#include "eyemBarCode.h"

#pragma region 内部使用函数

static void split(const std::string &cStrText, const std::string &cStrDelim, std::vector<std::string> &vStrs)
{
	char *cpStr = new char[strlen(cStrText.c_str()) + 1];
	strcpy(cpStr, cStrText.c_str());
	//分割
	char *token = NULL, *ptr = NULL;
	token = strtok_s(cpStr, cStrDelim.c_str(), &ptr);
	while (NULL != token)
	{
		vStrs.push_back(token);
		token = strtok_s(NULL, cStrDelim.c_str(), &ptr);
	}
	delete[] cpStr;
	cpStr = NULL;
}

static void filterByApriltag(cv::Mat &binary, cv::Mat &labels, std::vector<tMap> &vPts, std::vector<uchar> &colors, int nccomps, double dToleErr = 0.5)
{
	//图像尺寸
	int X = binary.cols, Y = binary.rows;
	//背景
	colors[0] = 255;
	std::vector<tMap> temp;
	//水平扫描
	for (int c = 0; c < (int)vPts.size(); c++)
	{
		const uint8_t *ptrRow = binary.ptr<uint8_t>(vPts[c].Pt.y);

		//当前可能不是二维码区域
		if (ptrRow[vPts[c].Pt.x] == 0 || ptrRow[std::min(vPts[c].Pt.x + 1, X)] == 0 || ptrRow[std::max(vPts[c].Pt.x - 1, 0)] == 0 || \
			binary.ptr<uint8_t>(std::max(vPts[c].Pt.y - 1, 0))[vPts[c].Pt.x] == 0 || binary.ptr<uint8_t>(std::min(vPts[c].Pt.y + 1, Y))[vPts[c].Pt.x] == 0)
		{
			colors[vPts[c].Label] = 0;
			continue;
		}

		uint8_t future_pixel_right = 255;

		uint8_t next_pixel;

		//终止条件
		int flags = 0;

		double test_line[6]{ 0 };//0 中间那块；1那两块黑色；2外圈那两块
								 //向右扫描
		for (int x = vPts[c].Pt.x + 1; x < X - 1; x++)
		{
			//colors为0的不参与统计
			if ((colors[labels.ptr<int>(vPts[c].Pt.y)[x]] == 0))
			{
				continue;
			}
			next_pixel = ptrRow[x];
			//统计黑白像素
			test_line[flags]++;
			if (next_pixel != future_pixel_right)
			{
				flags++;
				future_pixel_right = 255 - future_pixel_right;
				if (flags == 3) { break; }
			}
		}
		uint8_t future_pixel_left = 255;

		//向左扫描
		for (int x = vPts[c].Pt.x - 1; x >= 1; x--)
		{
			//colors为0的不参与统计
			if ((colors[labels.ptr<int>(vPts[c].Pt.y)[x]] == 0))
			{
				continue;
			}
			next_pixel = ptrRow[x];
			//统计黑白像素
			test_line[flags]++;

			if (next_pixel != future_pixel_left)
			{
				flags++;
				future_pixel_left = 255 - future_pixel_left;
				if (flags == 6) { break; };
			}
		}
		//判断是否符合条件，[1]/[4]为1:1------[2]/[5]为1:1-----([1]+[4])/([0]+[3])-----约为0.67
		double rate = cv::min(test_line[1], test_line[4]) / cv::max(test_line[1], test_line[4]);
		if ((rate >= (1. - dToleErr) && rate <= (1. + dToleErr)))
		{
			rate = cv::min(test_line[2], test_line[5]) / cv::max(test_line[2], test_line[5]);
			if ((rate >= (1. - dToleErr) && rate <= (1. + dToleErr)))
			{
				rate = (test_line[1] + test_line[4]) / (test_line[0] + test_line[3]);
				//允许50%的误差
				if (rate >= ((2. / 3.)*(1. - dToleErr)) && rate <= ((2. / 3.)*(1. + dToleErr)))
				{
					temp.push_back(vPts[c]);
				}
				else
				{
					colors[vPts[c].Label] = 0;
				}
			}
			else
			{
				colors[vPts[c].Label] = 0;
			}
		}
		else
		{
			colors[vPts[c].Label] = 0;
		}
	}
	//垂直扫描
	for (int c = 0; c < (int)temp.size(); c++)
	{
		uint8_t future_pixel_down = 255;

		uint8_t next_pixel;

		//终止条件
		int flags = 0;

		double test_line[6]{ 0 };
		//向下扫描
		for (int y = temp[c].Pt.y + 1; y < Y - 1; y++)
		{
			//colors为0的不参与统计
			if ((colors[labels.ptr<int>(y)[temp[c].Pt.x]] == 0))
			{
				continue;
			}
			next_pixel = binary.ptr<uint8_t>(y)[temp[c].Pt.x];
			//统计黑白像素
			test_line[flags]++;
			if (next_pixel != future_pixel_down)
			{
				flags++;
				future_pixel_down = 255 - future_pixel_down;
				if (flags == 3) { break; };
			}
		}

		uint8_t future_pixel_up = 255;

		//向下扫描
		for (int y = temp[c].Pt.y - 1; y >= 1; y--)
		{
			//colors为0的不参与统计
			if ((colors[labels.ptr<int>(y)[temp[c].Pt.x]] == 0))
			{
				continue;
			}
			next_pixel = binary.ptr<uint8_t>(y)[temp[c].Pt.x];
			//统计黑白像素
			test_line[flags]++;

			if (next_pixel != future_pixel_up)
			{
				flags++;
				future_pixel_up = 255 - future_pixel_up;
				if (flags == 6) { break; };
			}
		}
		//判断是否符合条件，[1]/[4]为1:1------[2]/[5]为1:1-----([1]+[4])/([0]+[3])-----约为0.67
		double rate = cv::min(test_line[1], test_line[4]) / cv::max(test_line[1], test_line[4]);
		if (rate >= (1. - dToleErr) && rate <= (1 + dToleErr))
		{
			rate = cv::min(test_line[2], test_line[5]) / cv::max(test_line[2], test_line[5]);
			if (rate >= (1. - dToleErr) && rate <= (1 + dToleErr))
			{
				rate = (test_line[1] + test_line[4]) / (test_line[0] + test_line[3]);
				//允许50%的误差
				if (rate >= ((2. / 3.)*(1. - dToleErr)) && rate <= ((2. / 3.)*(1. + dToleErr)))
				{
					//大部分条件均满足，进入候选点
				}
				else
				{
					colors[temp[c].Label] = 0;
				}
			}
			else
			{
				colors[temp[c].Label] = 0;
			}
		}
		else
		{
			colors[temp[c].Label] = 0;
		}
	}
	colors[0] = 0;
	//过滤
	cv::parallel_for_(cv::Range(0, Y), [&](const cv::Range& range)->void {
		for (int y = range.start; y < range.end; y++)
		{
			uint8_t *ptrRow = binary.ptr<uint8_t>(y);
			for (int x = 0; x < X; x++)
			{
				int label = labels.ptr<int>(y)[x];
				CV_Assert(0 <= label && label <= nccomps);
				ptrRow[x] = colors[label];
			}
		}
	});
}

static double getThreshVal_Otsu_8u(const cv::Mat& _src)
{
	cv::Size size = _src.size();
	int step = (int)_src.step;
	if (_src.isContinuous())
	{
		size.width *= size.height;
		size.height = 1;
		step = size.width;
	}

#ifdef HAVE_IPP
	unsigned char thresh = 0;
	CV_IPP_RUN_FAST(ipp_getThreshVal_Otsu_8u(_src.ptr(), step, size, thresh), thresh);
#endif

	const int N = 256;
	int i, j, h[N] = { 0 };
#if CV_ENABLE_UNROLLED
	int h_unrolled[3][N] = {};
#endif
	for (i = 0; i < size.height; i++)
	{
		const uchar* src = _src.ptr() + step*i;
		j = 0;
#if CV_ENABLE_UNROLLED
		for (; j <= size.width - 4; j += 4)
		{
			int v0 = src[j], v1 = src[j + 1];
			h[v0]++; h_unrolled[0][v1]++;
			v0 = src[j + 2]; v1 = src[j + 3];
			h_unrolled[1][v0]++; h_unrolled[2][v1]++;
		}
#endif
		for (; j < size.width; j++)
			h[src[j]]++;
	}

	double mu = 0, scale = 1. / (size.width*size.height);
	for (i = 0; i < N; i++)
	{
#if CV_ENABLE_UNROLLED
		h[i] += h_unrolled[0][i] + h_unrolled[1][i] + h_unrolled[2][i];
#endif
		mu += i*(double)h[i];
	}

	mu *= scale;
	double mu1 = 0, q1 = 0;
	double max_sigma = 0, max_val = 0;

	for (i = 0; i < N; i++)
	{
		double p_i, q2, mu2, sigma;

		p_i = h[i] * scale;
		mu1 *= q1;
		q1 += p_i;
		q2 = 1. - q1;

		if (std::min(q1, q2) < FLT_EPSILON || std::max(q1, q2) > 1. - FLT_EPSILON)
			continue;

		mu1 = (mu1 + i*p_i) / q1;
		mu2 = (mu - q1*mu1) / q2;
		sigma = q1*q2*(mu1 - mu2)*(mu1 - mu2);
		if (sigma > max_sigma)
		{
			max_sigma = sigma;
			max_val = i;
		}
	}

	return max_val;
}

#pragma endregion

static bool decode(std::vector<WaitArea> &waitAreas, cv::Mat &showMat, std::vector<DecodeResult> &strDecodeResults, int iBlockSize, const int iRangeC, double dMinorStep)
{
	//全部解码成功
	bool allDecode = true;
	//进入线程锁
	mtx.lock();
	//处理解码
	for (int i = 0; i < waitAreas.size(); i++)
	{
		//如果已解码则不需要再次进行解码
		//if (waitAreas[i].decode) continue;
		//解码结果
		std::string strDecodeResult = ""; std::string strDecodeResultType = "QR-Code"; cv::Point ptDecodeResult = cv::Point();
		//二值化
		cv::Mat binary;
		//尝试多种参数解码
		for (int blockSize = iBlockSize - 2; blockSize <= iBlockSize + 2; blockSize += 2)
		{
			for (double d = waitAreas[i].C - (double)iRangeC; d <= waitAreas[i].C + (double)iRangeC; d += dMinorStep)
			{
				cv::adaptiveThreshold(waitAreas[i].waitArea, binary, 255, cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY, blockSize, d);
				try {
					Ref<LuminanceSource> source = MatSource::create(binary);
					Ref<Reader> reader;
					reader.reset(new QRCodeReader);
					Ref<Binarizer> binarizer(new GlobalHistogramBinarizer(source));
					Ref<BinaryBitmap> bitmap(new BinaryBitmap(binarizer));
					Ref<Result> result(reader->decode(bitmap, DecodeHints(DecodeHints::TRYHARDER_HINT)));
					if (!result.empty())
					{
						//waitAreas[i].decode = true;
						strDecodeResult = result->getText()->getText();
						goto breakLoop;
					}
				}
				catch (...) {
					//there is something wrong
				}
			}
		}
	breakLoop:
		{
			//做标记
			if (strDecodeResult != std::string())
			{
				cv::putText(showMat, strDecodeResult, waitAreas[i].Pt, cv::FONT_HERSHEY_PLAIN, 1, cv::Scalar(0, 0, 255));
				strDecodeResults.push_back(DecodeResult(0, waitAreas[i].Pt, strDecodeResult, strDecodeResultType));
			}
		}
		allDecode &= true/*waitAreas[i].decode*/;
	}
	//离开线程锁
	mtx.unlock();
	return allDecode;
}

static void decodeMul(std::vector<WaitArea> &waitAreas, std::vector<std::string> &hints, cv::Mat &showMat, std::vector<DecodeResult> &decodeResults, int iBlockSize, const int iRangeC, double dMinorStep)
{
	//进入线程锁
	mtx.lock();
	//处理解码
	for (int i = 0; i < waitAreas.size(); i++)
	{
		bool bDecode = false;
		//解码结果
		std::string strResult = ""; std::string strResultType = ""; cv::Point ptResult = cv::Point();
		//优先当作DM来解码，因为它比较快
		if (!waitAreas[i].oneD)
		{
			//DmtxMessage		*msg;
			//DmtxRegion		*reg;

			//DmtxImage		*img = NULL;
			//img = dmtxImageCreate(waitAreas[i].waitArea.data, waitAreas[i].waitArea.cols, waitAreas[i].waitArea.rows, DmtxPack8bppK);

			//DmtxDecode		*dec = dmtxDecodeCreate(img, 1);
			////超时
			//DmtxTime		beginTime = dmtxTimeNow();
			//DmtxTime		timeout = dmtxTimeAdd(beginTime, 15);
			//reg = dmtxRegionFindNext(dec, &timeout);
			//if (NULL != reg)
			//{
			//	//解码
			//	msg = dmtxDecodeMatrixRegion(dec, reg, DmtxUndefined);
			//	if (NULL != msg)
			//	{
			//		bDecode = true;
			//		ptResult = waitAreas[i].Pt;
			//		strResultType = "DATA_MATRIX";
			//		strResult = std::string(reinterpret_cast<const char *>(msg->output));
			//		//销毁资源
			//		dmtxMessageDestroy(&msg);
			//	}
			//	//解码失败
			//	dmtxRegionDestroy(&reg);
			//}
			//dmtxDecodeDestroy(&dec);
			//dmtxImageDestroy(&img);

#pragma region 有问题
			for (int d = waitAreas[i].C - iRangeC; d <= waitAreas[i].C + 2 * iRangeC; d += (int)dMinorStep)
			{
				cv::Mat binary;
				cv::adaptiveThreshold(waitAreas[i].waitArea, binary, 255, cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY, iBlockSize, d);

				DmtxMessage		*msg;
				DmtxRegion		*reg;

				DmtxImage		*img = NULL;
				if (abs(d) < DBL_EPS)
				{
					img = dmtxImageCreate(waitAreas[i].waitArea.data, waitAreas[i].waitArea.cols, waitAreas[i].waitArea.rows, DmtxPack8bppK);
				}
				else
				{
					img = dmtxImageCreate(binary.data, waitAreas[i].waitArea.cols, waitAreas[i].waitArea.rows, DmtxPack8bppK);
				}

				DmtxDecode		*dec = dmtxDecodeCreate(img, 1);
				//超时
				DmtxTime		beginTime = dmtxTimeNow();
				DmtxTime		timeout = dmtxTimeAdd(beginTime, 15);
				reg = dmtxRegionFindNext(dec, &timeout);
				if (NULL != reg)
				{
					//解码
					msg = dmtxDecodeMatrixRegion(dec, reg, DmtxUndefined);
					if (NULL != msg)
					{
						bDecode = true;
						ptResult = waitAreas[i].Pt;
						strResultType = "DATA_MATRIX";
						strResult = std::string(reinterpret_cast<const char *>(msg->output));
						//销毁资源
						dmtxMessageDestroy(&msg);
					}
					//解码失败
					dmtxRegionDestroy(&reg);
				}
				dmtxDecodeDestroy(&dec);
				dmtxImageDestroy(&img);
				//
				if (bDecode)
					break;
			}
#pragma endregion

		}
		//如果未解码，判断可能是QR或者一维码或者DATA_MATRIX
		if (!bDecode)
		{
			if (waitAreas[i].oneD)
			{
				//创建解码器
				Ref<Reader> reader_;
				reader_.reset(Ref<Reader>(new zxing::oned::MultiFormatOneDReader(DecodeHints::CODE_128_HINT | DecodeHints::CODE_39_HINT | DecodeHints::CODE_93_HINT)));
				//一维码识别
				for (int ii = 0; ii < waitAreas[i].oneDMats.size(); ii++)
				{
					cv::Mat src = waitAreas[i].oneDMats[ii];
					cv::pyrUp(src, src, cv::Size(src.cols * 2, src.rows * 2));
					//判断解码结果
					double threshVal = getThreshVal_Otsu_8u(src);
					for (double c = threshVal - 4 * iRangeC; c < threshVal + 4 * iRangeC; c += dMinorStep)
					{
						cv::Mat binary;
						cv::threshold(src, binary, c, 255, cv::THRESH_BINARY);
						try
						{
							//创建图像
							Ref<LuminanceSource> source = MatSource::create(binary);
							Ref<Binarizer> binarizer(new GlobalHistogramBinarizer(source));
							Ref<BinaryBitmap> bitmap(new BinaryBitmap(binarizer));
							//解码
							Ref<Result> result(reader_->decode(bitmap, DecodeHints::CODE_128_HINT | DecodeHints::CODE_39_HINT | DecodeHints::CODE_93_HINT));
							if (!result.empty())
							{
								bDecode = true;
								ptResult = waitAreas[i].Pt;
								strResult = result->getText()->getText();
								switch (result->getBarcodeFormat())
								{
								case NONE:
									strResultType = "NONE";
									break;
								case CODABAR:
									strResultType = "CODABAR";
									break;
								case CODE_39:
									strResultType = "CODE_39";
									break;
								case CODE_93:
									strResultType = "CODE_93";
									break;
								case CODE_128:
									strResultType = "CODE_128";
									break;
								case EAN_8:
									strResultType = "EAN_8";
								case EAN_13:
									strResultType = "EAN_13";
									break;
								case ITF:
									strResultType = "ITF";
									break;
								case MAXICODE:
									strResultType = "MAXICODE";
									break;
								case RSS_14:
									strResultType = "RSS_14";
									break;
								case RSS_EXPANDED:
									strResultType = "RSS_EXPANDED";
									break;
								case UPC_A:
									strResultType = "UPC_A";
									break;
								case UPC_E:
									strResultType = "UPC_E";
									break;
								case UPC_EAN_EXTENSION:
									strResultType = "UPC_EAN_EXTENSION";
									break;
								default:
									break;
								}
							}
						}
						catch (...) {
							//there is something wrong
						}
					}
					if (bDecode) {
						break;
					}
				}
			}
			else
			{
				//添加二维码解码器
				std::vector<Ref<Reader>> readers_;
				if (std::find(hints.begin(), hints.end(), "QR_CODE") != hints.end()) {
					readers_.push_back(Ref<Reader>(new zxing::qrcode::QRCodeReader));
				}
				if (std::find(hints.begin(), hints.end(), "DATA_MATRIX") != hints.end()) {
					readers_.push_back(Ref<Reader>(new zxing::datamatrix::DataMatrixReader));
				}
				if (std::find(hints.begin(), hints.end(), "AZTEC") != hints.end()) {
					readers_.push_back(Ref<Reader>(new zxing::aztec::AztecReader));
				}
				cv::Mat binary;
				cv::Mat src = waitAreas[i].waitArea;
				for (unsigned int ii = 0; ii < readers_.size(); ii++) {
					//尝试多种参数解码
					for (int blockSize = iBlockSize - 2; blockSize <= iBlockSize + 2; blockSize += 2)
					{
						for (double d = waitAreas[i].C - (double)iRangeC; d <= waitAreas[i].C + (double)iRangeC; d += dMinorStep)
						{
							cv::adaptiveThreshold(src, binary, 255, cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY, blockSize, d);
							try {
								//创建图像
								Ref<LuminanceSource> source = MatSource::create(binary);
								Ref<Binarizer> binarizer(new GlobalHistogramBinarizer(source));
								Ref<BinaryBitmap> bitmap(new BinaryBitmap(binarizer));
								Ref<Result> result(readers_[ii]->decode(bitmap, zxing::DecodeHints::TRYHARDER_HINT));
								//如果解码成功
								if (!result.empty())
								{
									ptResult = waitAreas[i].Pt;
									strResult = result->getText()->getText();
									switch (result->getBarcodeFormat())
									{
									case AZTEC:
										strResultType = "AZTEC";
										break;
									case DATA_MATRIX:
										strResultType = "DATA_MATRIX";
										break;
									case QR_CODE:
										strResultType = "QR_CODE";
										break;
									default:
										break;
									}
									goto breakLoop;
								}
							}
							catch (...) {
								//there is something wrong
							}
						}
					}
					//如果仍未解码
					if (!bDecode)
					{
						cv::Mat srcPyrUp;
						cv::pyrUp(src, srcPyrUp, cv::Size(src.cols * 2, src.rows * 2));
						for (int blockSize = (2 * iBlockSize + 1) - 2; blockSize <= (2 * iBlockSize + 1) + 2; blockSize += 2)
						{
							for (double d = waitAreas[i].C - (double)iRangeC; d <= waitAreas[i].C + (double)iRangeC; d += dMinorStep)
							{
								cv::adaptiveThreshold(srcPyrUp, binary, 255, cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY, blockSize, d);
								try {
									//创建图像
									Ref<LuminanceSource> source = MatSource::create(binary);
									Ref<Binarizer> binarizer(new GlobalHistogramBinarizer(source));
									Ref<BinaryBitmap> bitmap(new BinaryBitmap(binarizer));
									Ref<Result> result(readers_[ii]->decode(bitmap, zxing::DecodeHints::TRYHARDER_HINT));
									//如果解码成功
									if (!result.empty())
									{
										ptResult = waitAreas[i].Pt;
										strResult = result->getText()->getText();
										switch (result->getBarcodeFormat())
										{
										case AZTEC:
											strResultType = "AZTEC";
											break;
										case DATA_MATRIX:
											strResultType = "DATA_MATRIX";
											break;
										case QR_CODE:
											strResultType = "QR_CODE";
											break;
										default:
											break;
										}
										goto breakLoop;
									}
								}
								catch (...) {
									//there is something wrong
								}
							}
						}
					}
				}
			breakLoop:
				{
					if (strResult != std::string())
					{
						bDecode = true;
					}
				}
			}
		}
		//判断是否解码
		if (bDecode)
		{
			decodeResults.push_back(DecodeResult(waitAreas[i].angle, ptResult, strResult, strResultType));
			cv::putText(showMat, strResult, ptResult / 2, cv::FONT_HERSHEY_PLAIN, 1, cv::Scalar(0, 0, 255));
		}
	}
	//离开线程锁
	mtx.unlock();
}

int eyemDetectAndDecode(EyemImage tpImage, EyemRect tpRoi, const char *ccFileName, const char *ccCodeType, IntPtr *hObject, EyemBarCode **hResults, int *ipNum, bool bUseNiBlack, int iBlockSize, const int iRangeC, int iSymbolMin, int iSymbolMax, double dScaleUpAndDown, double dToleErr, double dMinorStep)
{
	cv::Mat src = cv::Mat(tpImage.iHeight, tpImage.iWidth, MAKETYPE(tpImage.iDepth, tpImage.iChannels), tpImage.vpImage);
	if (src.empty()) {
		return FUNC_IMAGE_NOT_EXIST;
	}
	//转单通道图像
	if (src.channels() != 1)
		cv::cvtColor(src, src, cv::COLOR_BGR2GRAY);
	//提取ROI
	src = src(cv::Rect(tpRoi.iXs, tpRoi.iYs, tpRoi.iWidth, tpRoi.iHeight));
	//图像原图备份
	cv::Mat backup = src.clone();
	//降采样
	if (dScaleUpAndDown != 1.)
		cv::pyrDown(src, src, cv::Size(cvRound(src.cols*dScaleUpAndDown), cvRound(src.rows*dScaleUpAndDown)));
	//用于显示
	cv::Mat showMat;
	cv::cvtColor(src, showMat, cv::COLOR_GRAY2BGR);
	//图像尺寸
	int X = src.cols, Y = src.rows;
	//解码结果
	std::vector<EyemBarCode> *tpResults = new std::vector<EyemBarCode>();
	//测试用
	cv::Mat srcPrev, binary;
	//高斯滤波去噪
	int ksize = cvRound((iBlockSize + 1)*dScaleUpAndDown) % 2 == 0 ? cvRound((iBlockSize + 1)*dScaleUpAndDown) + 1 : cvRound((iBlockSize + 1)*dScaleUpAndDown);
	cv::GaussianBlur(src, srcPrev, cv::Size(ksize, ksize), 0.3);
	//条码来说会比背景值小，二维码来说会比背景值大
	//计算导数
	cv::Mat dx, dy, mag;
	cv::Sobel(srcPrev, dx, CV_32F, 1, 0);
	cv::Sobel(srcPrev, dy, CV_32F, 0, 1);
	//计算梯度幅值
	cv::magnitude(dx, dy, mag);
	// 归一化
	cv::normalize(mag, mag, 0, 255, cv::NORM_MINMAX, CV_32FC1, cv::Mat());
	cv::convertScaleAbs(mag, srcPrev);
	//二值化
	cv::threshold(srcPrev, binary, 0, 255, cv::THRESH_BINARY | cv::THRESH_OTSU);
	//膨胀
	cv::morphologyEx(binary, binary, cv::MORPH_DILATE, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(cvRound(iBlockSize*dScaleUpAndDown / 3.), cvRound(iBlockSize*dScaleUpAndDown / 3.))));
	//计算角点响应
	cv::Mat harMap;
	cv::cornerHarris(src, harMap, cvRound(iBlockSize*dScaleUpAndDown), 3, 0.04);//对二维码效果比较好
	// 归一化与转换
	cv::normalize(harMap, harMap, 0, 255, cv::NORM_MINMAX, CV_32FC1, cv::Mat());
	cv::convertScaleAbs(harMap, harMap);
	//计算背景像素
	const int histSize = 256;
	float range[] = { 0,255 };
	const float* histRange = { range };
	//calculate the histogram
	cv::Mat hist;
	cv::calcHist(&harMap, 1, 0, cv::Mat(), hist, 1, &histSize, &histRange);
	//calculate the background pixels
	int maxIdx[2] = { 255,255 };
	cv::minMaxIdx(hist, NULL, NULL, NULL, maxIdx);
	//m1用于检测一维码；m2用于检测二维码
	cv::Mat m1(Y, X, CV_8UC1, cv::Scalar(0)), m2(Y, X, CV_8UC1, cv::Scalar(0));
	cv::parallel_for_(cv::Range(0, Y), [&](const cv::Range& range)->void {
		for (int y = range.start; y < range.end; y++)
		{
			for (int x = 0; x < X; x++)
			{
				if (harMap.ptr<uint8_t>(y)[x] < maxIdx[0])
				{
					m1.ptr<uint8_t>(y)[x] = 255;
				}
				else if (harMap.ptr<uint8_t>(y)[x] > maxIdx[0])
				{
					m2.ptr<uint8_t>(y)[x] = 255;
				}
			}
		}
	});
	//确定识别类型
	std::vector<std::string> hints_;
	split(ccCodeType, "|", hints_);
	//是否添加一维码检测
	bool addOneDReader = std::find(hints_.begin(), hints_.end(), "UPC_A") != hints_.end() ||
		std::find(hints_.begin(), hints_.end(), "UPC_E") != hints_.end() ||
		std::find(hints_.begin(), hints_.end(), "EAN_8") != hints_.end() ||
		std::find(hints_.begin(), hints_.end(), "EAN_13") != hints_.end() ||
		std::find(hints_.begin(), hints_.end(), "CODABAR") != hints_.end() ||
		std::find(hints_.begin(), hints_.end(), "CODE_39") != hints_.end() ||
		std::find(hints_.begin(), hints_.end(), "CODE_93") != hints_.end() ||
		std::find(hints_.begin(), hints_.end(), "CODE_128") != hints_.end() ||
		std::find(hints_.begin(), hints_.end(), "ITF") != hints_.end() ||
		std::find(hints_.begin(), hints_.end(), "RSS_14") != hints_.end() ||
		std::find(hints_.begin(), hints_.end(), "RSS_EXPANDED") != hints_.end();
	//是否添加二维码检测
	bool addTwoDReader = std::find(hints_.begin(), hints_.end(), "QR_CODE") != hints_.end() ||
		std::find(hints_.begin(), hints_.end(), "DATA_MATRIX") != hints_.end() ||
		std::find(hints_.begin(), hints_.end(), "AZTEC") != hints_.end();
	//未设置识别类型
	if (!addOneDReader && !addTwoDReader)
		return FUNC_CANNOT_CALC;
	//所有解码内容
	std::vector<DecodeResult> decodeResults;
	//待解码区域，区分条码类型来识别
	std::vector<WaitArea> waitAreas;
	if (addOneDReader)
	{
		cv::Mat labels, stats, centroids;
		int nccomps = cv::connectedComponentsWithStats(m1, labels, stats, centroids, 4);
		//过滤连通域面积及长/宽比例不符合的,允许50%误差
		std::vector<uchar> colors(nccomps + 1, 0);
		for (int i = 1; i < nccomps; i++) {
			colors[i] = 255;
			if ((stats.ptr<int>(i)[cv::CC_STAT_AREA] < std::pow(iBlockSize*dScaleUpAndDown, 2) * 5) | (m1.ptr<uint8_t>(cvRound(centroids.ptr<double>(i)[1]))[cvRound(centroids.ptr<double>(i)[0])] == 0))
			{
				colors[i] = 0;
			}
		}
		//过滤
		cv::parallel_for_(cv::Range(0, Y), [&](const cv::Range& range)->void {
			for (int y = range.start; y < range.end; y++)
			{
				uint8_t *ptrRow = m1.ptr<uint8_t>(y);
				for (int x = 0; x < X; x++)
				{
					int label = labels.ptr<int>(y)[x];
					CV_Assert(0 <= label && label <= nccomps);
					ptrRow[x] = colors[label];
				}
			}
		});
		//用于过滤非条码部分
		cv::Mat binFilter;
		cv::adaptiveThreshold(backup, binFilter, 255, cv::ADAPTIVE_THRESH_GAUSSIAN_C, cv::THRESH_BINARY_INV, ksize, 5);
		//处理断裂一维码
		//cv::morphologyEx(m1, m1, cv::MORPH_CLOSE, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(cvRound(iBlockSize*dScaleUpAndDown / 3.), cvRound(iBlockSize*dScaleUpAndDown / 3.))));
		//用于轮廓检测
		std::vector<std::vector<cv::Point>> contourAll, contourFilter;
		findContours(m1, contourAll, cv::noArray(), cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
		for (int i = 0; i < static_cast<int>(contourAll.size()); i++)
		{
			cv::RotatedRect rect = cv::minAreaRect(contourAll[i]);
			//最大宽度限制
			double minLen = cv::min(rect.size.height, rect.size.width);
			if (minLen < 8.*iBlockSize*dScaleUpAndDown*(1. + dToleErr))
			{
				//增加比例过滤条件
				cv::Point2f pts[4];
				rect.points(pts);
				//起点、终点、中点
				cv::Point ptStart, ptEnd, ptMid;
				if (cv::norm(pts[0] - pts[1]) > cv::norm(pts[1] - pts[2]))
				{
					ptStart = cv::Point((pts[0] + pts[3]) / 2. / dScaleUpAndDown); ptEnd = cv::Point((pts[1] + pts[2]) / 2. / dScaleUpAndDown);
				}
				else
				{
					ptStart = cv::Point((pts[0] + pts[1]) / 2. / dScaleUpAndDown); ptEnd = cv::Point((pts[2] + pts[3]) / 2. / dScaleUpAndDown);
				}
				ptMid = (ptStart + ptEnd) / 2;

				cv::LineIterator it(binFilter, (ptMid + ptEnd) / 2, (ptMid + ptStart) / 2, 4);

				double dis = cv::norm((ptMid + ptEnd) / 2 - (ptMid + ptStart) / 2);

				uint8_t future_pixel = 255;
				//扫描像素密度，比例接近1:1记录下来,并且黑白间隔数目小大于长度的一半
				int flag = 0;
				double test_line[2]{ 0 };
				for (int n = 0; n < it.count; n++, ++it)
				{
					if (m1.ptr<uint8_t>(cvRound(it.pos().y * dScaleUpAndDown))[cvRound(it.pos().x * dScaleUpAndDown)] == 0) continue;
					//统计均匀性
					uint8_t next_pixel = binFilter.ptr<uint8_t>(it.pos().y)[it.pos().x];
					test_line[next_pixel % 254]++;
					if (next_pixel != future_pixel)
					{
						flag++;
						future_pixel = 255 - future_pixel;
					}
					//showMat.at<cv::Vec3b>(it.pos()) = cv::Vec3b(0, 255, 0);
				}
				//满足比例
				double dRate = cv::min(test_line[0], test_line[1]) / cv::max(test_line[0], test_line[1]);
				if (dRate >= (1. - dToleErr) && dRate <= (1. + dToleErr) && flag > cvRound((dis / 4.)*(1. - dToleErr)))
				{
					cv::Point2f pt[4];
					cv::Size size(cvRound(cv::max(rect.size.height, rect.size.width) + iBlockSize*dScaleUpAndDown / 4.), cvRound(cv::min(rect.size.height, rect.size.width)));
					//获取roi位置
					double _angle = std::atan2((ptEnd.y - ptStart.y), (ptEnd.x - ptStart.x));
					float b = (float)cos(_angle)*0.5f;
					float a = (float)sin(_angle)*0.5f;

					pt[0].x = rect.center.x - a*size.height - b*size.width;
					pt[0].y = rect.center.y + b*size.height - a*size.width;
					pt[1].x = rect.center.x + a*size.height - b*size.width;
					pt[1].y = rect.center.y - b*size.height - a*size.width;
					pt[2].x = 2 * rect.center.x - pt[0].x;
					pt[2].y = 2 * rect.center.y - pt[0].y;
					pt[3].x = 2 * rect.center.x - pt[1].x;
					pt[3].y = 2 * rect.center.y - pt[1].y;

					//防止越界
					for (int n = 0; n < 4; n++)
					{
						if (pt[n].x < 0) pt[n].x = 0.f; if (pt[n].x >= X - 1) pt[n].x = float(X - 1); if (pt[n].y < 0) pt[n].y = 0.f; if (pt[n].y >= Y - 1) pt[n].y = float(Y - 1);
					}
					//用采样的方式提取待解码区域
					cv::LineIterator itStHeight(backup, pt[0], pt[1], 4);

					cv::LineIterator itEdHeight(backup, pt[3], pt[2], 4);

					cv::LineIterator itStWidth(backup, pt[0], pt[3], 4);

					cv::LineIterator itEdWidth(backup, pt[1], pt[2], 4);

					struct Track
					{
						cv::Point PosS;
						cv::Point PosE;

						Track() {};

						Track(cv::Point PosS, cv::Point PosE) :PosS(PosS), PosE(PosE) {};
					};

					std::vector<Track> pairStEd(cv::min(itStHeight.count, itEdHeight.count));

					for (int n = 0; n < pairStEd.size(); n++, ++itStHeight, ++itEdHeight)
					{
						pairStEd[n] = Track(itStHeight.pos(), itEdHeight.pos());
					}
					int iSamplingStep = int(pairStEd.size()) / 4;
					//线采样
					cv::Mat srcSampling(cv::Size(cv::max(itStWidth.count, itEdWidth.count), 1), CV_8UC1, cv::Scalar(255));
					//
					std::vector<cv::Mat> oneDMats;
					//行
					for (int n = 0; n < (int)pairStEd.size(); n += iSamplingStep)
					{
						cv::LineIterator it(backup, pairStEd[n].PosS, pairStEd[n].PosE, 4);
						for (int nn = 0; nn < it.count; nn++, ++it)//列
						{
							//showMat.at<cv::Vec3b>(it.pos()) = cv::Vec3b(0, 255, 0);
							srcSampling.ptr<uint8_t>(0)[nn] = backup.ptr<uint8_t>(it.pos().y)[it.pos().x];
						}
						//判断是否为二维码
						cv::Mat testMat;
						cv::threshold(srcSampling, testMat, 0, 255, cv::THRESH_BINARY_INV | cv::THRESH_OTSU);
						//
						cv::Mat testLabels;
						if (cv::connectedComponents(testMat, testLabels) < 6)
						{
							//判断非二维码
							break;
						}
						//扩展
						cv::Mat waitArea;
						cv::copyMakeBorder(srcSampling, waitArea, 0, 1, 60, 60, cv::BORDER_REPLICATE);
						oneDMats.push_back(waitArea);
					}
					//存储一维码待解码区域
					if ((int)oneDMats.size() > 0)
					{
						//画图
						for (int j = 0; j < 4; j++)
						{
							cv::line(showMat, pt[j], pt[(j + 1) % 4], cv::Scalar(0, 255, 255), 1);
						}
						cv::circle(showMat, pt[0], 2, cv::Scalar(255, 0, 0), -1);
						cv::circle(showMat, pt[1], 2, cv::Scalar(0, 255, 0), -1);
						cv::circle(showMat, pt[2], 2, cv::Scalar(0, 0, 255), -1);
						waitAreas.push_back(WaitArea(cv::Mat(), ptMid, getThreshVal_Otsu_8u(oneDMats[0]), rect.angle, true, oneDMats));
					}
				}
			}
		}
	}
	if (addTwoDReader)
	{
		//测试用
		cv::Mat srcFilter;
		cv::adaptiveThreshold(src, srcFilter, 255, cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY_INV, 43, 2);
		//去掉小于15个像素的
		cv::morphologyEx(srcFilter, srcFilter, cv::MORPH_DILATE, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(cvRound(iBlockSize*dScaleUpAndDown / 3.), cvRound(iBlockSize*dScaleUpAndDown / 3.))));
		//断裂处连接在一起
		cv::morphologyEx(m2, m2, cv::MORPH_DILATE, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(cvRound(iBlockSize*dScaleUpAndDown), cvRound(iBlockSize*dScaleUpAndDown))));
		//去掉无关区域
		cv::bitwise_and(srcFilter, m2, m2);
		//对二值图像过滤
		cv::Mat labels, stats, centroids;
		int nccomps = cv::connectedComponentsWithStats(m2, labels, stats, centroids, 4);
		//过滤连通域面积及长/宽比例不符合的,允许50%误差
		std::vector<uchar> colors(nccomps + 1, 0);
		for (int i = 1; i < nccomps; i++) {
			colors[i] = 255;
			double dRate = (double)stats.ptr<int>(i)[cv::CC_STAT_WIDTH] / (double)stats.ptr<int>(i)[cv::CC_STAT_HEIGHT];
			if ((!(dRate >= (1. - dToleErr) && dRate <= (1. + dToleErr))) | (stats.ptr<int>(i)[cv::CC_STAT_WIDTH] > iBlockSize*dScaleUpAndDown * 15 * 1.414*(1. + dToleErr)) | (stats.ptr<int>(i)[cv::CC_STAT_HEIGHT] > iBlockSize*dScaleUpAndDown * 15 * 1.414*(1. + dToleErr))\
				| ((double)stats.ptr<int>(i)[cv::CC_STAT_AREA] < std::pow(iBlockSize / 2, 2) * 15))
			{
				colors[i] = 0;
			}
		}
		//第一次过滤
		cv::parallel_for_(cv::Range(0, Y), [&](const cv::Range& range)->void {
			for (int y = range.start; y < range.end; y++)
			{
				uint8_t *ptrRow = m2.ptr<uint8_t>(y);
				for (int x = 0; x < X; x++)
				{
					int label = labels.ptr<int>(y)[x];
					CV_Assert(0 <= label && label <= nccomps);
					ptrRow[x] = colors[label];
				}
			}
		});
		//用于轮廓检测
		std::vector<std::vector<cv::Point>> contourAll, contourFilter;
		findContours(m2, contourAll, cv::noArray(), cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
		for (int i = 0; i < static_cast<int>(contourAll.size()); i++)
		{
			cv::RotatedRect rect = cv::minAreaRect(contourAll[i]);
			//满足矩形条件与面积条件
			double dRate = cv::min(rect.size.width, rect.size.height) / cv::max(rect.size.height, rect.size.width);
			if (dRate >= (1. - dToleErr) && dRate <= (1. + dToleErr) && ((double)rect.size.width > double(8. * iBlockSize*dScaleUpAndDown)) && ((double)rect.size.height > double(8. * iBlockSize*dScaleUpAndDown)))
			{
				contourFilter.push_back(contourAll[i]);
			}
		}

		for (int i = 0; i < contourFilter.size(); i++)
		{
			cv::Rect rect = cv::minAreaRect(contourFilter[i]).boundingRect();
			cv::RotatedRect rRect = cv::minAreaRect(contourFilter[i]);
			//外包矩形
			int dynSize = cvRound(cv::max((double)rect.size().height / dScaleUpAndDown, (double)rect.size().width / dScaleUpAndDown));
			//疑似二维码区域
			cv::Mat waitArea = backup(cv::Range(cv::max(0, cvRound(rRect.center.y / dScaleUpAndDown) - cvRound(4.*(double)iBlockSize*dScaleUpAndDown + dynSize / 2)), cv::min(backup.rows - 1, cvRound(rRect.center.y / dScaleUpAndDown) + cvRound(4.*(double)iBlockSize*dScaleUpAndDown + dynSize / 2))), cv::Range(cv::max(0, cvRound(rRect.center.x / dScaleUpAndDown) - cvRound(4.*(double)iBlockSize*dScaleUpAndDown + dynSize / 2)), cv::min(backup.cols - 1, cvRound(rRect.center.x / dScaleUpAndDown) + cvRound(4.*(double)iBlockSize*dScaleUpAndDown + dynSize / 2)))).clone();
			//处理后再压入识别
			waitAreas.push_back(WaitArea(waitArea, cv::Point(cvRound(rRect.center.x / dScaleUpAndDown), cvRound(rRect.center.y / dScaleUpAndDown)), 0, 0, false, std::vector<cv::Mat>()));
			//画图
			cv::rectangle(showMat, rect, cv::Scalar(0, 255, 0), 1);
		}
	}
	//解码
	decodeMul(waitAreas, hints_, showMat, decodeResults, iBlockSize, iRangeC, dMinorStep);
	//输出结果
	for (int i = 0; i < decodeResults.size(); i++)
	{
		EyemBarCode tpResult;
		tpResult.iCenterX = decodeResults[i].ptResult.x;
		tpResult.iCenterY = decodeResults[i].ptResult.y;
		tpResult.dAngle = decodeResults[i].dAngle;
		//分配内容所需内存
		tpResult.lpszText = (char *)CoTaskMemAlloc(512);
		if (NULL != tpResult.lpszText)
		{
			char file[512] = { 0 };
			sprintf_s(file, "%s", decodeResults[i].strResultText.c_str());
			strcpy(tpResult.lpszText, file);
		}
		else return FUNC_NOT_ENOUGH_MEM;
		//分配码型所需内存
		tpResult.lpszType = (char *)CoTaskMemAlloc(512);
		if (NULL != tpResult.lpszType)
		{
			char file[512] = { 0 };
			sprintf_s(file, "%s", decodeResults[i].strResultType.c_str());
			strcpy(tpResult.lpszType, file);
		}
		else return FUNC_NOT_ENOUGH_MEM;
		//添加结果
		tpResults->push_back(tpResult);
	}
	*hResults = tpResults->data();
	*ipNum = static_cast<int>(tpResults->size());
	*hObject = reinterpret_cast<IntPtr>(tpResults);
	////格式化文件名
	//const int bufSize = 32;
	//char file[bufSize * 4] = { 0 };
	//sprintf_s(file, "D:\\ResOut\\%s-Mark.png", ccFileName);
	//cv::imwrite(file, showMat);
	return FUNC_OK;

	////局部二值化
	//cv::adaptiveThreshold(src, binary, 255, cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY_INV, iBlockSize, 2);
	////去掉大部分干扰项
	//binary &= mask;
	////对二值图像过滤
	//cv::Mat labels, stats, centroids;
	//int nccomps = cv::connectedComponentsWithStats(binary, labels, stats, centroids, 4);
	////过滤连通域面积及长/宽比例不符合的,允许50%误差
	//std::vector<uchar> colors(nccomps + 1, 0);
	//for (int i = 1; i < nccomps; i++) {
	//	colors[i] = 255;
	//	if ((stats.ptr<int>(i)[cv::CC_STAT_WIDTH] > iBlockSize * 15 * 1.414*(1. + dToleErr)) | (stats.ptr<int>(i)[cv::CC_STAT_HEIGHT] > iBlockSize * 15 * 1.414*(1. + dToleErr))\
	//		| (false))
	//	{
	//		colors[i] = 0;
	//	}
	//}
	////第一次过滤
	//cv::parallel_for_(cv::Range(0, Y), [&](const cv::Range& range)->void {
	//	for (int y = range.start; y < range.end; y++)
	//	{
	//		uint8_t *ptrRow = binary.ptr<uint8_t>(y);
	//		for (int x = 0; x < X; x++)
	//		{
	//			int label = labels.ptr<int>(y)[x];
	//			CV_Assert(0 <= label && label <= nccomps);
	//			ptrRow[x] = colors[label];
	//		}
	//	}
	//});
	////cv::cvtColor(binary, showMat, cv::COLOR_GRAY2BGR);
	//const int iScanRadius = 35;
	////最好还是用线扫描的方法，具体扫描全图还是按照中心点来扫看时间
	////指定长度十字网格遍历是否满足条件黑白比例在1:1,考虑其他方式去扫描，可能速度上会慢一些，如果区分满足各自条件可能会好一些
	////，这样可以将其他不必要的过滤掉，最后再合成一张图;2，扫描宽度，根据宽度流来确定是否属于条码、qr、datamatrix，黑白宽度；
	////宽度打开都在一个很小变动范围内，允许50%的误差,宽度密度相较目前判断方式可也确定是否是黑白格分布，考虑到一维条码，八个方向只需满足一个方向即可
	////这样可以过滤掉一些孤立长条
	////vPts[0].Pt = cv::Point(3610, 2433);
	//cv::Mat label(Y, X, CV_8UC1, cv::Scalar(0));
	//cv::parallel_for_(cv::Range(0, Y), [&](const cv::Range& range)->void {
	//	for (int y = range.start; y < range.end; y++)
	//	{
	//		for (int x = 0; x < X; x++)
	//		{
	//			uint8_t future_pixel = binary.ptr<uint8_t>(y)[x];
	//			if (!future_pixel)
	//			{
	//				continue;
	//			}
	//			bool iFlag = 0;
	//			//判断白色像素部分占整条线的比例
	//			for (double t = -180; t < 180; t += 45)
	//			{
	//				float xx = float(x + iScanRadius * cos(t* 0.01745));
	//				float yy = float(y + iScanRadius * sin(t* 0.01745));

	//				//防止越界
	//				if (xx < 0) xx = 0; if (xx >= X - 1) xx = X - 1; if (yy < 0) yy = 0; if (yy >= Y - 1) yy = Y - 1;

	//				cv::LineIterator it(binary, cv::Point(x, y), cv::Point(cvRound(xx), cvRound(yy)), 4);
	//				//扫描像素密度，比例接近1:1记录下来
	//				int length = 0;
	//				std::vector<double> test_lines;
	//				double test_line[2]{ 0 };
	//				for (int n = 0; n < it.count; n++, ++it)
	//				{
	//					//统计相邻由明到暗个数,并且查看均匀性
	//					uint8_t next_pixel = binary.ptr<uint8_t>(it.pos().y)[it.pos().x];
	//					test_line[next_pixel % 254]++;
	//					length++;
	//					if (next_pixel != future_pixel)
	//					{
	//						if (!next_pixel)
	//						{
	//							test_lines.push_back(length);
	//						}
	//						future_pixel = 255 - future_pixel;
	//						length = 0;
	//					}
	//				}
	//				if (cv::max(test_line[0], test_line[1]) <= 0) continue;
	//				//至少存在l个方向满足黑白1:1比例,并且满足黑白交替比例大概在1:1
	//				double dRate = cv::min(test_line[0], test_line[1]) / cv::max(test_line[0], test_line[1]);
	//				if (dRate >= (1. - dToleErr) && dRate <= (1. + dToleErr))
	//				{
	//					//满足条件,再判断当前方向的宽度是否
	//					iFlag = true;
	//					//cv::putText(showMat, "OK", vPts[c].Pt, cv::FONT_HERSHEY_PLAIN, 1, cv::Scalar(0, 0, 255));
	//					//showMat.at<cv::Vec3b>(cv::Point(x, y)) = cv::Vec3b(0, 255, 0);
	//					label.ptr<uint8_t>(y)[x] = 255;
	//					break;
	//				}
	//			}
	//		}
	//	}
	//});

	//for (int c = 0; c < (int)vPts.size(); c++)
	//{
	//	bool iFlag = 0;
	//	uint8_t future_pixel = binary.ptr<uint8_t>(vPts[c].Pt.y)[vPts[c].Pt.x];
	//	//判断白色像素部分占整条线的比例
	//	for (double t = -180; t < 180; t += 45)
	//	{
	//		float x = float(vPts[c].Pt.x + iScanRadius * cos(t* 0.01745));
	//		float y = float(vPts[c].Pt.y + iScanRadius * sin(t* 0.01745));

	//		//防止越界
	//		if (x < 0) x = 0; if (x >= X - 1) x = X - 1; if (y < 0) y = 0; if (y >= Y - 1) y = Y - 1;

	//		cv::LineIterator it(binary, vPts[c].Pt, cv::Point(cvRound(x), cvRound(y)), 4);
	//		//扫描像素密度，比例接近1:1记录下来
	//		//测试用
	//		std::vector<cv::Point> test_point;

	//		int length = 0;
	//		std::vector<double> test_lines;
	//		double test_line[2]{ 0 };
	//		for (int n = 0; n < it.count; n++, ++it)
	//		{
	//			//统计相邻由明到暗个数,并且查看均匀性
	//			uint8_t next_pixel = binary.ptr<uint8_t>(it.pos().y)[it.pos().x];
	//			test_line[next_pixel % 254]++;
	//			length++;
	//			if (next_pixel != future_pixel)
	//			{
	//				if (length > 1)
	//				{
	//					test_lines.push_back(length);
	//				}
	//				future_pixel = 255 - future_pixel;
	//				length = 0;
	//			}
	//			//test_point.push_back(it.pos());
	//			//showMat.at<cv::Vec3b>(it.pos()) = cv::Vec3b(0, 255, 0);
	//		}
	//		//至少存在l个方向满足黑白1:1比例,并且满足黑白交替比例大概在1:1
	//		double dRate = cv::min(test_line[0], test_line[1]) / cv::max(test_line[0], test_line[1]);
	//		if (dRate >= (1. - dToleErr) && dRate <= (1. + dToleErr) && (test_lines.size() >= T))
	//		{
	//			//满足条件,再判断当前方向的宽度是否
	//			iFlag = true;
	//			//cv::putText(showMat, "OK", vPts[c].Pt, cv::FONT_HERSHEY_PLAIN, 1, cv::Scalar(0, 0, 255));
	//			//for (int n = 0; n < test_point.size(); n++)
	//			//{
	//			//	showMat.at<cv::Vec3b>(test_point[n]) = cv::Vec3b(0, 0, 255);
	//			//}
	//			//std::cout << "xx" << std::endl;
	//		}
	//	}
	//	//对四个方向进行进一步进行过滤,黑白间隔跨度阈值限定
	//	if ((!iFlag))
	//	{
	//		colors[vPts[c].Label] = 0;
	//	}
	//}
	//二次过滤


	//cv::parallel_for_(cv::Range(0, Y), [&](const cv::Range& range)->void {
	//	for (int y = range.start; y < range.end; y++)
	//	{
	//		uint8_t *ptrRow = binary.ptr<uint8_t>(y);
	//		for (int x = 0; x < X; x++)
	//		{
	//			int label = labels.ptr<int>(y)[x];
	//			CV_Assert(0 <= label && label <= nccomps);
	//			ptrRow[x] = colors[label];
	//		}
	//	}
	//});
	//cv::Mat binPrev;
	//cv::morphologyEx(label, binPrev, cv::MORPH_CLOSE, cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(iBlockSize, iBlockSize)));
	////用于轮廓检测（最终过滤过的图）
	//std::vector<cv::Vec4i> hierarchy;
	//std::vector<std::vector<cv::Point>> contourAll, contourFilter;
	//findContours(binPrev, contourAll, hierarchy, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);

	//for (int i = 0; i < static_cast<int>(contourAll.size()); i++)
	//{
	//	cv::RotatedRect rect = cv::minAreaRect(contourAll[i]);
	//	double dRate = rect.size.width / rect.size.height;
	//	std::vector<cv::Point> approx;
	//	cv::approxPolyDP(cv::Mat(contourAll[i]), approx, cv::arcLength(cv::Mat(contourAll[i]), true)*0.02, true);
	//	//满足四边形条件
	//	if (dRate >= (1. - dToleErr) && dRate <= (1. + dToleErr) && (approx.size() >= 4 && approx.size() < 8) && (cv::contourArea(contourAll[i]) > std::pow(iBlockSize * 12 * (1. - dToleErr), 2)))
	//	{
	//		contourFilter.push_back(contourAll[i]);
	//	}
	//}
	//if (contourFilter.size() < 1)
	//{
	//	return FUNC_CANNOT_CALC;
	//}

	return FUNC_OK;
}

int eyemDetectAndDecodeUseNN(EyemImage tpImage, EyemRect tpRoi, IntPtr *hObject, EyemBarCode **hResults, int *ipNum, int iBlockSize, int iRangeC, double dMinorStep)
{
	cv::Mat image = cv::Mat(tpImage.iHeight, tpImage.iWidth, MAKETYPE(tpImage.iDepth, tpImage.iChannels), tpImage.vpImage);
	if (image.empty()) {
		return FUNC_IMAGE_NOT_EXIST;
	}
	//识别
	std::vector<cv::Rect> points;
	auto ret = detector->detect(image, points);

	//解码
	for (int i = 0; i < ret.size(); i++)
	{
		cv::Mat dst;
		cv::resize(ret[i], dst, cv::Size(140, 140));
		std::vector<cv::Mat> scaled = detector->getScaleMap(dst);
	}

	//画图
	if (image.channels() != 3) {
		cv::cvtColor(image, image, cv::COLOR_GRAY2BGR);
	}

	for (auto point : points) {
		cv::rectangle(image, point, cv::Scalar(0, 255, 0), 2);
	}
	return FUNC_OK;
}

int eyemInitNNDataCodeModel(const char *detectorConfigPath, const char *detectorModelPath, const char *superResolutionConfigPath, const char *superResolutionModelPath)
{
	try
	{
		detector = cv::makePtr<NNDetector>(detectorConfigPath, detectorModelPath, superResolutionConfigPath, superResolutionModelPath);
	}
	catch (const std::exception& e)
	{
		std::cout << e.what() << std::endl;
		return FUNC_CANNOT_CALC;
	}
	return FUNC_OK;
}

bool eyemDetectAndDecodeFree(IntPtr hObject)
{
	std::vector<EyemBarCode>  *tpResults = reinterpret_cast<std::vector<EyemBarCode>*>(hObject);

	//清空容器
	tpResults->clear();

	//释放
	delete tpResults;
	tpResults = NULL;

	return true;
}