Computer Vision with MATLAB.zip_MATLAB 人脸定位_人脸定位_人脸追踪_人脸追踪matlab

%% Face Detection and Tracking Using the KLT Algorithm % This example shows how to automatically detect and track a face using % feature points. The approach in this example keeps track of the face even % when the person tilts his or her head, or moves toward or away from the % camera. % % Copyright 2013 The MathWorks, Inc. %% Detect a Face % Create a cascade detector object. faceDetector = vision.CascadeObjectDetector(); % Open a video file videoFileReader = vision.VideoFileReader('tilted_face.avi'); % Read a video frame and run the face detector. videoFrame = step(videoFileReader); bbox = step(faceDetector, videoFrame); % Convert the box to a polygon. This is needed to be able to visualize the % rotation of the object. x = bbox(1); y = bbox(2); w = bbox(3); h = bbox(4); bboxPolygon = [x, y, x+w, y, x+w, y+h, x, y+h]; % Draw the returned bounding box around the detected face. shapeInserter = vision.ShapeInserter('Shape', 'Polygons', 'BorderColor','Custom',... 'CustomBorderColor',[255 255 0]); videoFrame = step(shapeInserter, videoFrame, bboxPolygon); figure; imshow(videoFrame); title('Detected face'); %% Identify Facial Features To Track % Crop out the region of the image containing the face, and detect the % feature points inside it. cornerDetector = vision.CornerDetector('Method', ... 'Minimum eigenvalue (Shi & Tomasi)'); points = step(cornerDetector, rgb2gray(imcrop(videoFrame, bbox))); % The coordinates of the feature points are with respect to the cropped % region. They need to be translated back into the original image % coordinate system. points = double(points); points(:, 1) = points(:, 1) + double(bbox(1)); points(:, 2) = points(:, 2) + double(bbox(2)); % Display the detected points. markerInserter = vision.MarkerInserter('Shape', 'Plus', ... 'BorderColor', 'White'); videoFrame = step(markerInserter, videoFrame, points); figure, imshow(videoFrame), title('Detected features'); %% Initialize a Tracker to Track the Points % Create a point tracker and enable the bidirectional error constraint to % make it more robust in the presence of noise and clutter. pointTracker = vision.PointTracker('MaxBidirectionalError', 2); % Initialize the tracker with the initial point locations and the initial % video frame. initialize(pointTracker, double(points), rgb2gray(videoFrame)); %% Initialize a Video Player to Display the Results % Create a video player object for displaying video frames. videoInfo = info(videoFileReader); videoPlayer = vision.VideoPlayer('Position',... [100 100 videoInfo.VideoSize(1:2)+30]); %% Initialize a Geometric Transform Estimator geometricTransformEstimator = vision.GeometricTransformEstimator(... 'PixelDistanceThreshold', 4, 'Transform', 'Nonreflective similarity'); % Make a copy of the points to be used for computing the geometric % transformation between the points in the previous and the current frames oldPoints = double(points); %% Track the Points while ~isDone(videoFileReader) % get the next frame videoFrame = step(videoFileReader); % Track the points. Note that some points may be lost. [points, isFound] = step(pointTracker, rgb2gray(videoFrame)); visiblePoints = points(isFound, :); oldInliers = oldPoints(isFound, :); if ~isempty(visiblePoints) % Estimate the geometric transformation between the old points % and the new points. [xform, geometricInlierIdx] = step(geometricTransformEstimator, ... double(oldInliers), double(visiblePoints)); % Eliminate outliers visiblePoints = visiblePoints(geometricInlierIdx, :); oldInliers = oldInliers(geometricInlierIdx, :); % Apply the transformation to the bounding box boxPoints = [reshape(bboxPolygon, 2, 4)', ones(4, 1)]; boxPoints = boxPoints * xform; bboxPolygon = reshape(boxPoints', 1, numel(boxPoints)); % Insert a bounding box around the object being tracked videoFrame = step(shapeInserter, videoFrame, bboxPolygon); % Display tracked points videoFrame = step(markerInserter, videoFrame, visiblePoints); % Reset the points oldPoints = visiblePoints; setPoints(pointTracker, oldPoints); end % Display the annotated video frame using the video player object step(videoPlayer, videoFrame); end %% Clean up release(videoFileReader); release(videoPlayer); release(geometricTransformEstimator); release(pointTracker); close all