gusucode.com > vision工具箱matlab源码程序 > vision/+vision/en/ForegroundDetector.m
classdef ForegroundDetector< matlab.System
%ForegroundDetector Detect foreground using Gaussian Mixture Models
%
% detector = vision.ForegroundDetector returns a foreground detector System
% object, detector, that computes foreground mask using Gaussian Mixture
% Models (GMM) given a series of either grayscale or color video frames.
%
% detector = vision.ForegroundDetector('PropertyName', PropertyValue, ...)
% returns a foreground detector System object, H, with each specified
% property set to the specified value.
%
% Step method syntax:
%
% foregroundMask = step(detector, I) computes the foreground mask for input
% image I, and returns a logical mask where true represents foreground
% pixels. Image I can be grayscale or color. This form of the step
% function call is allowed when AdaptLearningRate is true (default).
%
% foregroundMask = step(detector, I, learningRate) computes the foreground
% mask for input image I using the LearningRate provided by the user. This
% form of the step function call is allowed when AdaptLearningRate is
% false.
%
% 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.
%
% ForegroundDetector methods:
%
% step - See above description for use of this method
% reset - Resets the GMM model to its initial state
% release - Allow property value and input characteristics changes
% clone - Create foreground detection object with same property values
% isLocked - Locked status (logical)
%
% ForegroundDetector properties:
%
% AdaptLearningRate - Enables the adapting of LearningRate as
% 1/(current frame number) during the
% training period specified by
% NumTrainingFrames
% NumTrainingFrames - Number of initial video frames used for
% training the background model
% LearningRate - Learning rate used for parameter updates
% MinimumBackgroundRatio - Threshold to determine the gaussian modes
% in the mixture model that constitute the
% background process
% NumGaussians - Number of distributions that make up the
% foreground-background mixture model
% InitialVariance - Initial variance to initialize all
% distributions that compose the
% foreground-background mixture model
%
% Example: Detect moving cars in video
% ------------------------------------
% reader = vision.VideoFileReader('visiontraffic.avi', ...
% 'VideoOutputDataType', 'uint8');
%
% detector = vision.ForegroundDetector();
%
% blobAnalyzer = vision.BlobAnalysis(...
% 'CentroidOutputPort', false, 'AreaOutputPort', false, ...
% 'BoundingBoxOutputPort', true, 'MinimumBlobArea', 250);
%
% player = vision.DeployableVideoPlayer();
%
% while ~isDone(reader)
% frame = step(reader);
% fgMask = step(detector, frame);
% bbox = step(blobAnalyzer, fgMask);
%
% % draw bounding boxes around cars
% out = insertShape(frame, 'Rectangle', bbox, 'Color', 'Yellow');
% step(player, out); % view results in the video player
% end
%
% release(player);
% release(reader);
%
% See also: vision.BlobAnalysis, regionprops, vision.KalmanFilter, imopen,
% imclose
% Copyright 2010-2016 The MathWorks, Inc.
methods
function out=ForegroundDetector
%ForegroundDetector Detect foreground using Gaussian Mixture Models
%
% detector = vision.ForegroundDetector returns a foreground detector System
% object, detector, that computes foreground mask using Gaussian Mixture
% Models (GMM) given a series of either grayscale or color video frames.
%
% detector = vision.ForegroundDetector('PropertyName', PropertyValue, ...)
% returns a foreground detector System object, H, with each specified
% property set to the specified value.
%
% Step method syntax:
%
% foregroundMask = step(detector, I) computes the foreground mask for input
% image I, and returns a logical mask where true represents foreground
% pixels. Image I can be grayscale or color. This form of the step
% function call is allowed when AdaptLearningRate is true (default).
%
% foregroundMask = step(detector, I, learningRate) computes the foreground
% mask for input image I using the LearningRate provided by the user. This
% form of the step function call is allowed when AdaptLearningRate is
% false.
%
% 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.
%
% ForegroundDetector methods:
%
% step - See above description for use of this method
% reset - Resets the GMM model to its initial state
% release - Allow property value and input characteristics changes
% clone - Create foreground detection object with same property values
% isLocked - Locked status (logical)
%
% ForegroundDetector properties:
%
% AdaptLearningRate - Enables the adapting of LearningRate as
% 1/(current frame number) during the
% training period specified by
% NumTrainingFrames
% NumTrainingFrames - Number of initial video frames used for
% training the background model
% LearningRate - Learning rate used for parameter updates
% MinimumBackgroundRatio - Threshold to determine the gaussian modes
% in the mixture model that constitute the
% background process
% NumGaussians - Number of distributions that make up the
% foreground-background mixture model
% InitialVariance - Initial variance to initialize all
% distributions that compose the
% foreground-background mixture model
%
% Example: Detect moving cars in video
% ------------------------------------
% reader = vision.VideoFileReader('visiontraffic.avi', ...
% 'VideoOutputDataType', 'uint8');
%
% detector = vision.ForegroundDetector();
%
% blobAnalyzer = vision.BlobAnalysis(...
% 'CentroidOutputPort', false, 'AreaOutputPort', false, ...
% 'BoundingBoxOutputPort', true, 'MinimumBlobArea', 250);
%
% player = vision.DeployableVideoPlayer();
%
% while ~isDone(reader)
% frame = step(reader);
% fgMask = step(detector, frame);
% bbox = step(blobAnalyzer, fgMask);
%
% % draw bounding boxes around cars
% out = insertShape(frame, 'Rectangle', bbox, 'Color', 'Yellow');
% step(player, out); % view results in the video player
% end
%
% release(player);
% release(reader);
%
% See also: vision.BlobAnalysis, regionprops, vision.KalmanFilter, imopen,
% imclose
end
function getNumInputsImpl(in) %#ok<MANU>
end
function getNumOutputsImpl(in) %#ok<MANU>
end
function isInactivePropertyImpl(in) %#ok<MANU>
end
function isInputComplexityLockedImpl(in) %#ok<MANU>
end
function isInputSizeLockedImpl(in) %#ok<MANU>
end
function isOutputComplexityLockedImpl(in) %#ok<MANU>
end
function loadObjectImpl(in) %#ok<MANU>
% load object properties
end
function releaseImpl(in) %#ok<MANU>
end
function resetImpl(in) %#ok<MANU>
end
function saveObjectImpl(in) %#ok<MANU>
% save object properties
end
function setupImpl(in) %#ok<MANU>
end
function setupTypes(in) %#ok<MANU>
end
function stepImpl(in) %#ok<MANU>
end
function validateInputsImpl(in) %#ok<MANU>
end
end
methods (Abstract)
end
properties
%AdaptLearningRate Adapt learning rate
% Set this property to true to adapt the LearningRate as 1/(current
% frame number) during the training period specified by
% NumTrainingFrames. Set this property to false to specify learning
% rate as input at each time step.
%
% Default: true
AdaptLearningRate;
%InitialVariance Initial mixture model variance
% Specify the initial variance as a numeric scalar or 'Auto'.
% When 'Auto' is specified, the initial variance is set based on
% the input image data type:
%
% Image Data Type Initial Variance
% ---------------- ---------------
% double/single (30/255)^2
% uint8 30^2
%
% If input is color, this value applies to all color channels.
%
% Default: 'Auto'
InitialVariance;
%LearningRate Learning rate for parameter updates
% This property controls how quickly the model adapts to changing
% conditions. Set this property appropriately to ensure algorithm
% stability. When AdaptLearningRate is true, the LearningRate
% property takes effect only after the training period specified by
% NumTrainingFrames is over. This property is not available when
% AdaptLearningRate is false. This property is tunable.
%
% Default: 0.005
LearningRate;
%MinimumBackgroundRatio Threshold to determine background model
% This property is the minimum of the apriori probabilities for
% pixels to be background. If this value is too small, multimodal
% backgrounds can not be handled.
%
% Default: 0.7
MinimumBackgroundRatio;
%NumGaussians Number of Gaussian modes
% Set this property to a positive integer. Typically this value is
% 3, 4 or 5. Set this value to 3 or greater to be able to model
% multiple background modes.
%
% Default: 5
NumGaussians;
%NumTrainingFrames Number of initial training frames
% Set this property to the number of training frames at the start of
% the video sequence. This property is not available when
% AdaptLearningRate is false.
%
% Default: 150
NumTrainingFrames;
end
end