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* Travis OpenCV4 update, testing Ubuntu with new OpenCV * Fix to Ubuntu travis * Another attempt at OpenCV 4.0 for Ubuntu * And another OpenCV attempt. * Simplifying the travis script * Ubuntu OpenCV 4 support. * Updating to OpenCV 4, for x64 windows. * Fixes to move to OpenCV 4 on windows. * Travis fix for OpenCV 4 on OSX * Renaming a lib. * Travis opencv4 fix. * Building OpenCV4 versions using appveyor. * Attempt mac travis fix. * Small travis fix. * Travis fix attempt. * First iteration in boost removal and upgrade to C++17 * Test with ocv 4.0 * Moving filesystem out of stdafx * Some more boost testing with cmake. * More CMAKE options * More compiler flag changes * Another attempt at compiler options. * Another attempt. * More filesystem stuff. * Linking to filesystem. * Cmake fix with target linking. * Attempting travis with g++-8 * Attempting to setup g++8 on travis linux. * Another travis change. * Adding OpenBLAS to travis and removing g++-8 * Fixing typo * More travis experiments. * More travis debugging. * A small directory change. * Adding some more travis changes. * travis typo fix. * Some reordering of travis, for cleaner yml * Removing `using namespace std` in order to avoid clash with byte and to make the code more consistent. * Working towards removing std::filesystem requirement, allow boost::filesystem as well. * Making boost an optional dependency * Fixing std issue. * Fixing cmake issue. * Fixing the precompiled header issue. * Another cmake boost fix. * Including missing files. * Removing unnecessary includes. * Removing more includes. * Changes to appveyor build, proper removal of VS2015 * If boost is present, do not need to link to filesystem. * Removing un-needed link library. * oops * Mac attempt at opencv4 travis. * Upgrading OCV to 4.1 on VS2018 * Downloading OpenCV binaries through a script * Triger an appveyor build. * Upgrading VS version. * Attempting VS2017 build * Adding win-32 libraries for OpenCV 4.1 * Adding OpenCV 32 bit libraries.
63 lines
3.3 KiB
C++
63 lines
3.3 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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#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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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, TODO do these still work
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void convolution_fft2(std::vector<cv::Mat_<float> >& outputs, const std::vector<cv::Mat_<float> >& input_maps,
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const std::vector<std::vector<cv::Mat_<float> > >& kernels, const std::vector<float >& biases,
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std::vector<std::map<int, std::vector<cv::Mat_<double> > > >& precomp_dfts);
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// Convolution using matrix multiplication and OpenBLAS optimization, can also provide a pre-allocated im2col result for faster processing
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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, cv::Mat_<float>& pre_alloc_im2col);
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}
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#endif // CNN_UTILS_H
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