OpenCV4入门教程118：特征综合

就是将前面提到的特征方法综合起来，使用统一的接口。

#include "opencv2/opencv.hpp"

using namespace std;
using namespace cv;

int main() {
    double matchThresh;

    // ***** Read images
    Mat object = imread("box.png");
    Mat scene  = imread("box_in_scene.png");

    // ***** Create the algorithm accordingly
    int            algorithm = 1;
    Ptr<Feature2D> detector;
    switch (algorithm) {
        case 1:
            detector    = BRISK::create();
            matchThresh = 350;
            break;

        case 2:
            detector    = KAZE::create();
            matchThresh = 0.1;
            break;

        case 3:
            detector    = AKAZE::create();
            matchThresh = 250;
            break;
    }

    // ***** Detect features
    vector<KeyPoint> objKPs, scnKPs;
    detector->detect(object, objKPs);
    detector->detect(scene, scnKPs);

    // ***** Extract descriptors
    Mat objDesc, scnDesc;
    detector->compute(object, objKPs, objDesc);
    detector->compute(scene, scnKPs, scnDesc);

    // ***** Match descriptors
    Ptr<BFMatcher> matcher = BFMatcher::create();
    vector<DMatch> matches;
    matcher->match(objDesc, scnDesc, matches);

    // ***** Remove "bad" matches
    vector<DMatch> goodMatches;
    for (int i = 0; i < objDesc.rows; i++) {
        if (matches[ i ].distance < matchThresh)
            goodMatches.push_back(matches[ i ]);
    }

    if (goodMatches.size() > 0) {
        cout << "Found " << goodMatches.size() << " good matches.";
    } else {
        cout << "Didn't find a single good match. Quitting!";
        return -1;
    }

    // ***** Draw matches
    Mat result;
    drawMatches(object, objKPs, scene, scnKPs, goodMatches, result,
                Scalar(0, 255, 0), // green for matched
                Scalar::all(-1),   // unmatched color (default)
                vector<char>(),    // empty mask
                DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);

    // ***** Find the homography change between object and scene
    vector<Point2f> goodP1, goodP2;
    for (int i = 0; i < goodMatches.size(); i++) {
        goodP1.push_back(objKPs[ goodMatches[ i ].queryIdx ].pt);
        goodP2.push_back(scnKPs[ goodMatches[ i ].trainIdx ].pt);
    }
    Mat homoChange = findHomography(goodP1, goodP2);

    // ***** Apply perpective change to find the result points
    vector<Point2f> corners1(4), corners2(4);
    corners1[ 0 ] = Point2f(0, 0);
    corners1[ 1 ] = Point2f(object.cols - 1, 0);
    corners1[ 2 ] = Point2f(object.cols - 1, object.rows - 1);
    corners1[ 3 ] = Point2f(0, object.rows - 1);
    perspectiveTransform(corners1, corners2, homoChange);

    // ***** Correct the coordinates to fir the drawMatches result image
    for (int i = 0; i < 4; i++)
        corners2[ i ].x += object.cols;

    // ***** Draw the bounding lines
    line(result, corners2[ 0 ], corners2[ 1 ], Scalar::all(255), 2);
    line(result, corners2[ 1 ], corners2[ 2 ], Scalar::all(255), 2);
    line(result, corners2[ 2 ], corners2[ 3 ], Scalar::all(255), 2);
    line(result, corners2[ 3 ], corners2[ 0 ], Scalar::all(255), 2);

    imshow("Result", result);
    waitKey();

    return 0;
}