Detecting contours.
The process of detecting contours is one of the simples.
Load the image -> Change to gray --> use the threshold function --> find the contour --> draw the contours
The code
//Load the image in colour
flag=(double) getTickCount();
imagen=imread(argv[1],CV_LOAD_IMAGE_UNCHANGED);
flagLoad=getTick(flag);
//Change the image to gray
flag=(double)getTickCount();
cvtColor(imagen,imgGris,CV_BGR2GRAY);
flag2Gray=getTick(flag);
//use the threshold to separate few objects
flag=(double)getTickCount();
threshold(imgGris,imgContorno,122,255,THRESH_BINARY);
flagThreshold=getTick(flag);
//we need a place to leave the new iamge
flag=(double)getTickCount();
Mat dst = Mat::zeros(imgGris.rows,imgGris.cols,CV_8UC3);
flagCreate=getTick(flag);
namedWindow("gris",CV_WINDOW_NORMAL);
namedWindow("contorno",CV_WINDOW_NORMAL);
namedWindow("binario",CV_WINDOW_NORMAL);
imshow ("gris",imgGris);
imshow ("contorno",imgContorno);
cvMoveWindow("gris",300,50);
cvMoveWindow("contorno",600,50);
vector< vector<Point> > vecContornos;
vector<Vec4i>jerarquia;
//Find the contours
flag = (double)getTickCount();
findContours(imgContorno,vecContornos,jerarquia,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE);
flagFind =getTick(flag);
flag = (double)getTickCount();
for (int idx=0;idx >=0;idx=jerarquia[idx][0])
{
drawContours(dst,vecContornos,idx,WHITE,5,8,jerarquia);
}
The results
And finally we have this results from the original image
 |
| Original image |
 |
| Result image |
|
All the values are in milliseconds
This exercise give us a useful information
The main information is the threshold to separate the right image (12 ms) and find the contours (31 ms) around 50 ms to detect objects, it's not really bad for a dive which could use this information.
But the structural analysis could be take more time, we will what happen in the next exercises
Any comment will be appreciated
