error message : Undefined function 'bbox2points' for input arguments of type 'double'.

Hi,

I am currently using Matlab R2013a and am running the code as stated below. Was trying to test out the codes but received an error message : Undefined function 'bbox2points' for input arguments of type 'double'.

I am not sure what this message is as i am new to matlab.

Really appreciate your help.

%% 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 2014 The MathWorks, Inc.

%% Introduction % Object detection and tracking are important in many computer vision % applications including activity recognition, automotive safety, and % surveillance. In this example, you will develop a simple face tracking % system by dividing the tracking problem into three parts: % % # Detect a face % # Identify facial features to track % # Track the face

%% Detect a Face % First, you must detect the face. Use the vision.CascadeObjectDetector % System object™ to detect the location of a face in a video frame. The % cascade object detector uses the Viola-Jones detection algorithm and a % trained classification model for detection. By default, the detector is % configured to detect faces, but it can be used to detect other types of % objects.

% Create a cascade detector object. faceDetector = vision.CascadeObjectDetector();

% Read a video frame and run the face detector. videoFileReader = vision.VideoFileReader('tilted_face.avi'); videoFrame = step(videoFileReader); bbox = step(faceDetector, videoFrame);

% Draw the returned bounding box around the detected face. videoFrame = insertShape(videoFrame, 'Rectangle', bbox); figure; imshow(videoFrame); title('Detected face');

% Convert the first box into a list of 4 points % This is needed to be able to visualize the rotation of the object. bboxPoints = bbox2points(bbox(1, :));

%% % To track the face over time, this example uses the Kanade-Lucas-Tomasi % (KLT) algorithm. While it is possible to use the cascade object detector % on every frame, it is computationally expensive. It may also fail to % detect the face, when the subject turns or tilts his head. This % limitation comes from the type of trained classification model used for % detection. The example detects the face only once, and then the KLT % algorithm tracks the face across the video frames.

%% Identify Facial Features To Track % The KLT algorithm tracks a set of feature points across the video frames. % Once the detection locates the face, the next step in the example % identifies feature points that can be reliably tracked. This example % uses the standard, "good features to track" proposed by Shi and Tomasi.

% Detect feature points in the face region. points = detectMinEigenFeatures(rgb2gray(videoFrame), 'ROI', bbox);

% Display the detected points. figure, imshow(videoFrame), hold on, title('Detected features'); plot(points);

%% Initialize a Tracker to Track the Points % With the feature points identified, you can now use the % vision.PointTracker System object to track them. For each point in the % previous frame, the point tracker attempts to find the corresponding % point in the current frame. Then the estimateGeometricTransform % function is used to estimate the translation, rotation, and scale between % the old points and the new points. This transformation is applied to the % bounding box around the face.

% 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. points = points.Location; initialize(pointTracker, points, videoFrame);

%% Initialize a Video Player to Display the Results % Create a video player object for displaying video frames. videoPlayer = vision.VideoPlayer('Position',... [100 100 [size(videoFrame, 2), size(videoFrame, 1)]+30]);

%% Track the Face % Track the points from frame to frame, and use % estimateGeometricTransform function to estimate the motion of the face.

% 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 = 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, videoFrame);
    visiblePoints = points(isFound, :);
    oldInliers = oldPoints(isFound, :);

    if size(visiblePoints, 1) >= 2 % need at least 2 points

        % Estimate the geometric transformation between the old points
        % and the new points and eliminate outliers
        [xform, oldInliers, visiblePoints] = estimateGeometricTransform(...
            oldInliers, visiblePoints, 'similarity', 'MaxDistance', 4);

        % Apply the transformation to the bounding box points
        bboxPoints = transformPointsForward(xform, bboxPoints);

        % Insert a bounding box around the object being tracked
        bboxPolygon = reshape(bboxPoints', 1, []);
        videoFrame = insertShape(videoFrame, 'Polygon', bboxPolygon, ...
            'LineWidth', 2);

        % Display tracked points
        videoFrame = insertMarker(videoFrame, visiblePoints, '+', ...
            'Color', 'white');       

        % 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(pointTracker);

%% Summary % In this example, you created a simple face tracking system that % automatically detects and tracks a single face. Try changing the input % video, and see if you are still able to detect and track a face. Make % sure the person is facing the camera in the initial frame for the % detection step.

%% References % % Viola, Paul A. and Jones, Michael J. "Rapid Object Detection using a % Boosted Cascade of Simple Features", IEEE CVPR, 2001. % % Bruce D. Lucas and Takeo Kanade. An Iterative Image Registration % Technique with an Application to Stereo Vision. % International Joint Conference on Artificial Intelligence, 1981. % % Carlo Tomasi and Takeo Kanade. Detection and Tracking of Point Features. % Carnegie Mellon University Technical Report CMU-CS-91-132, 1991. % % Jianbo Shi and Carlo Tomasi. Good Features to Track. % IEEE Conference on Computer Vision and Pattern Recognition, 1994. % % Zdenek Kalal, Krystian Mikolajczyk and Jiri Matas. Forward-Backward % Error: Automatic Detection of Tracking Failures. % International Conference on Pattern Recognition, 2010

displayEndOfDemoMessage(mfilename)

Answer 1

[Geoff Hayes]

Eliza - the bbox2points function is part of the Computer Vision System Toolbox. Do you have it? In the Command a window type

 ver

to see your version of MATLAB and all installed toolboxes. If you see it, then type

 which bbox2points -all

to see where in your MATLAB search path this function can be found. Though given the error message, it us doubtful that you have this function and so will have to purchase the toolbox.

[Dima Lisin]

You also need to have MATLAB version R2014b.

Answer 2

[RAKSHA KULKARNI]

%hello!! all you need to do is try this code.
% first u need create point as done by the code below.
function A=makePoint1(b)
b=b(:);
A(1,1)=b(1);
A(1,2)=b(2);

A(2,1)=b(1)+b(3); A(2,2)=b(2);

A(3,1)=b(1)+b(3); A(3,2)=b(2)+b(4);

A(4,1)=b(1); A(4,2)=b(2)+b(4);

end % code below is the function for bbox2points. function a=bbox2points1(B) a(1)=B(1,1); a(2)=B(1,2); a(3)=B(2,1); a(4)=B(2,2); a(5)=B(3,1); a(6)=B(3,2); a(7)=B(4,1); a(8)=B(4,2);

end

Answer 3

[shw zhang]

From the link last row we know that the 'bbox2points' Introduced in R2014b .<https://cn.mathworks.com/help/vision/ref/bbox2points.html%20 about bbox2points>