Java 冲浪特征检测-OpenCV
我正在开发一个android应用程序，主要目的是在场景中检测被询问的对象。为此，我使用OpenCV的SURF算法。我并没有“好运”的发现，因为我不知道什么时候“发现”了一个物体 我用我的设备摄像头获取一帧，然后按照以下步骤获取对象的关键点和描述符： Java代码Java 冲浪特征检测-OpenCV,java,android,c++,opencv,surf,Java,Android,C++,Opencv,Surf,我正在开发一个android应用程序，主要目的是在场景中检测被询问的对象。为此，我使用OpenCV的SURF算法。我并没有“好运”的发现，因为我不知道什么时候“发现”了一个物体 我用我的设备摄像头获取一帧，然后按照以下步骤获取对象的关键点和描述符： Java代码 public void onSnapClick(View v) { Imgproc.GaussianBlur(frameGray, frameGray, new Size(3, 3), 2); Imgproc.Canny
public void onSnapClick(View v) {
Imgproc.GaussianBlur(frameGray, frameGray, new Size(3, 3), 2);
Imgproc.Canny(frameGray, frameGray, 40, 120);
Imgproc.resize(frameGray, frameGray, new Size(320, 240));
FindFeatures(frameGray.getNativeObjAddr()); //JNI call
//Some code to store data in DB...
}
public void onSearchClick(View v) {
Imgproc.GaussianBlur(frameGray, frameGray, new Size(3, 3), 2);
Imgproc.Canny(frameGray, frameGray, 40, 120);
Imgproc.resize(frameGray, frameGray, new Size(320, 240));
nObject = FindObjects(frameGray.getNativeObjAddr()); //JNI call
if (nObject = searchObject)
//draw frame with a rectangle around the found object in the scenario....
}
double hessianThreshold=600;
int nOctaves=4;
int nOctaveLayers=2;
bool extended=true;
bool upright=false;
JNIEXPORT void JNICALL Java_es_ugr_reconocimiento_Juego_FindFeatures(JNIEnv* env, jobject, jlong addrGray) {
Mat& frameGray= *(Mat*) addrGray;
vector<KeyPoint> keyPoints;
Mat descriptores;
SurfFeatureDetector detector_Surf(hessianThreshold, nOctaves, nOctaveLayers, extended, upright);
SurfDescriptorExtractor extractor_Surf;
detector_Surf.detect(frameGray, keyPoints);
if (keyPoints.size() > 0)
extractor_Surf.compute(frameGray, keyPoints, descriptores);
}
double hessianThreshold=600;
int nOctaves=4;
int nOctaveLayers=2;
bool extended=true;
bool upright=false;
JNIEXPORT jint JNICALL Java_es_ugr_reconocimiento_Juego_FindObjects(JNIEnv* env, jobject, jlong addrGray) {
Mat& frameGray = *(Mat*) addrGray;
vector<KeyPoint> keyPoints_esc;
Mat descriptores_esc;
SurfFeatureDetector detector_Surf(hessianThreshold, nOctaves, nOctaveLayers, extended, upright);
SurfDescriptorExtractor extractor_Surf;
detector_Surf.detect(frameGray , keyPoints_esc);
if (keyPoints_esc.size() == 0) return -1;
extractor_Surf.compute(frameGray , keyPoints_esc, descriptores_esc);
if (descriptores_esc.rows() == 0) return -1;
for(int i=0;i<lstObjects.size();i++){
Mat descriptores_obj = lstDescriptors.at(i);
vector<KeyPoint> keyPoints_obj = lstKeyPoints.at(i);
FlannBasedMatcher matcher;
vector<vector<DMatch> > matches;
matcher.knnMatch(descriptores_obj, descriptores_esc, matches, 2);
// ----------------------------------------------------------------------
// Draw only "good" matches (i.e. whose distance is less than 2*min_dist,
// or a small arbitary value ( 0.02 ) in the event that min_dist is very
// small)
// PS.- radiusMatch can also be used here.
// ----------------------------------------------------------------------
vector<DMatch> good_matches;
//THIS LOOP IS SENSITIVE TO SEGFAULTS
for (int i = 0; i < min(descriptores_obj.rows - 1, (int) matches.size());i++){
if ( (matches[i][0].distance < 0.6 * (matches[i][1].distance)) &&
((int) matches[i].size() <= 2 && (int) matches[i].size() > 0) ) {
good_matches.push_back(matches[i][0]);
}
}
if (good_matches.size() >= nThreshold) {
vector < Point2f > obj;
vector < Point2f > scene;
for (int i = 0; i < good_matches.size(); i++) {
//-- Get the keypoints from the good matches
obj.push_back(keyPoints_obj[good_matches[i].queryIdx].pt);
scene.push_back(keyPoints_esc[good_matches[i].trainIdx].pt);
}
Mat H = findHomography(obj, scene, CV_RANSAC);
vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0, 0);
obj_corners[1] = cvPoint(240, 0);
obj_corners[2] = cvPoint(240, 320);
obj_corners[3] = cvPoint(0, 320);
vector<Point2f> scene_corners(4);
perspectiveTransform(obj_corners, scene_corners, H);
line(frameGray, scene_corners[0], scene_corners[1], Scalar(255, 0, 0), 4);
line(frameGray, scene_corners[1], scene_corners[2], Scalar(255, 0, 0), 4);
line(frameGray, scene_corners[2], scene_corners[3], Scalar(255, 0, 0), 4);
line(frameGray, scene_corners[3], scene_corners[0], Scalar(255, 0, 0), 4);
for (unsigned int i = 0; i < scene.size(); i++) {
const Point2f& kp = scene[i];
circle(frameGray, Point(kp.x, kp.y), 10, Scalar(255, 255, 255, 255));
}
return i; //position of the matched object
}
}
}
double hessianThreshold=600;
int nOctaves=4;
int nOctaveLayers=2;
bool extended=true;
bool upright=false;
JNIEXPORT void JNICALL Java_es_ugr_reconocimiento_Juego_FindFeatures(JNIEnv* env, jobject, jlong addrGray) {
Mat& frameGray= *(Mat*) addrGray;
vector<KeyPoint> keyPoints;
Mat descriptores;
SurfFeatureDetector detector_Surf(hessianThreshold, nOctaves, nOctaveLayers, extended, upright);
SurfDescriptorExtractor extractor_Surf;
detector_Surf.detect(frameGray, keyPoints);
if (keyPoints.size() > 0)
extractor_Surf.compute(frameGray, keyPoints, descriptores);
}
double hessianThreshold=600;
int nOctaves=4;
int nOctaveLayers=2;
bool extended=true;
bool upright=false;
JNIEXPORT jint JNICALL Java_es_ugr_reconocimiento_Juego_FindObjects(JNIEnv* env, jobject, jlong addrGray) {
Mat& frameGray = *(Mat*) addrGray;
vector<KeyPoint> keyPoints_esc;
Mat descriptores_esc;
SurfFeatureDetector detector_Surf(hessianThreshold, nOctaves, nOctaveLayers, extended, upright);
SurfDescriptorExtractor extractor_Surf;
detector_Surf.detect(frameGray , keyPoints_esc);
if (keyPoints_esc.size() == 0) return -1;
extractor_Surf.compute(frameGray , keyPoints_esc, descriptores_esc);
if (descriptores_esc.rows() == 0) return -1;
for(int i=0;i<lstObjects.size();i++){
Mat descriptores_obj = lstDescriptors.at(i);
vector<KeyPoint> keyPoints_obj = lstKeyPoints.at(i);
FlannBasedMatcher matcher;
vector<vector<DMatch> > matches;
matcher.knnMatch(descriptores_obj, descriptores_esc, matches, 2);
// ----------------------------------------------------------------------
// Draw only "good" matches (i.e. whose distance is less than 2*min_dist,
// or a small arbitary value ( 0.02 ) in the event that min_dist is very
// small)
// PS.- radiusMatch can also be used here.
// ----------------------------------------------------------------------
vector<DMatch> good_matches;
//THIS LOOP IS SENSITIVE TO SEGFAULTS
for (int i = 0; i < min(descriptores_obj.rows - 1, (int) matches.size());i++){
if ( (matches[i][0].distance < 0.6 * (matches[i][1].distance)) &&
((int) matches[i].size() <= 2 && (int) matches[i].size() > 0) ) {
good_matches.push_back(matches[i][0]);
}
}
if (good_matches.size() >= nThreshold) {
vector < Point2f > obj;
vector < Point2f > scene;
for (int i = 0; i < good_matches.size(); i++) {
//-- Get the keypoints from the good matches
obj.push_back(keyPoints_obj[good_matches[i].queryIdx].pt);
scene.push_back(keyPoints_esc[good_matches[i].trainIdx].pt);
}
Mat H = findHomography(obj, scene, CV_RANSAC);
vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0, 0);
obj_corners[1] = cvPoint(240, 0);
obj_corners[2] = cvPoint(240, 320);
obj_corners[3] = cvPoint(0, 320);
vector<Point2f> scene_corners(4);
perspectiveTransform(obj_corners, scene_corners, H);
line(frameGray, scene_corners[0], scene_corners[1], Scalar(255, 0, 0), 4);
line(frameGray, scene_corners[1], scene_corners[2], Scalar(255, 0, 0), 4);
line(frameGray, scene_corners[2], scene_corners[3], Scalar(255, 0, 0), 4);
line(frameGray, scene_corners[3], scene_corners[0], Scalar(255, 0, 0), 4);
for (unsigned int i = 0; i < scene.size(); i++) {
const Point2f& kp = scene[i];
circle(frameGray, Point(kp.x, kp.y), 10, Scalar(255, 255, 255, 255));
}
return i; //position of the matched object
}
}
}
还有其他更好的方法吗？比如使用不同的匹配器或另一个阈值，或者试图弄清楚执行单应性是否会创建类似于矩形的东西（我这样说是因为有时它会“找到”某个东西，但却绘制了四条不构成矩形的线）。请在代码中进行以下更改
int nThreshold= 100;
if (good_matches.size() >= nThreshold)
{
continue; // This line is to prevent further steps of matching if there are too many good matches (Lot of ambiguous points results in false match)
}
vector < Point2f > obj;
vector < Point2f > scene;
for (int i = 0; i < good_matches.size(); i++) {
//-- Get the keypoints from the good matches
obj.push_back(keyPoints_obj[good_matches[i].queryIdx].pt);
scene.push_back(keyPoints_esc[good_matches[i].trainIdx].pt);
}
// Skip doing homography if the object and scene contains less than four points(cant draw a rectangle if less than 4 points, hence your program will crash here if you do not handle the exception)
if(obj.size() < 4 || scene.size() < 4)
{
continue;
}
Mat H = findHomography(obj, scene, CV_RANSAC);
int nThreshold=100；
if（good_matches.size（）>=nThreshold）
{
continue；//如果有太多好的匹配（很多不明确的点导致错误匹配），则此行将阻止进一步的匹配步骤
}
向量obj；
向量场景；
for（int i=0；i 良好匹配。size（）>=0.6*object\u keypoints.size（）