OpenCV 具有转换为极坐标并返回的代码。这种转换对于通过相关查找对象旋转或以其他方式创建以对象为中心的“与旋转无关”的对象表示非常有用。可视化每个极坐标以及它们的联合图像很有用。下面的图像应该是自我解释的。极坐标图以角度为水平轴，半径为垂直轴，因此 4 个峰值对应于输入图像的 4 个角。附上代码（带有 OpenCV 的 C++）。
//================================
// Name : PolarCoord.cpp
// Author : V.Ivanchenko cudassimo@gmail.com
// Version :
// Copyright : Your copyright notice
// Description : Hello World in C++, Ansi-style
//======================================
#include <iostream>
#include "opencv.hpp"
using namespace std;
using namespace cv;
#define VALID(x, y, w, h) ((x)>=0 && (y)>=0 && (x)<(w) && (y)<(h)) // validates index
/*
* 1. Original binary image HxW CV_8U
* |
* |
* V
* 2. Two coordinate Mats HxW CV_32F
* |
* |
* V
* 3. Visualization CV_8U
* a. gray HxW for a single coordinate image
* b. binary Rx360 for two coordinate images
*/
// convert a binary 2D image into two Mats with float coordiantes
void imageToCoord(const Mat& img, Mat& X, Mat& Y, bool centered = true) {
if (img.empty())
return;
int h = img.rows;
int w = img.cols;
X.create(h, w, CV_32F);
Y.create(h, w, CV_32F);
float Cx = w/2.0f;
float Cy = h/2.0f;
for (int i=0; i<h; ++i){
const uchar* img_row = img.ptr<uchar>(i);
float* x_row = X.ptr<float>(i);
float* y_row = Y.ptr<float>(i);
for (int j=0; j<w; ++j) {
if (img_row[j]>0) {
float x = j;
float y = i;
if (centered) {
x-=Cx;
y-=Cy;
}
x_row[j] = x;
y_row[j] = y;
}
} // j
} // i
} //imageToCoord()
// convert a single float ploar coord Mat to a gray image
void polarToImg(const Mat& PolarCoord, Mat& img) {
if (PolarCoord.empty())
return;
int h = PolarCoord.rows;
int w = PolarCoord.cols;
img.create(h, w, CV_8U);
float maxVal = std::numeric_limits<float>::min();
// find maxVal
for (int i=0; i<h; ++i){
const float* x_row = PolarCoord.ptr<float>(i);
for (int j=0; j<w; ++j) {
if (maxVal < x_row[j])
maxVal = x_row[j];
} // j
} // i
// create an image
if (maxVal>0) {
float k = 255.0/maxVal;
for (int i=0; i<h; ++i){
uchar* img_row = img.ptr<uchar>(i);
const float* x_row = PolarCoord.ptr<float>(i);
for (int j=0; j<w; ++j) {
img_row[j] = saturate_cast<uchar>(k*x_row[j]);
}// j
} // i
} // if
} // plarToImg()
// convert two polar coord Mats to a binary image
void polarToImg(const Mat& radius, const Mat& angle, Mat& img) {
if (angle.empty() || radius.empty())
return;
int h = angle.rows;
int w = angle.cols;
assert(radius.cols==w && radius.rows==h);
const int imgH = sqrt(h*h+w*w)+0.5f; // radius
const int imgW = 360; // angle, deg
img.create(imgH, imgW, CV_8U);
// create an image
for (int i=0; i<h; ++i){
const float* ang_row = angle.ptr<float>(i);
const float* r_row = radius.ptr<float>(i);
for (int j=0; j<w; ++j) {
int x = ang_row[j] + 0.5f;
int y = r_row[j] + 0.5f;
if (x>0) {
cout<<x<<endl;
}
if (VALID(x, y, imgW, imgH))
img.at<uchar>(y, x) = 255;
else {
cout<<"Invalid x, y: "<<x<<", "<<y<<endl;
}
}// j
} // i
} // plarToImg()
int main() {
cout << "Cartesian to polar" << endl; // prints "Syntax training in openCV"
const int W=400, H=400;
Mat Minput(H, W, CV_8U);
Minput(Rect(W/4, H/4, W/2, H/2)) = 255;
Mat X, Y, Angle, Radius, Mr, Mang, Mpolar;
// processing
imageToCoord(Minput, X, Y); // extract coordinates
cartToPolar(X, Y, Radius, Angle, true);// convert coordiantes
// visualize
polarToImg(Radius, Mr);
polarToImg(Angle, Mang);
polarToImg(Radius, Angle, Mpolar);
// debug
//cout<<Mpolar<<endl;
namedWindow("input", 0);
namedWindow("angle", 0);
namedWindow("radius", 0);
namedWindow("Polar", 0);
const int winw=200, winh=200;
resizeWindow("input", winw, winh);
resizeWindow("angle", winw, winh);
resizeWindow("radius", winw, winh);
resizeWindow("Polar", 360, (int)sqrt(H*H + W*W));
moveWindow("input", 0, 0);
moveWindow("angle", winw, 0);
moveWindow("radius", 2*winw, 0);
moveWindow("Polar", 3*winw, 0);
imshow("input", Minput);
imshow("angle", Mang);
imshow("radius", Mr);
imshow("Polar", Mpolar);
waitKey(-1);
return 0;
}