///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
// all rights reserved.
//
// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
//
// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.  
// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
//
// License can be found in OpenFace-license.txt
//
//     * 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 <Face_utils.h>

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

// For FHOG visualisation
#include <dlib/opencv.h>

using namespace std;

namespace FaceAnalysis
{

	// Pick only the more stable/rigid points under changes of expression
	void extract_rigid_points(cv::Mat_<double>& source_points, cv::Mat_<double>& destination_points)
	{
		if(source_points.rows == 68)
		{
			cv::Mat_<double> tmp_source = source_points.clone();
			source_points = cv::Mat_<double>();

			// Push back the rigid points (some face outline, eyes, and nose)
			source_points.push_back(tmp_source.row(1));
			source_points.push_back(tmp_source.row(2));
			source_points.push_back(tmp_source.row(3));
			source_points.push_back(tmp_source.row(4));
			source_points.push_back(tmp_source.row(12));
			source_points.push_back(tmp_source.row(13));
			source_points.push_back(tmp_source.row(14));
			source_points.push_back(tmp_source.row(15));
			source_points.push_back(tmp_source.row(27));
			source_points.push_back(tmp_source.row(28));
			source_points.push_back(tmp_source.row(29));
			source_points.push_back(tmp_source.row(31));
			source_points.push_back(tmp_source.row(32));
			source_points.push_back(tmp_source.row(33));
			source_points.push_back(tmp_source.row(34));
			source_points.push_back(tmp_source.row(35));
			source_points.push_back(tmp_source.row(36));
			source_points.push_back(tmp_source.row(39));
			source_points.push_back(tmp_source.row(40));
			source_points.push_back(tmp_source.row(41));
			source_points.push_back(tmp_source.row(42));
			source_points.push_back(tmp_source.row(45));
			source_points.push_back(tmp_source.row(46));
			source_points.push_back(tmp_source.row(47));

			cv::Mat_<double> tmp_dest = destination_points.clone();
			destination_points = cv::Mat_<double>();

			// Push back the rigid points
			destination_points.push_back(tmp_dest.row(1));
			destination_points.push_back(tmp_dest.row(2));
			destination_points.push_back(tmp_dest.row(3));
			destination_points.push_back(tmp_dest.row(4));
			destination_points.push_back(tmp_dest.row(12));
			destination_points.push_back(tmp_dest.row(13));
			destination_points.push_back(tmp_dest.row(14));
			destination_points.push_back(tmp_dest.row(15));
			destination_points.push_back(tmp_dest.row(27));
			destination_points.push_back(tmp_dest.row(28));
			destination_points.push_back(tmp_dest.row(29));
			destination_points.push_back(tmp_dest.row(31));
			destination_points.push_back(tmp_dest.row(32));
			destination_points.push_back(tmp_dest.row(33));
			destination_points.push_back(tmp_dest.row(34));
			destination_points.push_back(tmp_dest.row(35));
			destination_points.push_back(tmp_dest.row(36));
			destination_points.push_back(tmp_dest.row(39));
			destination_points.push_back(tmp_dest.row(40));
			destination_points.push_back(tmp_dest.row(41));
			destination_points.push_back(tmp_dest.row(42));
			destination_points.push_back(tmp_dest.row(45));
			destination_points.push_back(tmp_dest.row(46));
			destination_points.push_back(tmp_dest.row(47));
		}
	}

	// Aligning a face to a common reference frame
	void AlignFace(cv::Mat& aligned_face, const cv::Mat& frame, const LandmarkDetector::CLNF& clnf_model, bool rigid, double sim_scale, int out_width, int out_height)
	{
		// Will warp to scaled mean shape
		cv::Mat_<double> similarity_normalised_shape = clnf_model.pdm.mean_shape * sim_scale;
	
		// Discard the z component
		similarity_normalised_shape = similarity_normalised_shape(cv::Rect(0, 0, 1, 2*similarity_normalised_shape.rows/3)).clone();

		cv::Mat_<double> source_landmarks = clnf_model.detected_landmarks.reshape(1, 2).t();
		cv::Mat_<double> destination_landmarks = similarity_normalised_shape.reshape(1, 2).t();

		// Aligning only the more rigid points
		if(rigid)
		{
			extract_rigid_points(source_landmarks, destination_landmarks);
		}

		cv::Matx22d scale_rot_matrix = LandmarkDetector::AlignShapesWithScale(source_landmarks, destination_landmarks);
		cv::Matx23d warp_matrix;

		warp_matrix(0,0) = scale_rot_matrix(0,0);
		warp_matrix(0,1) = scale_rot_matrix(0,1);
		warp_matrix(1,0) = scale_rot_matrix(1,0);
		warp_matrix(1,1) = scale_rot_matrix(1,1);

		double tx = clnf_model.params_global[4];
		double ty = clnf_model.params_global[5];

		cv::Vec2d T(tx, ty);
		T = scale_rot_matrix * T;

		// Make sure centering is correct
		warp_matrix(0,2) = -T(0) + out_width/2;
		warp_matrix(1,2) = -T(1) + out_height/2;

		cv::warpAffine(frame, aligned_face, warp_matrix, cv::Size(out_width, out_height), cv::INTER_LINEAR);
	}

	// Aligning a face to a common reference frame
	void AlignFaceMask(cv::Mat& aligned_face, const cv::Mat& frame, const LandmarkDetector::CLNF& clnf_model, const cv::Mat_<int>& triangulation, bool rigid, double sim_scale, int out_width, int out_height)
	{
		// Will warp to scaled mean shape
		cv::Mat_<double> similarity_normalised_shape = clnf_model.pdm.mean_shape * sim_scale;
	
		// Discard the z component
		similarity_normalised_shape = similarity_normalised_shape(cv::Rect(0, 0, 1, 2*similarity_normalised_shape.rows/3)).clone();

		cv::Mat_<double> source_landmarks = clnf_model.detected_landmarks.reshape(1, 2).t();
		cv::Mat_<double> destination_landmarks = similarity_normalised_shape.reshape(1, 2).t();

		// Aligning only the more rigid points
		if(rigid)
		{
			extract_rigid_points(source_landmarks, destination_landmarks);
		}

		cv::Matx22d scale_rot_matrix = LandmarkDetector::AlignShapesWithScale(source_landmarks, destination_landmarks);
		cv::Matx23d warp_matrix;

		warp_matrix(0,0) = scale_rot_matrix(0,0);
		warp_matrix(0,1) = scale_rot_matrix(0,1);
		warp_matrix(1,0) = scale_rot_matrix(1,0);
		warp_matrix(1,1) = scale_rot_matrix(1,1);

		double tx = clnf_model.params_global[4];
		double ty = clnf_model.params_global[5];

		cv::Vec2d T(tx, ty);
		T = scale_rot_matrix * T;

		// Make sure centering is correct
		warp_matrix(0,2) = -T(0) + out_width/2;
		warp_matrix(1,2) = -T(1) + out_height/2;

		cv::warpAffine(frame, aligned_face, warp_matrix, cv::Size(out_width, out_height), cv::INTER_LINEAR);

		// Move the destination landmarks there as well
		cv::Matx22d warp_matrix_2d(warp_matrix(0,0), warp_matrix(0,1), warp_matrix(1,0), warp_matrix(1,1));
		
		destination_landmarks = cv::Mat(clnf_model.detected_landmarks.reshape(1, 2).t()) * cv::Mat(warp_matrix_2d).t();

		destination_landmarks.col(0) = destination_landmarks.col(0) + warp_matrix(0,2);
		destination_landmarks.col(1) = destination_landmarks.col(1) + warp_matrix(1,2);
		
		// Move the eyebrows up to include more of upper face
		destination_landmarks.at<double>(0,1) -= (30/0.7)*sim_scale; 
		destination_landmarks.at<double>(16,1) -= (30 / 0.7)*sim_scale;

		destination_landmarks.at<double>(17,1) -= (30 / 0.7)*sim_scale;
		destination_landmarks.at<double>(18,1) -= (30 / 0.7)*sim_scale;
		destination_landmarks.at<double>(19,1) -= (30 / 0.7)*sim_scale;
		destination_landmarks.at<double>(20,1) -= (30 / 0.7)*sim_scale;
		destination_landmarks.at<double>(21,1) -= (30 / 0.7)*sim_scale;
		destination_landmarks.at<double>(22,1) -= (30 / 0.7)*sim_scale;
		destination_landmarks.at<double>(23,1) -= (30 / 0.7)*sim_scale;
		destination_landmarks.at<double>(24,1) -= (30 / 0.7)*sim_scale;
		destination_landmarks.at<double>(25,1) -= (30 / 0.7)*sim_scale;
		destination_landmarks.at<double>(26,1) -= (30 / 0.7)*sim_scale;

		destination_landmarks = cv::Mat(destination_landmarks.t()).reshape(1, 1).t();

		LandmarkDetector::PAW paw(destination_landmarks, triangulation, 0, 0, aligned_face.cols-1, aligned_face.rows-1);
		
		// Mask each of the channels (a bit of a roundabout way, but OpenCV 3.1 in debug mode doesn't seem to be able to handle a more direct way using split and merge)
		vector<cv::Mat> aligned_face_channels(aligned_face.channels());
		
		for (int c = 0; c < aligned_face.channels(); ++c)
		{
			cv::extractChannel(aligned_face, aligned_face_channels[c], c);
		}

		for(size_t i = 0; i < aligned_face_channels.size(); ++i)
		{
			cv::multiply(aligned_face_channels[i], paw.pixel_mask, aligned_face_channels[i], 1.0, CV_8U);
		}

		if(aligned_face.channels() == 3)
		{
			cv::Mat planes[] = { aligned_face_channels[0], aligned_face_channels[1], aligned_face_channels[2] };
			cv::merge(planes, 3, aligned_face);
		}
		else
		{
			aligned_face = aligned_face_channels[0];
		}
	}


	void Visualise_FHOG(const cv::Mat_<double>& descriptor, int num_rows, int num_cols, cv::Mat& visualisation)
	{

		// First convert to dlib format
		dlib::array2d<dlib::matrix<float,31,1> > hog(num_rows, num_cols);
		
		cv::MatConstIterator_<double> descriptor_it = descriptor.begin();
		for(int y = 0; y < num_cols; ++y)
		{
			for(int x = 0; x < num_rows; ++x)
			{
				for(unsigned int o = 0; o < 31; ++o)
				{
					hog[y][x](o) = *descriptor_it++;
				}
			}
		}

		// Draw the FHOG to OpenCV format
		auto fhog_vis = dlib::draw_fhog(hog);
		visualisation = dlib::toMat(fhog_vis).clone();
	}

	// Create a row vector Felzenszwalb HOG descriptor from a given image
	void Extract_FHOG_descriptor(cv::Mat_<double>& descriptor, const cv::Mat& image, int& num_rows, int& num_cols, int cell_size)
	{
		
		dlib::array2d<dlib::matrix<float,31,1> > hog;
		if(image.channels() == 1)
		{
			dlib::cv_image<uchar> dlib_warped_img(image);
			dlib::extract_fhog_features(dlib_warped_img, hog, cell_size);
		}
		else
		{
			dlib::cv_image<dlib::bgr_pixel> dlib_warped_img(image);
			dlib::extract_fhog_features(dlib_warped_img, hog, cell_size);
		}

		// Convert to a usable format
		num_cols = hog.nc();
		num_rows = hog.nr();

		descriptor = cv::Mat_<double>(1, num_cols * num_rows * 31);
		cv::MatIterator_<double> descriptor_it = descriptor.begin();
		for(int y = 0; y < num_cols; ++y)
		{
			for(int x = 0; x < num_rows; ++x)
			{
				for(unsigned int o = 0; o < 31; ++o)
				{
					*descriptor_it++ = (double)hog[y][x](o);
				}
			}
		}
	}

	// Extract summary statistics (mean, stdev, min, max) from each dimension of a descriptor, each row is a descriptor
	void ExtractSummaryStatistics(const cv::Mat_<double>& descriptors, cv::Mat_<double>& sum_stats, bool use_mean, bool use_stdev, bool use_max_min)
	{
		// Using four summary statistics at the moment 
		// Means, stds, mins, maxs
		int num_stats = 0;

		if(use_mean)
			num_stats++;

		if(use_stdev)
			num_stats++;

		if(use_max_min)
			num_stats++;

		sum_stats = cv::Mat_<double>(1, descriptors.cols * num_stats, 0.0);
		for(int i = 0; i < descriptors.cols; ++i)
		{
			cv::Scalar mean, stdev;
			cv::meanStdDev(descriptors.col(i), mean, stdev);

			int add = 0;

			if(use_mean)
			{
				sum_stats.at<double>(0, i*num_stats + add) = mean[0];
				add++;
			}

			if(use_stdev)
			{
				sum_stats.at<double>(0, i*num_stats + add) = stdev[0];
				add++;
			}

			if(use_max_min)
			{
				double min, max;
				cv::minMaxIdx(descriptors.col(i), &min, &max);
				sum_stats.at<double>(0, i*num_stats + add) = max - min;
				add++;
			}
		}		
	}

	void AddDescriptor(cv::Mat_<double>& descriptors, cv::Mat_<double> new_descriptor, int curr_frame, int num_frames_to_keep)
	{
		if(descriptors.empty())
		{
			descriptors = cv::Mat_<double>(num_frames_to_keep, new_descriptor.cols, 0.0);
		}

		int row_to_change = curr_frame % num_frames_to_keep;

		new_descriptor.copyTo(descriptors.row(row_to_change));
	}	

}