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725 lines
23 KiB
C++
725 lines
23 KiB
C++
///////////////////////////////////////////////////////////////////////////////
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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 “AS IS” 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 “open source” software licenses (“Open Source
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// Components”), 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’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 Baltrušaitis, 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 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-speci?c 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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// Constrained Local Neural Fields for robust facial landmark detection in the wild.
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// Tadas Baltrušaitis, 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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// Boost includes
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#include <filesystem.hpp>
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#include <filesystem/fstream.hpp>
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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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#include "LandmarkDetectorUtils.h"
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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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CNN::CNN(const CNN& other) : cnn_layer_types(other.cnn_layer_types), cnn_max_pooling_layers(other.cnn_max_pooling_layers), cnn_convolutional_layers_bias(other.cnn_convolutional_layers_bias)
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{
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this->cnn_convolutional_layers.resize(other.cnn_convolutional_layers.size());
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for (size_t l = 0; l < other.cnn_convolutional_layers.size(); ++l)
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{
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this->cnn_convolutional_layers[l].resize(other.cnn_convolutional_layers[l].size());
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for (size_t i = 0; i < other.cnn_convolutional_layers[l].size(); ++i)
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{
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this->cnn_convolutional_layers[l][i].resize(other.cnn_convolutional_layers[l][i].size());
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for (size_t k = 0; k < other.cnn_convolutional_layers[l][i].size(); ++k)
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{
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// Make sure the matrix is copied.
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this->cnn_convolutional_layers[l][i][k] = other.cnn_convolutional_layers[l][i][k].clone();
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}
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}
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}
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this->cnn_fully_connected_layers_weights.resize(other.cnn_fully_connected_layers_weights.size());
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for (size_t l = 0; l < other.cnn_fully_connected_layers_weights.size(); ++l)
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{
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// Make sure the matrix is copied.
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this->cnn_fully_connected_layers_weights[l] = other.cnn_fully_connected_layers_weights[l].clone();
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}
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this->cnn_fully_connected_layers_biases.resize(other.cnn_fully_connected_layers_biases.size());
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for (size_t l = 0; l < other.cnn_fully_connected_layers_biases.size(); ++l)
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{
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// Make sure the matrix is copied.
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this->cnn_fully_connected_layers_biases[l] = other.cnn_fully_connected_layers_biases[l].clone();
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}
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this->cnn_prelu_layer_weights.resize(other.cnn_prelu_layer_weights.size());
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for (size_t l = 0; l < other.cnn_prelu_layer_weights.size(); ++l)
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{
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// Make sure the matrix is copied.
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this->cnn_prelu_layer_weights[l] = other.cnn_prelu_layer_weights[l].clone();
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}
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}
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std::vector<cv::Mat_<float>> CNN::Inference(const cv::Mat& input_img)
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{
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if (input_img.channels() == 1)
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{
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cv::cvtColor(input_img, input_img, cv::COLOR_GRAY2BGR);
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}
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int cnn_layer = 0;
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int fully_connected_layer = 0;
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int prelu_layer = 0;
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int max_pool_layer = 0;
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// Slit a BGR image into three chnels
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cv::Mat channels[3];
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cv::split(input_img, channels);
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// Flip the BGR order to RGB
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vector<cv::Mat_<float> > input_maps;
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input_maps.push_back(channels[2]);
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input_maps.push_back(channels[1]);
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input_maps.push_back(channels[0]);
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vector<cv::Mat_<float> > outputs;
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for (size_t layer = 0; layer < cnn_layer_types.size(); ++layer)
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{
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// Determine layer type
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int layer_type = cnn_layer_types[layer];
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// Convolutional layer
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if (layer_type == 0)
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{
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outputs.clear();
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for (size_t in = 0; in < input_maps.size(); ++in)
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{
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cv::Mat_<float> input_image = input_maps[in];
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// Useful precomputed data placeholders for quick correlation (convolution)
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cv::Mat_<double> input_image_dft;
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cv::Mat integral_image;
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cv::Mat integral_image_sq;
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// TODO can TBB-ify this
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for (size_t k = 0; k < cnn_convolutional_layers[cnn_layer][in].size(); ++k)
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{
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cv::Mat_<float> kernel = cnn_convolutional_layers[cnn_layer][in][k];
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// The convolution (with precomputation)
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cv::Mat_<float> output;
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if (cnn_convolutional_layers_dft[cnn_layer][in][k].second.empty())
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{
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std::map<int, cv::Mat_<double> > precomputed_dft;
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LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
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cnn_convolutional_layers_dft[cnn_layer][in][k].first = precomputed_dft.begin()->first;
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cnn_convolutional_layers_dft[cnn_layer][in][k].second = precomputed_dft.begin()->second;
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}
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else
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{
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std::map<int, cv::Mat_<double> > precomputed_dft;
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precomputed_dft[cnn_convolutional_layers_dft[cnn_layer][in][k].first] = cnn_convolutional_layers_dft[cnn_layer][in][k].second;
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LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
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}
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// Combining the maps
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if (in == 0)
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{
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outputs.push_back(output);
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}
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else
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{
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outputs[k] = outputs[k] + output;
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}
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}
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}
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for (size_t k = 0; k < cnn_convolutional_layers[cnn_layer][0].size(); ++k)
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{
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outputs[k] = outputs[k] + cnn_convolutional_layers_bias[cnn_layer][k];
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}
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cnn_layer++;
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}
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if (layer_type == 1)
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{
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vector<cv::Mat_<float> > outputs_sub;
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int stride_x = std::get<2>(cnn_max_pooling_layers[max_pool_layer]);
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int stride_y = std::get<3>(cnn_max_pooling_layers[max_pool_layer]);
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int kernel_size_x = std::get<0>(cnn_max_pooling_layers[max_pool_layer]);
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int kernel_size_y = std::get<1>(cnn_max_pooling_layers[max_pool_layer]);
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// Iterate over kernel height and width, based on stride
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for (size_t in = 0; in < input_maps.size(); ++in)
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{
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int out_x = round((input_maps[in].cols - kernel_size_x) / stride_x) + 1;
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int out_y = round((input_maps[in].rows - kernel_size_y) / stride_y) + 1;
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cv::Mat_<float> sub_out(out_y, out_x, 0.0);
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cv::Mat_<float> in_map = input_maps[in];
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for (int x = 0; x < input_maps[in].cols; x += stride_x)
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{
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for (int y = 0; y < input_maps[in].rows; y += stride_y)
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{
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float curr_max = -FLT_MAX;
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for (int x_in = x; x_in < x + kernel_size_x; ++x_in)
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{
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for (int y_in = y; y_in < y + kernel_size_y; ++y_in)
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{
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float curr_val = in_map.at<float>(y_in, x_in);
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if (curr_val > curr_max)
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{
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curr_max = curr_val;
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}
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}
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}
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int x_in_out = floor(x / stride_x);
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int y_in_out = floor(y / stride_y);
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sub_out.at<float>(y_in_out, x_in_out) = curr_max;
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}
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}
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outputs_sub.push_back(sub_out);
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}
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outputs = outputs_sub;
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}
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if (layer_type == 2)
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{
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if(input_maps.size() > 1)
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{
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// Concatenate all the maps
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cv::Size orig_size = input_maps[0].size();
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cv::Mat_<float> input_concat = input_maps[0].t();
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input_concat = input_concat.reshape(0, 1);
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for (size_t in = 1; in < input_maps.size(); ++in)
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{
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cv::Mat_<float> add = input_maps[in].t();
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add = add.reshape(0, 1);
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cv::vconcat(input_concat, add, input_concat);
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}
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input_concat = input_concat.t() * cnn_fully_connected_layers_weights[fully_connected_layer];
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// Add biases
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for (size_t k = 0; k < cnn_fully_connected_layers_biases[fully_connected_layer].rows; ++k)
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{
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input_concat.col(k) = input_concat.col(k) + cnn_fully_connected_layers_biases[fully_connected_layer].at<float>(k);
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}
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outputs.clear();
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// Resize and add as output
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for (size_t k = 0; k < cnn_fully_connected_layers_biases[fully_connected_layer].rows; ++k)
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{
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cv::Mat_<float> reshaped = input_concat.col(k).clone();
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reshaped = reshaped.reshape(1, orig_size.width).t();
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outputs.push_back(reshaped);
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}
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}
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else
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{
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cv::Mat out = input_maps[0].t() * cnn_fully_connected_layers_weights[fully_connected_layer] + cnn_fully_connected_layers_biases[fully_connected_layer].t();
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outputs.clear();
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outputs.push_back(out);
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}
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fully_connected_layer++;
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}
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if (layer_type == 3) // PReLU
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{
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outputs.clear();
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for (size_t k = 0; k < input_maps.size(); ++k)
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{
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// Apply the PReLU
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cv::Mat_<float> pos;
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cv::threshold(input_maps[k], pos, 0, 0, cv::THRESH_TOZERO);
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cv::Mat_<float> neg;
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cv::threshold(input_maps[k], neg, 0, 0, cv::THRESH_TOZERO_INV);
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outputs.push_back(pos + neg * cnn_prelu_layer_weights[prelu_layer].at<float>(k));
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}
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prelu_layer++;
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}
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if (layer_type == 4)
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{
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outputs.clear();
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for (size_t k = 0; k < input_maps.size(); ++k)
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{
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// Apply the sigmoid
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cv::exp(-input_maps[k], input_maps[k]);
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input_maps[k] = 1.0 / (1.0 + input_maps[k]);
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outputs.push_back(input_maps[k]);
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}
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}
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// Set the outputs of this layer to inputs of the next
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input_maps = outputs;
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}
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return outputs;
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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 == 3)
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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
|
||
void FaceDetectorMTCNN::Read(string location)
|
||
{
|
||
|
||
cout << "Reading the MTCNN face detector from: " << location << endl;
|
||
|
||
ifstream locations(location.c_str(), ios_base::in);
|
||
if (!locations.is_open())
|
||
{
|
||
cout << "Couldn't open the model file, aborting" << endl;
|
||
return;
|
||
}
|
||
string line;
|
||
|
||
// The other module locations should be defined as relative paths from the main model
|
||
boost::filesystem::path root = boost::filesystem::path(location).parent_path();
|
||
|
||
// The main file contains the references to other files
|
||
while (!locations.eof())
|
||
{
|
||
getline(locations, line);
|
||
|
||
stringstream lineStream(line);
|
||
|
||
string module;
|
||
string location;
|
||
|
||
// figure out which module is to be read from which file
|
||
lineStream >> module;
|
||
|
||
lineStream >> location;
|
||
|
||
// remove carriage return at the end for compatibility with unix systems
|
||
if (location.size() > 0 && location.at(location.size() - 1) == '\r')
|
||
{
|
||
location = location.substr(0, location.size() - 1);
|
||
}
|
||
|
||
// append to root
|
||
location = (root / location).string();
|
||
if (module.compare("PNet") == 0)
|
||
{
|
||
cout << "Reading the PNet module from: " << location << endl;
|
||
PNet.Read(location);
|
||
}
|
||
else if(module.compare("RNet") == 0)
|
||
{
|
||
cout << "Reading the RNet module from: " << location << endl;
|
||
RNet.Read(location);
|
||
}
|
||
else if (module.compare("ONet") == 0)
|
||
{
|
||
cout << "Reading the ONet module from: " << location << endl;
|
||
ONet.Read(location);
|
||
}
|
||
}
|
||
}
|
||
|
||
// Perform non maximum supression on proposal bounding boxes prioritizing boxes with high score/confidence
|
||
std::vector<int> non_maximum_supression(const std::vector<cv::Rect_<float> >& original_bb, const std::vector<float>& scores, float thresh)
|
||
{
|
||
|
||
// Sort the input bounding boxes by the detection score, using the nice trick of multimap always being sorted internally
|
||
std::multimap<float, size_t> idxs;
|
||
for (size_t i = 0; i < original_bb.size(); ++i)
|
||
{
|
||
idxs.insert(std::pair<float, size_t>(scores[i], i));
|
||
}
|
||
|
||
std::vector<int> output_ids;
|
||
|
||
// keep looping while some indexes still remain in the indexes list
|
||
while (idxs.size() > 0)
|
||
{
|
||
// grab the last rectangle
|
||
auto lastElem = --std::end(idxs);
|
||
size_t curr_id = lastElem->second;
|
||
|
||
const cv::Rect& rect1 = original_bb[curr_id];
|
||
|
||
idxs.erase(lastElem);
|
||
|
||
// Iterate through remaining bounding boxes and choose which ones to remove
|
||
for (auto pos = std::begin(idxs); pos != std::end(idxs); )
|
||
{
|
||
// grab the current rectangle
|
||
const cv::Rect& rect2 = original_bb[pos->second];
|
||
|
||
float intArea = (rect1 & rect2).area();
|
||
float unionArea = rect1.area() + rect2.area() - intArea;
|
||
float overlap = intArea / unionArea;
|
||
|
||
// Remove the bounding boxes with less confidence but with significant overlap with the current one
|
||
if (overlap > thresh)
|
||
{
|
||
pos = idxs.erase(pos);
|
||
}
|
||
else
|
||
{
|
||
++pos;
|
||
}
|
||
}
|
||
output_ids.push_back(curr_id);
|
||
|
||
}
|
||
|
||
return output_ids;
|
||
|
||
}
|
||
|
||
// Helper function for selecting a subset of bounding boxes based on indices
|
||
void select_subset(const vector<int>& to_keep, vector<cv::Rect_<float> >& bounding_boxes, vector<float>& scores, vector<cv::Rect_<float> >& corrections)
|
||
{
|
||
vector<cv::Rect_<float> > bounding_boxes_tmp;
|
||
vector<float> scores_tmp;
|
||
vector<cv::Rect_<float> > corrections_tmp;
|
||
|
||
for (size_t i = 0; i < to_keep.size(); ++i)
|
||
{
|
||
bounding_boxes_tmp.push_back(bounding_boxes[to_keep[i]]);
|
||
scores_tmp.push_back(scores[to_keep[i]]);
|
||
corrections_tmp.push_back(corrections[to_keep[i]]);
|
||
}
|
||
|
||
bounding_boxes = bounding_boxes_tmp;
|
||
scores = scores_tmp;
|
||
corrections = corrections_tmp;
|
||
}
|
||
|
||
// Use the heatmap generated by PNet to generate bounding boxes in the original image space, also generate the correction values and scores of the bounding boxes as well
|
||
void generate_bounding_boxes(vector<cv::Rect_<float> >& o_bounding_boxes, vector<float>& o_scores, vector<cv::Rect_<float> >& o_corrections, const cv::Mat_<float>& heatmap, const vector<cv::Mat_<float> >& corrections, double scale, double threshold, int face_support)
|
||
{
|
||
|
||
// Correction for the pooling
|
||
int stride = 2;
|
||
|
||
o_bounding_boxes.clear();
|
||
o_scores.clear();
|
||
o_corrections.clear();
|
||
|
||
int counter = 0;
|
||
for (int x = 0; x < heatmap.cols; ++x)
|
||
{
|
||
for(int y = 0; y < heatmap.rows; ++y)
|
||
{
|
||
if (heatmap.at<float>(y, x) >= threshold)
|
||
{
|
||
float min_x = int((stride * x + 1) / scale);
|
||
float max_x = int((stride * x + face_support) / scale);
|
||
float min_y = int((stride * y + 1) / scale);
|
||
float max_y = int((stride * y + face_support) / scale);
|
||
|
||
o_bounding_boxes.push_back(cv::Rect_<float>(min_x, min_y, max_x - min_x, max_y - min_y));
|
||
o_scores.push_back(heatmap.at<float>(y, x));
|
||
|
||
float corr_x = corrections[0].at<float>(y, x);
|
||
float corr_y = corrections[1].at<float>(y, x);
|
||
float corr_width = corrections[2].at<float>(y, x);
|
||
float corr_height = corrections[3].at<float>(y, x);
|
||
o_corrections.push_back(cv::Rect_<float>(corr_x, corr_y, corr_width, corr_height));
|
||
|
||
counter++;
|
||
}
|
||
}
|
||
}
|
||
|
||
}
|
||
|
||
|
||
// The actual MTCNN face detection step
|
||
bool FaceDetectorMTCNN::DetectFaces(vector<cv::Rect_<double> >& o_regions, const cv::Mat& input_img, std::vector<double>& o_confidences, int min_face_size, double t1, double t2, double t3)
|
||
{
|
||
|
||
int height_orig = input_img.size().height;
|
||
int width_orig = input_img.size().width;
|
||
|
||
// Size ratio of image pyramids
|
||
double pyramid_factor = 0.709;
|
||
|
||
// Face support region is 12x12 px, so from that can work out the largest
|
||
// scale(which is 12 / min), and work down from there to smallest scale(no smaller than 12x12px)
|
||
int min_dim = std::min(height_orig, width_orig);
|
||
|
||
int face_support = 12;
|
||
int num_scales = floor(log((double)min_face_size / (double)min_dim) / log(pyramid_factor)) + 1;
|
||
|
||
if (input_img.channels() == 1)
|
||
{
|
||
cv::cvtColor(input_img, input_img, CV_GRAY2RGB);
|
||
}
|
||
|
||
cv::Mat img_float;
|
||
input_img.convertTo(img_float, CV_32FC3);
|
||
|
||
vector<cv::Rect_<float> > proposal_boxes_all;
|
||
vector<float> scores_all;
|
||
vector<cv::Rect_<float> > proposal_corrections_all;
|
||
|
||
for (int i = 0; i < num_scales; ++i)
|
||
{
|
||
double scale = ((double)face_support / (double)min_face_size)*cv::pow(pyramid_factor, i);
|
||
|
||
int h_pyr = ceil(height_orig * scale);
|
||
int w_pyr = ceil(width_orig * scale);
|
||
|
||
cv::Mat normalised_img;
|
||
cv::resize(img_float, normalised_img, cv::Size(w_pyr, h_pyr));
|
||
|
||
// Normalize the image
|
||
normalised_img = (normalised_img - 127.5) * 0.0078125;
|
||
|
||
// Actual PNet CNN step
|
||
std::vector<cv::Mat_<float> > pnet_out = PNet.Inference(normalised_img);
|
||
|
||
// Extract the probabilities from PNet response
|
||
cv::Mat_<float> prob_heatmap;
|
||
cv::exp(pnet_out[0]- pnet_out[1], prob_heatmap);
|
||
prob_heatmap = 1.0 / (1.0 + prob_heatmap);
|
||
|
||
// Extract the probabilities from PNet response
|
||
std::vector<cv::Mat_<float>> corrections_heatmap(pnet_out.begin() + 2, pnet_out.end());
|
||
|
||
// Grab the detections
|
||
vector<cv::Rect_<float> > proposal_boxes;
|
||
vector<float> scores;
|
||
vector<cv::Rect_<float> > proposal_corrections;
|
||
generate_bounding_boxes(proposal_boxes, scores, proposal_corrections, prob_heatmap, corrections_heatmap, scale, t1, face_support);
|
||
|
||
// Perform non-maximum supression on proposals in this scale
|
||
vector<int> to_keep = non_maximum_supression(proposal_boxes, scores, 0.5);
|
||
select_subset(to_keep, proposal_boxes, scores, proposal_corrections);
|
||
|
||
proposal_boxes_all.insert(proposal_boxes_all.end(), proposal_boxes.begin(), proposal_boxes.end());
|
||
scores_all.insert(scores_all.end(), scores.begin(), scores.end());
|
||
proposal_corrections_all.insert(proposal_corrections_all.end(), proposal_corrections.begin(), proposal_corrections.end());
|
||
|
||
}
|
||
|
||
// Preparation for RNet step
|
||
|
||
// Non maximum supression accross bounding boxes, and their offset correction
|
||
vector<int> to_keep = non_maximum_supression(proposal_boxes_all, scores_all, 0.7);
|
||
select_subset(to_keep, proposal_boxes_all, scores_all, proposal_corrections_all);
|
||
|
||
//total_bboxes = apply_correction(total_bboxes, corrections, false);
|
||
|
||
//% Making them into rectangles
|
||
// total_bboxes(:, 1 : 4) = rectify(total_bboxes(:, 1 : 4));
|
||
|
||
//% Rounding to pixels
|
||
//
|
||
|
||
return true;
|
||
|
||
}
|
||
|