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92 lines
3.4 KiB
C++
92 lines
3.4 KiB
C++
///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
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//
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// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
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//
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// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.
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// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
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//
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// License can be found in OpenFace-license.txt
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//
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// * Any publications arising from the use of this software, including but
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// not limited to academic journal and conference publications, technical
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// reports and manuals, must cite at least one of the following works:
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//
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// OpenFace 2.0: Facial Behavior Analysis Toolkit
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// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
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// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
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//
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// Convolutional experts constrained local model for facial landmark detection.
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// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
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// in Computer Vision and Pattern Recognition Workshops, 2017.
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//
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// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
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// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
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// in IEEE International. Conference on Computer Vision (ICCV), 2015
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//
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// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
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// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
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// in Facial Expression Recognition and Analysis Challenge,
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// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
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//
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///////////////////////////////////////////////////////////////////////////////
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#ifndef __CEN_PATCH_EXPERT_h_
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#define __CEN_PATCH_EXPERT_h_
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// system includes
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#include <vector>
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// OpenCV includes
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#include <opencv2/core/core.hpp>
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namespace LandmarkDetector
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{
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//===========================================================================
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/**
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The classes describing the CEN patch experts
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*/
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class CEN_patch_expert {
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public:
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// Width and height of the patch expert support area
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int width_support;
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int height_support;
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// Neural weights
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std::vector<cv::Mat_<float>> biases;
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// Neural weights
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std::vector<cv::Mat_<float>> weights;
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std::vector<int> activation_function;
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// Confidence of the current patch expert (used for NU_RLMS optimisation)
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double confidence;
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CEN_patch_expert() { ; }
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// A copy constructor
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CEN_patch_expert(const CEN_patch_expert& other);
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// Reading in the patch expert
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void Read(std::ifstream &stream);
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// The actual response computation from intensity image
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void Response(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
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void ResponseInternal(cv::Mat_<float>& response);
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// For frontal faces can apply mirrored and non-mirrored experts at the same time
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void ResponseSparse(const cv::Mat_<float> &area_of_interest_left, const cv::Mat_<float> &area_of_interest_right, cv::Mat_<float> &response_left, cv::Mat_<float> &response_right, cv::Mat_<float>& mapMatrix, cv::Mat_<float>& im2col_prealloc_left, cv::Mat_<float>& im2col_prealloc_right);
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};
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void interpolationMatrix(cv::Mat_<float>& mapMatrix, int response_height, int response_width, int input_width, int input_height);
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}
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#endif
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