#include "stdafx.h"
#include "DarkChannelDehaze.h"

DarkChannelDehaze::DarkChannelDehaze(void):block(7),w(0.9)
{
	ROI_rect.x = 0;
	ROI_rect.y = 0;
	double min_channel1 = 0;
	double min_channel2 = 0;
	double min_channel3 = 0;
	double max_channel1 = 0;
	double max_channel2 = 0;
	double max_channel3 = 0;
	double min = 0;
	
	double A_final = 10;
}


DarkChannelDehaze::~DarkChannelDehaze(void)
{
}

void DarkChannelDehaze::doDehaze( const Mat& src, Mat& dst )
{
	dcc.setTo(cv::Scalar::all(0));
	cv::split(src, dsts);
	ROI_rect.width = block;
	ROI_rect.height = block;

	Mat Vm, Vop,Vnorm;
	uchar pixel;
	cv::Scalar e2x,ex2;
	cv::Rect meanRect;
	meanRect.width = 5;
	meanRect.height = 5;
	Vm.create(src.size(), CV_8UC1);
	Vm.setTo(cv::Scalar::all(0));
	for(int i = 0;i<src.rows;i++)
	{
		for(int j = 0;j<src.cols;j++)
		{
			pixel = min(dsts[1].at<uchar>(i,j),dsts[0].at<uchar>(i,j));
			pixel = min(pixel,dsts[2].at<uchar>(i,j));
			Vm.at<uchar>(i,j) = pixel;
		}
	}

	Vnorm = Vm.clone();
	Sobel(Vm,Vop,-1,1,1,3,1,0,BORDER_DEFAULT);
	Mat element = getStructuringElement(MORPH_RECT,Size(15, 15));
	morphologyEx(Vm,Vnorm,MORPH_OPEN,element);
	normalize(Vop, Vop, 255,0,NORM_MINMAX);

	for(int row = 0;row<src.rows;row++)
	{
		for(int col = 0;col<src.cols;col++)
		{
			dcc.at<uchar>(row,col) = (Vm.at<uchar>(row,col)*Vop.at<uchar>(row,col)+Vnorm.at<uchar>(row,col)*(255-Vop.at<uchar>(row,col)))/255;

		}
	}

	Adcc = dcc.clone();

	cv::Point minloc, maxloc;
	cv::minMaxLoc(Adcc, &min_dark, &max_dark, &minloc, &maxloc);
	ROI_rect.x = maxloc.x;
	ROI_rect.y = maxloc.y;
	ROI_rect.width = block;
	ROI_rect.height = block;
	ROI_rect = ROI_rect&(cv::Rect(0, 0, src.cols, src.rows));

	imgroi1 = dsts[0](ROI_rect).clone();

	cv::minMaxLoc(imgroi1, &dst1_min, &A_dst1, NULL, NULL);
	
	imgroi2 = dsts[1](ROI_rect).clone();
	
	cv::minMaxLoc(imgroi2, &dst2_min, &A_dst2, NULL, NULL);
	
	imgroi3 = dsts[2](ROI_rect).clone();

	cv::minMaxLoc(imgroi3, &dst3_min, &A_dst3, NULL, NULL);
	
	
	
	if ((src.cols<=100)&&(src.rows<=100))
	{
		double td = 20;
	}
	else
	{
		 td = 120;
	}
	
	if(A_dst1 > A_dst2)
		Amax = A_dst1;
	else
		Amax = A_dst2;
	if(Amax > A_dst3)
		Amax = A_dst3;
	A1 = abs(Amax - A_final);

	if (A1 >= td)
		
	    A_final = Amax;

#pragma omp parallel for
	for (int row = 0; row < src.rows; row++)
	{
		for (int col = 0; col < src.cols; col++)
		{
			toushelv.at<uchar>(row, col) = static_cast<uchar>((1.0 - w * dcc.at<uchar>(row, col)/A_final) * 255.0);
		}
	}
	int gfresize = 4;
	cv::resize(toushelv, dstpinghua,cv::Size(src.cols/gfresize, src.rows/gfresize));
	cv::resize(src, Ihalf, cv::Size(src.cols/gfresize, src.rows/gfresize));

	pinghua = guidedFilter(Ihalf, dstpinghua, 7, 0.01);
 
	double sumtxd = 0;
	for (int row = 0; row < dst.rows; row++)
	{
		for (int col = 0; col < dst.cols; col++)
		{
			sumtxd = sumtxd + pinghua.at<double>(row/gfresize, col/gfresize);
		}
	}
	double avetxd = sumtxd/(dst.rows * dst.cols);
	double adjust = 1.5 * avetxd * A_final;

	//ÇóÈ¡ÎÞÎíͼÏñ
#pragma omp parallel for
	for (int row = 0; row < dst.rows; row++)
	{
		for (int col = 0; col < dst.cols; col++)
		{
			double txd;
			txd = pinghua.at<double>(row/gfresize, col/gfresize);//
			txd = txd/255.0;
			if(txd < 0.1)
				txd = 0.1;
			const cv::Vec3b& ixv = src.at<cv::Vec3b>(row, col);

			double v1 = (((double)(ixv.val[0]) - A_final)/txd + A_final);
			double v2 = (((double)(ixv.val[1]) - A_final)/txd + A_final);
			double v3 = (((double)(ixv.val[2]) - A_final)/txd + A_final);
		    v1 = (v1 * (255 + adjust))/(v1 + adjust);
			v2 = (v2 * (255 + adjust))/(v2 + adjust);
			v3 = (v3 * (255 + adjust))/(v3 + adjust);
			v1 = v1<1.0?1.0:v1>254.0?254:v1;
			v2 = v2<1.0?1.0:v2>254.0?254:v2;
			v3 = v3<1.0?1.0:v3>254.0?254:v3;
			cv::Vec3d val(v1, v2, v3);
			dst.at<cv::Vec3b>(row, col) = val;//cv::Vec3b(txd*255,txd*255,txd*255);
		}
	}

}

cv::Mat DarkChannelDehaze::calDev(Mat src, Rect meanRect)
{
	int sum_value,sum_value2;
	double e1,e2;
	Mat Vop;

	#pragma omp parallel for
	for(int ii = 0;ii<src.rows-meanRect.width;ii++)
	{
		for(int jj = 0;jj<src.cols-meanRect.height;jj++)
		{
			meanRect.x = jj;
			meanRect.y = ii;
			sum_value = 0;
			sum_value2 = 0;
			e1 = 0.0;
			e2 = 0.0;
			for (int i = 0;i<meanRect.width;i++)
			{
				for (int j = 0;j<meanRect.height;j++)
				{
					sum_value += src(meanRect).at<uchar>(i,j);
					sum_value2 += src(meanRect).at<uchar>(i,j)*src(meanRect).at<uchar>(i,j);

				}
			}
			e1 = sum_value/(meanRect.width*meanRect.height);
			e2 = sum_value2/(meanRect.width*meanRect.height);
			Vop.create(src.size(), CV_32FC1);
			Vop.setTo(cv::Scalar::all(0.0));
			
			Vop.at<float>(ii,jj) = static_cast<float>(e2 - e1);
		}
	}
	normalize(Vop,Vop,255.0,0.0,NORM_MINMAX);
	return Vop;
}

void DarkChannelDehaze::calcDarkChannel( const Mat& src, Mat& dark )
{
	std::vector<Mat> rgbs;
	cv::split(src, rgbs);

	int totalrow = ((src.rows)/block);
	totalrow += ((src.rows) % block == 0 ? 0 : 1);
	int totalcol = ((src.cols) / block);
	totalcol += ((src.cols) % block == 0)?0:1;
	Mat block_dark = Mat(totalrow, totalcol, CV_8UC1);
	block_dark.setTo(cv::Scalar::all(0));
	cv::Rect imgRect(0, 0, src.cols, src.rows);

	#pragma omp parallel for
	for (int row = 0; row < totalrow; row++)
	{
		for (int col = 0;col < totalcol;col++)
		{
			cv::Rect block_rect(col*block, row*block, block, block);
			block_rect = block_rect&imgRect;
			cv::Vec3d mins, maxs;
			for (size_t i = 0;i < rgbs.size();i++)
			{
				cv::minMaxLoc(rgbs[i](block_rect), mins.val + i, maxs.val + i, NULL, NULL);
			}
			
			uchar min = 0;
			if(mins.val[0] < mins.val[1])
			{
				min = static_cast<uchar>(mins.val[0]);
			}
			else
			{
				min = static_cast<uchar>(mins.val[1]);
			}

			if(min > mins.val[2])
			{
				min = static_cast<uchar>(mins.val[2]);
			}

			block_dark.at<uchar>(row,col) = min;
		}
	}

	dark.create(src.size(), CV_8UC1);
	dark.setTo(cv::Scalar::all(0));
	
	#pragma omp parallel for
	for (int row = 0; row < dark.rows; row++)
	{
		for (int col = 0; col < dark.cols; col++)
		{
			int rrow = row/block;
			int rcol = col/block;
			dark.at<uchar>(row, col) = block_dark.at<uchar>(rrow, rcol);
		}
	}
}

void DarkChannelDehaze::Init(int width, int height)
{
	dcc.create(height, width, CV_8UC1);
	toushelv.create(height, width, CV_8UC1);
}

cv::Mat DarkChannelDehaze::guidedFilter( cv::Mat I, cv::Mat ip, int rid, double eps )
{
	cv::Mat _I;
	cv::Mat _ip;
	cvtColor(I, _I, CV_BGR2GRAY);
	_I.convertTo(I, CV_64FC1);
	ip.convertTo(_ip, CV_64FC1);
	ip = _ip;
	
	int hei = I.rows;
	int wid = I.cols;

	cv::Mat N;
	cv::boxFilter(cv::Mat::ones(hei, wid, I.type()), N, CV_64FC1, cv::Size(rid, rid));

	cv::Mat mean_I;
	cv::boxFilter(I, mean_I, CV_64FC1, cv::Size(rid, rid));

	cv::Mat mean_p;
	cv::boxFilter(ip, mean_p, CV_64FC1, cv::Size(rid, rid));

	cv::Mat mean_Ip;
	cv::boxFilter(I.mul(ip), mean_Ip, CV_64FC1, cv::Size(rid, rid));

	cv::Mat cov_Ip = mean_Ip - mean_I.mul(mean_p);

	cv::Mat mean_II;
	cv::boxFilter(I.mul(I), mean_II, CV_64FC1, cv::Size(rid, rid));

	cv::Mat var_I = mean_II - mean_I.mul(mean_I);
	cv::Mat amask = cov_Ip/(var_I + eps);

	cv::Mat bmask = mean_p - amask.mul(mean_I);

	cv::Mat mean_a;
	cv::boxFilter(amask, mean_a, CV_64FC1, cv::Size(rid, rid));
	mean_a = mean_a/N;

	cv::Mat mean_b;
	cv::boxFilter(bmask, mean_b, CV_64FC1, cv::Size(rid, rid));
	mean_a = mean_a/N;

	cv::Mat pinghua = mean_a.mul(I) + mean_b;

	return pinghua;
}