opencv-083-角点检测(亚像素级别角点检测)

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知识点

OpenCV中角点检测的结果实际不够精准，角点检测最后的结果是整数值，因为真实的计算中有些位置可能是在浮点数的空间内才最大值，这样就需要我们通过给定的响应值，在像素邻域空间进行拟合，实现亚像素级别的角点检测。如：(100,5)实际上应该是(100.126,4.329) .

API

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void cv::cornerSubPix(
	InputArray image,
	InputOutputArray corners,
	Size winSize,
	Size zeroZone,
	TermCriteria criteria 
)
image单通道输入图像，八位或者浮点数
corners是输入输出的关键点坐标集合
winSize表示插值计算时候窗口大小
zeroZone表示搜索区域中间的dead region边长的一半，有时用于避免自相关矩阵的奇异性。如果值设为(-1,-1)则表示没有这个区域。
criteria角点精准化迭代过程的终止条件

代码（c++,python）

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#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

void process_frame(Mat &image);

RNG rng(12345);

/*
 * 角点检测(亚像素级别角点检测)
 */
int main() {
    VideoCapture capture("../images/color_object.mp4");
    if (!capture.isOpened()) {
        cout << "could not open video..." << endl;
        return -1;
    }

    Mat frame;
    while (true) {
        bool ret = capture.read(frame);
        imshow("input", frame);
        if (!ret) break;

        process_frame(frame);
        imshow("result", frame);

        char c = waitKey(5);
        if (c == 27) {
            break;
        }
    }

    waitKey(0);
    return 0;
}

void process_frame(Mat &image) {
    // Detector parameters
    int maxCorners = 100;
    double quality_level = 0.01;
    double minDistance = 0.04;

    // detecting corners
    Mat gray, dst;
    cvtColor(image, gray, COLOR_BGR2GRAY);
    vector<Point2f> corners;
    goodFeaturesToTrack(gray, corners, maxCorners, quality_level,
                        minDistance, Mat(), 3, false);

    // detect sub-pixel 亚像素检测
    Size winSize = Size(5,5);
    Size zeroZone = Size(-1,-1);
    TermCriteria criteria = TermCriteria(TermCriteria::EPS +
            TermCriteria::COUNT, 40, 0.001);
    cornerSubPix(gray, corners, winSize, zeroZone, criteria);

    // drawing corner
    for (int i = 0; i < corners.size(); ++i) {
        int b = rng.uniform(0, 255);
        int g = rng.uniform(0, 255);
        int r = rng.uniform(0, 255);
        circle(image, corners[i], 5, Scalar(b, g, r), 3);
    }
}
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import numpy as np
import cv2 as cv


def process(image, opt=1):
    # Detecting corners
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    corners = cv.goodFeaturesToTrack(gray, 100, 0.05, 10)
    print(len(corners))
    for pt in corners:
        print(pt)
        b = np.random.random_integers(0, 256)
        g = np.random.random_integers(0, 256)
        r = np.random.random_integers(0, 256)
        x = np.int32(pt[0][0])
        y = np.int32(pt[0][1])
        cv.circle(image, (x, y), 5, (int(b), int(g), int(r)), 2)

    # detect sub-pixel
    winSize = (5, 5)
    zeroZone = (-1, -1)
    criteria = (cv.TERM_CRITERIA_EPS + cv.TermCriteria_COUNT, 40, 0.001)
    # Calculate the refined corner locations
    corners = cv.cornerSubPix(gray, corners, winSize, zeroZone, criteria)
    # display
    for i in range(corners.shape[0]):
        print(" -- Refined Corner [", i, "]  (", corners[i, 0, 0], ",", corners[i, 0, 1], ")")
    return image


cap = cv.VideoCapture("D:/images/video/vtest.avi")
while True:
    ret, frame = cap.read()
    frame = cv.flip(frame, 1)
    cv.imwrite("D:/input.png", frame)
    cv.imshow('input', frame)
    result = process(frame)
    cv.imshow('result', result)
    k = cv.waitKey(5)&0xff
    if k == 27:
        break
cap.release()
cv.destroyAllWindows()

结果

代码地址

github