Real time QR Code / Bar code detection with webcam using OpenCV and ZBar

Categories Computer Vision, Uncategorized
Screen-Shot-2014-04-02-at-11.49.05-PM

Tutorial: Real time QR Code / Bar code detection using webcam video feed / stream using OpenCV and ZBar

Pre-requisites:

You will need to have installed OpenCV and ZBar (see previous tutorials) for this to work.

Source on Github:  https://github.com/ayoungprogrammer/WebcamCodeScanner

Code:

 #include <opencv2/highgui/highgui.hpp>  
 #include <opencv2/imgproc/imgproc.hpp>  
 #include <zbar.h>  
 #include <iostream>  
 using namespace cv;  
 using namespace std;  
 using namespace zbar;  
 //g++ main.cpp /usr/local/include/ /usr/local/lib/ -lopencv_highgui.2.4.8 -lopencv_core.2.4.8  
 int main(int argc, char* argv[])  
 {  
   VideoCapture cap(0); // open the video camera no. 0  
   // cap.set(CV_CAP_PROP_FRAME_WIDTH,800);  
   // cap.set(CV_CAP_PROP_FRAME_HEIGHT,640);  
   if (!cap.isOpened()) // if not success, exit program  
   {  
     cout << "Cannot open the video cam" << endl;  
     return -1;  
   }  
   ImageScanner scanner;   
    scanner.set_config(ZBAR_NONE, ZBAR_CFG_ENABLE, 1);   
   double dWidth = cap.get(CV_CAP_PROP_FRAME_WIDTH); //get the width of frames of the video  
   double dHeight = cap.get(CV_CAP_PROP_FRAME_HEIGHT); //get the height of frames of the video  
   cout << "Frame size : " << dWidth << " x " << dHeight << endl;  
   namedWindow("MyVideo",CV_WINDOW_AUTOSIZE); //create a window called "MyVideo"  
   while (1)  
   {  
     Mat frame;  
     bool bSuccess = cap.read(frame); // read a new frame from video  
      if (!bSuccess) //if not success, break loop  
     {  
        cout << "Cannot read a frame from video stream" << endl;  
        break;  
     }  
     Mat grey;  
     cvtColor(frame,grey,CV_BGR2GRAY);  
     int width = frame.cols;   
     int height = frame.rows;   
     uchar *raw = (uchar *)grey.data;   
     // wrap image data   
     Image image(width, height, "Y800", raw, width * height);   
     // scan the image for barcodes   
     int n = scanner.scan(image);   
     // extract results   
     for(Image::SymbolIterator symbol = image.symbol_begin();   
     symbol != image.symbol_end();   
     ++symbol) {   
         vector<Point> vp;   
     // do something useful with results   
     cout << "decoded " << symbol->get_type_name() << " symbol "" << symbol->get_data() << '"' <<" "<< endl;   
       int n = symbol->get_location_size();   
       for(int i=0;i<n;i++){   
         vp.push_back(Point(symbol->get_location_x(i),symbol->get_location_y(i)));   
       }   
       RotatedRect r = minAreaRect(vp);   
       Point2f pts[4];   
       r.points(pts);   
       for(int i=0;i<4;i++){   
         line(frame,pts[i],pts[(i+1)%4],Scalar(255,0,0),3);   
       }   
       //cout<<"Angle: "<<r.angle<<endl;   
     }   
     imshow("MyVideo", frame); //show the frame in "MyVideo" window  
     if (waitKey(30) == 27) //wait for 'esc' key press for 30ms. If 'esc' key is pressed, break loop  
     {  
       cout << "esc key is pressed by user" << endl;  
       break;   
     }  
   }  
   return 0;  
 }  

To Test

Find any QR code or bar code and hold it close to your webcam and it should pick up.

Tutorial: Scanning Barcodes / QR Codes with OpenCV using ZBar

Categories Computer Vision, Uncategorized
screenshot.12
With the ZBar library, scanning Barcodes / QR codes is quite simple. ZBar is able to identify multiple bar code /qr code types and able to give the coords of their locations.

This tutorial was written using:
Microsoft Visual Studio 2008 Express
OpenCV 2.4.2
Windows Vista 32-bit
ZBar 0.1

You will need OpenCV installed before doing this tutorial

Tutorial here: http://ayoungprogrammer.blogspot.ca/2012/10/tutorial-install-opencv-242-for-windows.html

1. Install ZBar (Windows Installer)

http://sourceforge.net/projects/zbar/files/zbar/0.10/zbar-0.10-setup.exe/download

Check install developmental libraries and headers (You will need this)

Install ZBar in the default directory
“C:Program FilesZBar”

2. Import headers and libraries

Tools ->Options 

Projects & Solutions -> VC++ Directories
Go to “Include files” and add: “C:Program FilesZBarinclude”

 Go to “Library files and add: “C:Program FilesZBarlib”

3. Link libraries in current project

Create an empy blank console project
Right click your project -> Properties -> Configuration Properties -> Linker -> Input

In additional dependencies copy and paste the following:
libzbar-0.lib
opencv_core242d.lib
opencv_imgproc242d.lib
opencv_highgui242d.lib
opencv_ml242d.lib
opencv_video242d.lib
opencv_features2d242d.lib
opencv_calib3d242d.lib
opencv_objdetect242d.lib
opencv_contrib242d.lib
opencv_legacy242d.lib
opencv_flann242d.lib

4. Test Program

Make a new file in your project: main.cpp
 #include "zbar.h"  
 #include "cv.h"  
 #include "highgui.h"  
 #include <iostream>  
 using namespace std;  
 using namespace zbar;  
 using namespace cv;  
 int main(void){  
      ImageScanner scanner;  
      scanner.set_config(ZBAR_NONE, ZBAR_CFG_ENABLE, 1);  
       // obtain image data  
      char file[256];  
      cin>>file;  
      Mat img = imread(file,0);  
      Mat imgout;  
      cvtColor(img,imgout,CV_GRAY2RGB);  
      int width = img.cols;  
      int height = img.rows;  
   uchar *raw = (uchar *)img.data;  
   // wrap image data  
   Image image(width, height, "Y800", raw, width * height);  
   // scan the image for barcodes  
   int n = scanner.scan(image);  
   // extract results  
   for(Image::SymbolIterator symbol = image.symbol_begin();  
     symbol != image.symbol_end();  
     ++symbol) {  
                vector<Point> vp;  
     // do something useful with results  
     cout << "decoded " << symbol->get_type_name()  
        << " symbol "" << symbol->get_data() << '"' <<" "<< endl;  
           int n = symbol->get_location_size();  
           for(int i=0;i<n;i++){  
                vp.push_back(Point(symbol->get_location_x(i),symbol->get_location_y(i))); 
           }  
           RotatedRect r = minAreaRect(vp);  
           Point2f pts[4];  
           r.points(pts);  
           for(int i=0;i<4;i++){  
                line(imgout,pts[i],pts[(i+1)%4],Scalar(255,0,0),3);  
           }  
           cout<<"Angle: "<<r.angle<<endl;  
   }  
      imshow("imgout.jpg",imgout);  
   // clean up  
   image.set_data(NULL, 0);  
       waitKey();  
 }  

5. Copy libzbar-0.dll from C:/Program Files/ZBar/bin to your project folder

6. Run program 

Sample Images

Tutorial: Detection / recognition of multiple rectangles and extracting with OpenCV

Categories Computer Vision, Uncategorized
orig

 This tutorial will be focused on being able to take a picture and extract the rectangles in the image that are above a certain size:

I am using OpenCV 2.4.2 on Microsoft Visual Express 2008 but it should work with other version as well.

Thanks to: opencv-code.com for their helpful guides

Step 1: Clean up

So once again, we’ll use my favourite snippet for cleaning up an image:
Apply a Gaussian blur and using an adaptive threshold for binarzing the image
//Apply blur to smooth edges and use adapative thresholding  
 cv::Size size(3,3);  
 cv::GaussianBlur(img,img,size,0);  
 adaptiveThreshold(img, img,255,CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY,75,10);  
 cv::bitwise_not(img, img);  

Step 2: Hough Line detection

Use a probabilistic Hough line detection to figure out where the lines are. This algorithm works by going through every point in the image and checking every angle. 
 vector<Vec4i> lines;  
 HoughLinesP(img, lines, 1, CV_PI/180, 80, 100, 10);  
And here we have the results of the algorithm:

Step 3: Use connected components to determine what they shapes are

This is the most complex part of the algorithm (general pseudocode):
First, initialize every line to be in an undefined group
For every line compute the intersection of the two line segments (if they do not intersect ignore the point)
      If both lines are undefined, make a new group out of them
      If only one line is defined in a group, add the other line into the group. 
      If both lines are defined than add all the lines from one group into the other group
      If both lines are in the same group, do nothing
cv::Point2f computeIntersect(cv::Vec4i a, cv::Vec4i b)  
 {  
   int x1 = a[0], y1 = a[1], x2 = a[2], y2 = a[3];  
   int x3 = b[0], y3 = b[1], x4 = b[2], y4 = b[3];  
   if (float d = ((float)(x1-x2) * (y3-y4)) - ((y1-y2) * (x3-x4)))  
   {  
     cv::Point2f pt;  
     pt.x = ((x1*y2 - y1*x2) * (x3-x4) - (x1-x2) * (x3*y4 - y3*x4)) / d;  
     pt.y = ((x1*y2 - y1*x2) * (y3-y4) - (y1-y2) * (x3*y4 - y3*x4)) / d;  
           //-10 is a threshold, the POI can be off by at most 10 pixels
           if(pt.x<min(x1,x2)-10||pt.x>max(x1,x2)+10||pt.y<min(y1,y2)-10||pt.y>max(y1,y2)+10){  
                return Point2f(-1,-1);  
           }  
           if(pt.x<min(x3,x4)-10||pt.x>max(x3,x4)+10||pt.y<min(y3,y4)-10||pt.y>max(y3,y4)+10){  
                return Point2f(-1,-1);  
           }  
     return pt;  
   }  
   else  
     return cv::Point2f(-1, -1);  
 }  
Connected components
int* poly = new int[lines.size()];  
  for(int i=0;i<lines.size();i++)poly[i] = - 1;  
  int curPoly = 0;  
       vector<vector<cv::Point2f> > corners;  
      for (int i = 0; i < lines.size(); i++)  
      {  
           for (int j = i+1; j < lines.size(); j++)  
           {  
          
                cv::Point2f pt = computeIntersect(lines[i], lines[j]);  
                if (pt.x >= 0 && pt.y >= 0&&pt.x<img2.size().width&&pt.y<img2.size().height){  
              
                     if(poly[i]==-1&&poly[j] == -1){  
                          vector<Point2f> v;  
                          v.push_back(pt);  
                          corners.push_back(v);       
                          poly[i] = curPoly;  
                          poly[j] = curPoly;  
                          curPoly++;  
                          continue;  
                     }  
                     if(poly[i]==-1&&poly[j]>=0){  
                          corners[poly[j]].push_back(pt);  
                          poly[i] = poly[j];  
                          continue;  
                     }  
                     if(poly[i]>=0&&poly[j]==-1){  
                          corners[poly[i]].push_back(pt);  
                          poly[j] = poly[i];  
                          continue;  
                     }  
                     if(poly[i]>=0&&poly[j]>=0){  
                          if(poly[i]==poly[j]){  
                               corners[poly[i]].push_back(pt);  
                               continue;  
                          }  
                        
                          for(int k=0;k<corners[poly[j]].size();k++){  
                               corners[poly[i]].push_back(corners[poly[j]][k]);  
                          }  
                       
                          corners[poly[j]].clear();  
                          poly[j] = poly[i];  
                          continue;  
                     }  
                }  
           }  
      }  
The circles represent the points of intersection and the colours represent the different shapes. 

Step 4: Find corners of the polygon

Now we need to find corners of the polygons to get the polygon formed from the point of intersections.
Pseudocode:
For each group of points:
       Compute mass center (average of points)
        For each point that is above the mass center, add to top list
        For each point that is below the mass center, add to bottom list
        Sort top list and bottom list by x val
       first element of top list is  left most (top left point)
        last element of top list is right most (top right point) 
       first element of bottom list is  left most  (bottom left point)
       last element of bottom list is right most  (bottom right point) 

       

 bool comparator(Point2f a,Point2f b){  
           return a.x<b.x;  
      }  
 void sortCorners(std::vector<cv::Point2f>& corners, cv::Point2f center)  
 {  
   std::vector<cv::Point2f> top, bot;  
   for (int i = 0; i < corners.size(); i++)  
   {  
     if (corners[i].y < center.y)  
       top.push_back(corners[i]);  
     else  
       bot.push_back(corners[i]);  
   }  
      sort(top.begin(),top.end(),comparator);  
      sort(bot.begin(),bot.end(),comparator);  
   cv::Point2f tl = top[0];
   cv::Point2f tr = top[top.size()-1];
   cv::Point2f bl = bot[0];
   cv::Point2f br = bot[bot.size()-1];  
   corners.clear();  
   corners.push_back(tl);  
   corners.push_back(tr);  
   corners.push_back(br);  
   corners.push_back(bl);  
 }  
for(int i=0;i<corners.size();i++){  
           cv::Point2f center(0,0);  
           if(corners[i].size()<4)continue;  
           for(int j=0;j<corners[i].size();j++){  
                center += corners[i][j];  
           }  
           center *= (1. / corners[i].size());  
           sortCorners(corners[i], center);  
      }  

Step 5: Extraction

The final step is extract each rectangle from the image. We can do this quite easily with the perspective transform from OpenCV. To get an estimate of the dimensions of the rectangle we can use a bounding rectangle of the corners. If the dimensions of that rectangle are under our wanted area, we ignore the polygon. If the polygon also has less than 4 points we can ignore it as well. 
for(int i=0;i<corners.size();i++){  
           if(corners[i].size()<4)continue;  
           Rect r = boundingRect(corners[i]);  
           if(r.area()<50000)continue;  
           cout<<r.area()<<endl;  
           // Define the destination image  
           cv::Mat quad = cv::Mat::zeros(r.height, r.width, CV_8UC3);  
           // Corners of the destination image  
           std::vector<cv::Point2f> quad_pts;  
           quad_pts.push_back(cv::Point2f(0, 0));  
           quad_pts.push_back(cv::Point2f(quad.cols, 0));  
           quad_pts.push_back(cv::Point2f(quad.cols, quad.rows));  
           quad_pts.push_back(cv::Point2f(0, quad.rows));  
           // Get transformation matrix  
           cv::Mat transmtx = cv::getPerspectiveTransform(corners[i], quad_pts);  
           // Apply perspective transformation  
           cv::warpPerspective(img3, quad, transmtx, quad.size());  
           stringstream ss;  
           ss<<i<<".jpg";  
           imshow(ss.str(), quad);  
      }