twain3/ImageProcess/ImageApplyBWBinaray.cpp

#include "ImageApplyBWBinaray.h"

CImageApplyBWBinaray::CImageApplyBWBinaray(ThresholdType type, int threshold, int blockSize, int constant)
	: m_threshold(threshold)
	, m_type(type)
	, m_blockSize(blockSize)
	, m_constant(constant)
	, m_table(new uchar[256])
{
	memset(m_table, 255, 256);
	memset(m_table, 0, static_cast<size_t>(m_threshold));
}

CImageApplyBWBinaray::CImageApplyBWBinaray()
	: m_threshold(120)
	, m_type(ThresholdType::THRESH_BINARY)
	, m_blockSize(25)
	, m_constant(5)
	, m_table(new uchar[256])
{
	memset(m_table, 255, 256);
	memset(m_table, 0, static_cast<size_t>(m_threshold));
}

CImageApplyBWBinaray::~CImageApplyBWBinaray(void)
{
	delete[] m_table;
}

void CImageApplyBWBinaray::apply(cv::Mat& pDib, int side)
{
	(void)side;
	if (pDib.empty()) return;

	if (pDib.channels() == 3)
		cv::cvtColor(pDib, pDib, cv::COLOR_BGR2GRAY);

	//20.12.29  修改参数为51 10 30 235
	//20.12.30  修改参数为51 20 30 235
	// 修改参数为17 20 110 235
 	cv::Mat integ;
	int blockSize = 17;//邻域尺寸
	int threshold = 20;
	int low = 110;
	int up = 235;
	int halfSize = blockSize / 2;
	int square_blockSize = blockSize * blockSize;
	switch (m_type)
	{
	case ThresholdType::THRESH_BINARY:
		cv::integral(pDib, integ, CV_32S);

		for (int j = halfSize; j < integ.rows - halfSize - 1; j++)
		{
			uchar* data = pDib.ptr<uchar>(j);
			int* idata1 = integ.ptr<int>(j - halfSize);
			int* idata2 = integ.ptr<int>(j + halfSize + 1);
			for (int i = halfSize; i < integ.cols - halfSize - 1; i++)
			{
				if (data[i] < low)
					data[i] = 0;
				else if (data[i] > up)
					data[i] = 255;
				else
					data[i] = data[i] < ((idata2[i + halfSize + 1] - idata2[i - halfSize] - idata1[i + halfSize + 1] + idata1[i - halfSize]) / square_blockSize - threshold) ? 0 : 255;
			}
		}

		cv::threshold(pDib(cv::Rect(0, 0, halfSize, pDib.rows)), pDib(cv::Rect(0, 0, halfSize, pDib.rows)), m_threshold, 255, cv::THRESH_BINARY);
		cv::threshold(pDib(cv::Rect(pDib.cols - halfSize, 0, halfSize, pDib.rows)), pDib(cv::Rect(pDib.cols - halfSize, 0, halfSize, pDib.rows)), m_threshold, 255, cv::THRESH_BINARY);
		cv::threshold(pDib(cv::Rect(0, 0, pDib.cols, halfSize)), pDib(cv::Rect(0, 0, pDib.cols, halfSize)), m_threshold, 255, cv::THRESH_BINARY);
		cv::threshold(pDib(cv::Rect(0, pDib.rows - halfSize, pDib.cols, halfSize)), pDib(cv::Rect(0, pDib.rows - halfSize, pDib.cols, halfSize)), m_threshold, 255, cv::THRESH_BINARY);
		break;
	case ThresholdType::THRESH_OTSU:
		cv::threshold(pDib, pDib, m_threshold, 255, CV_THRESH_OTSU);
		break;
	case ThresholdType::ADAPTIVE_GAUSSIAN:
		cv::adaptiveThreshold(pDib, pDib, 255, cv::ADAPTIVE_THRESH_GAUSSIAN_C, CV_THRESH_BINARY, m_blockSize, m_constant);
		break;
	case ThresholdType::ADAPTIVE_MEAN:
		cv::adaptiveThreshold(pDib, pDib, 255, cv::ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, m_blockSize, m_constant);
		break;
	case ThresholdType::ERROR_DIFFUSION:
		errorDiffuse(pDib);
		break;
	default:
		break;
	}
}

void CImageApplyBWBinaray::apply(std::vector<cv::Mat>& mats, bool isTwoSide)
{
	(void)isTwoSide;
	int i = 0;
	for (cv::Mat& var : mats) {
		if (i != 0 && isTwoSide == false)
			break;
		if (!var.empty())
			apply(var, 0);
		i++;
	}
}

void CImageApplyBWBinaray::errorDiffuse(cv::Mat& image)
{
	if (image.rows < 3 || image.cols < 3)
	{
		cv::threshold(image, image, m_threshold, 255, CV_THRESH_BINARY);
		return;
	}

	cv::Mat dst;
	image.convertTo(dst, CV_16S);

	size_t rows = static_cast<size_t>(image.rows) - 1;
	size_t cols = static_cast<size_t>(image.cols) - 1;

	short** pixels_dst = new short* [static_cast<size_t>(image.rows)];
	for (int i = 0; i < image.rows; i++)
		pixels_dst[i] = reinterpret_cast<short*>(dst.data + i * static_cast<int>(dst.step));

	short error;
	for (size_t y = 0; y < rows; y++)
		for (size_t x = 1; x < cols; x++)
		{
			short dstPix = pixels_dst[y][x];
			if (dstPix >= m_threshold)
			{
				pixels_dst[y][x] = 255;
				error = dstPix - 255;
			}
			else
			{
				pixels_dst[y][x] = 0;
				error = dstPix;
			}

			pixels_dst[y][x + 1] += error * 1 / 16;
			pixels_dst[y + 1][x - 1] += error * 1 / 16;
			pixels_dst[y + 1][x] += error * 1 / 16;
			pixels_dst[y + 1][x + 1] += error * 1 / 16;
		}
	image.release();
	dst.convertTo(image, CV_8U);

	rows++;
	uchar* ptr = image.data;
	size_t step = image.step;
	size_t offset;
	for (size_t y = 0; y < rows; y++)
	{
		offset = y * step;
		ptr[offset] = m_table[ptr[offset]];
		offset += cols;
		ptr[offset] = m_table[ptr[offset]];
	}

	cols++;
	ptr = image.data + step * (rows - 1);
	for (size_t x = 0; x < cols; x++)
		ptr[x] = m_table[ptr[x]];

	delete[] pixels_dst;
}