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我想找/检查子图/模板图像在主图像,想知道它的坐标,我已经使用在以下链接来实现它定的代码匹配模板IMGE(缩放)主/图片放大
,
Check presence of subimage in image in iOS
它工作正常,如果模板图像的尺寸是较大的图像的匹配部分的大小完全相同。
但是,如果子图像缩小或放大比匹配较大图像的一部分,它不能正确给出结果。
我想找/检查子图/模板图像在主图像,想知道它的坐标,我已经使用在以下链接来实现它定的代码匹配模板IMGE(缩放)主/图片放大
,
Check presence of subimage in image in iOS
它工作正常,如果模板图像的尺寸是较大的图像的匹配部分的大小完全相同。
但是,如果子图像缩小或放大比匹配较大图像的一部分,它不能正确给出结果。
使用OpenCV特征检测。它比模板匹配更精确..
请使用此代码尝试..
-(void)featureDetection:(UIImage*)largerImage withImage:(UIImage*)subImage
{
cv::Mat tempMat1 = [largerImage CVMat];
cv::Mat tempMat2 = [subImage CVMat];
cv::cvtColor(tempMat1, tempMat1, CV_RGB2GRAY);
cv::cvtColor(tempMat2, tempMat2, CV_RGB2GRAY);
if(!tempMat1.data || !tempMat2.data) {
return;
}
//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 25;
cv::SurfFeatureDetector detector(minHessian); // More Accurate bt take more time..
//cv::FastFeatureDetector detector(minHessian); //Less Accurate bt take less time..
std::vector<cv::KeyPoint> keypoints_1, keypoints_2;
detector.detect(tempMat1, keypoints_1);
detector.detect(tempMat2, keypoints_2);
//-- Step 2: Calculate descriptors (feature vectors)
cv::SurfDescriptorExtractor extractor;
cv::Mat descriptors_1, descriptors_2;
extractor.compute(tempMat1, keypoints_1, descriptors_1);
extractor.compute(tempMat2, keypoints_2, descriptors_2);
std::vector<cv::Point2f> obj_corners(4);
//Get the corners from the object
obj_corners[0] = (cvPoint(0,0));
obj_corners[1] = (cvPoint(tempMat2.cols,0));
obj_corners[2] = (cvPoint(tempMat2.cols,tempMat2.rows));
obj_corners[3] = (cvPoint(0, tempMat2.rows));
//-- Step 3: Matching descriptor vectors with a brute force matcher
//cv::BruteForceMatcher < cv::L2<float> > matcher;
cv::FlannBasedMatcher matcher;
//std::vector<cv::DMatch> matches;
std::vector<cv::vector<cv::DMatch > > matches;
std::vector<cv::DMatch > good_matches;
std::vector<cv::Point2f> obj;
std::vector<cv::Point2f> scene;
std::vector<cv::Point2f> scene_corners(4);
cv::Mat H;
matcher.knnMatch(descriptors_2, descriptors_1, matches,2);
for(int i = 0; i < cv::min(tempMat1.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]);
}
}
cv::Mat img_matches;
drawMatches(tempMat2, keypoints_2, tempMat1, keypoints_1, good_matches, img_matches);
NSLog(@"good matches %lu",good_matches.size());
if (good_matches.size() >= 4) {
for(int i = 0; i < good_matches.size(); i++) {
//Get the keypoints from the good matches
obj.push_back(keypoints_2[ good_matches[i].queryIdx ].pt);
scene.push_back(keypoints_1[ good_matches[i].trainIdx ].pt);
}
H = findHomography(obj, scene, CV_RANSAC);
perspectiveTransform(obj_corners, scene_corners, H);
NSLog(@"%f %f",scene_corners[0].x,scene_corners[0].y);
NSLog(@"%f %f",scene_corners[1].x,scene_corners[1].y);
NSLog(@"%f %f",scene_corners[2].x,scene_corners[2].y);
NSLog(@"%f %f",scene_corners[3].x,scene_corners[3].y);
//Draw lines between the corners (the mapped object in the scene image)
line(tempMat1, scene_corners[0], scene_corners[1], cvScalar(0, 255, 0), 4);
line(tempMat1, scene_corners[1], scene_corners[2], cvScalar(0, 255, 0), 4);
line(tempMat1, scene_corners[2], scene_corners[3], cvScalar(0, 255, 0), 4);
line(tempMat1, scene_corners[3], scene_corners[0], cvScalar(0, 255, 0), 4);
}
// View matching..
UIImage *resultimage = [UIImage imageWithCVMat:img_matches];
UIImageView *imageview = [[UIImageView alloc] initWithImage:resultimage];
imageview.frame = CGRectMake(0, 0, 320, 240);
[self.view addSubview:imageview];
// View Result
UIImage *resultimage2 = [UIImage imageWithCVMat:tempMat1];
UIImageView *imageview2 = [[UIImageView alloc] initWithImage:resultimage2];
imageview2.frame = CGRectMake(0, 240, 320, 240);
[self.view addSubview:imageview2];
}
非常感谢你,很好地工作... –
你可以尝试变换数极坐标http://stackoverflow.com/questions/14132951/how-to-obtain-the - 范围和旋转角度从对数极坐标变换 – mrgloom
我已经看到了一个,但在upvoted的答案中,我无法理解任何东西 –
两个简单(但效率不高)的方法: 重新缩放模板并匹配原始图像或重新缩放图像并匹配原始模板。 模板匹配不是比例不变的。您可能希望搜索文献以获得“尺度不变”(也可能是旋转不变)模板匹配方法,或者您可能希望切换到像SIFT或SURF这样的尺度不变特征(并使用特征匹配)的更健壮的方法。 – Micka