how can i make face clusters using k means after detecting face from video?Here is my face detecting code.

Question

prashanth 2014-2-19

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/116819-how-can-i-make-face-clusters-using-k-means-after-detecting-face-from-video-here-is-my-face-detecting

评论： prashanth 2014-2-20

% Create a cascade detector object. faceDetector = vision.CascadeObjectDetector(); % Read a video frame and run the detector. videoFileReader = vision.VideoFileReader('visionface.avi'); videoFrame = step(videoFileReader); bbox = step(faceDetector, videoFrame);

% Draw the returned bounding box around the detected face. boxInserter = vision.ShapeInserter('BorderColor','Custom',... 'CustomBorderColor',[255 255 0]); videoOut = step(boxInserter, videoFrame,bbox); figure, imshow(videoOut), title('Detected face'); % Get the skin tone information by extracting the Hue from the video frame % converted to the HSV color space. [hueChannel,~,~] = rgb2hsv(videoFrame);

% Display the Hue Channel data and draw the bounding box around the face. figure, imshow(hueChannel), title('Hue channel data'); rectangle('Position',bbox(1,:),'LineWidth',2,'EdgeColor',[1 1 0]) % Detect the nose within the face region. The nose provides a more accurate % measure of the skin tone because it does not contain any background % pixels. noseDetector = vision.CascadeObjectDetector('Nose'); faceImage = imcrop(videoFrame,bbox); noseBBox = step(noseDetector,faceImage);

% The nose bounding box is defined relative to the cropped face image. % Adjust the nose bounding box so that it is relative to the original video % frame. noseBBox(1:2) = noseBBox(1:2) + bbox(1:2);

% Create a tracker object. tracker = vision.HistogramBasedTracker;

% Initialize the tracker histogram using the Hue channel pixels from the % nose. initializeObject(tracker, hueChannel, noseBBox);

% Create a video player object for displaying video frames. videoInfo = info(videoFileReader); videoPlayer = vision.VideoPlayer('Position',[100 100 videoInfo.VideoSize+30]);

% Track the face over successive video frames until the video is finished. while ~isDone(videoFileReader)

    % Extract the next video frame
    videoFrame = step(videoFileReader);
    % RGB -> HSV
    [hueChannel,~,~] = rgb2hsv(videoFrame);
    % Track using the Hue channel data
    bbox = step(tracker, hueChannel);
    % Insert a bounding box around the object being tracked
    videoOut = step(boxInserter, videoFrame, bbox);
    % Display the annotated video frame using the video player object
    step(videoPlayer, videoOut);

end

% Release resources release(videoFileReader); release(videoPlayer);

a

3 个评论
显示 1更早的评论隐藏 1更早的评论

prashanth 2014-2-20

在 MATLAB Online 中打开

 % Create a cascade detector object.
 faceDetector = vision.CascadeObjectDetector();
 % Read a video frame and run the detector.
 videoFileReader = vision.VideoFileReader('visionface.avi');
 videoFrame      = step(videoFileReader);
 bbox            = step(faceDetector, videoFrame);
 % Draw the returned bounding box around the detected face.
 boxInserter  = vision.ShapeInserter('BorderColor','Custom',...
    'CustomBorderColor',[255 255 0]);
 videoOut = step(boxInserter, videoFrame,bbox);
 figure, imshow(videoOut), title('Detected face');
 % Get the skin tone information by extracting the Hue from the video frame
 % converted to the HSV color space.
 [hueChannel,~,~] = rgb2hsv(videoFrame);
 % Display the Hue Channel data and draw the bounding box around the face.
 figure, imshow(hueChannel), title('Hue channel data');
 rectangle('Position',bbox(1,:),'LineWidth',2,'EdgeColor',[1 1 0])
 % Detect the nose within the face region. The nose provides a more  accurate
 % measure of the skin tone because it does not contain any background
 % pixels.
 noseDetector = vision.CascadeObjectDetector('Nose');
 faceImage    = imcrop(videoFrame,bbox);
 noseBBox     = step(noseDetector,faceImage);
 % The nose bounding box is defined relative to the cropped face image.
 % Adjust the nose bounding box so that it is relative to the original video
 % frame.
 noseBBox(1:2) = noseBBox(1:2) + bbox(1:2);
 % Create a tracker object.
 tracker = vision.HistogramBasedTracker;
 % Initialize the tracker histogram using the Hue channel pixels from the
 % nose.
 initializeObject(tracker, hueChannel, noseBBox);
 % Create a video player object for displaying video frames.
 videoInfo    = info(videoFileReader);
 videoPlayer  = vision.VideoPlayer('Position',[100 100    videoInfo.VideoSize+30]);
 % Track the face over successive video frames until the video is finished.
 while ~isDone(videoFileReader)
    % Extract the next video frame
    videoFrame = step(videoFileReader);
    % RGB -> HSV
    [hueChannel,~,~] = rgb2hsv(videoFrame);
    % Track using the Hue channel data
    bbox = step(tracker, hueChannel);
    % Insert a bounding box around the object being tracked
    videoOut = step(boxInserter, videoFrame, bbox);
    % Display the annotated video frame using the video player object
    step(videoPlayer, videoOut);
 end
 % Release resources
 release(videoFileReader);
 release(videoPlayer);

prashanth 2014-2-20

I just detected face from video.Now i want to do clusters of faces.So Now i am planning to use kmeans cluster method.I dont know how cropped face should be stored in arrays and clustered.

请先登录，再进行评论。

请先登录，再回答此问题。