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Diffstat (limited to 'macros/ForegroundDetector.sci')
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diff --git a/macros/ForegroundDetector.sci b/macros/ForegroundDetector.sci new file mode 100644 index 0000000..16a1e81 --- /dev/null +++ b/macros/ForegroundDetector.sci @@ -0,0 +1,55 @@ +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 |