blockSize, false, k);
// add corners to result vector
for (auto it = corners.begin(); it != corners.end(); ++it)
{
cv::KeyPoint newKeyPoint;
newKeyPoint.pt = cv::Point2f((*it).x, (*it).y);
newKeyPoint.size = blockSize;
keypoints.push_back(newKeyPoint);
}
t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
cout << "Shi-Tomasi detection with n=" << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
// visualize results
if (bVis)
{
cv::Mat visImage = img.clone();
cv::drawKeypoints(img, keypoints, visImage, cv::Scalar::all(-1), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
string windowName = "Shi-Tomasi Corner Detector Results";
cv::namedWindow(windowName, 6);
imshow(windowName, visImage);
cv::waitKey(0);
}
}
//FAST, BRISK, ORB, AKAZE, SIFT
void detKeypointsModern(std::vector<cv::KeyPoint> &keypoints, cv::Mat &img, std::string detectorType, bool bVis)
{
string windowName;
if (detectorType.compare("FAST") == 0)
{
int threshold = 30;
bool bNMS = true;
cv::FastFeatureDetector::DetectorType type = cv::FastFeatureDetector::TYPE_9_16; // TYPE_9_16, TYPE_7_12, TYPE_5_8
cv::Ptr<cv::FeatureDetector> detector = cv::FastFeatureDetector::create(threshold, bNMS, type);
double t = (double)cv::getTickCount();
detector->detect(img, keypoints);
t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
cout << "FAST detection with n= " << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
windowName = "FAST Detector Results";
}
else if (detectorType.compare("BRISK") == 0)
{
cv::Ptr<cv::FeatureDetector> detector = cv::BRISK::create();
double t = (double)cv::getTickCount();
detector->detect(img, keypoints);
t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
cout << "BRISK detection with n= " << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
windowName = "BRISK Detector Results";
}
else if (detectorType.compare("ORB") == 0)
{
cv::Ptr<cv::FeatureDetector> detector = cv::ORB::create();
double t = (double)cv::getTickCount();
detector->detect(img, keypoints);
t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
cout << "ORB detection with n= " << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
windowName = "ORB Detector Results";
}
else if (detectorType.compare("AKAZE") == 0)
{
cv::Ptr<cv::FeatureDetector> detector = cv::AKAZE::create();
double t = (double)cv::getTickCount();
detector->detect(img, keypoints);
t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
cout << "AKAZE detection with n= " << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
windowName = "AKAZE Detector Results";
}
else if (detectorType.compare("SIFT") == 0)
{
cv::Ptr<cv::FeatureDetector> detector = cv::xfeatures2d::SIFT::create();
double t = (double)cv::getTickCount();
detector->detect(img, keypoints);
t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
cout << "SIFT detection with n= " << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
windowName = "SIFT Detector Results";
}
// visualize results
if (bVis)
{
cv::Mat visImage = img.clone();
cv::drawKeypoints(img, keypoints, visImage, cv::Scalar::all(-1), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
cv::namedWindow(windowName, 6);
imshow(windowName, visImage);
cv::waitKey(0);
}
}
// Detect keypoints in image using the traditional Harris detector
void detKeypointsHarris(std::vector<cv::KeyPoint> &keypoints, cv::Mat &img, bool bVis)
{
// Detector parameters
int blockSize = 2; // for every pixel, a blockSize × blockSize neighborhood is considered
int apertureSize = 3; // aperture parameter for Sobel operator (must be odd)
int minResponse = 100; // minimum value for a corner in the 8bit scaled response matrix
double k = 0.04; // Harris parameter (see equation for details)
// Detect Harris corners and normalize output
double t = (double)cv::getTickCount();
cv::Mat dst, dst_norm, dst_norm_scaled;
dst = cv::Mat::zeros(img.size(), CV_32FC1);
cv::cornerHarris(img, dst, blockSize, apertureSize, k, cv::BORDER_DEFAULT);
cv::normalize(dst, dst_norm, 0, 255, cv::NORM_MINMAX, CV_32FC1, cv::Mat());
cv::convertScaleAbs(dst_norm, dst_norm_scaled);
double maxOverlap = 0.0; // max. permissible overlap between two features in %, used during non-maxima suppression
for (size_t j = 0; j < dst_norm.rows; j++)
{
for (size_t i = 0; i < dst_norm.cols; i++)
{
int response = (int)dst_norm.at<float>(j, i);
if (response > minResponse)
{ // only store points above a threshold
cv::KeyPoint newKeyPoint;
newKeyPoint.pt = cv::Point2f(i, j);
newKeyPoint.size = 2 * apertureSize;
newKeyPoint.response = response;
// perform non-maximum suppression (NMS) in local neighbourhood around new key point
bool bOverlap = false;
for (auto it = keypoints.begin(); it != keypoints.end(); ++it)
{
double kptOverlap = cv::KeyPoint::overlap(newKeyPoint, *it);
if (kptOverlap > maxOverlap)
{
bOverlap = true;
if (newKeyPoint.response > (*it).response)
{ // if overlap is >t AND response is higher for new kpt
*it = newKeyPoint; // replace old key point with new one
break; // quit loop over keypoints
}
}
}
if (!bOverlap)
{ // only add new key point if no overlap has been found in previous NMS
keypoints.push_back(newKeyPoint); // store new keypoint in dynamic list
}
}
} // eof loop over cols
} // eof loop over rows
t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
cout << "Harris detection with n=" << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
// visualize results
if (bVis)
{
cv::Mat visImage = img.clone();
cv::drawKeypoints(img, keypoints, visImage, cv::Scalar::all(-1), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
string windowName = "Harris Corner Detector Results";
cv::namedWindow(windowName, 6);
imshow(windowName, visImage);
cv::waitKey(0);
}
}