When i use the canny edge algorithm, it produces the 2 edges opposite the thick colored line as expected, but i want only one edge to be displayed so as to make my line and curve detection algorithm much less complicated, any ideas on how i can make that happen ?
Here is the code :
bool CannyEdgeDetection(DataStructure& col)
{
Mat src, src_gray;
Mat dst, detected_edges, fin;
int WhiteCount = 0, BCount = 0;
char szFil1[32] = "ocv.bmp";
char szFil2[32] = "dst.bmp";
src = imread(szFil1);
dst = imread(szFil1);
blur( src_gray, detected_edges, Size(3,3) );
Canny( src, dst, 100, 200, 3 );
imwrite(szFil2, dst );
IplImage* img = cvLoadImage(szFil2);
int height = img->height;
int width = img->width;
int step = img->widthStep;
int channels = img->nChannels;
uchar * datau = (uchar *)img->imageData;
for(int i=0;i<height;i++){
for(int j=0;j<width;j++){
for(int k=0;k<channels;k++){
datau[i*step+j*channels+k] = 255 - datau[i*step+j*channels+k];
if (datau[i*step+j*channels+k]==0){
WhiteCount++;
col.pixel_col [i][j] = 2;
}
else{BCount++;
col.pixel_col[i][j] = 0;
}
}
}
}
cvSaveImage("img.bmp" ,img);
return 0;
}
This is not the original image but similar :
Which part do i comment out to be able to read black images in white backgrounds ? or any colored image ?
bool done;
do
{
cv::morphologyEx(img, temp, cv::MORPH_OPEN, element);
cv::bitwise_not(temp, temp);
cv::bitwise_and(img, temp, temp);
cv::bitwise_or(skel, temp, skel);
cv::erode(img, img, element);
double max;
cv::minMaxLoc(img, 0, &max);
done = (max == 0);
} while (!done);
High threshold is used to identify the strong pixels (intensity higher than the high threshold) Low threshold is used to identify the non-relevant pixels (intensity lower than the low threshold)
The 'Canny' method uses two thresholds. For example, if the threshold is [0.1 0.15] then the edge pixels above the upper limit(0.15) are considered and edge pixels below the threshold(0.1) are discarded. Now, you may have a question "what about the pixels in between upper and lower threshold"?
The Canny edge detector requires the user to input three parameters. The first is sigma, the standard deviation of the Gaussian filter specified in pixels. The second parameter low is the low threshold which is specified as a fraction of a computed high threshold.
That process is called skeletonization
or thinning
. You can google for that.
Here is a simple method for skeletonization
: skeletonization OpenCV In C#
Below is the output
I got when applied above method to your image ( Image is inverted before skeletonization because above method work for white images in black background
, just opposite case of your input image).
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