Canny Edge Detection- Non Maximum Surpression Implementation - c++

I am trying to implement the Canny edge detection algorithm from scratch with the help of OpenCV. I am facing a problem implementing the Non-Maximum Suppression step which helps to thin the edges.
My logic is to first compute the intensity gradient vector, then group it in either 0,45,90,135 degrees direction and then try to find local maxima. The method to find this local maxima is by making sure that current pixel is greater than succeeding and preceeding pixel in same direction. If not, I assign value of zero to this pixel. Using this logic, I'm still not able to thin the edges. I feel the error is when I'm computing the intensity gradient vector for each pixel.
Here is my code-
#include <iostream>
#include <bits/stdc++.h>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/opencv.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <opencv2/objdetect/objdetect.hpp>
#include <math.h>
using namespace cv;
using namespace std;
int main()
{
// Reading image
Mat img = imread("1.jpg");
// Displaying image
//imshow("Original Image",img);
//waitKey(0);
// Converting to grayscale
Mat img_gray,image_gray;
cvtColor(img,image_gray,CV_RGB2GRAY);
GaussianBlur( image_gray, img_gray, Size(15,15), 3, 3);
// Displaying grayscale image
imshow("Original Image",img_gray);
waitKey(0);
int cols = img_gray.cols;
int rows = img_gray.rows;
// Creating sobel operator in x direction
int sobel_x[3][3] = {-1,0,1,-2,0,2,-1,0,1};
// Creating sobel operator in y direction
int sobel_y[3][3] = {1,2,1,0,0,0,-1,-2,-1};
int radius = 1;
// Handle border issues
Mat _src;
copyMakeBorder(img_gray, _src, radius, radius, radius, radius, BORDER_REFLECT101);
// Create output matrix
Mat gradient_x = img_gray.clone();
Mat gradient_y = img_gray.clone();
Mat gradient_f = img_gray.clone();
Mat gradient_mag = img_gray.clone();
// Conrrelation loop in x direction
// Iterate on image
for (int r = radius; r < _src.rows - radius; ++r)
{
for (int c = radius; c < _src.cols - radius; ++c)
{
int s = 0;
// Iterate on kernel
for (int i = -radius; i <= radius; ++i)
{
for (int j = -radius; j <= radius; ++j)
{
s += _src.at<uchar>(r + i, c + j) * sobel_x[i + radius][j + radius];
}
}
gradient_x.at<uchar>(r - radius, c - radius) = s/8;
/*if(s>200)
gradient.at<uchar>(r - radius, c - radius) = 255;
else
gradient.at<uchar>(r - radius, c - radius) = 0;
*/
}
}
// Conrrelation loop in y direction
// Iterate on image
for (int r = radius; r < _src.rows - radius; ++r)
{
for (int c = radius; c < _src.cols - radius; ++c)
{
int s = 0;
// Iterate on kernel
for (int i = -radius; i <= radius; ++i)
{
for (int j = -radius; j <= radius; ++j)
{
s += _src.at<uchar>(r + i, c + j) * sobel_y[i + radius][j + radius];
}
}
gradient_y.at<uchar>(r - radius, c - radius) = s/8;
/*if(s>200)
gradient.at<uchar>(r - radius, c - radius) = 255;
else
gradient.at<uchar>(r - radius, c - radius) = 0;
*/
}
}
///cout<<endl<<"max:"<<max;
//cout<<img_gray.rows;
//cout<<endl<<_src.rows;
cout<<endl<<gradient_x.rows;
cout<<endl<<gradient_y.rows;
cout<<endl<<gradient_f.rows<<gradient_f.cols;
//Calculating gradient magnitude
for(int i=0; i<gradient_mag.rows; i++)
{
for(int j=0; j<gradient_mag.cols; j++)
{
gradient_mag.at<uchar>(i,j) = sqrt( pow(gradient_x.at<uchar>(i,j),2) + pow(gradient_y.at<uchar>(i,j),2) );
if(gradient_mag.at<uchar>(i,j) >250)
gradient_f.at<uchar>(i,j) = 255;
else
gradient_f.at<uchar>(i,j) = 0;
}
}
/*
imshow("grad x",gradient_x);
waitKey(0);
imshow("grad y",gradient_y);
waitKey(0);
*/
imshow("grad magnitude",gradient_f);
waitKey(0);
int max=0;
// Performing Non-Maximum Surpression
float theta; // Calculate intensity gradient vector theta=atan2(Gy,Gx);
Mat nonMaxSupp= Mat(gradient_mag.rows-2, gradient_mag.cols-2, CV_8UC1); //CV_8UC1 is 8-bit single channel image i.e grayscale
for(int i=1; i<gradient_x.rows-1; i++)
{
for(int j=1; j<gradient_x.cols-1; j++)
{
//if(gradient_x.at<uchar>(i,j) ==0) //Arctan Fix
// theta = 90;
//else
theta = atan2(gradient_y.at<uchar>(i,j),gradient_x.at<uchar>(i,j))*(180/3.14);
//theta = atan(gradient_y.at<uchar>(i,j)/gradient_x.at<uchar>(i,j))*(180/3.14);
//cout<<theta<<endl;
//if(theta>max)
// max=theta;
nonMaxSupp.at<uchar>(i-1, j-1) = gradient_mag.at<uchar>(i,j);
// For horizontal edge
if(((-22.5 < theta) && (theta <= 22.5)) || ((157.5 < theta) && (theta <= -157.5)))
{
if ((gradient_mag.at<uchar>(i,j) < gradient_mag.at<uchar>(i,j+1)) || (gradient_mag.at<uchar>(i,j) < gradient_mag.at<uchar>(i,j-1)))
nonMaxSupp.at<uchar>(i-1, j-1) = 0;
}
//For vertical edge
if (((-112.5 < theta) && (theta <= -67.5)) || ((67.5 < theta) && (theta <= 112.5)))
{
if ((gradient_mag.at<uchar>(i,j) < gradient_mag.at<uchar>(i+1,j)) || (gradient_mag.at<uchar>(i,j) < gradient_mag.at<uchar>(i-1,j)))
nonMaxSupp.at<uchar>(i-1, j-1) = 0;
}
// For 135 degree or -45 degree edge
if (((-67.5 < theta) && (theta <= -22.5)) || ((112.5 < theta) && (theta <= 157.5)))
{
if ((gradient_mag.at<uchar>(i,j) < gradient_mag.at<uchar>(i-1,j+1)) || (gradient_mag.at<uchar>(i,j) < gradient_mag.at<uchar>(i+1,j-1)))
nonMaxSupp.at<uchar>(i-1, j-1) = 0;
}
// For 45 Degree Edge
if (((-157.5 < theta) && (theta <= -112.5)) || ((22.5 < theta) && (theta <= 67.5)))
{
if ((gradient_mag.at<uchar>(i,j) < gradient_mag.at<uchar>(i+1,j+1)) || (gradient_mag.at<uchar>(i,j) < gradient_mag.at<uchar>(i-1,j-1)))
nonMaxSupp.at<uchar>(i-1, j-1) = 0;
}
}
}
//cout<<endl<<"max"<<max;
imshow("Non-Maximum Surpression",nonMaxSupp);
waitKey(0);
return 0;
}

Related

Warp Image by Diagonal Sine Wave

I'm trying to warp colour image using sin function in OpenCV and I was successful in doing so. However, how can I make a 'diagonal' warping using sine wave?
My code is this:
Mat result = src.clone();
for (int i = 0; i < src.rows; i++) { // to y
for (int j = 0; j < src.cols; j++) { // to x
for (int ch = 0; ch < 3; ch++) { // each colour
int offset_x = 0;
int offset_y = (int)(25.0 * sin(3.14 * j / 150));
if (i + offset_y < src.rows) {
result.at<Vec3b>(i, j)[ch] = src.at<Vec3b>((i + offset_y) % src.rows, j)[ch];
}
else
result.at<Vec3b>(i, j)[ch] = 0.0;
}
}
}
imshow("result", result);
How can I do this? Not drawing a sine graph, but warping an image.
Solved this! Several times ago, I've received a message by someone who told me that the image is stolen. It was from Google, actually, but I've deleted it to fulfill not to cause any situations. Thx!
I think it should look like this:
void deform()
{
float alpha = 45 * CV_PI / 180.0; // wave direction
float ox = cos(alpha);
float oy = sin(alpha);
cv::Mat src = cv::imread("F:/ImagesForTest/lena.jpg");
for (int i = 0; i < src.rows; i+=8)
{
cv::line(src, cv::Point(i, 0), cv::Point(i, src.rows),cv::Scalar(255,255,255));
}
for (int j = 0; j < src.cols; j += 8)
{
cv::line(src, cv::Point(0,j), cv::Point(src.cols,j), cv::Scalar(255, 255, 255));
}
cv::Mat result = src.clone();
for (int i = 0; i < src.rows; i++)
{ // to y
for (int j = 0; j < src.cols; j++)
{ // to x
float t =(i * oy)+ (j * ox); // wave parameter
for (int ch = 0; ch < 3; ch++)
{ // each colour
int offset_x =ox* (int)(25.0 * (sin(3.14 * t/ 150)));
int offset_y =oy* (int)(25.0 * (sin(3.14 * t / 150)));
if (i + offset_y < src.rows && j + offset_x < src.rows && i + offset_y >=0 && j + offset_x>=0)
{
result.at<cv::Vec3b>(i, j)[ch] = src.at<cv::Vec3b>(i + offset_y, j + offset_x )[ch];
}
else
result.at<cv::Vec3b>(i, j)[ch] = 0.0;
}
}
}
cv:: imshow("result", result);
cv::imwrite("result.jpg", result);
cv::waitKey();
}
The result:
BTW, may be better to use cv::remap ?

Sobel algorithm without library, greyscale output

I'm trying to get this output b&w positives and negatives and gray neutrals
But instead I'm getting this B&W only
I'm not sure what to change about my code.
Mat sobelX(const cv::Mat &m){
Mat im = m.clone();
int gx, mag;
for (int i = 0; i < m.rows; i++){
for (int j = 0; j < m.cols;
//find x gradient
gx = m.at<uchar>(i-1, j-1)
+ 2*m.at<uchar>(i, j-1)
+ m.at<uchar>(i+1, j-1)
- m.at<uchar>(i-1, j+1)
- 2*m.at<uchar>(i, j+1)
- m.at<uchar>(i+1, j+1);
mag = sqrt(gx*gx);
if (mag < 0) {
mag = 0; //set to black
}
else if (mag > 255) {
mag = 255; //set to white
}
im.at<uchar>(i, j) = mag;
}
}
return im;
}
Thanks.

normalize histogram in c++ - function normalize in openCV

I need to normalize the histogram of an image f which mean to applicated an transformation of histogram from image in order to extend the range of value of f to all available values.
the norm(fmin) = Vmin ( minimal value we want to reach) and normal(fmin) = Vmax ( maximal value we want to reach)
I have this formula too
the goal is to have the same result that the function normalize which openCV gives.
Mat normalize(Mat image, float minValue, float maxValue)
{
Mat res = image.clone();
assert(minValue <= maxValue);
float Fmax = 0;
float Fmin = 0;
for(int i = 0; i < res.rows; i++)
{
for(int j = 0; j < res.cols; j++)
{
float x = res.at<float>(i,j);
if(i < minValue)
{
Fmin = i;
}
if( i > maxValue)
{
Fmax = i;
}
res.at<float>(i,j) = (x - Fmin) * ((maxValue - minValue) / (Fmax - Fmin)) + minValue;
}
}
return res;
}
I have this error : !!! Warning, saved image values not between 0 and 1.
!!! Warning, saved image values not between 0 and 1.
I think I didn't understand how to calculate fmin/ fmax
So, as I explained in my comment, there are some mistakes, here's the corrected version. You need to run the double loop twice, once to find the min-max, and a second time to apply the formula. There were also errors in the comparisons:
cv::Mat normalize(cv::Mat image, float minValue, float maxValue)
{
cv::Mat res = image.clone();
assert(minValue <= maxValue);
// 1) find min and max values
float Fmax = 0.0f;
float Fmin = 1.0f; // set it to 1, not 0
for (int i = 0; i < res.rows; i++)
{
float* pixels = res.ptr<float>(i); // this is quicker
for (int j = 0; j < res.cols; j++)
{
float x = pixels[j];
if (x < Fmin) // compare x and Fmin, not i and minValue
{
Fmin = x;
}
if (x > Fmax) // compare x and Fmax, not i and maxValue
{
Fmax = x;
}
}
}
// 1 color image => don't normalize + avoid crash
if (Fmin >= Fmax)
return res;
// 2) normalize using your formula
for (int i = 0; i < res.rows; i++)
{
float* pixels = res.ptr<float>(i);
for (int j = 0; j < res.cols; j++)
{
pixels[j] = (pixels[j] - Fmin) * ((maxValue - minValue) / (Fmax - Fmin)) + minValue;
}
}
return res;
}
If your source image is a grayscale image in 8 bit, you can convert it like that:
cv::Mat floatImage;
grayImage.convertTo(floatImage, CV_32F, 1.0 / 255, 0);
floatImage = normalize(floatImage, 0, 1.0f);
floatImage.convertTo(grayImage, CV_8UC1, 255.0, 0);
Also, if you use cv::minMaxLoc, your normalize function can be made shorter =>
cv::Mat normalize(cv::Mat image, float minValue, float maxValue)
{
cv::Mat res = image.clone();
assert(minValue <= maxValue);
// 1) find min and max values
double Fmax;
double Fmin;
cv::minMaxLoc(image, &Fmin, &Fmax);
if (Fmin >= Fmax)
return res;
// 2) normalize using your formula
for (int i = 0; i < res.rows; i++)
{
float* pixels = res.ptr<float>(i);
for (int j = 0; j < res.cols; j++)
{
pixels[j] = (pixels[j] - Fmin) * ((maxValue - minValue) / (Fmax - Fmin)) + minValue;
}
}
return res;
}

Image Shearing C++

I'm trying to Shear an image along the X-axis using OpenCV to load the image, and the following algorithm to shear the image: x′=x+y·Bx, but for some reason, I end up with the following shear:
My source code looks like this:
#include "stdafx.h"
#include "opencv2\opencv.hpp"
using namespace std;
using namespace cv;
int main()
{
Mat src = imread("B2DBy.jpg", 1);
if (src.empty())
cout << "Error: Loading image" << endl;
int r1, c1; // tranformed point
int rows, cols; // original image rows and columns
rows = src.rows;
cols = src.cols;
float Bx = 2; // amount of shearing in x-axis
float By = 0; // amount of shearing in y-axis
int maxXOffset = abs(cols * Bx);
int maxYOffset = abs(rows * By);
Mat out = Mat::ones(src.rows + maxYOffset, src.cols + maxXOffset, src.type()); // create output image to be the same as the source
for (int r = 0; r < out.rows; r++) // loop through the image
{
for (int c = 0; c < out.cols; c++)
{
r1 = r + c * By - maxYOffset; // map old point to new
c1 = r * Bx + c - maxXOffset;
if (r1 >= 0 && r1 <= out.rows && c1 >= 0 && c1 <= out.cols) // check if the point is within the boundaries
{
out.at<uchar>(r, c) = src.at<uchar>(r1, c1); // set value
}
}
}
namedWindow("Source image", CV_WINDOW_AUTOSIZE);
namedWindow("Rotated image", CV_WINDOW_AUTOSIZE);
imshow("Source image", src);
imshow("Rotated image", out);
waitKey(0);
return 0;
}
EDIT
Fixed it myself.
Didn't need to substract the offset. Heres the updated source code:
Mat forward(Mat img) {
Mat umg = img;
int y1, x1; // tranformed point
int rows, cols; // original image rows and columns
rows = umg.rows;
cols = umg.cols;
float Bx = 0.7; // amount of shearing in x-axis
float By = 0; // amount of shearing in y-axis
int maxXOffset = abs(rows * Bx);
int maxYOffset = abs(cols * By);
Mat out = Mat::ones(rows + maxYOffset, cols + maxXOffset, umg.type()); // create output image to be the same as the source
for (int y = 0; y < rows; y++) // loop through the image
{
for (int x = 0; x < cols; x++)
{
y1 = y + x * By; // map old point to new
x1 = y * Bx + x;
out.at<uchar>(y1, x1) = umg.at<uchar>(y, x); // set value
}
}
return out;
}
Mat backwards(Mat img) {
Mat umg = img;
int y1, x1; // tranformed point
int rows, cols; // original image rows and columns
rows = umg.rows;
cols = umg.cols;
float Bx = 0.7; // amount of shearing in x-axis
float By = 0; // amount of shearing in y-axis
int maxXOffset = abs(rows * Bx);
int maxYOffset = abs(cols * By);
Mat out = Mat::ones(rows + maxYOffset, cols + maxXOffset, umg.type()); // create output image to be the same as the source
for (int y = 0; y < rows; y++) // loop through the image
{
for (int x = 0; x < cols; x++)
{
//y1 = y + x * By; // map old point to new
//x1 = y * Bx + x;
y1 = (1 / (1 - Bx*By)) * (y + x * By);
x1 = (1 / (1 - Bx*By)) * (y * Bx + x);
out.at<uchar>(y1, x1) = umg.at<uchar>(y, x); // set value
}
}
return out;
}
int main()
{
Mat src = imread("B2DBy.jpg", 0);
if (src.empty())
cout << "Error: Loading image" << endl;
Mat forwards = forward(src);
Mat back = backwards(src);
namedWindow("Source image", CV_WINDOW_NORMAL);
imshow("Source image", src);
imshow("back", back);
imshow("forward image", forwards);
waitKey(0);
return 0;
}
I found some time to work on this.
Now I understand what you tried to achieve with the offset computation, but I'm not sure whether yours is correct.
Just change all the cv::Vec3b to unsigned char or uchar and load as grayscale, if wanted.
Please try this code and maybe you'll find your error:
// no interpolation yet
// cv::Vec3b only
cv::Mat shear(const cv::Mat & input, float Bx, float By)
{
if (Bx*By == 1)
{
throw("Shearing: Bx*By==1 is forbidden");
}
if (input.type() != CV_8UC3) return cv::Mat();
// shearing:
// x'=x+y·Bx
// y'=y+x*By
// shear the extreme positions to find out new image size:
std::vector<cv::Point2f> extremePoints;
extremePoints.push_back(cv::Point2f(0, 0));
extremePoints.push_back(cv::Point2f(input.cols, 0));
extremePoints.push_back(cv::Point2f(input.cols, input.rows));
extremePoints.push_back(cv::Point2f(0, input.rows));
for (unsigned int i = 0; i < extremePoints.size(); ++i)
{
cv::Point2f & pt = extremePoints[i];
pt = cv::Point2f(pt.x + pt.y*Bx, pt.y + pt.x*By);
}
cv::Rect offsets = cv::boundingRect(extremePoints);
cv::Point2f offset = -offsets.tl();
cv::Size resultSize = offsets.size();
cv::Mat shearedImage = cv::Mat::zeros(resultSize, input.type()); // every pixel here is implicitely shifted by "offset"
// perform the shearing by back-transformation
for (int j = 0; j < shearedImage.rows; ++j)
{
for (int i = 0; i < shearedImage.cols; ++i)
{
cv::Point2f pp(i, j);
pp = pp - offset; // go back to original coordinate system
// go back to original pixel:
// x'=x+y·Bx
// y'=y+x*By
// y = y'-x*By
// x = x' -(y'-x*By)*Bx
// x = +x*By*Bx - y'*Bx +x'
// x*(1-By*Bx) = -y'*Bx +x'
// x = (-y'*Bx +x')/(1-By*Bx)
cv::Point2f p;
p.x = (-pp.y*Bx + pp.x) / (1 - By*Bx);
p.y = pp.y - p.x*By;
if ((p.x >= 0 && p.x < input.cols) && (p.y >= 0 && p.y < input.rows))
{
// TODO: interpolate, if wanted (p is floating point precision and can be placed between two pixels)!
shearedImage.at<cv::Vec3b>(j, i) = input.at<cv::Vec3b>(p);
}
}
}
return shearedImage;
}
int main(int argc, char* argv[])
{
cv::Mat input = cv::imread("C:/StackOverflow/Input/Lenna.png");
cv::Mat output = shear(input, 0.7, 0);
//cv::Mat output = shear(input, -0.7, 0);
//cv::Mat output = shear(input, 0, 0.7);
cv::imshow("input", input);
cv::imshow("output", output);
cv::waitKey(0);
return 0;
}
Giving me these outputs for the 3 sample lines:

corruption of the heap

I have one function dealing with image. In that function, i am trying to find several particular ellipses. The code is working if i call it individually in a separate project, but in the whole project, it crashed when it returns.
I used many vectors in the processing to store some information during the process.
The error information:
Windows has triggered a breakpoint in KinectBridgeWithOpenCVBasics-D2D.exe.
This may be due to a corruption of the heap, which indicates a bug in KinectBridgeWithOpenCVBasics-D2D.exe or any of the DLLs it has loaded.
This may also be due to the user pressing F12 while KinectBridgeWithOpenCVBasics-D2D.exe has focus.
The output window may have more diagnostic information.
could any one tell me where is wrong to cause this crash. More weird is it is working in the separate project.
The code is a little long, but it is really noting, just looking for several particular ellipses with some pattern.
Thank you.
int FindNao(Mat* pImg, double* x, double* y)
{
// Fail if pointer is invalid
if (!pImg)
{
return 2;
}
// Fail if Mat contains no data
if (pImg->empty())
{
return 3;
}
//*x = 0; *y = 0;
Mat localMat = *pImg; // save a local copy of the image
cvtColor(~localMat, localMat, CV_BGR2GRAY); // Convert to gray image
threshold(localMat, localMat, 165, 255, THRESH_BINARY); // Convert into black-white image
Mat elementOpen = getStructuringElement(MORPH_ELLIPSE, Size(5,5), Point(-1,-1));
morphologyEx(localMat, localMat, MORPH_OPEN, elementOpen, Point(-1,-1), 1);
// Find all the contours in the blak-white image
vector<vector<Point>> contours;
findContours(localMat.clone(), contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
localMat.release();
// Calculate the area of each contour
vector<double> areas; int num = contours.size();
/* If no contours are found, return S_OK */
if(num < 1)
return 1;
for(int i = 0; i < num; i++)
{
areas.push_back(contourArea(contours[i]));
}
// First round of selection
// The area is small, and they are like a ellipse and around the middle in X direction and at the upper part of the image
vector<RotatedRect> selected_ellipses; // store the fitted ellipse fitted to the potential contour
vector<double> selected_areas; // store the contour area of the potential contour
int imgX = localMat.cols; int imgY = localMat.rows; // get the demension of the image
for(int i=0; i < num - 1; i++)
{
if(areas[i] < 350 && areas[i] > 10)
{
// fit an ellipse
RotatedRect ellipse1 = fitEllipse(Mat(contours[i]));
// it is a reasonable ellipse, and the area should be close to the
double length1 = ellipse1.size.height;
double length2 = ellipse1.size.width;
if( abs(1 - length1/length2) <= 0.8 &&
abs(1 - areas[i] / (CV_PI * length1 * length2 / 4) ) <= 0.2 )
{
selected_ellipses.push_back(ellipse1);
selected_areas.push_back(areas[i]);
}
}
}
/************ Second round of selection **************/
// Calculate each ellipse's dimension
vector<double> diff_dimension;
vector<double> ave_dimention;
/* If no contours are found, return S_OK */
if(selected_ellipses.size() < 1)
return 1;
for(int i = 0; i < selected_ellipses.size(); i++)
{
double difference = abs(1 - selected_ellipses[i].size.height / selected_ellipses[i].size.width);
diff_dimension.push_back(difference);
double average = (selected_ellipses[i].size.height + selected_ellipses[i].size.width) / 2;
ave_dimention.push_back(average);
}
vector<vector<int>> eyematches;
vector<vector<int>> cammatches;
// go over all the ellipses to find the matches with close area and dimension.
for(int i = 0; i < selected_ellipses.size() - 1; i++)
{
for(int j = i+1; j < selected_ellipses.size(); j++)
{
// looking for the eyes
if(diff_dimension[i] < 0.05 && diff_dimension[j] < 0.05)
{
double diff_area = abs( 1 - selected_areas[i] / selected_areas[j] );
if (diff_area < 0.05)
{
double diff_y = abs(selected_ellipses[i].center.y - selected_ellipses[j].center.y);
if(diff_y < 10)
{
vector<int> match1;
match1.push_back(i); match1.push_back(j);
eyematches.push_back(match1);
}
}
}
// looking for the cameras
double diff_x = abs(selected_ellipses[i].center.x - selected_ellipses[j].center.x);
if (diff_x < 10)
{
vector<int> match2;
match2.push_back(i); match2.push_back(j);
cammatches.push_back(match2);
}
}
}
/* Last check */
int num_eyes = eyematches.size();
int num_cams = cammatches.size();
if(num_eyes == 0 || num_cams == 0)
return 1;
// Calculate the vector between two eyes and the center
vector<Point> vector_eyes; vector<Point> center_eyes;
vector<vector<int>>::iterator ite = eyematches.begin();
while(ite < eyematches.end())
{
Point point;
point.x = selected_ellipses[(*ite)[0]].center.x - selected_ellipses[(*ite)[1]].center.x;
point.y = selected_ellipses[(*ite)[0]].center.y - selected_ellipses[(*ite)[1]].center.y;
vector_eyes.push_back(point);
point.x = (selected_ellipses[(*ite)[0]].center.x + selected_ellipses[(*ite)[1]].center.x)/2;
point.y = (selected_ellipses[(*ite)[0]].center.y + selected_ellipses[(*ite)[1]].center.y)/2;
center_eyes.push_back(point);
ite++;
}
// Calculate the vector between two cameras and the center
vector<Point> vector_cams; vector<Point> center_cams;
ite = cammatches.begin();
while(ite < cammatches.end())
{
Point point;
point.x = selected_ellipses[(*ite)[0]].center.x - selected_ellipses[(*ite)[1]].center.x;
point.y = selected_ellipses[(*ite)[0]].center.y - selected_ellipses[(*ite)[1]].center.y;
vector_cams.push_back(point);
point.x = (selected_ellipses[(*ite)[0]].center.x + selected_ellipses[(*ite)[1]].center.x)/2;
point.y = (selected_ellipses[(*ite)[0]].center.y + selected_ellipses[(*ite)[1]].center.y)/2;
center_cams.push_back(point);
ite++;
}
// Match the eyes and cameras, by calculating the center distances and intersection angle
vector<vector<int>> matches_eye_cam;
vector<vector<double>> matches_parameters;
for(int i = 0; i < num_eyes; i++)
{
for(int j = 0; j < num_cams; j++)
{
vector<int> temp1;
vector<double> temp2;
// calculate the distances
double distance = sqrt( double( (center_eyes[i].x - center_cams[j].x)^2 + (center_eyes[i].y - center_cams[j].y)^2 ) );
// calculate the cosine intersection angle
double cosAngle = vector_eyes[i].x * vector_cams[j].x + vector_eyes[i].y * vector_cams[j].y;
// store everything
temp1.push_back(i); temp1.push_back(j);
temp2.push_back(distance); temp2.push_back(cosAngle);
matches_eye_cam.push_back(temp1);
matches_parameters.push_back(temp2);
}
}
// go over to find the minimum
int min_dis = 0; int min_angle = 0;
vector<vector<double>>::iterator ite_para = matches_parameters.begin();
/* If no contours are found, return S_OK */
if(matches_parameters.size() < 1)
return 1;
for(int i = 1; i < matches_parameters.size(); i++)
{
if( (*(ite_para+min_dis))[0] > (*(ite_para+i))[0] )
min_dis = i;
if( (*(ite_para+min_angle))[1] > (*(ite_para+i))[1] )
min_angle = i;
}
// get the best match of eyes and cameras 's index
int eyes_index, cams_index;
vector<vector<int>>::iterator ite_match_eye_cam = matches_eye_cam.begin();
if(min_dis == min_angle)
{
// perfect match
eyes_index = (*(ite_match_eye_cam + min_dis))[0];
cams_index = (*(ite_match_eye_cam + min_dis))[1];
}
else
{
// tried to fuse them and find a better sulotion, but didnot work out, so
// go with the min_dis
eyes_index = (*(ite_match_eye_cam + min_dis))[0];
cams_index = (*(ite_match_eye_cam + min_dis))[1];
}
vector<vector<int>>::iterator ite_eyes = eyematches.begin();
vector<vector<int>>::iterator ite_cams = cammatches.begin();
// draw the eyes
ellipse(*pImg, selected_ellipses[(*(ite_eyes+eyes_index))[0]], Scalar(0, 255, 255), 2, 8);
ellipse(*pImg, selected_ellipses[(*(ite_eyes+eyes_index))[1]], Scalar(0, 255, 255), 2, 8);
// draw the camera
ellipse(*pImg, selected_ellipses[(*(ite_cams+cams_index))[0]], Scalar(0, 255, 0), 2, 8);
ellipse(*pImg, selected_ellipses[(*(ite_cams+cams_index))[1]], Scalar(0, 255, 0), 2, 8);
imshow("show", *pImg);
// find the upper camera
int m1 = (*(ite_cams+cams_index))[0];
int m2 = (*(ite_cams+cams_index))[1];
int upper;
if(selected_ellipses[m1].center.y < selected_ellipses[m2].center.y)
upper = m1;
else
upper = m2;
*x = selected_ellipses[upper].center.x;
*y = selected_ellipses[upper].center.y;
return 1;
}
int main()
{
Mat imO = imread("Capture.PNG");
double x, y;
FindNao(&imO, &x, &y);
cout<<x<<" "<<y<<endl;
cvWaitKey(0);
}