OpenFace/lib/local/LandmarkDetector/src/FaceDetectorMTCNN.cpp

///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
// all rights reserved.
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// THIS SOFTWARE IS PROVIDED <20>AS IS<49> FOR ACADEMIC USE ONLY AND ANY EXPRESS
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// Notwithstanding the license granted herein, Licensee acknowledges that certain components
// of the Software may be covered by so-called <20>open source<63> software licenses (<28>Open Source
// Components<74>), which means any software licenses approved as open source licenses by the
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// license that, as a condition of distribution of the software licensed under such license,
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// provide a list of Open Source Components for a particular version of the Software upon
// Licensee<65>s request. Licensee will comply with the applicable terms of such licenses and to
// the extent required by the licenses covering Open Source Components, the terms of such
// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
// licenses applicable to Open Source Components prohibit any of the restrictions in this
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// Licensee acknowledges receipt of notices for the Open Source Components for the initial
// delivery of the Software.

//     * Any publications arising from the use of this software, including but
//       not limited to academic journal and conference publications, technical
//       reports and manuals, must cite at least one of the following works:
//
//       OpenFace: an open source facial behavior analysis toolkit
//       Tadas Baltru<72>aitis, Peter Robinson, and Louis-Philippe Morency
//       in IEEE Winter Conference on Applications of Computer Vision, 2016  
//
//       Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
//       Erroll Wood, Tadas Baltru<72>aitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling 
//       in IEEE International. Conference on Computer Vision (ICCV),  2015 
//
//       Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
//       Tadas Baltru<72>aitis, Marwa Mahmoud, and Peter Robinson 
//       in Facial Expression Recognition and Analysis Challenge, 
//       IEEE International Conference on Automatic Face and Gesture Recognition, 2015 
//
//       Constrained Local Neural Fields for robust facial landmark detection in the wild.
//       Tadas Baltru<72>aitis, Peter Robinson, and Louis-Philippe Morency. 
//       in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.    
//
///////////////////////////////////////////////////////////////////////////////

#include "stdafx.h"

#include "FaceDetectorMTCNN.h"

// OpenCV includes
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc.hpp>

// TBB includes
#include <tbb/tbb.h>

// System includes
#include <fstream>

// Math includes
#define _USE_MATH_DEFINES
#include <cmath>

// Boost includes
#include <filesystem.hpp>
#include <filesystem/fstream.hpp>


#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif

#include "LandmarkDetectorUtils.h"

using namespace LandmarkDetector;

// Constructor from model file location
FaceDetectorMTCNN::FaceDetectorMTCNN(const string& location)
{
	this->Read(location);
}
// Copy constructor
FaceDetectorMTCNN::FaceDetectorMTCNN(const FaceDetectorMTCNN& other) : PNet(other.PNet), RNet(other.RNet), ONet(other.ONet)
{
}

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)
{

	this->cnn_convolutional_layers_weights.resize(other.cnn_convolutional_layers_weights.size());
	for (size_t l = 0; l < other.cnn_convolutional_layers_weights.size(); ++l)
	{
		// Make sure the matrix is copied.
		this->cnn_convolutional_layers_weights[l] = other.cnn_convolutional_layers_weights[l].clone();
	}

	this->cnn_convolutional_layers.resize(other.cnn_convolutional_layers.size());
	for (size_t l = 0; l < other.cnn_convolutional_layers.size(); ++l)
	{
		this->cnn_convolutional_layers[l].resize(other.cnn_convolutional_layers[l].size());

		for (size_t i = 0; i < other.cnn_convolutional_layers[l].size(); ++i)
		{
			this->cnn_convolutional_layers[l][i].resize(other.cnn_convolutional_layers[l][i].size());

			for (size_t k = 0; k < other.cnn_convolutional_layers[l][i].size(); ++k)
			{
				// Make sure the matrix is copied.
				this->cnn_convolutional_layers[l][i][k] = other.cnn_convolutional_layers[l][i][k].clone();
			}
		}
	}

	this->cnn_fully_connected_layers_weights.resize(other.cnn_fully_connected_layers_weights.size());

	for (size_t l = 0; l < other.cnn_fully_connected_layers_weights.size(); ++l)
	{
		// Make sure the matrix is copied.
		this->cnn_fully_connected_layers_weights[l] = other.cnn_fully_connected_layers_weights[l].clone();
	}

	this->cnn_fully_connected_layers_biases.resize(other.cnn_fully_connected_layers_biases.size());

	for (size_t l = 0; l < other.cnn_fully_connected_layers_biases.size(); ++l)
	{
		// Make sure the matrix is copied.
		this->cnn_fully_connected_layers_biases[l] = other.cnn_fully_connected_layers_biases[l].clone();
	}

	this->cnn_prelu_layer_weights.resize(other.cnn_prelu_layer_weights.size());

	for (size_t l = 0; l < other.cnn_prelu_layer_weights.size(); ++l)
	{
		// Make sure the matrix is copied.
		this->cnn_prelu_layer_weights[l] = other.cnn_prelu_layer_weights[l].clone();
	}
}

void PReLU(std::vector<cv::Mat_<float> >& input_output_maps, cv::Mat_<float> prelu_weights)
{

	if (input_output_maps.size() > 1)
	{
		int h = input_output_maps[0].rows;
		int w = input_output_maps[0].cols;
		size_t size_in = h * w;

		for (size_t k = 0; k < input_output_maps.size(); ++k)
		{
			// Apply the PReLU
			auto iter = input_output_maps[k].begin();

			float neg_mult = prelu_weights.at<float>(k);

			for(size_t i = 0; i < size_in; ++i)
			{
				float in_val = *iter;

				// The prelu step
				*iter++ = in_val >= 0 ? in_val : in_val * neg_mult;

			}
		}
	}
	else
	{
		
		int w = input_output_maps[0].cols;

		for (size_t k = 0; k < prelu_weights.rows; ++k)
		{
			auto iter = input_output_maps[0].row(k).begin();
			float neg_mult = prelu_weights.at<float>(k);

			for (size_t i = 0; i < w; ++i)
			{
				float in_val = *iter;
				// Apply the PReLU
				*iter++ = in_val >= 0 ? in_val : in_val * neg_mult;
			}
		}

	}

}

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)
{
	outputs.clear();

	if (input_maps.size() > 1)
	{
		// Concatenate all the maps
		cv::Size orig_size = input_maps[0].size();
		cv::Mat_<float> input_concat(input_maps.size(), input_maps[0].cols * input_maps[0].rows);

		for (size_t in = 0; in < input_maps.size(); ++in)
		{
			cv::Mat_<float> add = input_maps[in];
			
			// Reshape if all of the data will be flattened
			if (input_concat.rows != weights.cols)
			{
				add = add.t();
			}

			add = add.reshape(0, 1);
			add.copyTo(input_concat.row(in));
		}

		// Treat the input as separate feature maps
		if (input_concat.rows == weights.cols)
		{
			input_concat = weights * input_concat;
			// Add biases
			for (size_t k = 0; k < biases.rows; ++k)
			{
				input_concat.row(k) = input_concat.row(k) + biases.at<float>(k);
			}

			outputs.clear();
			// Resize and add as output
			for (size_t k = 0; k < biases.rows; ++k)
			{
				cv::Mat_<float> reshaped = input_concat.row(k).clone();
				reshaped = reshaped.reshape(1, orig_size.height);
				outputs.push_back(reshaped);
			}
		}
		else
		{
			// Flatten the input
			input_concat = input_concat.reshape(0, input_concat.rows * input_concat.cols);

			input_concat = weights * input_concat + biases;

			outputs.clear();
			outputs.push_back(input_concat);
		}

	}
	else
	{
		cv::Mat out = weights * input_maps[0] + biases;
		outputs.clear();
		outputs.push_back(out.t());
	}

}

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)
{
	vector<cv::Mat_<float> > outputs_sub;

	// Iterate over kernel height and width, based on stride
	for (size_t in = 0; in < input_maps.size(); ++in)
	{
		// Help with rounding up a bit, to match caffe style output
		int out_x = round((double)(input_maps[in].cols - kernel_size_x) / (double)stride_x) + 1;
		int out_y = round((double)(input_maps[in].rows - kernel_size_y) / (double)stride_y) + 1;

		cv::Mat_<float> sub_out(out_y, out_x, 0.0);
		cv::Mat_<float> in_map = input_maps[in];

		for (int x = 0; x < input_maps[in].cols; x += stride_x)
		{
			int max_x = cv::min(input_maps[in].cols, x + kernel_size_x);
			int x_in_out = floor(x / stride_x);

			if (x_in_out >= out_x)
				continue;

			for (int y = 0; y < input_maps[in].rows; y += stride_y)
			{
				int y_in_out = floor(y / stride_y);

				if (y_in_out >= out_y)
					continue;

				int max_y = cv::min(input_maps[in].rows, y + kernel_size_y);

				float curr_max = -FLT_MAX;

				for (int x_in = x; x_in < max_x; ++x_in)
				{
					for (int y_in = y; y_in < max_y; ++y_in)
					{
						float curr_val = in_map.at<float>(y_in, x_in);
						if (curr_val > curr_max)
						{
							curr_max = curr_val;
						}
					}
				}
				sub_out.at<float>(y_in_out, x_in_out) = curr_max;
			}
		}

		outputs_sub.push_back(sub_out);

	}
	outputs = outputs_sub;

}

////////////////////////////////////////////////////////////////////////////////////////////////////////

void convolution_single_kern_fft(const vector<cv::Mat_<float> >& input_imgs, vector<cv::Mat_<double> >& img_dfts, const vector<cv::Mat_<float> >&  _templs, map<int, vector<cv::Mat_<double> > >& _templ_dfts, cv::Mat_<float>& result)
{
	// Assume result is defined properly
	if (result.empty())
	{
		cv::Size corrSize(input_imgs[0].cols - _templs[0].cols + 1, input_imgs[0].rows - _templs[0].rows + 1);
		result.create(corrSize);
	}

	// Our model will always be under min block size so can ignore this
	//const double blockScale = 4.5;
	//const int minBlockSize = 256;

	int maxDepth = CV_64F;

	cv::Size dftsize;

	dftsize.width = cv::getOptimalDFTSize(result.cols + _templs[0].cols - 1);
	dftsize.height = cv::getOptimalDFTSize(result.rows + _templs[0].rows - 1);

	// Compute block size
	cv::Size blocksize;
	blocksize.width = dftsize.width - _templs[0].cols + 1;
	blocksize.width = MIN(blocksize.width, result.cols);
	blocksize.height = dftsize.height - _templs[0].rows + 1;
	blocksize.height = MIN(blocksize.height, result.rows);

	vector<cv::Mat_<double>> dftTempl;

	// if this has not been precomputed, precompute it, otherwise use it
	if (_templ_dfts.find(dftsize.width) == _templ_dfts.end())
	{
		dftTempl.resize(_templs.size());
		for (size_t k = 0; k < _templs.size(); ++k)
		{
			dftTempl[k].create(dftsize.height, dftsize.width);

			cv::Mat_<float> src = _templs[k];

			cv::Mat_<double> dst(dftTempl[k], cv::Rect(0, 0, dftsize.width, dftsize.height));

			cv::Mat_<double> dst1(dftTempl[k], cv::Rect(0, 0, _templs[k].cols, _templs[k].rows));

			if (dst1.data != src.data)
				src.convertTo(dst1, dst1.depth());

			if (dst.cols > _templs[k].cols)
			{
				cv::Mat_<double> part(dst, cv::Range(0, _templs[k].rows), cv::Range(_templs[k].cols, dst.cols));
				part.setTo(0);
			}

			// Perform DFT of the template
			dft(dst, dst, 0, _templs[k].rows);

		}
		_templ_dfts[dftsize.width] = dftTempl;

	}
	else
	{
		dftTempl = _templ_dfts[dftsize.width];
	}

	cv::Size bsz(std::min(blocksize.width, result.cols), std::min(blocksize.height, result.rows));
	cv::Mat src;

	cv::Mat cdst(result, cv::Rect(0, 0, bsz.width, bsz.height));

	vector<cv::Mat_<double> > dftImgs;
	dftImgs.resize(input_imgs.size());

	if (img_dfts.empty())
	{
		for(size_t k = 0; k < input_imgs.size(); ++k)
		{
			dftImgs[k].create(dftsize);
			dftImgs[k].setTo(0.0);

			cv::Size dsz(bsz.width + _templs[k].cols - 1, bsz.height + _templs[k].rows - 1);

			int x2 = std::min(input_imgs[k].cols, dsz.width);
			int y2 = std::min(input_imgs[k].rows, dsz.height);

			cv::Mat src0(input_imgs[k], cv::Range(0, y2), cv::Range(0, x2));
			cv::Mat dst(dftImgs[k], cv::Rect(0, 0, dsz.width, dsz.height));
			cv::Mat dst1(dftImgs[k], cv::Rect(0, 0, x2, y2));

			src = src0;

			if (dst1.data != src.data)
				src.convertTo(dst1, dst1.depth());

			dft(dftImgs[k], dftImgs[k], 0, dsz.height);
			img_dfts.push_back(dftImgs[k].clone());
		}
	}

	cv::Mat_<double> dft_img(img_dfts[0].rows, img_dfts[0].cols, 0.0);
	for (size_t k = 0; k < input_imgs.size(); ++k)
	{
		cv::Mat dftTempl1(dftTempl[k], cv::Rect(0, 0, dftsize.width, dftsize.height));
		if (k == 0)
		{
			cv::mulSpectrums(img_dfts[k], dftTempl1, dft_img, 0, true);
		}
		else
		{
			cv::mulSpectrums(img_dfts[k], dftTempl1, dftImgs[k], 0, true);
			dft_img = dft_img + dftImgs[k];
		}
	}

	cv::dft(dft_img, dft_img, cv::DFT_INVERSE + cv::DFT_SCALE, bsz.height);

	src = dft_img(cv::Rect(0, 0, bsz.width, bsz.height));

	src.convertTo(cdst, CV_32F);

}

void im2col_t(const cv::Mat_<float>& input, int width, int height, cv::Mat_<float>& output)
{

	int m = input.cols;
	int n = input.rows;

	// determine how many blocks there will be with a sliding window of width x height in the input
	int yB = m - height + 1;
	int xB = n - width + 1;

	// Allocate the output size
	if (output.rows != width * height && output.cols != xB*yB)
	{
		output = cv::Mat::ones(width * height, xB*yB, CV_32F);
	}

	// Iterate over the whole image
	for (int i = 0; i< yB; i++)
	{
		int rowIdx = i;
		for (int j = 0; j< xB; j++)
		{
			//int rowIdx = i; +j*yB;
			// iterate over the blocks within the image
			for (unsigned int yy = 0; yy < height; ++yy)
			{
				// Faster iteration over the image
				const float* Mi = input.ptr<float>(j + yy);
				for (unsigned int xx = 0; xx < width; ++xx)
				{
					int colIdx = xx*height + yy;

					output.at<float>(colIdx, rowIdx) = Mi[i + xx];
				}
			}
			rowIdx += yB;

		}
	}
}

void convolution_direct(std::vector<cv::Mat_<float> >& outputs, const std::vector<cv::Mat_<float> >& input_maps, const cv::Mat_<float>& weight_matrix, const std::vector<float >& biases, int height_k, int width_k)
{
	outputs.clear();

	int height_in = input_maps[0].rows;
	int width_n = input_maps[0].cols;

	// determine how many blocks there will be with a sliding window of width x height in the input
	int yB = height_in - height_k + 1;
	int xB = width_n - width_k + 1;

	cv::Mat_<float> input_matrix(input_maps.size() * height_k * width_k + 1.0, yB * xB, 1.0f);

	// Comibine im2col accross channels to prepare for matrix multiplication
	for (size_t i = 0; i < input_maps.size(); ++i)
	{
		im2col_t(input_maps[i], width_k, height_k, input_matrix(cv::Rect(0, i * height_k * width_k, yB * xB, height_k * width_k)));
	}

	// Actual convolution (through multiplication)
	cv::Mat_<float> out = weight_matrix * input_matrix;

	// Move back to vectors and reshape accordingly (also add the bias)
	for (size_t k = 0; k < out.rows; ++k)
	{
		outputs.push_back(out.row(k).reshape(1, yB));
	}

}

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)
{
	outputs.clear();

	// Useful precomputed data placeholders for quick correlation (convolution)
	vector<cv::Mat_<double> > input_image_dft;

	for (size_t k = 0; k < kernels.size(); ++k)
	{

		// The convolution (with precomputation)
		cv::Mat_<float> output;
		convolution_single_kern_fft(input_maps, input_image_dft, kernels[k], precomp_dfts[k], output);

		// Combining the maps
		outputs.push_back(output + biases[k]);

	}
}

void convolution_fft(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<vector<pair<int, cv::Mat_<double> > > >& precomp_dfts)
{
	outputs.clear();
	for (size_t in = 0; in < input_maps.size(); ++in)
	{
		cv::Mat_<float> input_image = input_maps[in];

		// Useful precomputed data placeholders for quick correlation (convolution)
		cv::Mat_<double> input_image_dft;
		cv::Mat integral_image;
		cv::Mat integral_image_sq;

		for (size_t k = 0; k < kernels[in].size(); ++k)
		{
			cv::Mat_<float> kernel = kernels[in][k];

			// The convolution (with precomputation)
			cv::Mat_<float> output;
			if (precomp_dfts[in][k].second.empty())
			{
				std::map<int, cv::Mat_<double> > precomputed_dft;

				LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);

				precomp_dfts[in][k].first = precomputed_dft.begin()->first;
				precomp_dfts[in][k].second = precomputed_dft.begin()->second;
			}
			else
			{
				std::map<int, cv::Mat_<double> > precomputed_dft;
				precomputed_dft[precomp_dfts[in][k].first] = precomp_dfts[in][k].second;
				LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
			}

			// Combining the maps
			if (in == 0)
			{
				outputs.push_back(output);
			}
			else
			{
				outputs[k] = outputs[k] + output;
			}

		}

	}

	for (size_t k = 0; k < biases.size(); ++k)
	{
		outputs[k] = outputs[k] + biases[k];
	}
}

std::vector<cv::Mat_<float>> CNN::Inference(const cv::Mat& input_img, bool direct)
{
	if (input_img.channels() == 1)
	{
		cv::cvtColor(input_img, input_img, cv::COLOR_GRAY2BGR);
	}

	int cnn_layer = 0;
	int fully_connected_layer = 0;
	int prelu_layer = 0;
	int max_pool_layer = 0;

	// Slit a BGR image into three chnels
	cv::Mat channels[3]; 
	cv::split(input_img, channels);  

	// Flip the BGR order to RGB
	vector<cv::Mat_<float> > input_maps;
	input_maps.push_back(channels[2]);
	input_maps.push_back(channels[1]);
	input_maps.push_back(channels[0]);

	vector<cv::Mat_<float> > outputs;

	for (size_t layer = 0; layer < cnn_layer_types.size(); ++layer)
	{

		// Determine layer type
		int layer_type = cnn_layer_types[layer];

		// Convolutional layer
		if (layer_type == 0)		
		{

			// Either perform direct convolution through matrix multiplication or use an FFT optimized version, which one is optimal depends on the kernel and input sizes
			if (direct)
			{
				convolution_direct(outputs, input_maps, cnn_convolutional_layers_weights[cnn_layer], cnn_convolutional_layers_bias[cnn_layer], cnn_convolutional_layers[cnn_layer][0][0].rows, cnn_convolutional_layers[cnn_layer][0][0].cols);
			}
			else
			{
				convolution_fft2(outputs, input_maps, cnn_convolutional_layers[cnn_layer], cnn_convolutional_layers_bias[cnn_layer], cnn_convolutional_layers_dft[cnn_layer]);
			}
			//vector<cv::Mat_<float> > outs;
			//convolution_fft(outs, input_maps, cnn_convolutional_layers[cnn_layer], cnn_convolutional_layers_bias[cnn_layer], cnn_convolutional_layers_dft[cnn_layer]);



			cnn_layer++;
		}
		if (layer_type == 1)
		{

			int stride_x = std::get<2>(cnn_max_pooling_layers[max_pool_layer]);
			int stride_y = std::get<3>(cnn_max_pooling_layers[max_pool_layer]);
			
			int kernel_size_x = std::get<0>(cnn_max_pooling_layers[max_pool_layer]);
			int kernel_size_y = std::get<1>(cnn_max_pooling_layers[max_pool_layer]);

			max_pooling(outputs, input_maps, stride_x, stride_y, kernel_size_x, kernel_size_y);
			max_pool_layer++;
		}
		if (layer_type == 2)
		{
			fully_connected(outputs, input_maps, cnn_fully_connected_layers_weights[fully_connected_layer], cnn_fully_connected_layers_biases[fully_connected_layer]);
			fully_connected_layer++;
		}
		if (layer_type == 3) // PReLU
		{
			// In place prelu computation
			PReLU(input_maps, cnn_prelu_layer_weights[prelu_layer]);
			outputs = input_maps;
			prelu_layer++;
		}
		if (layer_type == 4)
		{
			outputs.clear();
			for (size_t k = 0; k < input_maps.size(); ++k)
			{
				// Apply the sigmoid
				cv::exp(-input_maps[k], input_maps[k]);
				input_maps[k] = 1.0 / (1.0 + input_maps[k]);

				outputs.push_back(input_maps[k]);

			}
		}
		// Set the outputs of this layer to inputs of the next one
		input_maps = outputs;		
	}

	
	return outputs;

}

void ReadMatBin(std::ifstream& stream, cv::Mat &output_mat)
{
	// Read in the number of rows, columns and the data type
	int row, col, type;

	stream.read((char*)&row, 4);
	stream.read((char*)&col, 4);
	stream.read((char*)&type, 4);

	output_mat = cv::Mat(row, col, type);
	int size = output_mat.rows * output_mat.cols * output_mat.elemSize();
	stream.read((char *)output_mat.data, size);

}

void CNN::ClearPrecomp()
{
	for (size_t k1 = 0; k1 < cnn_convolutional_layers_dft.size(); ++k1)
	{
		for (size_t k2 = 0; k2 < cnn_convolutional_layers_dft[k1].size(); ++k2)
		{
			cnn_convolutional_layers_dft[k1][k2].clear();
		}
	}
}

void CNN::Read(const string& location)
{
	ifstream cnn_stream(location, ios::in | ios::binary);
	if (cnn_stream.is_open())
	{
		cnn_stream.seekg(0, ios::beg);

		// Reading in CNNs

		int network_depth;
		cnn_stream.read((char*)&network_depth, 4);

		cnn_layer_types.resize(network_depth);

		for (int layer = 0; layer < network_depth; ++layer)
		{

			int layer_type;
			cnn_stream.read((char*)&layer_type, 4);
			cnn_layer_types[layer] = layer_type;

			// convolutional
			if (layer_type == 0)
			{

				// Read the number of input maps
				int num_in_maps;
				cnn_stream.read((char*)&num_in_maps, 4);

				// Read the number of kernels for each input map
				int num_kernels;
				cnn_stream.read((char*)&num_kernels, 4);

				vector<vector<cv::Mat_<float> > > kernels;

				kernels.resize(num_in_maps);

				vector<float> biases;
				for (int k = 0; k < num_kernels; ++k)
				{
					float bias;
					cnn_stream.read((char*)&bias, 4);
					biases.push_back(bias);
				}

				cnn_convolutional_layers_bias.push_back(biases);

				// For every input map
				for (int in = 0; in < num_in_maps; ++in)
				{
					kernels[in].resize(num_kernels);

					// For every kernel on that input map
					for (int k = 0; k < num_kernels; ++k)
					{
						ReadMatBin(cnn_stream, kernels[in][k]);

					}
				}

				// Rearrange the kernels for faster inference with FFT
				vector<vector<cv::Mat_<float> > > kernels_rearr;
				kernels_rearr.resize(num_kernels);

				// Fill up the rearranged layer
				for (int k = 0; k < num_kernels; ++k)
				{
					for (int in = 0; in < num_in_maps; ++in)
					{
						kernels_rearr[k].push_back(kernels[in][k]);
					}
				}

				cnn_convolutional_layers.push_back(kernels_rearr);

				// Place-holders for DFT precomputation
				vector<map<int, vector<cv::Mat_<double> > > > cnn_convolutional_layers_dft_curr_layer;
				cnn_convolutional_layers_dft_curr_layer.resize(num_kernels);
				cnn_convolutional_layers_dft.push_back(cnn_convolutional_layers_dft_curr_layer);

				// Rearrange the flattened kernels into weight matrices for direct convolution computation
				cv::Mat_<float> weight_matrix(num_in_maps * kernels_rearr[0][0].rows * kernels_rearr[0][0].cols, num_kernels);
				for (size_t k = 0; k < num_kernels; ++k)
				{
					for (size_t i = 0; i < num_in_maps; ++i)
					{
						// Flatten the kernel
						cv::Mat_<float> k_flat = kernels_rearr[k][i].t();
						k_flat = k_flat.reshape(0, 1).t();
						k_flat.copyTo(weight_matrix(cv::Rect(k, i * kernels_rearr[0][0].rows * kernels_rearr[0][0].cols, 1, kernels_rearr[0][0].rows * kernels_rearr[0][0].cols)));
					}
				}

				// Transpose the weight matrix for more convenient computation
				weight_matrix = weight_matrix.t();

				// Add a bias term to the weight matrix for efficiency
				cv::Mat_<float> W(weight_matrix.rows, weight_matrix.cols + 1, 1.0);
				for (size_t k = 0; k < weight_matrix.rows; ++k)
				{
					W.at<float>(k, weight_matrix.cols) = biases[k];
				}
				weight_matrix.copyTo(W(cv::Rect(0, 0, weight_matrix.cols, weight_matrix.rows)));

				cnn_convolutional_layers_weights.push_back(W);

			}
			else if (layer_type == 1)
			{
				int kernel_x, kernel_y, stride_x, stride_y;
				cnn_stream.read((char*)&kernel_x, 4);
				cnn_stream.read((char*)&kernel_y, 4);
				cnn_stream.read((char*)&stride_x, 4);
				cnn_stream.read((char*)&stride_y, 4);
				cnn_max_pooling_layers.push_back(std::tuple<int, int, int, int>(kernel_x, kernel_y, stride_x, stride_y));
			}
			else if (layer_type == 2)
			{
				cv::Mat_<float> biases;
				ReadMatBin(cnn_stream, biases);
				cnn_fully_connected_layers_biases.push_back(biases);

				// Fully connected layer
				cv::Mat_<float> weights;
				ReadMatBin(cnn_stream, weights);
				cnn_fully_connected_layers_weights.push_back(weights.t());
			}

			else if (layer_type == 3)
			{
				cv::Mat_<float> weights;
				ReadMatBin(cnn_stream, weights);
				cnn_prelu_layer_weights.push_back(weights);
			}
		}
	}
	else
	{
		cout << "WARNING: Can't find the CNN location" << endl;
	}
}

//===========================================================================
// Read in the MTCNN detector
void FaceDetectorMTCNN::Read(const 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, bool minimum)
{

	// 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;
			if (minimum)
			{
				unionArea = cv::min(rect1.area(), rect2.area());
			}
			else 
			{
				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++;
			}
		}
	}
	
}

// Converting the bounding boxes to squares
void rectify(vector<cv::Rect_<float> >& total_bboxes)
{

	// Apply size and location offsets
	for (size_t i = 0; i < total_bboxes.size(); ++i)
	{
		float height = total_bboxes[i].height;
		float width = total_bboxes[i].width;

		float max_side = max(width, height);

		// Correct the starts based on new size
		float new_min_x = total_bboxes[i].x + 0.5 * (width - max_side);
		float new_min_y = total_bboxes[i].y + 0.5 * (height - max_side);

		total_bboxes[i].x = (int)new_min_x;
		total_bboxes[i].y = (int)new_min_y;
		total_bboxes[i].width = (int)max_side;
		total_bboxes[i].height = (int)max_side;
	}
}

void apply_correction(vector<cv::Rect_<float> >& total_bboxes, const vector<cv::Rect_<float> > corrections, bool add1)
{

	// Apply size and location offsets
	for (size_t i = 0; i < total_bboxes.size(); ++i)
	{
		cv::Rect curr_box = total_bboxes[i];
		if (add1)
		{
			curr_box.width++;
			curr_box.height++;
		}

		float new_min_x = curr_box.x + corrections[i].x * curr_box.width;
		float new_min_y = curr_box.y + corrections[i].y * curr_box.height;
		float new_max_x = curr_box.x + curr_box.width + curr_box.width * corrections[i].width;
		float new_max_y = curr_box.y + curr_box.height + curr_box.height * corrections[i].height;
		total_bboxes[i] = cv::Rect_<float>(new_min_x, new_min_y, new_max_x - new_min_x, new_max_y - new_min_y);

	}


}


// The actual MTCNN face detection step
bool FaceDetectorMTCNN::DetectFaces(vector<cv::Rect_<double> >& o_regions, const cv::Mat& img_in, std::vector<double>& o_confidences, int min_face_size, double t1, double t2, double t3)
{

	int height_orig = img_in.size().height;
	int width_orig = img_in.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;

	cv::Mat input_img;

	if (img_in.channels() == 1)
	{
		cv::cvtColor(img_in, input_img, CV_GRAY2RGB);
	}
	else
	{
		input_img = img_in;
	}

	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;

	// As the scales will be done in parallel have some containers for them
	vector<vector<cv::Rect_<float> > > proposal_boxes_cross_scale(num_scales);
	vector<vector<float> > scores_cross_scale(num_scales);
	vector<vector<cv::Rect_<float> > > proposal_corrections_cross_scale(num_scales);

	//tbb::parallel_for(0, (int)num_scales, [&](int i) {
	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, true);

		// Clear the precomputations, as the image sizes will be different
		PNet.ClearPrecomp();

		// 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);

		proposal_boxes_cross_scale[i] = proposal_boxes;
		scores_cross_scale[i] = scores;
		proposal_corrections_cross_scale[i] = proposal_corrections;
	}
	//});

	// Perform non-maximum supression on proposals accross scales and combine them
	for (int i = 0; i < num_scales; ++i)
	{
		vector<int> to_keep = non_maximum_supression(proposal_boxes_cross_scale[i], scores_cross_scale[i], 0.5, false);
		select_subset(to_keep, proposal_boxes_cross_scale[i], scores_cross_scale[i], proposal_corrections_cross_scale[i]);

		proposal_boxes_all.insert(proposal_boxes_all.end(), proposal_boxes_cross_scale[i].begin(), proposal_boxes_cross_scale[i].end());
		scores_all.insert(scores_all.end(), scores_cross_scale[i].begin(), scores_cross_scale[i].end());
		proposal_corrections_all.insert(proposal_corrections_all.end(), proposal_corrections_cross_scale[i].begin(), proposal_corrections_cross_scale[i].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, false);
	select_subset(to_keep, proposal_boxes_all, scores_all, proposal_corrections_all);

	apply_correction(proposal_boxes_all, proposal_corrections_all, false);

	// Convert to rectangles and round
	rectify(proposal_boxes_all);

	// Creating proposal images from previous step detections
	vector<bool> above_thresh(proposal_boxes_all.size());
	//tbb::parallel_for(0, (int)proposal_boxes_all.size(), [&](int k) {
	for (size_t k = 0; k < proposal_boxes_all.size(); ++k) 
	{
		float width_target = proposal_boxes_all[k].width + 1;
		float height_target = proposal_boxes_all[k].height + 1;

		// Work out the start and end indices in the original image
		int start_x_in = cv::max((int)(proposal_boxes_all[k].x - 1), 0);
		int start_y_in = cv::max((int)(proposal_boxes_all[k].y - 1), 0);
		int end_x_in = cv::min((int)(proposal_boxes_all[k].x + width_target - 1), width_orig);
		int end_y_in = cv::min((int)(proposal_boxes_all[k].y + height_target - 1), height_orig);

		// Work out the start and end indices in the target image
		int	start_x_out = cv::max((int)(-proposal_boxes_all[k].x + 1), 0);
		int start_y_out = cv::max((int)(-proposal_boxes_all[k].y + 1), 0);
		int end_x_out = cv::min(width_target - (proposal_boxes_all[k].x + proposal_boxes_all[k].width - width_orig), width_target);
		int end_y_out = cv::min(height_target - (proposal_boxes_all[k].y + proposal_boxes_all[k].height - height_orig), height_target);

		cv::Mat tmp(height_target, width_target, CV_32FC3, cv::Scalar(0.0f,0.0f,0.0f));

		img_float(cv::Rect(start_x_in, start_y_in, end_x_in - start_x_in, end_y_in - start_y_in)).copyTo(
			tmp(cv::Rect(start_x_out, start_y_out, end_x_out - start_x_out, end_y_out - start_y_out)));
		
		cv::Mat prop_img;
		cv::resize(tmp, prop_img, cv::Size(24, 24));
			
		prop_img = (prop_img - 127.5) * 0.0078125;
		
		// Perform RNet on the proposal image
		std::vector<cv::Mat_<float> > rnet_out = RNet.Inference(prop_img, true);

		float prob = 1.0 / (1.0 + cv::exp(rnet_out[0].at<float>(0) - rnet_out[0].at<float>(1)));
		scores_all[k] = prob;
		proposal_corrections_all[k].x = rnet_out[0].at<float>(2);
		proposal_corrections_all[k].y = rnet_out[0].at<float>(3);
		proposal_corrections_all[k].width = rnet_out[0].at<float>(4);
		proposal_corrections_all[k].height = rnet_out[0].at<float>(5);
		if(prob >= t2)
		{
			above_thresh[k] = true;
		}
		else
		{
			above_thresh[k] = false;
		}

	}
	//});

	to_keep.clear();
	for (size_t i = 0; i < above_thresh.size(); ++i)
	{
		if (above_thresh[i])
		{
			to_keep.push_back(i);
		}
	}

	// Pick only the bounding boxes above the threshold
	select_subset(to_keep, proposal_boxes_all, scores_all, proposal_corrections_all);

	// Non maximum supression accross bounding boxes, and their offset correction
	to_keep = non_maximum_supression(proposal_boxes_all, scores_all, 0.7, false);
	select_subset(to_keep, proposal_boxes_all, scores_all, proposal_corrections_all);

	apply_correction(proposal_boxes_all, proposal_corrections_all, false);

	// Convert to rectangles and round
	rectify(proposal_boxes_all);

	// Preparing for the ONet stage
	above_thresh.clear();
	above_thresh.resize(proposal_boxes_all.size());
	//tbb::parallel_for(0, (int)proposal_boxes_all.size(), [&](int k) {
	for (size_t k = 0; k < proposal_boxes_all.size(); ++k)
	{
		float width_target = proposal_boxes_all[k].width + 1;
		float height_target = proposal_boxes_all[k].height + 1;

		// Work out the start and end indices in the original image
		int start_x_in = cv::max((int)(proposal_boxes_all[k].x - 1), 0);
		int start_y_in = cv::max((int)(proposal_boxes_all[k].y - 1), 0);
		int end_x_in = cv::min((int)(proposal_boxes_all[k].x + width_target - 1), width_orig);
		int end_y_in = cv::min((int)(proposal_boxes_all[k].y + height_target - 1), height_orig);

		// Work out the start and end indices in the target image
		int	start_x_out = cv::max((int)(-proposal_boxes_all[k].x + 1), 0);
		int start_y_out = cv::max((int)(-proposal_boxes_all[k].y + 1), 0);
		int end_x_out = cv::min(width_target - (proposal_boxes_all[k].x + proposal_boxes_all[k].width - width_orig), width_target);
		int end_y_out = cv::min(height_target - (proposal_boxes_all[k].y + proposal_boxes_all[k].height - height_orig), height_target);

		cv::Mat tmp(height_target, width_target, CV_32FC3, cv::Scalar(0.0f, 0.0f, 0.0f));

		img_float(cv::Rect(start_x_in, start_y_in, end_x_in - start_x_in, end_y_in - start_y_in)).copyTo(
			tmp(cv::Rect(start_x_out, start_y_out, end_x_out - start_x_out, end_y_out - start_y_out)));

		cv::Mat prop_img;
		cv::resize(tmp, prop_img, cv::Size(48, 48));

		prop_img = (prop_img - 127.5) * 0.0078125;

		// Perform RNet on the proposal image
		std::vector<cv::Mat_<float> > onet_out = ONet.Inference(prop_img, true);

		float prob = 1.0 / (1.0 + cv::exp(onet_out[0].at<float>(0) - onet_out[0].at<float>(1)));
		scores_all[k] = prob;
		proposal_corrections_all[k].x = onet_out[0].at<float>(2);
		proposal_corrections_all[k].y = onet_out[0].at<float>(3);
		proposal_corrections_all[k].width = onet_out[0].at<float>(4);
		proposal_corrections_all[k].height = onet_out[0].at<float>(5);
		if (prob >= t3)
		{
			above_thresh[k] = true;
		}
		else
		{
			above_thresh[k] = false;
		}
	}
	//});

	to_keep.clear();
	for (size_t i = 0; i < above_thresh.size(); ++i)
	{
		if (above_thresh[i])
		{
			to_keep.push_back(i);
		}
	}

	// Pick only the bounding boxes above the threshold
	select_subset(to_keep, proposal_boxes_all, scores_all, proposal_corrections_all);
	apply_correction(proposal_boxes_all, proposal_corrections_all, true);

	// Non maximum supression accross bounding boxes, and their offset correction
	to_keep = non_maximum_supression(proposal_boxes_all, scores_all, 0.7, true);
	select_subset(to_keep, proposal_boxes_all, scores_all, proposal_corrections_all);

	// Correct the box to expectation to be tight around facial landmarks
	for (size_t k = 0; k < proposal_boxes_all.size(); ++k)
	{
		proposal_boxes_all[k].x = proposal_boxes_all[k].width * -0.0075 + proposal_boxes_all[k].x;
		proposal_boxes_all[k].y = proposal_boxes_all[k].height * 0.2459 + proposal_boxes_all[k].y;
		proposal_boxes_all[k].width = 1.0323 * proposal_boxes_all[k].width;
		proposal_boxes_all[k].height = 0.7751 * proposal_boxes_all[k].height;

		o_regions.push_back(cv::Rect_<double>(proposal_boxes_all[k].x, proposal_boxes_all[k].y, proposal_boxes_all[k].width, proposal_boxes_all[k].height));
		o_confidences.push_back(scores_all[k]);

	}

	if(o_regions.size() > 0)
	{
		return true;
	}
	else
	{
		return false;
	}
}