Blockquote

I have to take a bmp file with multiple rectangles and determine corner points and angle of rotation. I've started with a canny and a hough transform, but can't seem to determine the corners to continue. Below is what I have written so far. Any guidance in the right direction would be greatly appreciated.

include<opencv\cv.h>

include<opencv\highgui.h>

include<iostream>

using namespace cv;

using namespace std;

using namespace cv;

/// Global variables

Mat src, src_gray;

Mat dst, cdst, ldst, detected_edges;

vector<vec4i> lines;

int edgeThresh = 1;

int const lowThreshold = 5;

int const maxThreshold = 200;

int ratio = 3;

int kernel_size = 3;

char* window_name1 = "Edge Map";

char* window_name2 = "Line Map";

int main()

{

//Load boxes.bmp into a matrix

src = imread( "boxes.bmp",1 );

//convert boxes.bmp into grayscale

cvtColor( src, src_gray, CV_BGR2GRAY );

/// Reduce noise with a kernel 3x3

blur( src_gray, detected_edges, Size(3,3) );

/// Canny detector

Canny( detected_edges, detected_edges, lowThreshold, maxThreshold, kernel_size );

/// Using Canny's output as a mask, we display our result

src.copyTo( dst, detected_edges);

cvtColor( dst, cdst, CV_BGR2GRAY );

HoughLinesP( cdst, lines, 25, CV_PI/180, 1, 0, 0);

for( size_t i = 0; i < lines.size(); i++ ) {

int x1 = lines[i][0];

int x2 = lines[i][2];

int y1 = lines[i][1];

int y2 = lines[i][3];

line( cdst, Point(x1, y1),

Point(x2, y2), Scalar(255,255,255), 1, 8 ); }

imshow( "Source", src );

imshow( "Detected Lines", cdst );

waitKey(); };

Blockquote

I have to take a bmp file with multiple rectangles and determine corner points and angle of rotation. I've started with a canny and a hough transform, but can't seem to determine the corners to continue. Below is what I have written so far. Any guidance in the right direction would be greatly appreciated.

#include<opencv\cv.h>

#include<opencv\highgui.h>

#include<iostream>

using namespace cv;
 
cv;
using namespace std;
 
std;
using namespace cv;
 
cv;
/// Global variables
 
variables
Mat src, src_gray;
 
src_gray;
Mat dst, cdst, ldst, detected_edges;
 
vector<vec4i> lines;
 
detected_edges;
vector<Vec4i> lines;
int edgeThresh = 1;
 
1;
int const lowThreshold = 5;
 
5;
int const maxThreshold = 200;
 
200;
int ratio = 3;
 
3;
int kernel_size = 3;
 
3;
char* window_name1 = "Edge Map";
 
Map";
char* window_name2 = "Line Map";
 
int main()
 
{
 
Map";
int main()
{
//Load boxes.bmp into a matrix
 
matrix
src = imread( "boxes.bmp",1 );
 
);
//convert boxes.bmp into grayscale
 
grayscale
cvtColor( src, src_gray, CV_BGR2GRAY );
 
);
/// Reduce noise with a kernel 3x3
 
3x3
blur( src_gray, detected_edges, Size(3,3) );
 
);
/// Canny detector
 
detector
Canny( detected_edges, detected_edges, lowThreshold, maxThreshold, kernel_size );
 
);
/// Using Canny's output as a mask, we display our result
 
result
src.copyTo( dst, detected_edges);
 
detected_edges);
cvtColor( dst, cdst, CV_BGR2GRAY );
 
);
HoughLinesP( cdst, lines, 25, CV_PI/180, 1, 0, 0);
 
0);
for( size_t i = 0; i < lines.size(); i++ )
{
 
{
int x1 = lines[i][0];
 
lines[i][0];
int x2 = lines[i][2];
 
lines[i][2];
int y1 = lines[i][1];
 
lines[i][1];
int y2 = lines[i][3];
 
lines[i][3];
line( cdst, Point(x1, y1),
 
y1),
Point(x2, y2), Scalar(255,255,255), 1, 8 );
}
 
}
imshow( "Source", src );
 
);
imshow( "Detected Lines", cdst );
 
);
waitKey();
};};