OpenCV3.0: SURF detection in parallel?

asked 2016-02-01 03:30:45 -0500

MarKS9 gravatar image

I need to make this SURF detection code run in parallel to gain faster fps. Now with this code i am getting max of 4 fps. what is the best method i could make it faster by running it in parallel using parallel_for_ , multithreading ? If yes, how do i do it ? Need help badly! Below is my implemented code.

void surf_detection::surf_detect(){

Mat img_extractor, snap_extractor;

if (crop_image_.empty())
    cv_snapshot.copyTo(dst);
else
    crop_image_.copyTo(dst);
//dst = QImagetocv(crop_image_);

imshow("dst", dst);

Ptr<SURF> detector = SURF::create(minHessian);
Ptr<DescriptorExtractor> extractor = SURF::create(minHessian);

cvtColor(dst, src, CV_BGR2GRAY);
cvtColor(frame, gray_image, CV_BGR2GRAY);


detector->detect(src, keypoints_1);
//printf("Object: %d keypoints detected\n", (int)keypoints_1.size());
detector->detect(gray_image, keypoints_2);
//printf("Object: %d keypoints detected\n", (int)keypoints_1.size());

extractor->compute(src, keypoints_1, img_extractor);
// printf("Object: %d descriptors extracted\n", img_extractor.rows);
extractor->compute(gray_image, keypoints_2, snap_extractor);

std::vector<Point2f> scene_corners(4);
std::vector<Point2f> obj_corners(4);

obj_corners[0] = (cvPoint(0, 0));
obj_corners[1] = (cvPoint(src.cols, 0));
obj_corners[2] = (cvPoint(src.cols, src.rows));
obj_corners[3] = (cvPoint(0, src.rows));

vector<DMatch> matches;
matcher.match(img_extractor, snap_extractor, matches);

double max_dist = 0; double min_dist = 100;

//-- Quick calculation of max and min distances between keypoints
for (int i = 0; i < img_extractor.rows; i++)
{
    double dist = matches[i].distance;
    if (dist < min_dist) min_dist = dist;
    if (dist > max_dist) max_dist = dist;
}
//printf("-- Max dist : %f \n", max_dist);
//printf("-- Min dist : %f \n", min_dist);

vector< DMatch > good_matches;

for (int i = 0; i < img_extractor.rows; i++)
{
    if (matches[i].distance <= max(2 * min_dist, 0.02))
    {
        good_matches.push_back(matches[i]);
    }
}

Mat img_matches;
drawMatches(src, keypoints_1, gray_image, keypoints_2,
    good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
    vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);

if (good_matches.size() >= 4){

    for (int i = 0; i<good_matches.size(); i++){

        //get the keypoints from good matches
        obj.push_back(keypoints_1[good_matches[i].queryIdx].pt);
        scene.push_back(keypoints_2[good_matches[i].trainIdx].pt);

    }
}

H = findHomography(obj, scene, CV_RANSAC);

perspectiveTransform(obj_corners, scene_corners, H);

line(img_matches, scene_corners[0] + Point2f(src.cols, 0), scene_corners[1] + Point2f(src.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[1] + Point2f(src.cols, 0), scene_corners[2] + Point2f(src.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[2] + Point2f(src.cols, 0), scene_corners[3] + Point2f(src.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[3] + Point2f(src.cols, 0), scene_corners[0] + Point2f(src.cols, 0), Scalar(0, 255, 0), 4);

//    drawKeypoints(src,keypoints_1,img_keypoint1,Scalar::all(-1),DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
//    drawKeypoints(gray_image,keypoints_2,img_keypoint2,Scalar::all(-1),DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);

//    QImage img_1 = cvToQImage(img_keypoint1);
//    QImage img_2 = cvToQImage(img_keypoint2);

imshow("Good matches", img_matches);
//    ui->label_2->setScaledContents(true);
//    ui->label->setPixmap(QPixmap::fromImage(img_2));
//    ui->label_2->setPixmap(QPixmap::fromImage(img_1));
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