/* -*- mia-c++  -*-
 *
 * This file is part of MIA - a toolbox for medical image analysis 
 * Copyright (c) Leipzig, Madrid 1999-2015 Gert Wollny
 *
 * MIA is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with MIA; if not, see <http://www.gnu.org/licenses/>.
 *
 */

#include <mia/2d/cost/lncc.hh>

#include <mia/core/threadedmsg.hh>
#include <mia/core/nccsum.hh>
#include <tbb/parallel_reduce.h>
#include <tbb/blocked_range.h>


NS_BEGIN(NS)

using namespace mia; 
using std::vector; 
using std::pair; 
using std::make_pair; 

CLNCC2DImageCost::CLNCC2DImageCost(int hw):
m_hwidth(hw)
{
}

inline pair<C2DBounds, C2DBounds> prepare_range(const C2DBounds& size, int cx, int cy, int hw) 
{
	int yb = cy - hw;
	if (yb < 0) yb = 0; 
	unsigned ye = cy + hw + 1; 
	if (ye > size.y) ye = size.y; 
	
	int xb = cx - hw;
	if (xb < 0) xb = 0; 
	unsigned xe = cx + hw + 1; 
	if (xe > size.x) xe = size.x; 
	
	return make_pair(C2DBounds(xb,yb), C2DBounds(xe,ye)); 
}



class FEvalCost : public TFilter<float> {
	int m_hw;
public:
	FEvalCost(int hw):
		m_hw(hw)
		{}
	
	template <typename T, typename R> 
	float operator () ( const T& mov, const R& ref) const {
		auto evaluate_local_cost = [this, &mov, &ref](const tbb::blocked_range<size_t>& range, const pair<float, int>& result) -> pair<float, int> {
			CThreadMsgStream msks; 
			float lresult = 0.0; 
			int count = 0; 
			
			for (auto y = range.begin(); y != range.end(); ++y) {
				for (size_t x = 0; x < mov.get_size().x; ++x) {
					
					auto c_block = prepare_range(mov.get_size(), x, y, m_hw); 
					auto ia = mov.begin_range(c_block.first,c_block.second); 
					auto ea = mov.end_range(c_block.first,c_block.second); 
					auto ib = ref.begin_range(c_block.first,c_block.second); 
					
					auto delta_size = c_block.second - c_block.first; 
					auto n = delta_size.product(); 
					
					if (n > 1) {
						
						NCCSums sum; 
						
						while (ia != ea) {
							sum.add(*ia, *ib); 
							++ia; ++ib; 
						}
						
						lresult += sum.value(); 
						++count; 
					}				}
			}
			return make_pair(result.first + lresult, result.second + count); 
		};
		
		pair<float,int> init{0, 0}; 
		auto r = parallel_reduce(tbb::blocked_range<size_t>(0, mov.get_size().y, 1), init, evaluate_local_cost, 
					 [](const pair<float,int>& x, const pair<float,int>& y){
						 return make_pair(x.first + y.first, x.second + y.second);
					 });	
		return r.second > 0 ? r.first / r.second : 0.0; 
	}
}; 


double CLNCC2DImageCost::do_value(const Data& a, const Data& b) const
{
	FEvalCost ecost(m_hwidth); 
	return mia::filter(ecost, a, b); 
}


class FEvalCostForce : public TFilter<float> {
	int m_hw;
	C2DFVectorfield& m_force; 
public: 
	FEvalCostForce(int hw, C2DFVectorfield& force):
		m_hw(hw), 
		m_force(force)
		{}
	
	template <typename T, typename R> 
	float operator () ( const T& mov, const R& ref) const {
		auto ag = get_gradient(mov); 
		auto evaluate_local_cost_force = [this, &mov, &ref, &ag](const tbb::blocked_range<size_t>& range, 
									 const pair<float, int>& result) -> pair<float, int> {
			
			CThreadMsgStream msks; 		
			float lresult = 0.0; 
			int count = 0; 
			for (auto y = range.begin(); y != range.end(); ++y) {
                        
				auto iforce = m_force.begin_at(0,y);
				auto ig = ag.begin_at(0,y);
				auto imov = mov.begin_at(0,y);
				auto iref = ref.begin_at(0,y);
                        
				for (size_t x = 0; x < mov.get_size().x; ++x, ++iforce, ++ig, ++iref, ++imov) {
					auto c_block = prepare_range(mov.get_size(), x, y, m_hw); 
					auto ia = mov.begin_range(c_block.first,c_block.second); 
					auto ea = mov.end_range(c_block.first,c_block.second); 
					auto ib = ref.begin_range(c_block.first,c_block.second); 
					
					auto delta_size = c_block.second - c_block.first; 
					auto n = delta_size.product(); 
					
					if (n > 1) {
						NCCSums sum; 
						
						while (ia != ea) {
							sum.add(*ia, *ib); 
							++ia; ++ib; 
						}
						
						auto res = sum.get_grad_helper(); 
						lresult += res.first;
						*iforce = res.second.get_gradient_scale(*imov, *iref) * *ig; 
						++count; 
					}
				}
			}
			return make_pair(result.first + lresult, result.second + count); 
		};
		pair<float,int> init{0, 0}; 		
		auto r = parallel_reduce(tbb::blocked_range<size_t>(0, mov.get_size().y, 1), init, evaluate_local_cost_force, 
					 [](const pair<float,int>& x, const pair<float,int>& y){
						 return make_pair(x.first + y.first, x.second + y.second);
					 });
		
		return r.second > 0 ? r.first / r.second : 0.0; 
	}
	
};

double CLNCC2DImageCost::do_evaluate_force(const Data& a, const Data& b, Force& force) const
{
	FEvalCostForce ecostforce(m_hwidth, force); 
	return mia::filter(ecostforce, a, b); 
}


CLNCC2DImageCostPlugin::CLNCC2DImageCostPlugin():
        C2DImageCostPlugin("lncc"), 
	m_hw(5)
{
	this->add_parameter("w", new CUIBoundedParameter(m_hw, EParameterBounds::bf_closed_interval, {1, 256}, false, 
							 "half width of the window used for evaluating the localized cross correlation")); 
}

C2DImageCost *CLNCC2DImageCostPlugin::do_create() const
{
	return new CLNCC2DImageCost(m_hw);
}

const std::string CLNCC2DImageCostPlugin::do_get_descr() const
{
	return "local normalized cross correlation with masking support."; 
}


extern "C" EXPORT CPluginBase *get_plugin_interface()
{
	return new CLNCC2DImageCostPlugin();
}

NS_END
