gusucode.com > vision工具箱matlab源码程序 > vision/+vision/en/HistogramBasedTracker.m
classdef HistogramBasedTracker< matlab.System
%HistogramBasedTracker Track object in video based on histogram
%
% H = vision.HistogramBasedTracker returns a System object, H, that
% tracks an object by using the Continuously Adaptive Mean Shift
% (CAMShift) algorithm. It uses the histogram of pixel values to identify
% the tracked object. To initialize the tracking process, you must use
% the initializeObject method to specify an exemplar image of the object.
% Then, use the step method to track the object in consecutive video
% frames.
%
% H = vision.HistogramBasedTracker('PropertyName', PropertyValue, ...)
% returns a tracker System object, H, with each specified property set to
% the specified value.
%
% initializeObject method syntax:
%
% initializeObject(H, I, R) sets the object to track by extracting it
% from the [x y width height] region R, in an M-by-N image I. I can be
% any 2-D feature map that distinguishes the object from the background.
% For example, I can be a hue channel of the HSV color space. R also
% represents the initial search window for the next call to the step
% method. Typically, I is the first frame of a video in which the object
% appears. For best results, the object must occupy the majority of R.
%
% initializeObject(H, I, R, N) additionally, lets you specify N, the
% number of histogram bins. By default, N is set to 16. Increasing N
% enhances the ability of the tracker to discriminate the object.
% However, it also narrows the range of changes to the object's visual
% characteristics that the tracker can accommodate, making it more
% susceptible to losing track.
%
% step method syntax:
%
% BBOX = step(H, I) returns the [x y width height] bounding box, BBOX, of
% the tracked object. Before calling the step method, use the
% initializeObject method to identify the object to track, and to set the
% initial search window.
%
% [BBOX, ORIENTATION] = step(H, I) additionally returns the angle between
% the x-axis and the major axis of the ellipse, which has the same second
% order moments as the object. The returned angle ranges from -pi/2 to
% pi/2.
%
% [BBOX, ORIENTATION, SCORE] = step(H, I) additionally returns the
% confidence score indicating whether the returned bounding box, BBOX,
% contains the tracked object. SCORE is between 0 and 1, with the
% greatest confidence equal to 1.
%
% initializeSearchWindow method syntax:
%
% initializeSearchWindow(H, R) sets the initial search window, R,
% specified as [x y width height]. The next call to the step method will
% use R as the initial window to search for the object. This method is
% useful when you lose track of the object. You can use it to
% re-initialize object's initial location and size.
%
% System objects may be called directly like a function instead of using
% the step method. For example, y = step(obj, x) and y = obj(x) are
% equivalent.
%
% HistogramBasedTracker methods:
%
% step - See above description for use of this method
% initializeObject - See above description for use of this method
% initializeSearchWindow - See above description for use of this method
% release - Allow property value and input characteristics changes
% clone - Create a tracker object with the same property values
% isLocked - Locked status (logical)
%
% HistogramBasedTracker properties:
%
% ObjectHistogram - Normalized pixel value histogram
%
% Notes:
%
% - The HistogramBasedTracker is most suitable for tracking a single
% object.
% - You can improve the computational speed of the HistogramBasedTracker
% by setting the class of the image, I, to uint8.
%
% Example: Tracking a face
%
% % Create System objects for reading and displaying video, and for
% % drawing bounding box of the object.
% videoFileReader = vision.VideoFileReader('vipcolorsegmentation.avi');
% videoPlayer = vision.VideoPlayer();
%
% % Read the first video frame which contains the object and then show
% % the object region
% objectFrame = step(videoFileReader); % read the first video frame
% objectHSV = rgb2hsv(objectFrame); % convert to HSV color space
% objectRegion = [40, 45, 25, 25]; % define the object region
% objectImage = insertShape(objectFrame, 'Rectangle', objectRegion, ...
% 'Color', [1 0 0]);
% figure; imshow(objectImage); title('Red box shows object region');
% % You can also use the following commands to select the object region
% % using a mouse. The object must occupy majority of the region.
% % figure; imshow(objectFrame); objectRegion=round(getPosition(imrect))
%
% % Set the object based on the hue channel of the first video frame
% tracker = vision.HistogramBasedTracker;
% initializeObject(tracker, objectHSV(:,:,1) , objectRegion);
%
% % Track and display the object in each video frame
% while ~isDone(videoFileReader)
% frame = step(videoFileReader); % Read next image frame
% hsv = rgb2hsv(frame); % Convert to HSV color space
% bbox = step(tracker, hsv(:,:,1)); % Track object in hue channel
% % where it's distinct from
% % the background
% out = insertShape(frame, 'Rectangle', ...
% bbox, 'Color', 'red'); % Draw a box around the object
% step(videoPlayer, out); % Show results
% end
%
% release(videoPlayer);
% release(videoFileReader);
% Copyright 2011-2016 The MathWorks, Inc.
methods
function out=HistogramBasedTracker
%HistogramBasedTracker Track object in video based on histogram
%
% H = vision.HistogramBasedTracker returns a System object, H, that
% tracks an object by using the Continuously Adaptive Mean Shift
% (CAMShift) algorithm. It uses the histogram of pixel values to identify
% the tracked object. To initialize the tracking process, you must use
% the initializeObject method to specify an exemplar image of the object.
% Then, use the step method to track the object in consecutive video
% frames.
%
% H = vision.HistogramBasedTracker('PropertyName', PropertyValue, ...)
% returns a tracker System object, H, with each specified property set to
% the specified value.
%
% initializeObject method syntax:
%
% initializeObject(H, I, R) sets the object to track by extracting it
% from the [x y width height] region R, in an M-by-N image I. I can be
% any 2-D feature map that distinguishes the object from the background.
% For example, I can be a hue channel of the HSV color space. R also
% represents the initial search window for the next call to the step
% method. Typically, I is the first frame of a video in which the object
% appears. For best results, the object must occupy the majority of R.
%
% initializeObject(H, I, R, N) additionally, lets you specify N, the
% number of histogram bins. By default, N is set to 16. Increasing N
% enhances the ability of the tracker to discriminate the object.
% However, it also narrows the range of changes to the object's visual
% characteristics that the tracker can accommodate, making it more
% susceptible to losing track.
%
% step method syntax:
%
% BBOX = step(H, I) returns the [x y width height] bounding box, BBOX, of
% the tracked object. Before calling the step method, use the
% initializeObject method to identify the object to track, and to set the
% initial search window.
%
% [BBOX, ORIENTATION] = step(H, I) additionally returns the angle between
% the x-axis and the major axis of the ellipse, which has the same second
% order moments as the object. The returned angle ranges from -pi/2 to
% pi/2.
%
% [BBOX, ORIENTATION, SCORE] = step(H, I) additionally returns the
% confidence score indicating whether the returned bounding box, BBOX,
% contains the tracked object. SCORE is between 0 and 1, with the
% greatest confidence equal to 1.
%
% initializeSearchWindow method syntax:
%
% initializeSearchWindow(H, R) sets the initial search window, R,
% specified as [x y width height]. The next call to the step method will
% use R as the initial window to search for the object. This method is
% useful when you lose track of the object. You can use it to
% re-initialize object's initial location and size.
%
% System objects may be called directly like a function instead of using
% the step method. For example, y = step(obj, x) and y = obj(x) are
% equivalent.
%
% HistogramBasedTracker methods:
%
% step - See above description for use of this method
% initializeObject - See above description for use of this method
% initializeSearchWindow - See above description for use of this method
% release - Allow property value and input characteristics changes
% clone - Create a tracker object with the same property values
% isLocked - Locked status (logical)
%
% HistogramBasedTracker properties:
%
% ObjectHistogram - Normalized pixel value histogram
%
% Notes:
%
% - The HistogramBasedTracker is most suitable for tracking a single
% object.
% - You can improve the computational speed of the HistogramBasedTracker
% by setting the class of the image, I, to uint8.
%
% Example: Tracking a face
%
% % Create System objects for reading and displaying video, and for
% % drawing bounding box of the object.
% videoFileReader = vision.VideoFileReader('vipcolorsegmentation.avi');
% videoPlayer = vision.VideoPlayer();
%
% % Read the first video frame which contains the object and then show
% % the object region
% objectFrame = step(videoFileReader); % read the first video frame
% objectHSV = rgb2hsv(objectFrame); % convert to HSV color space
% objectRegion = [40, 45, 25, 25]; % define the object region
% objectImage = insertShape(objectFrame, 'Rectangle', objectRegion, ...
% 'Color', [1 0 0]);
% figure; imshow(objectImage); title('Red box shows object region');
% % You can also use the following commands to select the object region
% % using a mouse. The object must occupy majority of the region.
% % figure; imshow(objectFrame); objectRegion=round(getPosition(imrect))
%
% % Set the object based on the hue channel of the first video frame
% tracker = vision.HistogramBasedTracker;
% initializeObject(tracker, objectHSV(:,:,1) , objectRegion);
%
% % Track and display the object in each video frame
% while ~isDone(videoFileReader)
% frame = step(videoFileReader); % Read next image frame
% hsv = rgb2hsv(frame); % Convert to HSV color space
% bbox = step(tracker, hsv(:,:,1)); % Track object in hue channel
% % where it's distinct from
% % the background
% out = insertShape(frame, 'Rectangle', ...
% bbox, 'Color', 'red'); % Draw a box around the object
% step(videoPlayer, out); % Show results
% end
%
% release(videoPlayer);
% release(videoFileReader);
end
function backProject(in) %#ok<MANU>
end
function clipROI(in) %#ok<MANU>
end
function computeBoundingBox(in) %#ok<MANU>
end
function cropImage(in) %#ok<MANU>
end
function fitEllipse(in) %#ok<MANU>
% Compute the moments of the probability map in the extended ROI.
end
function getNumOutputsImpl(in) %#ok<MANU>
end
function hist2D(in) %#ok<MANU>
end
function initializeObject(in) %#ok<MANU>
% initializeObject Sets the object to track
% initializeObject(H, I, R) sets the object to track by extracting
% it from the [x y width height] region R, in an M-by-N image I. I
% can be any 2-D feature map that distinguishes the object from the
% background. For example, I can be a hue channel of the HSV color
% space. R also represents the initial search window for the next
% call to the step method. Typically, I is the first frame of a
% video in which the object appears. For best results, the object
% must occupy the majority of R.
%
% initializeObject(H, I, R, N) additionally, lets you specify the
% number of histogram bins N. By default, N is set to 16. N must be
% chosen according to the image data. A larger N does not always
% produce better result.
end
function initializeSearchWindow(in) %#ok<MANU>
% initializeSearchWindow Sets the initial search window
% initializeSearchWindow(H, R) sets the initial search window, R,
% specified as [x y width height]. The next call to the step method
% will use R as the initial window to search for the object. This
% method is useful when you lose track of the object. You can use
% it to re-initialize object's initial location and size.
end
function isInputComplexityLockedImpl(in) %#ok<MANU>
end
function isOutputComplexityLockedImpl(in) %#ok<MANU>
end
function loadObjectImpl(in) %#ok<MANU>
end
function meanShift(in) %#ok<MANU>
end
function moment2D(in) %#ok<MANU>
end
function saveObjectImpl(in) %#ok<MANU>
end
function stepImpl(in) %#ok<MANU>
end
function validateImage(in) %#ok<MANU>
end
function validateInputsImpl(in) %#ok<MANU>
end
end
methods (Abstract)
end
properties
%ObjectHistogram Normalized pixel value histogram
% Set this property to an N-element vector which is the normalized
% histogram of the object's pixel values. Histogram values must be
% normalized to between 0 and 1. You can use the initializeObject
% method to set this property. This property is tunable.
%
% Default: []
ObjectHistogram;
end
end