opencv-052-二值图像分析(使用几何矩计算轮廓中心与横纵比过滤)

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知识点

对图像二值图像的每个轮廓，可以计算轮廓几何矩，根据几何矩可以计算图像的中心位置，估计得到中心位置可以计算中心矩、然后再根据中心矩可以计算胡矩。

几何矩

API

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Moments cv::moments(
	InputArray 	array,
	bool binaryImage = false
)
array是输入的图像轮廓点集合
输出图像的几何矩。

代码（c++,python）

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

using namespace std;
using namespace cv;

int main() {
    Mat src = imread("../images/stuff.jpg");
    if (src.empty()) {
        cout << "could not load image.." << endl;
    }
    imshow("input", src);

    // 去噪声与二值化
    Mat dst, gray, binary;
    Canny(src, binary, 80, 160);

    Mat k = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
    dilate(binary, binary, k);

    // 轮廓发现与绘制
    vector<vector<Point>> contours;
    vector<Vec4i> hierarchy;
    findContours(binary, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point());
    for (size_t t = 0; t < contours.size(); t++) {
        // 最小外接轮廓
        RotatedRect rrt = minAreaRect(contours[t]);
        float w = rrt.size.width;
        float h = rrt.size.height;
        Point2f pts[4];
        rrt.points(pts);

        // 计算横纵比
        float ratio = min(w,h) / max(w,h);
        Point2f cpt = rrt.center;
        circle(src, cpt, 2, Scalar(255,0,0), 2);
        if(ratio > 0.9){
            circle(src, cpt, 2, Scalar(255,0,0), 2);
            // 绘制旋转矩形
            for (int i = 0; i < 4; i++) {
                line(src, pts[i % 4], pts[(i + 1) % 4], Scalar(0, 0, 255), 2);
            }
        }
        if(ratio < 0.5){
            circle(src, cpt, 2, Scalar(255,0,0), 2);
            // 绘制旋转矩形
            for (int i = 0; i < 4; i++) {
                line(src, pts[i % 4], pts[(i + 1) % 4], Scalar(0, 255, 0), 2);
            }
        }
    }

    imshow("contours", src);

    waitKey(0);
    return 0;
}
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import cv2 as cv
import numpy as np


def canny_demo(image):
    t = 80
    canny_output = cv.Canny(image, t, t * 2)
    cv.imshow("canny_output", canny_output)
    cv.imwrite("D:/canny_output.png", canny_output)
    return canny_output


src = cv.imread("D:/images/stuff.jpg")
cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
cv.imshow("input", src)
binary = canny_demo(src)
k = np.ones((3, 3), dtype=np.uint8)
binary = cv.morphologyEx(binary, cv.MORPH_DILATE, k)

# 轮廓发现
out, contours, hierarchy = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
for c in range(len(contours)):
    rect = cv.minAreaRect(contours[c])
    cx, cy = rect[0]
    ww, hh = rect[1]
    ratio = np.minimum(ww, hh) / np.maximum(ww, hh)
    print(ratio)
    mm = cv.moments(contours[c])
    m00 = mm['m00']
    m10 = mm['m10']
    m01 = mm['m01']
    cx = np.int(m10 / m00)
    cy = np.int(m01 / m00)
    box = cv.boxPoints(rect)
    box = np.int0(box)
    if ratio > 0.9:
        cv.drawContours(src, [box], 0, (0, 0, 255), 2)
        cv.circle(src, (np.int32(cx), np.int32(cy)), 2, (255, 0, 0), 2, 8, 0)
    if ratio < 0.5:
        cv.drawContours(src, [box], 0, (255, 0, 255), 2)
        cv.circle(src, (np.int32(cx), np.int32(cy)), 2, (0, 0, 255), 2, 8, 0)


# 显示
cv.imshow("contours_analysis", src)
cv.imwrite("D:/contours_analysis.png", src)
cv.waitKey(0)
cv.destroyAllWindows()

结果

代码地址

github