#8a. Object Detection version 1.0

Moving on to object detection, you will find this post very interesting, provided you understand it.
In this tutorial, I will show you how to detect an object based on difference in color of the object and that of the surroundings.

I hope you've read 'A piece of advice', my previous post.
Try it in this tutorial. See if you understand the program in one shot.

#include
#include
#include
#include
using namespace std;
using namespace cv;

Mat frame , frame_threshold;
//Refer Main

int rgb_slider=0;
int low_slider=30, high_slider=100;

int low_r=30, low_g=30, low_b=30, high_r=100, high_g=100, high_b=100;
//r g b r g b

void on_rgb_trackbar(int ,void *){
 switch(rgb_slider){
 case 0:
  setTrackbarPos("Low threshold","Segmentation",low_r);
  setTrackbarPos("High threshold","Segmentation",high_r);
  break;
 case 1:
  setTrackbarPos("Low threshold", "Segmentation", low_g);
  setTrackbarPos("High threshold","Segmentation", high_g);
  break;
 case 2:
  setTrackbarPos("Low threshold","Segmentation",low_b);
  setTrackbarPos("High threshold", "Segmentation",high_b);
  break;
 }
}

//go to Main again

void on_low_thresh_trackbar(int, void*){
 switch(rgb_slider){
 case 0:
  low_r=min(high_slider-1, low_slider);
  setTrackbarPos("Low threshold","Segmentation",low_r);
  break;
 case 1:
  low_g=min(high_slider-1,low_slider);
  setTrackbarPos("Low threshold","Segmentation",low_g);
  break;
 case 2:
  low_b=min(high_slider-1,low_slider);
  setTrackbarPos("Low threshold","Segmentation",low_b);
  break;
 }
}

//go to Main again

void on_high_thresh_trackbar(int, void*){
 switch(rgb_slider){
 case 0:
  high_r=max(high_slider, low_slider+1);
  setTrackbarPos("High threshold","Segmentation",high_r);
  break;
 case 1:
  high_g=max(high_slider,low_slider+1);
  setTrackbarPos("High threshold","Segmentation",high_g);
  break;
 case 2:
  high_b=max(high_slider,low_slider+1);
  setTrackbarPos("High threshold","Segmentation",high_b);
  break;
 }
}


int main()
{

 VideoCapture cap(0);
 namedWindow("Video");
 namedWindow("Segmentation");

  createTrackbar("0.R\n1.G\n2.B","Segmentation",&rgb_slider, 2 ,on_rgb_trackbar);
//observe the variables and callback function.
// go back to where you've left.

 createTrackbar("Low threshold","Segmentation",&low_slider,255,on_low_thresh_trackbar);
//observe the variables and callback function.
// go back to where you've left.

 createTrackbar("High threshold","Segmentation",&high_slider,255,on_high_thresh_trackbar);
//observe the variables and callback function.
// go back to where you've left.

 while(char(waitKey(1)) != 'q') {
  cap>>frame;
  if(frame.empty())
   break;
  inRange(frame,Scalar(low_b,low_g,low_r),Scalar(high_b,high_g,high_r),frame_threshold);

//inRange function, refer text.

  imshow("Video",frame);
  imshow("Segmentation",frame_threshold);
 }

 return 0;
}

So, inRange function basically filters out an image based on the color channels.

• The color channel sequence in opencv is B, G ,R  and not R,G,B.
• Scalar(B,G,R) defines the strength of an image in terms of B, G, R.\
• If you remember, we had done a tutorial on increasing brightness of an image using TrackBar(Tutorial #6). The same function was used. I hope you got the idea.
• If you understood Scalar(), you've understood inRange.
• If not, refer DOCUMENTATION.

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Screenshot of the input video:

(My room is completely messed up. So I cropped the screenshot. Ubuntu doesn't have a cropping utility, but, we know OpenCV now! First Tutorial, Cropping app!)

Output:
e.g. Red color detector

Wonderful, isn't it?