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function [varargout] = ForegroundDetector(varargin)
// This function creates a ForeggroundDetector struct which is used to detect Foreground with Gaussian Mixture Models.
//
// Calling Sequence
// result = ForegroundDetector()
// result = ForegroundDetector(Name, Value, ..)
//
// Parameters
// results: ForegroundDetector struct containing AdaptLearningRate, NumTrainingFrames, MinimumBackgroundRatio, NumGaussians and LearningRate.
// AdaptLearningRate : (optional) If true, it enables the adaptation of Learning Rate during the period specified by number of training frames.
// NumTrainingFrames : (optional) The number of initial video frames employed to train the Background Model.
// MinimumBackgroundRatio : (optional)
// NumGaussians : (optional)
// LearningRate : (optional) This comes into action when AdaptLearningRate is enabled.
//
// Description
// This function returns a ForegreoundDetector Struct which basically sets a background model and tracks changes in an input video frame with respect to the background and then computes the Foreground Mask.
//
// Examples
// myStr = ForegroundDetector("NumGaussians", 4)
//
// Authors
// Shashank Shekhar
[ lhs, rhs ] = argn(0)
if modulo(rhs,2)~=0 then
error(msprintf("Incorrect number of input arguments"))
end
if rhs > 10 then
error(msprintf("Too many input arguments"))
end
if lhs > 1 then
error(msprintf("Too many output arguments"))
end
select rhs
case 0 then
[fdAdaptLearningRate, fdNumTrainingFrames,fdMinimumBackgroundRatio, fdNumGaussians, fdLearningRate ]= ocv_ForegroundDetector()
case 2 then
[fdAdaptLearningRate, fdNumTrainingFrames,fdMinimumBackgroundRatio, fdNumGaussians, fdLearningRate ] = ocv_ForegroundDetector(varargin(1), varargin(2))
case 4 then
[fdAdaptLearningRate, fdNumTrainingFrames,fdMinimumBackgroundRatio, fdNumGaussians, fdLearningRate ] = ocv_ForegroundDetector(varargin(1), varargin(2), varargin(3), varargin(4))
case 6 then
[fdAdaptLearningRate, fdNumTrainingFrames,fdMinimumBackgroundRatio, fdNumGaussians, fdLearningRate ] = ocv_ForegroundDetector(varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6))
case 8 then
[fdAdaptLearningRate, fdNumTrainingFrames,fdMinimumBackgroundRatio, fdNumGaussians, fdLearningRate ] = ocv_ForegroundDetector(varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8))
case 10 then
[fdAdaptLearningRate, fdNumTrainingFrames,fdMinimumBackgroundRatio, fdNumGaussians, fdLearningRate ] = ocv_ForegroundDetector(varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8), varargin(9), varargin(10))
end
varargout(1) = struct("AdaptLearningRate", fdAdaptLearningRate, "NumTrainingFrames", fdNumTrainingFrames, "MinimumBackgroundRatio", fdMinimumBackgroundRatio, "NumGaussians", fdNumGaussians, "LearningRate", fdLearningRate);
endfunction
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