多边形点测试

1.学习内容

本节我们将学习OpenCV的pointPolygonTest()函数：

C++：
double cv::pointPolygonTest	(InputArray contour, Point2f pt, bool measureDist)	
	
Python:
cv.pointPolygonTest(contour, pt, measureDist) ->retval

参数：
contour：输入轮廓。
pt：要测试的点。
measureDist：如果为真，该函数估计从点到最近的轮廓边缘的符号距离。否则，函数只检查点是否在轮廓线内。

该函数确定点是在轮廓线内、外，还是位于边缘(或与顶点重合)。它会相应地返回正(内部)、负(外部)或零(边缘)值。当measureDist=false时，返回值分别是**+1**，-1和0。否则，返回值是点和最近的轮廓边缘之间的带符号距离。

2.代码案例

2.1 Python代码

from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
# Create an image
r = 100
src = np.zeros((4*r, 4*r), dtype=np.uint8)
# 创建六边形的6个顶点
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))
# 根据六个顶点画6边形
for i in range(6):
    cv.line(src, vert[i],  vert[(i+1)%6], ( 255 ), 3)
# 获取六边形的轮廓
contours, _ = cv.findContours(src, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
# 计算图上点到六边形的距离（带符号）
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)
# 查找带符号的最大最小值
minVal, maxVal, _, maxDistPt = cv.minMaxLoc(raw_dist)
minVal = abs(minVal)
maxVal = abs(maxVal)
# 用图形表示距离
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
        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()

2.2 C++代码

#include 
#include 
#include 
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;
}

参考目录

https://docs.opencv.org/4.x/dc/d48/tutorial_point_polygon_test.html