///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
// all rights reserved.
//
// THIS SOFTWARE IS PROVIDED “AS IS” FOR ACADEMIC USE ONLY AND ANY EXPRESS
// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Notwithstanding the license granted herein, Licensee acknowledges that certain components
// of the Software may be covered by so-called “open source” software licenses (“Open Source
// Components”), which means any software licenses approved as open source licenses by the
// Open Source Initiative or any substantially similar licenses, including without limitation any
// license that, as a condition of distribution of the software licensed under such license,
// requires that the distributor make the software available in source code format. Licensor shall
// provide a list of Open Source Components for a particular version of the Software upon
// Licensee’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
// License Agreement with respect to such Open Source Component, such restrictions will not
// apply to such Open Source Component. To the extent the terms of the licenses applicable to
// Open Source Components require Licensor to make an offer to provide source code or
// related information in connection with the Software, such offer is hereby made. Any request
// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
// 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 Baltrušaitis, 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 Baltrušaitis, 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 Baltrušaitis, 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 Baltrušaitis, 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 "LandmarkDetectionValidator.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>

#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
// Local includes
#include "LandmarkDetectorUtils.h"

using namespace LandmarkDetector;

// Copy constructor
DetectionValidator::DetectionValidator(const DetectionValidator& other) : orientations(other.orientations), bs(other.bs), paws(other.paws),
cnn_subsampling_layers(other.cnn_subsampling_layers), cnn_layer_types(other.cnn_layer_types), cnn_fully_connected_layers_bias(other.cnn_fully_connected_layers_bias),
cnn_convolutional_layers_bias(other.cnn_convolutional_layers_bias), cnn_convolutional_layers_dft(other.cnn_convolutional_layers_dft)
{

	this->validator_type = other.validator_type;

	this->activation_fun = other.activation_fun;
	this->output_fun = other.output_fun;

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

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

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

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

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

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

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

			}
		}
	}

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

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

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

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

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

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

}

//===========================================================================
// Read in the landmark detection validation module
void DetectionValidator::Read(string location)
{

	ifstream detection_validator_stream (location, ios::in|ios::binary);
	if (detection_validator_stream.is_open())	
	{				
		detection_validator_stream.seekg (0, ios::beg);

		// Read validator type
		detection_validator_stream.read ((char*)&validator_type, 4);
		
		// Read the number of views (orientations) within the validator
		int n;
		detection_validator_stream.read ((char*)&n, 4);
	
		orientations.resize(n);

		for(int i = 0; i < n; i++)
		{
			cv::Mat_<double> orientation_tmp;
			LandmarkDetector::ReadMatBin(detection_validator_stream, orientation_tmp);		
		
			orientations[i] = cv::Vec3d(orientation_tmp.at<double>(0), orientation_tmp.at<double>(1), orientation_tmp.at<double>(2));

			// Convert from degrees to radians
			orientations[i] = orientations[i] * M_PI / 180.0;
		}

		// Initialise the piece-wise affine warps, biases and weights
		paws.resize(n);

		if( validator_type == 0)
		{
			// Reading in SVRs
			bs.resize(n);
			ws.resize(n);
		}
		else if(validator_type == 1)
		{
			// Reading in NNs
			ws_nn.resize(n);

			activation_fun.resize(n);
			output_fun.resize(n);
		}
		else if(validator_type == 2)
		{
			cnn_convolutional_layers.resize(n);
			cnn_convolutional_layers_dft.resize(n);
			cnn_subsampling_layers.resize(n);
			cnn_fully_connected_layers_weights.resize(n);
			cnn_layer_types.resize(n);
			cnn_fully_connected_layers_bias.resize(n);
			cnn_convolutional_layers_bias.resize(n);
		}			
		else if (validator_type == 3)
		{
			cnn_convolutional_layers.resize(n);
			cnn_convolutional_layers_dft.resize(n);
			cnn_fully_connected_layers_weights.resize(n);
			cnn_layer_types.resize(n);
			cnn_fully_connected_layers_biases.resize(n);
			cnn_convolutional_layers_bias.resize(n);
		}

		// Initialise the normalisation terms
		mean_images.resize(n);
		standard_deviations.resize(n);

		// Read in the validators for each of the views
		for(int i = 0; i < n; i++)
		{

			// Read in the mean images
			LandmarkDetector::ReadMatBin(detection_validator_stream, mean_images[i]);
			mean_images[i] = mean_images[i].t();
	
			LandmarkDetector::ReadMatBin(detection_validator_stream, standard_deviations[i]);
			standard_deviations[i] = standard_deviations[i].t();

			// Model specifics
			if(validator_type == 0)
			{
				// Reading in the biases and weights
				detection_validator_stream.read ((char*)&bs[i], 8);
				LandmarkDetector::ReadMatBin(detection_validator_stream, ws[i]);
	
			}
			else if(validator_type == 1)
			{
				
				// Reading in the number of layers in the neural net
				int num_depth_layers;
				detection_validator_stream.read ((char*)&num_depth_layers, 4);
				
				// Reading in activation and output function types
				detection_validator_stream.read ((char*)&activation_fun[i], 4);
				detection_validator_stream.read ((char*)&output_fun[i], 4);

				ws_nn[i].resize(num_depth_layers);
				for(int layer = 0; layer < num_depth_layers; layer++)
				{
					LandmarkDetector::ReadMatBin(detection_validator_stream, ws_nn[i][layer]);

					// Transpose for efficiency during multiplication
					ws_nn[i][layer] = ws_nn[i][layer].t();
				}
			}
			else if(validator_type == 2)
			{
				// Reading in CNNs

				int network_depth;
				detection_validator_stream.read ((char*)&network_depth, 4);
	
				cnn_layer_types[i].resize(network_depth);

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

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

					// convolutional
					if(layer_type == 0)
					{

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

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

						vector<vector<cv::Mat_<float> > > kernels;
						vector<vector<pair<int, cv::Mat_<double> > > > kernel_dfts;

						kernels.resize(num_in_maps);
						kernel_dfts.resize(num_in_maps);

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

						cnn_convolutional_layers_bias[i].push_back(biases);

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

							// For every kernel on that input map
							for (int k = 0; k < num_kernels; ++k)
							{
								ReadMatBin(detection_validator_stream, kernels[in][k]);				
								
								// Flip the kernel in order to do convolution and not correlation
								cv::flip(kernels[in][k], kernels[in][k], -1);
							}
						}

						cnn_convolutional_layers[i].push_back(kernels);
						cnn_convolutional_layers_dft[i].push_back(kernel_dfts);
					}
					else if(layer_type == 1)
					{
						// Subsampling layer

						int scale;
						detection_validator_stream.read ((char*)&scale, 4);

						cnn_subsampling_layers[i].push_back(scale);
					}
					else if(layer_type == 2)
					{
						float bias;
						detection_validator_stream.read ((char*)&bias, 4);
						cnn_fully_connected_layers_bias[i].push_back(bias);

						// Fully connected layer
						cv::Mat_<float> weights;
						ReadMatBin(detection_validator_stream, weights);
						cnn_fully_connected_layers_weights[i].push_back(weights);
					}
				}
			}
			else if (validator_type == 3)
			{
				int network_depth;
				detection_validator_stream.read((char*)&network_depth, 4);

				cnn_layer_types[i].resize(network_depth);

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

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

					// convolutional
					if (layer_type == 0)
					{

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

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

						vector<vector<cv::Mat_<float> > > kernels;
						vector<vector<pair<int, cv::Mat_<double> > > > kernel_dfts;

						kernels.resize(num_in_maps);
						kernel_dfts.resize(num_in_maps);

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

						cnn_convolutional_layers_bias[i].push_back(biases);

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

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

							}
						}

						cnn_convolutional_layers[i].push_back(kernels);
						cnn_convolutional_layers_dft[i].push_back(kernel_dfts);
					}
					else if (layer_type == 2)
					{
						cv::Mat_<float> biases;
						ReadMatBin(detection_validator_stream, biases);
						cnn_fully_connected_layers_biases[i].push_back(biases);

						// Fully connected layer
						cv::Mat_<float> weights;
						ReadMatBin(detection_validator_stream, weights);
						cnn_fully_connected_layers_weights[i].push_back(weights);
					}
				}
			}
			// Read in the piece-wise affine warps
			paws[i].Read(detection_validator_stream);
		}
		
	}
	else
	{
		cout << "WARNING: Can't find the Face checker location" << endl;
	}
}

//===========================================================================
// Check if the fitting actually succeeded
double DetectionValidator::Check(const cv::Vec3d& orientation, const cv::Mat_<uchar>& intensity_img, cv::Mat_<double>& detected_landmarks)
{

	int id = GetViewId(orientation);
	
	// The warped (cropped) image, corresponding to a face lying withing the detected lanmarks
	cv::Mat_<double> warped;
	
	// the piece-wise affine image
	cv::Mat_<double> intensity_img_double;
	intensity_img.convertTo(intensity_img_double, CV_64F);

	paws[id].Warp(intensity_img_double, warped, detected_landmarks);	
	
	double dec;
	if(validator_type == 0)
	{
		dec = CheckSVR(warped, id);
	}
	else if(validator_type == 1)
	{
		dec = CheckNN(warped, id);
	}
	else if(validator_type == 2)
	{
		dec = CheckCNN_old(warped, id);
	}
	else if (validator_type == 3)
	{
		// On some machines the non-TBB version may be faster
		//dec = CheckCNN(warped, id);
		dec = CheckCNN_tbb(warped, id);
	}
	return dec;
}

double DetectionValidator::CheckNN(const cv::Mat_<double>& warped_img, int view_id)
{
	cv::Mat_<double> feature_vec;
	NormaliseWarpedToVector(warped_img, feature_vec, view_id);
	feature_vec = feature_vec.t();
			
	for(size_t layer = 0; layer < ws_nn[view_id].size(); ++layer)
	{
		// Add a bias term
		cv::hconcat(cv::Mat_<double>(1,1, 1.0), feature_vec, feature_vec);
		
		// Apply the weights
		feature_vec = feature_vec * ws_nn[view_id][layer];

		// Activation or output
		int fun_type;
		if(layer != ws_nn[view_id].size() - 1)
		{
			fun_type = activation_fun[view_id];
		}
		else
		{
			fun_type = output_fun[view_id];
		}

		if(fun_type == 0)
		{
			cv::exp(-feature_vec, feature_vec);
			feature_vec = 1.0 /(1.0 + feature_vec);		
		}
		else if(fun_type == 1)
		{
			cv::MatIterator_<double> q1 = feature_vec.begin(); // respone for each pixel
			cv::MatIterator_<double> q2 = feature_vec.end();

			// the logistic function (sigmoid) applied to the response
			while(q1 != q2)
			{
				*q1 = 1.7159 * tanh((2.0/3.0) * (*q1));
				q1++;
			}
		}
		// TODO ReLU

	}

	// Turn it to -1, 1 range
	double dec = (feature_vec.at<double>(0) - 0.5) * 2;

	return dec;

}

double DetectionValidator::CheckSVR(const cv::Mat_<double>& warped_img, int view_id)
{

	cv::Mat_<double> feature_vec;
	NormaliseWarpedToVector(warped_img, feature_vec, view_id);
		

	double dec = (ws[view_id].dot(feature_vec.t()) + bs[view_id]);

	return dec;

}

// Convolutional Neural Network
double DetectionValidator::CheckCNN_old(const cv::Mat_<double>& warped_img, int view_id)
{

	cv::Mat_<double> feature_vec;
	NormaliseWarpedToVector(warped_img, feature_vec, view_id);
	
	// Create a normalised image from the crop vector
	cv::Mat_<float> img(warped_img.size(), 0.0);
	img = img.t();

	cv::Mat mask = paws[view_id].pixel_mask.t();
	cv::MatIterator_<uchar>  mask_it = mask.begin<uchar>();
	
	cv::MatIterator_<double> feature_it = feature_vec.begin();
	cv::MatIterator_<float> img_it = img.begin();		

	int wInt = img.cols;
	int hInt = img.rows;

	for(int i=0; i < wInt; ++i)
	{
		for(int j=0; j < hInt; ++j, ++mask_it, ++img_it)
		{
			// if is within mask
			if(*mask_it)
			{
				// assign the feature to image if it is within the mask
				*img_it = (float)*feature_it++;
			}
		}
	}
	img = img.t();
	
	int cnn_layer = 0;
	int subsample_layer = 0;
	int fully_connected_layer = 0;

	vector<cv::Mat_<float> > input_maps;
	input_maps.push_back(img);
	
	vector<cv::Mat_<float> > outputs;

	for(size_t layer = 0; layer < cnn_layer_types[view_id].size(); ++layer)
	{
		// Determine layer type
		int layer_type = cnn_layer_types[view_id][layer];
		
		
		// Convolutional layer
		if(layer_type == 0)
		{
			vector<cv::Mat_<float> > outputs_kern;
			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 < cnn_convolutional_layers[view_id][cnn_layer][in].size(); ++k)
				{
					cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
										
					// The convolution (with precomputation)
					cv::Mat_<float> output;
					if(cnn_convolutional_layers_dft[view_id][cnn_layer][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);
						
						cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first;
						cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second;
					}
					else
					{
						std::map<int, cv::Mat_<double> > precomputed_dft;
						precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][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_kern.push_back(output);
					}
					else
					{
						outputs_kern[k] = outputs_kern[k] + output;
					}

				}
								
			}
			
			outputs.clear();
			for(size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][0].size(); ++k)
			{
				// Apply the sigmoid
				cv::exp(-outputs_kern[k] - cnn_convolutional_layers_bias[view_id][cnn_layer][k], outputs_kern[k]);
				outputs_kern[k] = 1.0 /(1.0 + outputs_kern[k]);	

				outputs.push_back(outputs_kern[k]);

			}			

			cnn_layer++;
		}				
		if(layer_type == 1)
		{
			// Subsampling layer
			int scale = cnn_subsampling_layers[view_id][subsample_layer];
			
			cv::Mat kx = cv::Mat::ones(2, 1, CV_32F)*1.0f/scale;
			cv::Mat ky = cv::Mat::ones(1, 2, CV_32F)*1.0f/scale;

			vector<cv::Mat_<float>> outputs_sub;
			for(size_t in = 0; in < input_maps.size(); ++in)
			{

				cv::Mat_<float> conv_out;

				cv::sepFilter2D(input_maps[in], conv_out, CV_32F, kx, ky);
				conv_out = conv_out(cv::Rect(1, 1, conv_out.cols - 1, conv_out.rows - 1));

				int res_rows = conv_out.rows / scale;
				int res_cols = conv_out.cols / scale;

				if(conv_out.rows % scale != 0)
				{
					res_rows++;
				}
				if(conv_out.cols % scale != 0)
				{
					res_cols++;
				}

				cv::Mat_<float> sub_out(res_rows, res_cols);
				for(int w = 0; w < conv_out.cols; w+=scale)
				{
					for(int h=0; h < conv_out.rows; h+=scale)
					{
						sub_out.at<float>(h/scale, w/scale) = conv_out(h, w);
					}
				}
				outputs_sub.push_back(sub_out);
			}
			outputs = outputs_sub;
			subsample_layer++;

		}
		if(layer_type == 2)
		{
			// Concatenate all the maps
			cv::Mat_<float> input_concat = input_maps[0].t();
			input_concat = input_concat.reshape(0, 1);

			for(size_t in = 1; in < input_maps.size(); ++in)
			{
				cv::Mat_<float> add = input_maps[in].t();
				add = add.reshape(0,1);
				cv::hconcat(input_concat, add, input_concat);
			}
						
			input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer].t();

			cv::exp(-input_concat - cnn_fully_connected_layers_bias[view_id][fully_connected_layer], input_concat);
			input_concat = 1.0 /(1.0 + input_concat);		

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

			fully_connected_layer++;
		}
		// Max pooling layer
		if (layer_type == 3)
		{

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

			// Iterate over pool height and width, all the stride is 2x2 and no padding is used
			int stride_x = 2;
			int stride_y = 2;

			int pool_x = 2;
			int pool_y = 2;

			for (size_t in = 0; in < input_maps.size(); ++in)
			{
				int out_x = input_maps[in].cols / stride_x;
				int out_y = input_maps[in].rows / stride_y;

				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)
				{
					for (int y = 0; y < input_maps[in].rows; y+= stride_y) 
					{
						float curr_max = -FLT_MAX;
						for (int x_in = x; x_in < x+pool_x; ++x_in) 
						{
							for (int y_in = y; y_in < y + pool_y; ++y_in)
							{
								float curr_val = in_map.at<float>(y_in, x_in);
								if (curr_val > curr_max)
								{
									curr_max = curr_val;
								}
							}
						}
						int x_in_out = x / stride_x;
						int y_in_out = y / stride_y;
						sub_out.at<float>(y_in_out, x_in_out) = curr_max;
					}
				}

				outputs_sub.push_back(sub_out);
			}
			outputs = outputs_sub;
			subsample_layer++;
		}

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

	}
	
	// Turn it to -1, 1 range
	double dec = (outputs[0].at<float>(0) - 0.5) * 2.0;

	return dec;
}

// Convolutional Neural Network
double DetectionValidator::CheckCNN_tbb(const cv::Mat_<double>& warped_img, int view_id)
{

	cv::Mat_<double> feature_vec;
	NormaliseWarpedToVector(warped_img, feature_vec, view_id);

	// Create a normalised image from the crop vector
	cv::Mat_<float> img(warped_img.size(), 0.0);
	img = img.t();

	cv::Mat mask = paws[view_id].pixel_mask.t();
	cv::MatIterator_<uchar>  mask_it = mask.begin<uchar>();

	cv::MatIterator_<double> feature_it = feature_vec.begin();
	cv::MatIterator_<float> img_it = img.begin();

	int wInt = img.cols;
	int hInt = img.rows;

	for (int i = 0; i < wInt; ++i)
	{
		for (int j = 0; j < hInt; ++j, ++mask_it, ++img_it)
		{
			// if is within mask
			if (*mask_it)
			{
				// assign the feature to image if it is within the mask
				*img_it = (float)*feature_it++;
			}
		}
	}
	img = img.t();

	int cnn_layer = 0;
	int fully_connected_layer = 0;

	vector<cv::Mat_<float> > input_maps;
	input_maps.push_back(img);

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

	for (size_t layer = 0; layer < cnn_layer_types[view_id].size(); ++layer)
	{
		// Determine layer type
		int layer_type = cnn_layer_types[view_id][layer];

		// Convolutional layer
		if (layer_type == 0)
		{
			outputs.clear();
			// Pre-allocate the output feature maps
			outputs.resize(cnn_convolutional_layers[view_id][cnn_layer][0].size());
			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;

				// To adapt for TBB, perform the first convolution in a non TBB way so that dft, and integral images are computed
				cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][0];

				// The convolution (with precomputation)
				cv::Mat_<float> output;
				if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second.empty()) // This will only be needed during the first pass
				{
					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);

					cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].first = precomputed_dft.begin()->first;
					cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second = precomputed_dft.begin()->second;
				}
				else
				{
					std::map<int, cv::Mat_<double> > precomputed_dft;
					precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].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[0] = output;
				}
				else
				{
					outputs[0] = outputs[0] + output;
				}


				// TBB pass for the remaining kernels, empirically helps with layers with more kernels
				tbb::parallel_for(1, (int)cnn_convolutional_layers[view_id][cnn_layer][in].size(), [&](int k) {
				{
					cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];

					// The convolution (with precomputation)
					cv::Mat_<float> output;
					if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty()) // This will only be needed during the first pass
					{
						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);

						cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first;
						cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second;
					}
					else
					{
						std::map<int, cv::Mat_<double> > precomputed_dft;
						precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][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[k] = output;
					}
					else
					{
						outputs[k] = outputs[k] + output;
					}
				}
				});
				
			}

			for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][0].size(); ++k)
			{
				outputs[k] = outputs[k] + cnn_convolutional_layers_bias[view_id][cnn_layer][k];
			}
			cnn_layer++;
		}
		if (layer_type == 1)
		{
			vector<cv::Mat_<float>> outputs_sub;

			// Iterate over pool height and width, all the stride is 2x2 and no padding is used
			int stride_x = 2;
			int stride_y = 2;

			int pool_x = 2;
			int pool_y = 2;

			for (size_t in = 0; in < input_maps.size(); ++in)
			{
				int out_x = input_maps[in].cols / stride_x;
				int out_y = input_maps[in].rows / stride_y;

				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)
				{
					for (int y = 0; y < input_maps[in].rows; y += stride_y)
					{
						float curr_max = -FLT_MAX;
						for (int x_in = x; x_in < x + pool_x; ++x_in)
						{
							for (int y_in = y; y_in < y + pool_y; ++y_in)
							{
								float curr_val = in_map.at<float>(y_in, x_in);
								if (curr_val > curr_max)
								{
									curr_max = curr_val;
								}
							}
						}
						int x_in_out = x / stride_x;
						int y_in_out = y / stride_y;
						sub_out.at<float>(y_in_out, x_in_out) = curr_max;
					}
				}

				outputs_sub.push_back(sub_out);

			}
			outputs = outputs_sub;
		}
		if (layer_type == 2)
		{
			// Concatenate all the maps
			cv::Mat_<float> input_concat = input_maps[0].t();
			input_concat = input_concat.reshape(0, 1);

			for (size_t in = 1; in < input_maps.size(); ++in)
			{
				cv::Mat_<float> add = input_maps[in].t();
				add = add.reshape(0, 1);
				cv::hconcat(input_concat, add, input_concat);
			}

			input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer];
			input_concat = input_concat + cnn_fully_connected_layers_biases[view_id][fully_connected_layer].t();

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

			fully_connected_layer++;
		}
		if (layer_type == 3) // ReLU
		{
			outputs.clear();
			for (size_t k = 0; k < input_maps.size(); ++k)
			{
				// Apply the ReLU
				cv::threshold(input_maps[k], input_maps[k], 0, 0, cv::THRESH_TOZERO);
				outputs.push_back(input_maps[k]);

			}
		}
		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
		input_maps = outputs;

	}

	// Convert the class label to a continuous value
	double max_val = 0;
	cv::Point max_loc;
	cv::minMaxLoc(outputs[0].t(), 0, &max_val, 0, &max_loc);
	int max_idx = max_loc.y;
	double max = 1;
	double min = -1;
	double bins = (double)outputs[0].cols;
	// Unquantizing the softmax layer to continuous value
	double step_size = (max - min) / bins; // This should be saved somewhere
	double unquantized = min + step_size / 2.0 + max_idx * step_size;

	return unquantized;
}

// Convolutional Neural Network
double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view_id)
{
	
	cv::Mat_<double> feature_vec;
	NormaliseWarpedToVector(warped_img, feature_vec, view_id);

	// Create a normalised image from the crop vector
	cv::Mat_<float> img(warped_img.size(), 0.0);
	img = img.t();

	cv::Mat mask = paws[view_id].pixel_mask.t();
	cv::MatIterator_<uchar>  mask_it = mask.begin<uchar>();

	cv::MatIterator_<double> feature_it = feature_vec.begin();
	cv::MatIterator_<float> img_it = img.begin();

	int wInt = img.cols;
	int hInt = img.rows;

	for (int i = 0; i < wInt; ++i)
	{
		for (int j = 0; j < hInt; ++j, ++mask_it, ++img_it)
		{
			// if is within mask
			if (*mask_it)
			{
				// assign the feature to image if it is within the mask
				*img_it = (float)*feature_it++;
			}
		}
	}
	img = img.t();

	int cnn_layer = 0;
	int fully_connected_layer = 0;

	vector<cv::Mat_<float> > input_maps;
	input_maps.push_back(img);

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

	for (size_t layer = 0; layer < cnn_layer_types[view_id].size(); ++layer)
	{
		// Determine layer type
		int layer_type = cnn_layer_types[view_id][layer];

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

				// TODO can TBB-ify this
				for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][in].size(); ++k)
				{
					cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];

					// The convolution (with precomputation)
					cv::Mat_<float> output;
					if (cnn_convolutional_layers_dft[view_id][cnn_layer][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);

						cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first;
						cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second;
					}
					else
					{
						std::map<int, cv::Mat_<double> > precomputed_dft;
						precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][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 < cnn_convolutional_layers[view_id][cnn_layer][0].size(); ++k)
			{
				outputs[k] = outputs[k] + cnn_convolutional_layers_bias[view_id][cnn_layer][k];
			}
			cnn_layer++;
		}
		if (layer_type == 1)
		{
			vector<cv::Mat_<float>> outputs_sub;

			// Iterate over pool height and width, all the stride is 2x2 and no padding is used
			int stride_x = 2;
			int stride_y = 2;

			int pool_x = 2;
			int pool_y = 2;

			for (size_t in = 0; in < input_maps.size(); ++in)
			{
				int out_x = input_maps[in].cols / stride_x;
				int out_y = input_maps[in].rows / stride_y;

				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)
				{
					for (int y = 0; y < input_maps[in].rows; y += stride_y)
					{
						float curr_max = -FLT_MAX;
						for (int x_in = x; x_in < x + pool_x; ++x_in)
						{
							for (int y_in = y; y_in < y + pool_y; ++y_in)
							{
								float curr_val = in_map.at<float>(y_in, x_in);
								if (curr_val > curr_max)
								{
									curr_max = curr_val;
								}
							}
						}
						int x_in_out = x / stride_x;
						int y_in_out = y / stride_y;
						sub_out.at<float>(y_in_out, x_in_out) = curr_max;
					}
				}

				outputs_sub.push_back(sub_out);

			}
			outputs = outputs_sub;
		}
		if (layer_type == 2)
		{
			// Concatenate all the maps
			cv::Mat_<float> input_concat = input_maps[0].t();
			input_concat = input_concat.reshape(0, 1);

			for (size_t in = 1; in < input_maps.size(); ++in)
			{
				cv::Mat_<float> add = input_maps[in].t();
				add = add.reshape(0, 1);
				cv::hconcat(input_concat, add, input_concat);
			}

			input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer];
			input_concat = input_concat + cnn_fully_connected_layers_biases[view_id][fully_connected_layer].t();

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

			fully_connected_layer++;
		}
		if (layer_type == 3) // ReLU
		{
			outputs.clear();
			for (size_t k = 0; k < input_maps.size(); ++k)
			{
				// Apply the ReLU
				cv::threshold(input_maps[k], input_maps[k], 0, 0, cv::THRESH_TOZERO);
				outputs.push_back(input_maps[k]);

			}
		}
		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
		input_maps = outputs;

	}

	// First turn to the 0-3 range
	double max_val = 0;
	cv::Point max_loc;
	cv::minMaxLoc(outputs[0].t(), 0, &max_val, 0, &max_loc);
	int max_idx = max_loc.y;
	double max = 3;
	double min = 0;
	double bins = (double)outputs[0].cols;
	// Unquantizing the softmax layer to continuous value
	double step_size = (max - min) / bins; // This should be saved somewhere
	double unquantized = min + step_size / 2.0 + max_idx * step_size;

	// Turn it to -1, 1 range
	double dec = (unquantized - 1.5) / 1.5;

	return dec;
}

void DetectionValidator::NormaliseWarpedToVector(const cv::Mat_<double>& warped_img, cv::Mat_<double>& feature_vec, int view_id)
{
	cv::Mat_<double> warped_t = warped_img.t();
	
	// the vector to be filled with paw values
	cv::MatIterator_<double> vp;	
	cv::MatIterator_<double>  cp;

	cv::Mat_<double> vec(paws[view_id].number_of_pixels,1);
	vp = vec.begin();

	cp = warped_t.begin();		

	int wInt = warped_img.cols;
	int hInt = warped_img.rows;

	// the mask indicating if point is within or outside the face region
	
	cv::Mat maskT = paws[view_id].pixel_mask.t();

	cv::MatIterator_<uchar>  mp = maskT.begin<uchar>();

	for(int i=0; i < wInt; ++i)
	{
		for(int j=0; j < hInt; ++j, ++mp, ++cp)
		{
			// if is within mask
			if(*mp)
			{
				*vp++ = *cp;
			}
		}
	}

	// Local normalisation
	cv::Scalar mean;
	cv::Scalar std;
	cv::meanStdDev(vec, mean, std);

	// subtract the mean image
	vec -= mean[0];

	// Normalise the image
	if(std[0] == 0)
	{
		std[0] = 1;
	}
	
	vec /= std[0];

	// Global normalisation
	feature_vec = (vec - mean_images[view_id])  / standard_deviations[view_id];
}

// Getting the closest view center based on orientation
int DetectionValidator::GetViewId(const cv::Vec3d& orientation) const
{
	int id = 0;

	double dbest = -1.0;

	for(size_t i = 0; i < this->orientations.size(); i++)
	{
	
		// Distance to current view
		double d = cv::norm(orientation, this->orientations[i]);

		if(i == 0 || d < dbest)
		{
			dbest = d;
			id = i;
		}
	}
	return id;
	
}