點多邊形測試

目錄

• 目標
• 理論
• 程式碼
• 解釋
• 結果

上一個教程: 影像矩
下一個教程: 使用距離變換和分水嶺演算法進行影像分割

原始作者	Ana Huamán
相容性	OpenCV >= 3.0

目標

在本教程中，您將學習如何

• 使用 OpenCV 函式 cv::pointPolygonTest

理論

程式碼

C++

本教程的程式碼如下所示。您也可以從 這裡 下載

 
#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 );
    }
 
vector<vector<Point> > contours;
findContours( src, contours, RETR_TREE, CHAIN_APPROX_SIMPLE);
 
    Mat raw_dist( src.size(), CV_32F );
    for( int i = 0; i < src.rows; i++ )
    {
        for( int j = 0; j < src.cols; j++ )
        {
raw_dist.at<float>(i,j) = (float)pointPolygonTest( contours[0], Point2f((float)j, (float)i), true );
        }
    }
 
    double minVal, maxVal;
    Point maxDistPt; // inscribed circle center
minMaxLoc(raw_dist, &minVal, &maxVal, NULL, &maxDistPt);
minVal = abs(minVal);
maxVal = abs(maxVal);
 
    Mat drawing = Mat::zeros( src.size(), CV_8UC3 );
    for( int i = 0; i < src.rows; i++ )
    {
        for( int j = 0; j < src.cols; j++ )
        {
            if( raw_dist.at<float>(i,j) < 0 )
            {
drawing.at<Vec3b>(i,j)[0] = (uchar)(255 - abs(raw_dist.at<float>(i,j)) * 255 / minVal);
            }
            else if( raw_dist.at<float>(i,j) > 0 )
            {
drawing.at<Vec3b>(i,j)[2] = (uchar)(255 - raw_dist.at<float>(i,j) * 255 / maxVal);
            }
            else
            {
drawing.at<Vec3b>(i,j)[0] = 255;
drawing.at<Vec3b>(i,j)[1] = 255;
drawing.at<Vec3b>(i,j)[2] = 255;
            }
        }
    }
    circle(drawing, maxDistPt, (int)maxVal, Scalar(255,255,255));
 
    imshow( "Source", src );
    imshow( "Distance and inscribed circle", drawing );
 
    waitKey();
    return 0;
}

Java

本教程的程式碼如下所示。您也可以從 這裡 下載

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("Source", src);
HighGui.imshow("Distance and inscribed circle", drawing);
 
HighGui.waitKey();
System.exit(0);
    }
}
 
public class PointPolygonTestDemo {
    public static void main(String[] args) {
        // 載入本地 OpenCV 庫
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
 
        new PointPolygonTest().run();
    }
 
}

Python

本教程的程式碼如下所示。您也可以從 這裡 下載

from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
 
# 建立影像
r = 100
src = np.zeros((4*r, 4*r), dtype=np.uint8)
 
# 建立一系列點來構成輪廓
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 中繪製
for i in range(6)
    cv.line(src, vert[i], vert[(i+1)%6], ( 255 ), 3)
 
# 獲取輪廓
contours, _ = cv.findContours(src, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
 
# 計算到輪廓的距離
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)
 
# 圖形化地描述距離
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
        else:
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('Distance and inscribed circle', drawing)
cv.waitKey()

解釋

結果

結果如下: