使用无专利描述符进行特征检测

Question

我需要特征检测算法。我已经厌倦了在网上搜索除SURF示例和提示如何做到这一点之外，没有找到其他专利描述符（如SIFT或SURF）的示例。

有人能否编写使用免费特征检测算法（例如ORB / BRISK [据我所知，SURF和FLAAN是非免费的]）的示例？

我正在使用OpenCV 3.0.0。

- denis631

Answer 1

不要使用SURF关键点检测器和描述符提取器，而是转换为使用ORB。您只需更改传递给create的字符串即可拥有不同的提取器和描述符。

以下内容适用于OpenCV 2.4.11。

特征检测器

• "FAST" - FastFeatureDetector
• "STAR" - StarFeatureDetector
• "SIFT" - SIFT（非免费模块）
• "SURF" - SURF（非免费模块）
• "ORB" - ORB
• "BRISK" - BRISK
• "MSER" - MSER
• "GFTT" - GoodFeaturesToTrackDetector
• "HARRIS" - 启用Harris检测器的GoodFeaturesToTrackDetector
• "Dense" - DenseFeatureDetector
• "SimpleBlob" - SimpleBlobDetector

描述符提取器

• "SIFT" - SIFT
• "SURF" - SURF
• "BRIEF" - BriefDescriptorExtractor
• "BRISK" - BRISK
• "ORB" - ORB
• "FREAK" - FREAK

描述符匹配器

• BruteForce（使用L2）
• BruteForce-L1
• BruteForce-Hamming
• BruteForce-Hamming（2）
• FlannBased

FLANN不在非免费模块中。但是，您可以使用其他匹配程序，例如BruteForce。

以下是示例：

#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 \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;
}

更新

OpenCV 3.0.0有不同的API。

你可以在这里找到非专利特征检测器和描述符提取器的列表。

#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;
}

- Miki

---

你使用的是哪个OpenCV版本？这在OpenCV 2.4.9和2.4.11中都可以正常工作。 - Miki

最新的iOS版本可以在OpenCV网站上获取。 - denis631

在 'cv::Feature2d' 中没有名为 'create' 的成员。 - denis631

AKAZE是另一个不错的选择，它的匹配精度接近SURF，而运行时性能类似于SIFT，比ORB慢得多。http://docs.opencv.org/3.0-beta/doc/tutorials/features2d/akaze_matching/akaze_matching.html - Saharsh Bishnoi

@SaharshBishnoi 当然，OpenCV 3.0 中有许多新的描述符，包括 AKAZE。我在我的答案中添加了文档链接。 - Miki