opencv-082-角点检测(shi-tomas角点检测)

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知识点

Harris角点检测是一种计算速度很慢的角点检测算法，很难实时计算，所有最常用的是shi-tomas角点检测算法，它的运行速度很快。

API

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void cv::goodFeaturesToTrack(
	InputArray image,
	OutputArray corners,
	int maxCorners,
	double qualityLevel,
	double minDistance,
	InputArray mask = noArray(),
	int blockSize = 3,
	bool useHarrisDetector = false,
	double k = 0.04 
)
src单通道输入图像，八位或者浮点数
corners是输出的关键点坐标集合
maxCorners表示最大返回关键点数目
qualityLevel表示拒绝的关键点 R < qualityLevel × max response将会被直接丢弃
minDistance 表示两个关键点之间的最短距离
mask 表示mask区域，如果有表明只对mask区域做计算
blockSize 计算梯度与微分的窗口区域
useHarrisDetector 表示是否使用harris角点检测，默认是false 为shi-tomas
k = 0.04默认值，当useHarrisDetector为ture时候起作用

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(shi-tomas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);

    // 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)
    # output
    return image


src = cv.imread("D:/images/ele_panel.png")
cv.imshow("input", src)
result = process(src)
cv.imshow('result', result)
cv.waitKey(0)
cv.destroyAllWindows()
"""
cap = cv.VideoCapture(0)
while True:
    ret, frame = cap.read()
    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