rectangle, box face -> mosaic, pixelate

 

refer to mosaic function

..

def mosaic(img, rect, size):

(x1, y1, x2, y2) = rect

w = x2 - x1

h = y2 - y1

i_rect = img[y1:y2, x1:x2]

i_small = cv2.resize(i_rect, ( size, size))

i_mos = cv2.resize(i_small, (w, h), interpolation=cv2.INTER_AREA)

img2 = img.copy()

img2[y1:y2, x1:x2] = i_mos

return img2

#.... detect face

for face_dict in faces_dict:

x1,y1 = ( int(face_dict['x1']), int(face_dict['y1']))

x2,y2 = ( int(face_dict['x2']), int(face_dict['y2']))

# image = anonymize_face_pixelate(image[y1:y2, x1:x2, :] , blocks=3)

image = mosaic(image, (x1, y1, x2, y2), 10 )

#.... face mosaic

..

Ex) result

Thank you.

www.marearts.com

Two view of cam to one screen using stitching algorithm(OpenCV, example source code), (mosaic)

This source code based on ->
http://feelmare.blogspot.kr/2011/08/two-image-mosaic-paranoma-based-on-sift.html
This link page introduces how to make a mosaic image from two adjacent images.
I made two cam video to one stitching video using the source code.

After run, operate the program of 3 keys.
'q' key is quit, 'p' key is processing(stitching), 'r' is reset.

Mat TwoInOneOut(Mat Left, Mat Right);

void main()
{
 VideoCapture stream1(0);   //0 is the id of video device.0 if you have only one camera
 VideoCapture stream2(1);   //0 is the id of video device.0 if you have only one camera
 
 if (!stream1.isOpened()) { //check if video device has been initialised
  cout << "cannot open camera 1";
 }

 if (!stream2.isOpened()) { //check if video device has been initialised
  cout << "cannot open camera 2";
 }


// namedWindow("Processing");
// namedWindow("Left");
// namedWindow("Right");

 Mat H;
 int mode=0;
 //unconditional loop
 while (true) {
  Mat cameraFrame1;
  stream1.read(cameraFrame1); //get one frame form video
  

  Mat cameraFrame2;
  stream2.read(cameraFrame2); //get one frame form video

  if(mode == 0)
  {
   imshow("Left", cameraFrame1);
   imshow("Right", cameraFrame2);
  }

  Mat Left(cameraFrame1.rows, cameraFrame1.cols, CV_8U);
  Mat Right(cameraFrame1.rows, cameraFrame1.cols, CV_8U);
  cvtColor(cameraFrame1, Left, CV_RGB2GRAY, CV_8U);
  cvtColor(cameraFrame2, Right, CV_RGB2GRAY, CV_8U);

  if (waitKey(30) == 'p')
  {
   printf("Homography Matrix Processing\n");
   H = TwoInOneOut(Left, Right);
   mode=1;
   destroyWindow("Left");
   destroyWindow("Right");
  }

  if(waitKey(30) == 'r')
  {
   printf("normal mode\n");
   destroyWindow("Processing");
   mode=0;
  }
  
  //printf("%d %d\n", H.cols, H.rows);
  if(H.cols == 3 && H.rows == 3)
  {
   Mat WarpImg( Left.rows*2, Left.cols*2, cameraFrame1.depth() );
      //printf("%d %d\n", A.depth(), A.channels());
   warpPerspective(cameraFrame2, WarpImg, H, Size(WarpImg.cols, WarpImg.rows));
   Mat tempWarpImg = WarpImg(Rect(0,0,Left.cols,Left.rows));
   cameraFrame1.copyTo(tempWarpImg);

   /*
   Mat WarpImg( Left.rows*2, Left.cols*2, CV_8U);
      //printf("%d %d\n", A.depth(), A.channels());
   warpPerspective(Right, WarpImg, H, Size(WarpImg.cols, WarpImg.rows));
   Mat tempWarpImg = WarpImg(Rect(0,0,Left.cols,Left.rows));
   Left.copyTo(tempWarpImg);
   */
   if(mode ==1)
    imshow("Processing", WarpImg );
  //Mat t = WarpImg( Rect(0,0,B.cols, B.rows));

  }

  //imshow("Processing", t );

  if (waitKey(30) == 'q')
   break;
 }

 destroyAllWindows();
 
 
}


Mat TwoInOneOut(Mat Left, Mat Right)
{
 Mat H;
 

 if(Left.channels() != 1 || Right.channels() != 1)
 {
  printf("Channel Error\n");
  return H;
 }

 /////////////////
 //Detect the keypoints using SURF Detector
    int minHessian = 300; //1500; 
    SurfFeatureDetector detector( minHessian );
 SurfDescriptorExtractor extractor;

 /////////////////
 //A
    std::vector< KeyPoint> kp_Left;
    detector.detect( Left, kp_Left );    
 Mat des_Left;
    extractor.compute( Left, kp_Left, des_Left );

 /////////////////
 //B 
 std::vector< KeyPoint> kp_Right;
 detector.detect( Right, kp_Right );
 Mat des_Right;
 extractor.compute( Right, kp_Right, des_Right );

 /////////////////
 //Match
 std::vector< vector< DMatch > > matches;
 FlannBasedMatcher matcher;
 matcher.knnMatch(des_Left, des_Right, matches, 2);
 //matcher.knnMatch(des_Right, des_Left, matches, 2);
 std::vector< DMatch > good_matches;
 good_matches.reserve(matches.size());  

 for (size_t i = 0; i < matches.size(); ++i)
 { 
  if (matches[i].size() < 2)
   continue;

  const DMatch &m1 = matches[i][0];
  const DMatch &m2 = matches[i][1];

  if(m1.distance <= 0.7 * m2.distance)        
   good_matches.push_back(m1);     
 }

 //Draw only "good" matches
 Mat img_matches;
    drawMatches( Left, kp_Left, Right, kp_Right, good_matches, 
  img_matches, Scalar::all(-1), Scalar::all(-1), 
  vector< char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
 imshow("Match", img_matches);

    /////////////////
 //Find H
 if(good_matches.size() > 20 )
 {
  std::vector< Point2f >  LeftMatchPT;
  std::vector< Point2f >  RightMatchPT;
  
  for( unsigned int i = 0; i < good_matches.size(); i++ )
  {
   //-- Get the keypoints from the good matches
   LeftMatchPT.push_back( kp_Left[ good_matches[i].queryIdx ].pt );
   RightMatchPT.push_back( kp_Right[ good_matches[i].trainIdx ].pt );
  }

  H = findHomography( RightMatchPT, LeftMatchPT, CV_RANSAC );
  //H = findHomography( LeftMatchPT,RightMatchPT, CV_RANSAC );
 }


 return H;


}

////
The source code
-> here