首先,我对匹配技术比较陌生,所以请耐心等待:
我正在开发一个应用程序,将训练图像与收集的图像(单个细胞样本)相匹配。
我已经使用SIFT检测器和SURF检测器与基于FLANN的匹配来匹配一组训练数据到收集的图像。但我得到的结果真的很差。我使用与openCV文档中相同的代码:
void foramsMatching(Mat img_object, Mat img_scene){
int minHessian = 400;
SiftFeatureDetector detector(minHessian);
std::vector<KeyPoint> keypoints_object, keypoints_scene;
detector.detect(img_object, keypoints_object);
detector.detect(img_scene, keypoints_scene);
//-- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat descriptors_object, descriptors_scene;
extractor.compute(img_object, keypoints_object, descriptors_object);
extractor.compute(img_scene, keypoints_scene, descriptors_scene);
//-- Step 3: Matching descriptor vectors using FLANN matcher
FlannBasedMatcher matcher;
//BFMatcher matcher;
std::vector< DMatch > matches;
matcher.match(descriptors_object, descriptors_scene, matches);
double max_dist = 0; double min_dist = 100;
//-- Quick calculation of max and min distances between keypoints
for (int i = 0; i < descriptors_object.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);
//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
std::vector< DMatch > good_matches;
for (int i = 0; i < descriptors_object.rows; i++)
{
if (matches[i].distance < 3 * min_dist)
{
good_matches.push_back(matches[i]);
}
}
Mat img_matches;
drawMatches(img_object, keypoints_object, img_scene, keypoints_scene,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
//-- Localize the object
std::vector<Point2f> obj;
std::vector<Point2f> scene;
for (int i = 0; i < good_matches.size(); i++)
{
//-- Get the keypoints from the good matches
obj.push_back(keypoints_object[good_matches[i].queryIdx].pt);
scene.push_back(keypoints_scene[good_matches[i].trainIdx].pt);
}
Mat H = findHomography(obj, scene, CV_RANSAC);
//-- Get the corners from the image_1 ( the object to be "detected" )
std::vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0, 0); obj_corners[1] = cvPoint(img_object.cols, 0);
obj_corners[2] = cvPoint(img_object.cols, img_object.rows); obj_corners[3] = cvPoint(0, img_object.rows);
std::vector<Point2f> scene_corners(4);
perspectiveTransform(obj_corners, scene_corners, H);
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
line(img_matches, scene_corners[0] + Point2f(img_object.cols, 0), scene_corners[1] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[1] + Point2f(img_object.cols, 0), scene_corners[2] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[2] + Point2f(img_object.cols, 0), scene_corners[3] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[3] + Point2f(img_object.cols, 0), scene_corners[0] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);
//-- Show detected matches
namedWindow("Good Matches & Object detection", CV_WINDOW_NORMAL);
imshow("Good Matches & Object detection", img_matches);
//imwrite("../../Samples/Matching.jpg", img_matches);
}
以下是结果 -
与我使用这些方法看到的其他一些结果相比,它们真的很差。屏幕底部的两个blob(单元格)应该有两个匹配。
关于我做错了什么或如何改善这些结果的任何想法? 我正在考虑编写自己的Matcher / Discription Extractor,因为我的训练图像并不是我要查询的细胞的精确复制品。 这是一个好主意吗?如果是这样,我应该看看任何教程?
此致
答案 0 :(得分:0)
将评论转换为答案:
在运行SIFT / SURF之前,您应该使用可用的知识进行某种预处理,以便找到感兴趣的区域并消除噪音。这是一般的想法:
(*)您可以用于此步骤的内容包括:区域大小,形状,颜色分布等。从您提供的示例中,它可以例如可以看出你的物体是圆形的并且具有一定的最小尺寸。使用您拥有的任何知识来消除进一步的误报。当然,您需要进行一些调整,以使您的规则集不会过于严格,即保持真正的正面。