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Starting to work on MTCNN C++ implementation.
This commit is contained in:
Tadas Baltrusaitis
263
lib/local/LandmarkDetector/src/FaceDetectorMTCNN.cpp
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263
lib/local/LandmarkDetector/src/FaceDetectorMTCNN.cpp
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///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
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//
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// THIS SOFTWARE IS PROVIDED <20>AS IS<49> FOR ACADEMIC USE ONLY AND ANY EXPRESS
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// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
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// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
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// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Notwithstanding the license granted herein, Licensee acknowledges that certain components
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// of the Software may be covered by so-called <20>open source<63> software licenses (<28>Open Source
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// Components<74>), which means any software licenses approved as open source licenses by the
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// Open Source Initiative or any substantially similar licenses, including without limitation any
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// license that, as a condition of distribution of the software licensed under such license,
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// requires that the distributor make the software available in source code format. Licensor shall
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// provide a list of Open Source Components for a particular version of the Software upon
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// Licensee<65>s request. Licensee will comply with the applicable terms of such licenses and to
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// the extent required by the licenses covering Open Source Components, the terms of such
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// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
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// licenses applicable to Open Source Components prohibit any of the restrictions in this
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// License Agreement with respect to such Open Source Component, such restrictions will not
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// apply to such Open Source Component. To the extent the terms of the licenses applicable to
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// Open Source Components require Licensor to make an offer to provide source code or
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// related information in connection with the Software, such offer is hereby made. Any request
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// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
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// Licensee acknowledges receipt of notices for the Open Source Components for the initial
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// delivery of the Software.
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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: an open source facial behavior analysis toolkit
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// Tadas Baltru<72>aitis, Peter Robinson, and Louis-Philippe Morency
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// in IEEE Winter Conference on Applications of Computer Vision, 2016
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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 Baltru<72>aitis, 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-speci?c normalisation for automatic Action Unit detection
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// Tadas Baltru<72>aitis, 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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// Constrained Local Neural Fields for robust facial landmark detection in the wild.
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// Tadas Baltru<72>aitis, Peter Robinson, and Louis-Philippe Morency.
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// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
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//
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///////////////////////////////////////////////////////////////////////////////
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#include "stdafx.h"
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#include "FaceDetectorMTCNN.h"
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// OpenCV includes
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#include <opencv2/core/core.hpp>
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#include <opencv2/imgproc.hpp>
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// TBB includes
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#include <tbb/tbb.h>
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// System includes
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#include <fstream>
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// Math includes
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#define _USE_MATH_DEFINES
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#include <cmath>
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#ifndef M_PI
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#define M_PI 3.14159265358979323846
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#endif
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using namespace LandmarkDetector;
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// Copy constructor
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FaceDetectorMTCNN::FaceDetectorMTCNN(const FaceDetectorMTCNN& other) : PNet(other.PNet), RNet(other.RNet), ONet(other.ONet)
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{
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}
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void ReadMatBin(std::ifstream& stream, cv::Mat &output_mat)
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{
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// Read in the number of rows, columns and the data type
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int row, col, type;
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stream.read((char*)&row, 4);
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stream.read((char*)&col, 4);
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stream.read((char*)&type, 4);
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output_mat = cv::Mat(row, col, type);
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int size = output_mat.rows * output_mat.cols * output_mat.elemSize();
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stream.read((char *)output_mat.data, size);
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}
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void CNN::Read(string location)
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{
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ifstream cnn_stream(location, ios::in | ios::binary);
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if (cnn_stream.is_open())
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{
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cnn_stream.seekg(0, ios::beg);
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// Reading in CNNs
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int network_depth;
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cnn_stream.read((char*)&network_depth, 4);
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cnn_layer_types.resize(network_depth);
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for (int layer = 0; layer < network_depth; ++layer)
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{
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int layer_type;
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cnn_stream.read((char*)&layer_type, 4);
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cnn_layer_types[layer] = layer_type;
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// convolutional
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if (layer_type == 0)
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{
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// Read the number of input maps
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int num_in_maps;
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cnn_stream.read((char*)&num_in_maps, 4);
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// Read the number of kernels for each input map
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int num_kernels;
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cnn_stream.read((char*)&num_kernels, 4);
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vector<vector<cv::Mat_<float> > > kernels;
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vector<vector<pair<int, cv::Mat_<double> > > > kernel_dfts;
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kernels.resize(num_in_maps);
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kernel_dfts.resize(num_in_maps);
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vector<float> biases;
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for (int k = 0; k < num_kernels; ++k)
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{
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float bias;
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cnn_stream.read((char*)&bias, 4);
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biases.push_back(bias);
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}
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cnn_convolutional_layers_bias.push_back(biases);
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// For every input map
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for (int in = 0; in < num_in_maps; ++in)
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{
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kernels[in].resize(num_kernels);
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kernel_dfts[in].resize(num_kernels);
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// For every kernel on that input map
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for (int k = 0; k < num_kernels; ++k)
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{
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ReadMatBin(cnn_stream, kernels[in][k]);
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}
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}
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cnn_convolutional_layers.push_back(kernels);
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cnn_convolutional_layers_dft.push_back(kernel_dfts);
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}
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else if (layer_type == 1)
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{
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int kernel_x, kernel_y, stride_x, stride_y;
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cnn_stream.read((char*)&kernel_x, 4);
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cnn_stream.read((char*)&kernel_y, 4);
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cnn_stream.read((char*)&stride_x, 4);
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cnn_stream.read((char*)&stride_y, 4);
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cnn_max_pooling_layers.push_back(std::tuple<int, int, int, int>(kernel_x, kernel_y, stride_x, stride_y));
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}
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else if (layer_type == 2)
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{
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cv::Mat_<float> biases;
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ReadMatBin(cnn_stream, biases);
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cnn_fully_connected_layers_biases.push_back(biases);
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// Fully connected layer
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cv::Mat_<float> weights;
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ReadMatBin(cnn_stream, weights);
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cnn_fully_connected_layers_weights.push_back(weights);
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}
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else if (layer_type == 4)
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{
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cv::Mat_<float> weights;
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ReadMatBin(cnn_stream, weights);
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cnn_prelu_layer_weights.push_back(weights);
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}
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}
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}
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else
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{
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cout << "WARNING: Can't find the CNN location" << endl;
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}
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}
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//===========================================================================
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// Read in the MTCNN detector
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void FaceDetectorMTCNN::Read(string location)
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{
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cout << "Reading the MTCNN face detector from: " << location << endl;
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ifstream locations(location.c_str(), ios_base::in);
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if (!locations.is_open())
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{
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cout << "Couldn't open the model file, aborting" << endl;
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return;
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}
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string line;
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// The other module locations should be defined as relative paths from the main model
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boost::filesystem::path root = boost::filesystem::path(location).parent_path();
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// The main file contains the references to other files
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while (!locations.eof())
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{
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getline(locations, line);
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stringstream lineStream(line);
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string module;
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string location;
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// figure out which module is to be read from which file
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lineStream >> module;
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lineStream >> location;
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// remove carriage return at the end for compatibility with unix systems
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if (location.size() > 0 && location.at(location.size() - 1) == '\r')
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{
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location = location.substr(0, location.size() - 1);
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}
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// append to root
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location = (root / location).string();
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if (module.compare("PNet") == 0)
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{
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cout << "Reading the PNet module from: " << location << endl;
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PNet.Read(location);
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}
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else if(module.compare("RNet") == 0)
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{
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cout << "Reading the RNet module from: " << location << endl;
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RNet.Read(location);
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}
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else if (module.compare("ONet") == 0)
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{
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cout << "Reading the ONet module from: " << location << endl;
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ONet.Read(location);
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}
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}
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}
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