点多边形测试

目录

• 目标
• 理论
• 代码
• 解释
• 结果

上一篇教程： 图像矩
下一篇教程： 利用距离变换和分水岭算法进行图像分割

原作者	Ana Huamán
兼容性	OpenCV >= 3.0

目标

在本教程中，您将学习如何操作

• 使用 OpenCV 函数 cv::pointPolygonTest

理论

代码

C++

本教程的代码示例如以下列出。您还可以从 此处 下载代码

 
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
 
using namespace cv;
using namespace std;
 
int main( void )
{
    const int r = 100;
    Mat src = Mat::zeros( Size( 4*r, 4*r ), CV_8U );
 
vector<Point2f> vert(6);
vert[0] = Point( 3*r/2, static_cast<int>(1.34*r) );
vert[1] = Point( 1*r, 2*r );
vert[2] = Point( 3*r/2, static_cast<int>(2.866*r) );
vert[3] = Point( 5*r/2, static_cast<int>(2.866*r) );
vert[4] = Point( 3*r, 2*r );
vert[5] = Point( 5*r/2, static_cast<int>(1.34*r) );
 
    for( int i = 0; i < 6; i++ )
    {
line( src, vert[i], vert[(i+1)%6], Scalar( 255 ), 3 );
    }
 
矢量 <矢量 <点 > > 轮廓；
findContours(src，轮廓，RETR_TREE，CHAIN_APPROX_SIMPLE);
 
    矩阵 raw_dist(src.尺寸(), CV_32F);
    对于(整数i = 0；i < src.行；i++)
    {
        对于(整数 j = 0；j < src.列；j++)
        {
raw_dist.at<浮点数>(i，j) = (浮点数)pointPolygonTest(轮廓[0]，点 2f((浮点数)j，(浮点数)i)，true);
        }
    }
 
    双精度 minVal，maxVal；
    点 maxDistPt；// 内切圆圆心
minMaxLoc(raw_dist，&minVal，&maxVal，NULL，&maxDistPt);
minVal = abs(minVal);
maxVal = abs(maxVal);
 
    矩阵 赋值 = 矩阵::zeros(src.尺寸，CV_8UC3);
    对于(整数i = 0；i < src.行；i++)
    {
        对于(整数 j = 0；j < src.列；j++)
        {
            如果(raw_dist.at<浮点数>(i，j) < 0)
            {
赋值.at<Vec3b>(i，j)[0] = (无符号字符)(255 - abs(raw_dist.at<浮点数>(i，j)) * 255 / minVal);
            }
            否则如果(raw_dist.at<浮点数>(i，j) > 0)
            {
赋值.at<Vec3b>(i，j)[2] = (无符号字符)(255 - raw_dist.at<浮点数>(i，j) * 255 / maxVal);
            }
            否则
            {
赋值.at<Vec3b>(i，j)[0] = 255；
赋值.at<Vec3b>(i，j)[1] = 255；
赋值.at<Vec3b>(i，j)[2] = 255；
            }
        }
    }
    圆圈(赋值，maxDistPt，(整数)maxVal，标量(255,255,255));
 
    <
    imshow( "距离和内接圆", drawing );
 
    waitKey();
    return 0;
}

Java

此教程代码显示在以下行中。您还可从 此处 下载它。

import java.util.ArrayList;
import java.util.List;
 
import org.opencv.core.Core;
import org.opencv.core.Core.MinMaxLocResult;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfPoint;
import org.opencv.core.MatOfPoint2f;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.highgui.HighGui;
import org.opencv.imgproc.Imgproc;
 
class PointPolygonTest {
    public void run() {
        int r = 100;
Mat src = Mat.zeros(new Size(4 * r, 4 * r), CvType.CV_8U);
 
List<Point> vert = new ArrayList<>(6);
vert.add(new Point(3 * r / 2, 1.34 * r));
vert.add(new Point(1 * r, 2 * r));
vert.add(new Point(3 * r / 2, 2.866 * r));
vert.add(new Point(5 * r / 2, 2.866 * r));
vert.add(new Point(3 * r, 2 * r));
vert.add(new Point(5 * r / 2, 1.34 * r));
 
        for (int i = 0; i < 6; i++) {
Imgproc.line(src, vert.get(i), vert.get((i + 1) % 6), new Scalar(255), 3);
        }
 
List<MatOfPoint> contours = new ArrayList<>();
Mat hierarchy = new Mat();
Imgproc.findContours(src, contours, hierarchy, Imgproc.RETR_TREE, Imgproc.CHAIN_APPROX_SIMPLE);
 
Mat rawDist = new Mat(src.size(), CvType.CV_32F);
        float[] rawDistData = new float[(int) (rawDist.total() * rawDist.channels())];
        for (int i = 0; i < src.rows(); i++) {
            for (int j = 0; j < src.cols(); j++) {
rawDistData[i * src.cols() + j] = (float) Imgproc
.pointPolygonTest(new MatOfPoint2f(contours.get(0).toArray()), new Point(j, i), true);
            }
        }
rawDist.put(0, 0, rawDistData);
 
MinMaxLocResult res = Core.minMaxLoc(rawDist);
        double minVal = Math.abs(res.minVal);
        double maxVal = Math.abs(res.maxVal);
 
Mat drawing = Mat.zeros(src.size(), CvType.CV_8UC3);
        byte[] drawingData = new byte[(int) (drawing.total() * drawing.channels())];
        for (int i = 0; i < src.rows(); i++) {
            for (int j = 0; j < src.cols(); j++) {
                if (rawDistData[i * src.cols() + j] < 0) {
drawingData[(i * src.cols() + j) * 3] =
(byte) (255 - Math.abs(rawDistData[i * src.cols() + j]) * 255 / minVal);
} else if (rawDistData[i * src.cols() + j] > 0) {
drawingData[(i * src.cols() + j) * 3 + 2] =
(byte) (255 - rawDistData[i * src.cols() + j] * 255 / maxVal);
} else {
drawingData[(i * src.cols() + j) * 3] = (byte) 255;
drawingData[(i * src.cols() + j) * 3 + 1] = (byte) 255;
drawingData[(i * src.cols() + j) * 3 + 2] = (byte) 255;
                }
            }
        }
drawing.put(0, 0, drawingData);
Imgproc.circle(drawing, res.maxLoc, (int)res.maxVal, new Scalar(255, 255, 255), 2, 8, 0);
 
HighGui.imshow("原图像", src);
HighGui.imshow("距离图和内切圆", drawing);
 
HighGui.waitKey();
System.exit(0);
    }
}
 
public class PointPolygonTestDemo {
    public static void main(String[] args) {
        // 加载原生 OpenCV 库
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
 
        new PointPolygonTest().run();
    }
 
}

Python

本教程的代码如下所示。你也可以从 此处 下载

from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
 
# 创建一个图像
r = 100
src = np.zeros((4*r, 4*r), dtype=np.uint8)
 
# 创建一个点序列以形成轮廓
vert = [None]*6
vert[0] = (3*r//2, int(1.34*r))
vert[1] = (1*r, 2*r)
vert[2] = (3*r//2, int(2.866*r))
vert[3] = (5*r//2, int(2.866*r))
vert[4] = (3*r, 2*r)
vert[5] = (5*r//2, int(1.34*r))
 
# 在 src 中绘制它
for i in range(6)
    cv.line(src, vert[i], vert[(i+1)%6], ( 255 ), 3)
 
# 获取轮廓
contours, _ = cv.findContours(src, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
 
# 计算到轮廓的距离
raw_dist = np.empty(src.shape, dtype=np.float32)
for i in range(src.shape[0])
    for j in range(src.shape[1])
raw_dist[i,j] = cv.pointPolygonTest(contours[0], (j,i), True)
 
minVal, maxVal, _, maxDistPt = cv.minMaxLoc(raw_dist)
minVal = abs(minVal)
maxVal = abs(maxVal)
 
# 以图形方式描述距离
drawing = np.zeros((src.shape[0], src.shape[1], 3), dtype=np.uint8)
for i in range(src.shape[0])
    for j in range(src.shape[1])
        if raw_dist[i,j] < 0
drawing[i,j,0] = 255 - abs(raw_dist[i,j]) * 255 / minVal
        elif raw_dist[i,j] > 0
drawing[i,j,2] = 255 - raw_dist[i,j] * 255 / maxVal
        否则:
drawing[i,j,0] = 255
drawing[i,j,1] = 255
drawing[i,j,2] = 255
 
cv.circle(drawing,maxDistPt, int(maxVal),(255,255,255), 1, cv.LINE_8, 0)
cv.imshow('Source', src)
cv.imshow('距离和内切圆', drawing)
cv.waitKey()

解释

结果

如下：