特許のない記述子による特徴検出

Question

特徴検出アルゴリズムが必要です。私はウェブ上でサーフィンをすることにうんざりしていて、SURFの例とヒント以外の方法を見つけていませんが、特許取得済み以外の例は見つかりませんでしたSIFTやSURFなどの記述子。

無料機能検出アルゴリズム（ORB/BRISK [SURFとFLAANが非無料]であると理解している限り）の使用例を誰かが書くことができますか？

OpenCV3.0.0を使用しています。

Miki · Accepted Answer

SURFキーポイント検出器と記述子抽出機能を使用する代わりに、ORBを使用するように切り替えるだけです。 createに渡される文字列を変更するだけで、さまざまなエクストラクタと記述子を使用できます。

以下はOpenCV2.4.11に有効です。

機能検出器

「FAST」– FastFeatureDetector
「STAR」– StarFeatureDetector
「SIFT」– SIFT（非フリーモジュール）
「SURF」– SURF（非フリーモジュール）
「ORB」– ORB
「BRISK」– BRISK
「MSER」– MSER
「GFTT」– GoodFeaturesToTrackDetector
「HARRIS」–ハリス検出器が有効になっているGoodFeaturesToTrackDetector
「高密度」– DenseFeatureDetector
「SimpleBlob」– SimpleBlobDetector

記述子抽出

「SIFT」– SIFT
「SURF」– SURF
「BRIEF」– BriefDescriptorExtractor
「BRISK」– BRISK
「ORB」– ORB
「フリーク」–フリーク

記述子マッチャー

BruteForce（L2を使用）
ブルートフォース-L1
ブルートフォース-ハミング
ブルートフォース-ハミング（2）
フランベース

FLANNはnonfreeにありません。ただし、BruteForceのような他のマッチャーを使用することもできます。

以下の例：

#include <iostream> #include <opencv2\opencv.hpp> using namespace cv; /** @function main */ int main(int argc, char** argv) { Mat img_object = imread("D:\SO\img\box.png", CV_LOAD_IMAGE_GRAYSCALE); Mat img_scene = imread("D:\SO\img\box_in_scene.png", CV_LOAD_IMAGE_GRAYSCALE); if (!img_object.data || !img_scene.data) { std::cout << " --(!) Error reading images " << std::endl; return -1; } //-- Step 1: Detect the keypoints using SURF Detector Ptr<FeatureDetector> detector = FeatureDetector::create("ORB"); 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) Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create("ORB"); 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 Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce"); 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 
", max_dist); printf("-- Min dist : %f 
", 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 imshow("Good Matches & Object detection", img_matches); waitKey(0); return 0; }

[〜＃〜]更新[〜＃〜]

OpenCV3.0.0には異なるAPIがあります。

特許を取得していない特徴検出器と記述子抽出器のリストを見つけることができますここ。

#include <iostream> #include <opencv2\opencv.hpp> using namespace cv; /** @function main */ int main(int argc, char** argv) { Mat img_object = imread("D:\SO\img\box.png", CV_LOAD_IMAGE_GRAYSCALE); Mat img_scene = imread("D:\SO\img\box_in_scene.png", CV_LOAD_IMAGE_GRAYSCALE); if (!img_object.data || !img_scene.data) { std::cout << " --(!) Error reading images " << std::endl; return -1; } //-- Step 1: Detect the keypoints using SURF Detector Ptr<FeatureDetector> detector = ORB::create(); 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) Ptr<DescriptorExtractor> extractor = ORB::create(); 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 Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce"); 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 
", max_dist); printf("-- Min dist : %f 
", 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), std::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 imshow("Good Matches & Object detection", img_matches); waitKey(0); return 0; }