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67 lines
3.6 KiB
C++
67 lines
3.6 KiB
C++
///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2017, Tadas Baltrusaitis, 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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// Header for all external CLNF/CLM-Z/CLM methods of interest to the user
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#ifndef __CNN_UTILS_h_
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#define __CNN_UTILS_h_
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// OpenCV includes
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#include <opencv2/core/core.hpp>
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using namespace std;
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namespace LandmarkDetector
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{
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//===========================================================================
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// Various CNN layers
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// Parametric ReLU with leaky weights (separate ones per channel)
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void PReLU(std::vector<cv::Mat_<float> >& input_output_maps, cv::Mat_<float> prelu_weights);
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// The fully connected layer
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void fully_connected(std::vector<cv::Mat_<float> >& outputs, const std::vector<cv::Mat_<float> >& input_maps, cv::Mat_<float> weights, cv::Mat_<float> biases);
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// Max pooling layer with parametrized stride and kernel sizes
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void max_pooling(std::vector<cv::Mat_<float> >& outputs, const std::vector<cv::Mat_<float> >& input_maps, int stride_x, int stride_y, int kernel_size_x, int kernel_size_y);
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// Convolution using FFT optimization rather than matrix multiplication
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void convolution_fft2(std::vector<cv::Mat_<float> >& outputs, const std::vector<cv::Mat_<float> >& input_maps, const std::vector<std::vector<cv::Mat_<float> > >& kernels, const std::vector<float >& biases, vector<map<int, vector<cv::Mat_<double> > > >& precomp_dfts);
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// Convolution using matrix multiplication and OpenBLAS optimization
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void convolution_direct_blas(std::vector<cv::Mat_<float> >& outputs, const std::vector<cv::Mat_<float> >& input_maps, const cv::Mat_<float>& weight_matrix, int height_k, int width_k);
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// Convolution using matrix multiplication and OpenBLAS optimization (non thread safe but faster)
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void convolution_direct_blas_nts(std::vector<cv::Mat_<float> >& outputs, const std::vector<cv::Mat_<float> >& input_maps, const cv::Mat_<float>& weight_matrix, int height_k, int width_k, cv::Mat_<float>& pre_alloc_im2col);
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}
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#endif
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