预处理.cpp

/*
	预处理。cpp
	1.动态二值化,动态二值化会产生黑洞
	2.滤波（可选）
	3.去掉图像黑洞

	调参：二值化blocksize，C

*/
#include "opencv2/opencv.hpp"
#include "符号阵列.h"
using namespace std;
using namespace cv;

/*
input_img:相机拍摄图像名称/路径
dst：二值化图像
*/
void preprocessing(String input_img, Mat& dstImg)
{
	Mat srcImg; //原图
	Mat binary_img;//二值化图
	//Mat b2;
	srcImg = imread(input_img, 0); //读入灰度图像
	dstImg = Mat::zeros(srcImg.size(), CV_8UC1);
	yx_adaptiveThreshold(srcImg, binary_img, 7, -3);//二值化
	//threshold(srcImg, b2, 150,255, CV_THRESH_BINARY );
	//imshow("only b", binary_img);
	//imshow("固定阈值", b2);
	RemoveSmallRegion(binary_img, dstImg, 10, 0, 0);//去黑点
	RemoveSmallRegion(dstImg, dstImg, 10, 1, 0);//去白点


}
/*
function：图像中去除小区域，优化二值化后图像
para：
CheckMode: 0代表去除黑区域，1代表去除白区域;
NeihborMode：0代表4邻域，1代表8邻域;
AreaLimit：小区域面积上限

注意：dst必须是一个初始化后的Mat，大小不能为0
	 dst可以为src

*/

void RemoveSmallRegion(Mat& Src, Mat& Dst, int AreaLimit, int CheckMode, int NeihborMode)
{
	int RemoveCount = 0;       //记录除去的个数  
							   //记录每个像素点检验状态的标签，0代表未检查，1代表正在检查,2代表检查不合格（需要反转颜色），3代表检查合格或不需检查  
	Mat Pointlabel = Mat::zeros(Src.size(), CV_8UC1);

	if (CheckMode == 1)
	{
		//cout << "Mode: 去除小区域. ";
		for (int i = 0; i < Src.rows; ++i)
		{
			uchar* iData = Src.ptr<uchar>(i);
			uchar* iLabel = Pointlabel.ptr<uchar>(i);
			for (int j = 0; j < Src.cols; ++j)
			{
				if (iData[j] < 10)
				{
					iLabel[j] = 3;
				}
			}
		}
	}
	else
	{
		//cout << "Mode: 去除孔洞. ";
		for (int i = 0; i < Src.rows; ++i)
		{
			uchar* iData = Src.ptr<uchar>(i);
			uchar* iLabel = Pointlabel.ptr<uchar>(i);
			for (int j = 0; j < Src.cols; ++j)
			{
				if (iData[j] > 10)
				{
					iLabel[j] = 3;
				}
			}
		}
	}

	vector<Point2i> NeihborPos;  //记录邻域点位置  
	NeihborPos.push_back(Point2i(-1, 0));
	NeihborPos.push_back(Point2i(1, 0));
	NeihborPos.push_back(Point2i(0, -1));
	NeihborPos.push_back(Point2i(0, 1));
	if (NeihborMode == 1)
	{
		//cout << "Neighbor mode: 8邻域." << endl;
		NeihborPos.push_back(Point2i(-1, -1));
		NeihborPos.push_back(Point2i(-1, 1));
		NeihborPos.push_back(Point2i(1, -1));
		NeihborPos.push_back(Point2i(1, 1));
	}
	//else cout << "Neighbor mode: 4邻域." << endl;
	int NeihborCount = 4 + 4 * NeihborMode;
	int CurrX = 0, CurrY = 0;
	//开始检测  
	for (int i = 0; i < Src.rows; ++i)
	{
		uchar* iLabel = Pointlabel.ptr<uchar>(i);
		for (int j = 0; j < Src.cols; ++j)
		{
			if (iLabel[j] == 0)
			{
				//********开始该点处的检查**********  
				vector<Point2i> GrowBuffer;                                      //堆栈，用于存储生长点  
				GrowBuffer.push_back(Point2i(j, i));
				Pointlabel.at<uchar>(i, j) = 1;
				int CheckResult = 0;                                               //用于判断结果（是否超出大小），0为未超出，1为超出  

				for (int z = 0; z<GrowBuffer.size(); z++)
				{

					for (int q = 0; q<NeihborCount; q++)                                      //检查四个邻域点  
					{
						CurrX = GrowBuffer.at(z).x + NeihborPos.at(q).x;
						CurrY = GrowBuffer.at(z).y + NeihborPos.at(q).y;
						if (CurrX >= 0 && CurrX<Src.cols&&CurrY >= 0 && CurrY<Src.rows)  //防止越界  
						{
							if (Pointlabel.at<uchar>(CurrY, CurrX) == 0)
							{
								GrowBuffer.push_back(Point2i(CurrX, CurrY));  //邻域点加入buffer  
								Pointlabel.at<uchar>(CurrY, CurrX) = 1;           //更新邻域点的检查标签，避免重复检查  
							}
						}
					}

				}
				if (GrowBuffer.size()>AreaLimit) CheckResult = 2;                 //判断结果（是否超出限定的大小），1为未超出，2为超出  
				else { CheckResult = 1;   RemoveCount++; }
				for (int z = 0; z<GrowBuffer.size(); z++)                         //更新Label记录  
				{
					CurrX = GrowBuffer.at(z).x;
					CurrY = GrowBuffer.at(z).y;
					Pointlabel.at<uchar>(CurrY, CurrX) += CheckResult;
				}
				//********结束该点处的检查**********  


			}
		}
	}

	CheckMode = 255 * (1 - CheckMode);
	//开始反转面积过小的区域  
	for (int i = 0; i < Src.rows; ++i)
	{
		uchar* iData = Src.ptr<uchar>(i);
		uchar* iDstData = Dst.ptr<uchar>(i);
		uchar* iLabel = Pointlabel.ptr<uchar>(i);
		for (int j = 0; j < Src.cols; ++j)
		{
			if (iLabel[j] == 2)
			{
				iDstData[j] = CheckMode;
			}
			else if (iLabel[j] == 3)
			{
				iDstData[j] = iData[j];
			}
		}
	}

	//cout << RemoveCount << " objects removed." << endl;
}
/*
blockSize:用来计算每个像素的阈值的邻域大小，如3,5,7等。
C:从adaptiveMethod选择的方法中计算出来的平均值或加权平均值减去的参数值，可以是正数或负数。
  C为负数：C绝对值变大会减少图像白点
  C为正数：C变大会减少图像黑点
*/
void yx_adaptiveThreshold(InputArray src, OutputArray dst, int blockSize, double C)
{
	adaptiveThreshold(src, dst, 255, CV_ADAPTIVE_THRESH_GAUSSIAN_C, CV_THRESH_BINARY, blockSize, C);
}