Wykrywanie funkcji za pomocą niewymagających patentu deskryptorów

Question

Potrzebuję algorytmu wykrywania cech. Mam dość surfowania po Internecie, znajdując tylko przykład SURF i podpowiedzi jak to zrobić, ale nie znalazłem przykładu z innymi niż opatentowanymi deskryptorami, takimi jak SIFT lub SURF.

ktoś może napisać przykład przy użyciu wolnego algorytmu wykrywania właściwością jest (jak ORB/BRISK [ile dobrze rozumiane Surf i FLAAN są nonfree])?

Używam OpenCV 3.0.0.

Answer 1

Zamiast używać detektora punktów SURF i ekstraktora deskryptorów, wystarczy użyć ORB. Możesz po prostu zmienić ciąg przekazany do create, aby mieć różne ekstraktory i deskryptory.

Poniższe informacje dotyczą OpenCV 2.4.11.

Feature Detector

• "szybko" - FastFeatureDetector
• "gwiazdę" - StarFeatureDetector
• "SIFT" - SIFT (moduł niewolne)
• "surfowanie" - KIPIEL (moduł nonfree)
• "ORB" - ORB
• "BRISK" - BRISK
• "MSER" - MSER
• "GFTT" - GoodFeaturesToTrackDetector
• "HARRIS" - GoodFeaturesToTrackDetector z detektorem Harris włączona
• "Gęsty" - DenseFeatureDetector
• "SimpleBlob" - SimpleBlobDetector

Descriptor Extractor

• "SIFT" - SIFT
• "surfowanie" - KIPIEL
• "Krótki" - BriefDescriptorExtractor
• "BRISK" - BRISK
• "ORB" - ORB
• "FREAK" - FREAK

Descriptor Matcher

• BruteForce (używa L2)
• BruteForce-L1
• Bruteforce-Hamminga
• Bruteforce-Hamminga (2)
• FlannBased

Flann nie jest niewolnego.Możesz jednak użyć innych dopasowań, takich jak BruteForce.

Poniższy przykład:

#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 \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 
    imshow("Good Matches & Object detection", img_matches); 

    waitKey(0); 
    return 0; 
} 

UPDATE

OpenCV 3.0.0 inne API.

Możesz znaleźć listę nieopatentowanych detektorów cech i ekstraktora deskryptorów here.

#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 \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), 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; 
}