使用无专利描述符进行特征检测

Question

我需要特征检测算法。我受够了在网上冲浪，除了 SURF 示例和提示如何做到这一点，我什么也找不到，但是除了 SIFT 或 SURF 等专利描述符之外，我没有找到其他示例。

谁能写一个使用 free 特征检测算法的例子（比如 ORB/BRISK [据我了解 SURF 和 FLAAN 是 nonfree ]) ?

我正在使用 OpenCV 3.0.0。

Answer 1

不使用 SURF 关键点检测器和描述符提取器，只需切换到使用 ORB。您可以简单地更改传递给 create 的字符串以具有不同的提取器和描述符。

以下内容适用于 OpenCV 2.4.11。

Feature Detector

"FAST" – FastFeatureDetector
"STAR" – StarFeatureDetector
"SIFT" – SIFT（非自由模块）
"SURF" – SURF（非自由模块）
"ORB" – ORB
"BRISK" – 轻快
"MSER" – MSER
"GFTT" – GoodFeaturesToTrackDetector
"HARRIS" – 启用 Harris 检测器的 GoodFeaturesToTrackDetector
"Dense" – DenseFeatureDetector
"SimpleBlob" – SimpleBlobDetector

Descriptor Extractor

"SIFT" – 筛选
"SURF" – 冲浪
"BRIEF" – BriefDescriptorExtractor
"BRISK" – 轻快
"ORB" – ORB
"FREAK" – 怪胎

Descriptor Matcher

BruteForce（它使用 L2）
BruteForce-L1
BruteForce-汉明
BruteForce-Hamming(2)
FlannBased

FLANN 不在 nonfree 中。但是，您可以使用其他匹配器，例如 BruteForce.

下面的例子：

#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;
}

更新

OpenCV 3.0.0 有一个不同的API。

您可以找到非专利特征检测器和描述符提取器列表 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;
}

使用无专利描述符进行特征检测

Feature detection with patent-free descriptors

algorithm

opencv

image-recognition

feature-detection

opencv3.0