#include "ImageApplyOutHole.h"
#include "ImageProcess_Public.h"

#ifdef LOG
#include "Device/filetools.h"
#endif // LOG

CImageApplyOutHole::CImageApplyOutHole(void)
	: CImageApply()
	, m_borderSize(600)
	, m_edgeScale(0.1f)
	, m_threshold(100)
{
}

CImageApplyOutHole::CImageApplyOutHole(float borderSize, float edgeScale, double threshold)
	: CImageApply()
	, m_borderSize(borderSize)
	, m_edgeScale(edgeScale)
	, m_threshold(threshold)
{
}

CImageApplyOutHole::~CImageApplyOutHole(void)
{
}

void CImageApplyOutHole::apply(cv::Mat& pDib, int side)
{
	(void)pDib;
	(void)side;
}

void CImageApplyOutHole::apply(std::vector<cv::Mat>& mats, bool isTwoSide)
{
#ifdef LOG
	FileTools::write_log("imgprc.txt", "enter ImageOutHole apply");
#endif // LOG

	if (mats.size() < 2)
	{
#ifdef LOG
		FileTools::write_log("imgprc.txt", "exit ImageOutHole apply");
#endif // LOG
		return;
	}

	if (mats[0].empty() || mats[1].empty())
	{
#ifdef LOG
		FileTools::write_log("imgprc.txt", "exit ImageOutHole apply");
#endif // LOG
		return;
	}

	//二值化正反面图像
	cv::Mat front = mats[0];
	cv::Mat back = mats[1];
	cv::Mat front_thre, back_thre;
	hg::threshold_Mat(front, front_thre, m_threshold);
	hg::threshold_Mat(back, back_thre, m_threshold);

	cv::Mat element = getStructuringElement(cv::MORPH_RECT, cv::Size(10, 1));
	cv::morphologyEx(front_thre, front_thre, cv::MORPH_OPEN, element, cv::Point(-1, -1), 1, cv::BORDER_CONSTANT, cv::Scalar::all(0));
	cv::morphologyEx(back_thre, back_thre, cv::MORPH_OPEN, element, cv::Point(-1, -1), 1, cv::BORDER_CONSTANT, cv::Scalar::all(0));

	//反面二值化图像水平翻转
	cv::flip(back_thre, back_thre, 1);  //1:Horizontal

	//正反面图像寻边
	std::vector<std::vector<cv::Point>> contours_front, contours_back;
	std::vector<cv::Vec4i> b1_front, b1_back;
	hg::findContours(front_thre.clone(), contours_front, b1_front, cv::RETR_EXTERNAL);
	hg::findContours(back_thre.clone(), contours_back, b1_back, cv::RETR_EXTERNAL);

	//提取正反面图像最大轮廓
	std::vector<cv::Point> maxContour_front = hg::getMaxContour(contours_front, b1_front);
	std::vector<cv::Point> maxContour_back = hg::getMaxContour(contours_back, b1_back);

	cv::RotatedRect rrect_front = hg::getBoundingRect(maxContour_front);          //提取正面最大轮廓的最小外接矩形
	cv::RotatedRect rrect_back = hg::getBoundingRect(maxContour_back);            //提取反面最大轮廓的最小外接矩形

	//如果正反面图像尺寸差异超过20个像素，直接放弃处理
	if (cv::abs(rrect_front.size.width - rrect_back.size.width) > 20 ||
		cv::abs(rrect_front.size.height - rrect_back.size.height) > 20)
		return;

	//提取正反面图像重叠部分区域
	cv::Rect roi_front, roi_back;
	cv::RotatedRect mask_rotatedRect;
	getRoi(rrect_front, rrect_back, cv::Size(front.cols, front.rows), roi_front, roi_back, mask_rotatedRect);

	cv::Mat roiMat_front(front_thre, roi_front);                             //在正面二值图像中截取重叠部分
	cv::Mat roiMat_back(back_thre, roi_back);                                //在反面二值图像中截取重叠部分

	//正反面二值图像做或运算，真正镂空区域保留0，其他地方填充为255
	cv::Mat mask;
	cv::bitwise_or(roiMat_front, roiMat_back, mask);                            //或运算，正反面二值图像重叠

	cv::imwrite("roiMat_front.jpg", roiMat_front);
	cv::imwrite("roiMat_back.jpg", roiMat_back);

	//二值图像重叠图像颜色取反，膨胀，提取轮廓
	cv::bitwise_not(mask, mask);												//反色

	element = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(10, 10));
	cv::dilate(mask, mask, element, cv::Point(-1, -1), 1, cv::BORDER_CONSTANT, cv::Scalar(255));      //膨胀算法，增大孔洞连通区域面积

	//为了避免孔洞彻底贯穿纸边，人为绘制纸张轮廓，确保所有孔洞为封闭图形，不会与背景粘连
	cv::polylines(mask, hg::getVertices(mask_rotatedRect), true, cv::Scalar(0), 15);                         //绘制纸张矩形边缘

	std::vector<std::vector<cv::Point>> contours_mask;
	std::vector<cv::Vec4i> b1_mask;
	hg::findContours(mask, contours_mask, b1_mask, cv::RETR_TREE);                              //提取重叠图像轮廓

	//过滤非孔洞的联通区域
	std::vector<std::vector<cv::Point>> hole_contours = filterPoly(contours_mask, b1_mask, mask_rotatedRect, m_edgeScale, m_borderSize);
	//for (size_t i = 0; i < hole_contours.size(); i++)
	//	cv::drawContours(mask, hole_contours, static_cast<int>(i), cv::Scalar(127), 2);
	//cv::imwrite("mask.jpg", mask);
	cv::Scalar color = getBackGroudColor(front(roi_front), rrect_front.size.area());
	for (size_t i = 0; i < hole_contours.size(); i++)
	{
		std::vector<std::vector<cv::Point>> contourss_temp;
		contourss_temp.push_back(hole_contours[i]);
		cv::Mat front_temp = front(roi_front);
		hg::fillPolys(front_temp, contourss_temp, color);
	}

	if (isTwoSide)
	{
		int width_ = roi_back.width;
		roi_back.x = back.cols - roi_back.width - roi_back.x;               //因为之前反面图像翻转，所以现在ROI也要进行相应翻转
		color = getBackGroudColor(back(roi_back), rrect_front.size.area());
		for (size_t i = 0; i < hole_contours.size(); i++)
		{
			std::vector<cv::Point> hole_contour;
			for (size_t j = 0; j < hole_contours[i].size(); j++)
				hole_contour.push_back(cv::Point(width_ - hole_contours[i][j].x - 1, hole_contours[i][j].y));

			std::vector<std::vector<cv::Point>> contours_temp;
			contours_temp.push_back(hole_contour);
			cv::Mat back_temp = back(roi_back);
			hg::fillPolys(back_temp, contours_temp, color);
		}
	}
#ifdef LOG
	FileTools::write_log("imgprc.txt", "exit ImageOutHole apply");
#endif // LOG
}

void CImageApplyOutHole::getRoi(cv::RotatedRect rrect_front, cv::RotatedRect rrect_back, cv::Size srcSize,
	cv::Rect& roi_front, cv::Rect& roi_back, cv::RotatedRect& mask_rotatedRect)
{
	cv::Size size(static_cast<int>(rrect_front.size.width + rrect_back.size.width) / 2, static_cast<int>(rrect_front.size.height + rrect_back.size.height) / 2);
	float angle = (rrect_front.angle + rrect_back.angle) / 2;

	rrect_front.size = rrect_back.size = size;
	rrect_front.angle = rrect_back.angle = angle;

	roi_front = rrect_front.boundingRect();
	roi_back = rrect_back.boundingRect();

	if (roi_front.width != roi_back.width || roi_front.height != roi_back.height)
	{
		roi_front.height = roi_back.height;
		roi_front.width = roi_back.width;
	}

	cv::Point offset(0, 0);
	int top = std::min(roi_front.y, roi_back.y);
	if (top < 0)
	{
		roi_front.y -= top;
		roi_back.y -= top;
		roi_front.height += top;
		roi_back.height += top;
		offset.y += top;
	}

	int left = std::min(roi_front.x, roi_back.x);
	if (left < 0)
	{
		roi_front.x -= left;
		roi_back.x -= left;
		roi_front.width += left;
		roi_back.width += left;
		offset.x += left;
	}

	int right = std::max(roi_front.x + roi_front.width, roi_back.x + roi_back.width);
	if (right >= srcSize.width)
	{
		roi_front.width -= (right - srcSize.width + 1);
		roi_back.width -= (right - srcSize.width + 1);
	}

	int bottom = std::max(roi_front.y + roi_front.height, roi_back.y + roi_back.height);
	if (bottom >= srcSize.height)
	{
		roi_front.height -= (bottom - srcSize.height + 1);
		roi_back.height -= (bottom - srcSize.height + 1);
	}

	mask_rotatedRect.center = cv::Point((roi_front.width + offset.x) / 2, (roi_front.height + offset.y) / 2);
	mask_rotatedRect.size = size;
	mask_rotatedRect.angle = angle;
}

std::vector<std::vector<cv::Point>> CImageApplyOutHole::filterPoly(std::vector<std::vector<cv::Point>>& contours, const std::vector<cv::Vec4i>& m,
	cv::RotatedRect roi, float edgeScale, float areaThreshold)
{
	edgeScale = std::min(0.49f, std::max(edgeScale, 0.0f));
	cv::RotatedRect roi2(roi.center, cv::Size(static_cast<int>(roi.size.width * (1 - edgeScale * 2)),
		static_cast<int>(roi.size.height * (1 - edgeScale * 2))), roi.angle);

	std::vector<cv::Point> vertices_roi1 = hg::getVertices(roi);
	std::vector<cv::Point> vertices_roi2 = hg::getVertices(roi2);

	std::vector<std::vector<cv::Point>> hole_contours;
	for (size_t i = 0, length = contours.size(); i < length; i++)
	{
		if (m[i][2] != -1) continue;

		cv::RotatedRect rrect = hg::getBoundingRect(contours[i]);
		if (rrect.size.area() < areaThreshold) continue;

		bool enabled = true;
		for (size_t j = 0, count = contours[i].size(); j < count; j++)
		{
			cv::Point p(contours[i][j]);
			double temp1 = pointPolygonTest(vertices_roi1, p, false);     //判断是否在纸张内      1：内；0：上；-1：外
			double temp2 = pointPolygonTest(vertices_roi2, p, false);     //判断是否在边缘区域内  1：内；0：上；-1：外
			//如果在纸张外，或者边缘内，视为非孔洞
			if (temp1 < 0 || temp2 > 0)
			{
				enabled = false;
				break;
			}
		}

		if (enabled)
			hole_contours.push_back(contours[i]);
	}
	return hole_contours;
}

cv::Scalar CImageApplyOutHole::getBackGroudColor(const cv::Mat& image, const std::vector<cv::Point> pixelPoints)
{
	if (pixelPoints.empty()) return cv::Scalar(255, 255, 255);

	int channels = image.channels();

	int temp[3] = { 0 };
	for (size_t i = 0, length = pixelPoints.size(); i < length; ++i)
	{
		int x = cv::min(cv::max(0, pixelPoints[i].x), image.cols - 1);
		int y = cv::min(cv::max(0, pixelPoints[i].y), image.rows - 1);

		const unsigned char* ptr = image.ptr(y, x);
		for (int j = 0; j < channels; ++j)
			temp[j] += ptr[j];
	}

	return cv::Scalar(temp[0] / static_cast<int>(pixelPoints.size()),
		temp[1] / static_cast<int>(pixelPoints.size()),
		temp[2] / static_cast<int>(pixelPoints.size()));
}

cv::Scalar CImageApplyOutHole::getBackGroudColor(const cv::Mat& image, int total)
{
	if (image.channels() == 3)
	{
		cv::Mat image_bgr[3];
		cv::split(image, image_bgr);

		uchar bgr[3];
		for (size_t i = 0; i < 3; i++)
			bgr[i] = getBackGroudChannelMean(image_bgr[i], total);
		return cv::Scalar(bgr[0], bgr[1], bgr[2]);
	}
	else
		return cv::Scalar::all(getBackGroudChannelMean(image, total));
}

uchar CImageApplyOutHole::getBackGroudChannelMean(const cv::Mat& gray, int total)
{
	cv::Mat image_clone;
	cv::resize(gray, image_clone, cv::Size(), 0.25, 0.25);

	int threnshold = total / 32;
	int channels[] = { 0 };
	int nHistSize[] = { 256 };
	float range[] = { 0, 256 };
	const float* fHistRanges[] = { range };
	cv::Mat hist;
	cv::calcHist(&image_clone, 1, channels, cv::Mat(), hist, 1, nHistSize, fHistRanges, true, false);

	int hist_array[256];
	for (int i = 0; i < 256; i++)
		hist_array[i] = hist.at<float>(i, 0);

	int length = 1;
	const int length_max = 255 - m_threshold;
	while (length < length_max)
	{
		for (size_t i = m_threshold + 1; i < 256 - length; i++)
		{
			int count = 0;
			uint pixSum = 0;
			for (size_t j = 0; j < length; j++)
			{
				count += hist_array[j + i];
				pixSum += hist_array[j + i] * (i + j);
			}

			if (count >= threnshold)
				return pixSum / count;
		}
		length++;
	}
	return 255;
}