【影象處理筆記】特徵提取之整體影象特徵(Harris角點，MSER)

#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
int main() {
    Mat src = imread("./12.bmp", 0);
    Mat markImg = imread("./12.bmp");
    Mat kx = (Mat_<float>(1, 3) << -1, 0, 1);
    Mat ky = (Mat_<float>(3, 1) << -1, 0, 1);
    vector<Rect> rects = { Rect(2225,350,30,30), Rect(2225,405,30,30), Rect(2190,405,30,30) };

    for (int i = 0; i < rects.size(); i++) {
        rectangle(markImg, rects[i], Scalar(0, 255, 0), 1);
        Mat roi = src(rects[i]);//從一張圖上裁的三個區域
        Mat mask = Mat::zeros(Size(500, 500), CV_8UC1);
        Mat fx, fy;
        filter2D(roi, fx, CV_32F, kx);
        filter2D(roi, fy, CV_32F, ky);
        vector<Point> points;
        for (int i = 0; i < 30; i++) {
            for (int j = 0; j < 30; j++) {
                Point p = Point(fx.at<float>(i, j), fy.at<float>(i, j));
                Point p_offset = p + Point(250, 250);
                mask.at<uchar>(p_offset.y, p_offset.x) = 255;
                points.push_back(p_offset);
            }
        }
        Mat M = Mat::zeros(Size(2, 2), CV_32FC1);
        fx /= Scalar::all(255);
        fy /= Scalar::all(255);
        Mat q1, q2, q3;
        multiply(fx, fx, q1);
        multiply(fx, fy, q2);
        multiply(fy, fy, q3);
        M.at<float>(0, 0) = sum(sum(q1))[0];
        M.at<float>(0, 1) = sum(sum(q2))[0];
        M.at<float>(1, 0) = sum(sum(q2))[0];
        M.at<float>(1, 1) = sum(sum(q3))[0];

        Mat values;
        eigen(M, values);
        cout << "lamda1:" << values.ptr<float>(0)[0] << ", lamda2:" << values.ptr<float>(1)[0];
        double R = determinant(M) - 0.04 * pow(trace(M)[0], 2);
        cout << ", R:" << R << endl;
        imshow("roi"+to_string(i), roi);
        imshow("mask" + to_string(i), mask);
    }
    waitKey(0);
    return 0;
}

#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;

int main() {
    Mat src = imread("./13.bmp", 0);
    Mat markImg = imread("./13.bmp");
    imshow("src", markImg);
    Mat dst, binImg;
    cornerHarris(src, dst, 5, 9, 0.04);
    normalize(dst, dst, 0, 1.0, NORM_MINMAX);
    threshold(dst, binImg, 0.25, 1, THRESH_BINARY);
    binImg.convertTo(binImg, CV_8U, 255);
    vector<vector<Point>> contours;
    findContours(binImg, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
    vector<Point2f> corners;
    for (size_t i = 0; i < contours.size(); i++)
    {
        Point2f center; float radius;
        minEnclosingCircle(contours[i], center, radius);
        if(radius>2)
            corners.push_back(center);
    }
    TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::MAX_ITER, 1000, 0.001);
    cornerSubPix(src, corners, cv::Size(7, 7), cv::Size(-1, -1), criteria);
    for (size_t i = 0; i < corners.size(); i++)
        circle(markImg, corners[i], 3, Scalar(0, 255, 0), 3);
    imshow("markImg", markImg);
    waitKey(0);
    return 0;
}

#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;

bool cmp(Rect r1, Rect r2) {
    return r1.x < r2.x;
}
float IOU(const Rect& box1, const Rect& box2)
{
    if (box1.x > box2.x + box2.width) { return 0.0; }
    if (box1.y > box2.y + box2.height) { return 0.0; }
    if (box1.x + box1.width < box2.x) { return 0.0; }
    if (box1.y + box1.height < box2.y) { return 0.0; }
    float colInt = min(box1.x + box1.width, box2.x + box2.width) - max(box1.x, box2.x);
    float rowInt = min(box1.y + box1.height, box2.y + box2.height) - max(box1.y, box2.y);
    float intersection = colInt * rowInt;
    float area1 = box1.width * box1.height;
    float area2 = box2.width * box2.height;
    return intersection / (area1 + area2 - intersection);
}

vector<Rect> nms(vector<Rect> boxes, double overlapThreshold) {
    sort(boxes.begin(), boxes.end(), cmp); //將矩形框排序
    Rect r = boxes[0];
    vector<Rect> boxes_selectes = { r };
    int i = 1;
    while (i < boxes.size()) {
        if (!(IOU(r, boxes[i]) > overlapThreshold)) {//計算交併比
            boxes_selectes.push_back(boxes[i]);
            r = boxes[i];
        }
        i++;
    }
    return boxes_selectes;
}
int main() {
    Mat src = imread("./1.jpg", 0);
    Mat markImg = imread("./1.jpg");
    imshow("src", markImg);    
    vector<vector<Point> > points;
    vector<Rect> rects;
    Ptr<MSER> ptrMSER = MSER::create(1, 50, 10000);
    ptrMSER->detectRegions(src, points, rects);
    // 根據文字的大小篩選
    vector<Rect> boxes;
    for (int i = 0; i < rects.size(); i++) {
        if (rects[i].width < 60 && rects[i].height>60)
            boxes.push_back(rects[i]);
    }
    // 非極大值抑制
    vector<Rect> boxes_selectes = nms(boxes, 0.1);
    for (int i = 0; i < boxes_selectes.size(); i++)
        rectangle(markImg, boxes_selectes[i], Scalar(0, 255, 0), 1);
    imshow("markImg", markImg);
    waitKey(0);
    return 0;
}