fanfuhan OpenCV 教學057 ~ opencv-057-二值化圖像分析(點多邊形測試) – jashliao部落格

fanfuhan OpenCV 教學057 ~ opencv-057-二值化圖像分析(點多邊形測試)

資料來源: https://fanfuhan.github.io/

https://fanfuhan.github.io/2019/04/18/opencv-057/

GITHUB:https://github.com/jash-git/fanfuhan_ML_OpenCV

對於輪廓圖像，有時候還需要判斷一個點是在輪廓內部還是外部，OpenCV中實現這個功能的API叫做點多邊形測試，它可以準確的得到一個點距離多邊形的距離，如果點是輪廓點或者屬於輪廓多邊形上的點，距離是零，如果是多邊形內部的點是是正數，如果是負數返回表示點是外部。

C++

#include 
#include 

using namespace std;
using namespace cv;

/*
 * 二值图像分析(点多边形测试)
 */
int main() {
    Mat src = imread("../images/my_mask.png");
    if (src.empty()) {
        cout << "could not load image.." << endl;
    }
    imshow("input", src);

    // 二值化
    Mat dst, gray, binary;
    cvtColor(src, gray, COLOR_BGR2GRAY);
    threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU);

    // 轮廓发现与绘制
    vector> contours;
    vector hierarchy;
    findContours(binary, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point());

    Mat image = Mat::zeros(src.size(), CV_32FC3);
    // 对轮廓内外的点进行分类
    for (int row = 0; row < src.rows; ++row) {
        for (int col = 0; col < src.cols; ++col) {
            double dist = pointPolygonTest(contours[0], Point(col, row), true);
            if (dist == 0) {
                image.at(row, col) = Vec3f(255, 255, 255);
            } else if (dist > 0) {
                image.at(row, col) = Vec3f(255 - dist, 0, 0);
            } else {
                image.at(row, col) = Vec3f(0, 0, 255 + dist);
            }
        }
    }
    convertScaleAbs(image, image);
    imshow("points", image);

    waitKey(0);
    return 0;
}

Python

import cv2 as cv
import numpy as np

src = cv.imread("D:/images/my_mask.png")
cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
cv.imshow("input", src)
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
cv.imshow("binary", binary)

# 轮廓发现
image = np.zeros(src.shape, dtype=np.float32)
out, contours, hierarchy = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
h, w = src.shape[:2]
for row in range(h):
    for col in range(w):
        dist = cv.pointPolygonTest(contours[0], (col, row), True)
        if dist == 0:
            image[row, col] = (255, 255, 255)
        if dist > 0:
            image[row, col] = (255-dist, 0, 0)
        if dist < 0:
            image[row, col] = (0, 0, 255+dist)

dst = cv.convertScaleAbs(image)
dst = np.uint8(dst)

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