Algorithm 基于无专利描述符的特征检测

我需要特征检测算法。我厌倦了在网上冲浪，只找到冲浪的例子和提示，但我没有找到一个例子，除了专利描述符，如SIFT或SURF

任何人都可以写一个使用免费特征检测算法的例子（比如ORB/BRISK[据我所知，SURF和FLAAN是非免费的）

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我正在使用OpenCV 3.0.0。

而不是使用SURF关键点检测器和描述符提取器，只需切换到使用ORB即可。您可以简单地更改传递到

create

的字符串，使其具有不同的提取器和描述符

以下内容适用于OpenCV 2.4.11

• “快速”-快速特征检测器
• “恒星”——恒星特征探测器
• “筛选”-筛选（非自由模块）
• “冲浪”-冲浪（非自由模块）
• “球体”-球体
• “轻快”——轻快
• “MSER”-MSER
• “GFTT”-跟踪探测器的良好特性
• “HARRIS”-启用HARRIS检测器的TrackDetector的良好功能
• “稠密”-稠密特性探测器
• “SimpleBlob”-SimpleBlob检测器

• “筛选”-筛选
• “冲浪”——冲浪
• “简介”-简介描述符
• “轻快”——轻快
• “球体”-球体
• “怪胎”——怪胎

• BruteForce（它使用L2）
• 蛮力-L1
• 布鲁特福斯·哈明
• 布鲁特福斯·汉明（2）
• 法兰贝斯

法兰不在非自由区。但是，您可以使用其他匹配器，如

BruteForce

下面是一个例子：

#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;
}

#包括
#包括
使用名称空间cv；
/**@主功能*/
int main（int argc，字符**argv）
{
Mat img\u object=imread（“D:\\SO\\img\\box.png”，CV\u LOAD\u IMAGE\u灰度）；
Mat img_scene=imread（“D:\\SO\\img\\box_in_scene.png”，CV_LOAD_IMAGE_GRAYSCALE）；
如果（！img_object.data | |！img_scene.data）
{
std：：无法检测（img_场景、关键点_场景）；
//--步骤2：计算描述符（特征向量）
Ptr提取器=描述符牵引器：：创建（“ORB”）；
Mat描述符\u对象，描述符\u场景；
提取器->计算（img\u对象、关键点\u对象、描述符\u对象）；
提取器->计算（img\U场景、关键点\U场景、描述符\U场景）；
//--步骤3：使用FLANN匹配器匹配描述符向量
Ptr matcher=DescriptorMatcher:：create（“BruteForce”）；
标准：：向量匹配；
匹配器->匹配（描述符\对象、描述符\场景、匹配）；
双最大距离=0；双最小距离=100；
//--快速计算关键点之间的最大和最小距离
对于（int i=0；i最大距离）最大距离=距离；
}
printf（“--Max dist:%f\n”，Max\u dist）；
printf（“--最小距离：%f\n”，最小距离）；
//--仅绘制“良好”匹配（即距离小于3*min\u dist）
标准：：矢量良好匹配；
对于（int i=0；i

更新

OpenCV 3.0.0具有不同的API

您可以找到非专利特征检测器和描述符提取器的列表

#包括
#包括
使用名称空间cv；
/**@主功能*/
int main（int argc，字符**argv）
{
Mat img\u object=imread（“D:\\SO\\img\\box.png”，CV\u LOAD\u IMAGE\u灰度）；
Mat img_scene=imread（“D:\\SO\\img\\box_in_scene.png”，CV_LOAD_IMAGE_GRAYSCALE）；
如果（！img_object.data | |！img_scene.data）
{
std：：无法检测（img_场景、关键点_场景）；
//--步骤2：计算描述符（特征向量）
Ptr提取器=ORB:：create（）；
Mat描述符\u对象，描述符\u场景；
提取器->计算（img\u对象、关键点\u对象、描述符\u对象）；
提取器->计算（img\U场景、关键点\U场景、描述符\U场景）；
//--步骤3：使用FLANN匹配器匹配描述符向量
Ptr matcher=DescriptorMatcher:：create（“
#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;
}