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彩色图像的直方图绘制及灰度图像均衡化

分类: IT文章 • 2024-04-29 16:53:12

目录

Mat容器

  Mat E = Mat :: eye（4，4，CV_64F）;
  cout << “E =” << endl << “” << E << endl << endl;
  Mat O = Mat :: ones（2，2，CV_32F）;
  cout << “O =” << endl << “” <<“O << endl << endl;
  Mat Z = Mat :: zeros（3,3，CV_8UC1）;
  cout << “Z =” << endl << “” << Z << endl << endl;

像素值读写的几种方法：

  • 方法一:

    1. 单通道读像素uchar value = grayimg.at<uchar>(i,j);

      for(int i=0;i<grayimg.rows;i++)
{
  for(int j=0;j<grayimg.cols;j++)
  {
    grayimg.at<uchar>(i,j)=(i+j)%255;
  }
}

    2. 彩色图像（BGR）-3通道

      for(int i=0;i<colorimg.rows;i++)
{
  for(int j=0;j<colorimg.cols;j++) {
    Vec3b pixel;
    pixel[0]=i%255;//Blue
    pixel[1]=j%/255;//Green
    pixel[2]=0;//Red
    colorimg.at<Vec3b>(i,j)=pixel;
  }
}

  • 方法二:

    1. 单通道读像素cv::Mat Iterator_<uchar>grayit,grayend;

      cv::Mat Iterator_<uchar> grayit,grayend;
for(grayit = grayimg.begin<uchar>(),
grayend = grayimg.end<uchar>();
grayit != grayend;
grayit++)
{
    *grayit=rand()%255;
}

    2. 彩色图像（BGR）-3通道

      MatIterator_<Vec3b> colorit,colorend;
for(colorit=colorimg.begin<Vec3b>(),
colorend=colorimg.end<Vec3>();
colorit != colorend;
colorit++) {
  (*colorit)[0]=rand()%255;//Blue
  (*colorit)[1]=rand()%255;//Green
  (*colorit)[2]=rand()%255;//Red
}

  • 方法三：

    1. 单通道

      for(int i=0;i<grayimg.rows;i++)
{
  //获取第i行首像素指针
  uchar *p=grayimg.ptr<uchar>(i);
  for(int j=0;j<grayimg.cols;j++)
  {
    //对第i行每个像素（byte）操作
    p[j] = (i+j)%255;
  }
}

彩色图像的直方图实现

//绘制彩色图像的直方图
void colorGraph()
{
	string src = "../img/tiger.jpg";
	Mat img = imread(src,IMREAD_COLOR);
	namedWindow("colorimg",WINDOW_AUTOSIZE);
	imshow("colorimg",img);
	Mat colorimg = Mat::zeros(600, 800, CV_8UC3);

	//存放3个通道的各个灰度级像素数
	vector<int> num0(256);
	vector<int> num1(256);
	vector<int> num2(256);

	//遍历图形像素，
	for (int i = 0; i < img.rows; i++)
	{
		for (int j = 0; j < img.cols; j++)
		{
			Vec3b pix;
			pix = img.at<Vec3b>(i,j);
			//递增每个灰度级的像素数
			num0[(int)pix[0]]++;
			num1[pix[1]]++;
			num2[pix[2]]++;

		}
	}
	//绘制直方图
	//x，y,o的坐标终点
	Point o = Point(0,800);
	Point x = Point(500,700);
	Point y = Point(0,0);

	line(colorimg, o, y, Scalar(255, 0, 0));
	line(colorimg, o, x, Scalar(255, 0, 0));

	auto max0 = max_element(num0.begin(), num0.end());
	auto max1 = max_element(num1.begin(), num1.end());
	auto max2 = max_element(num2.begin(), num2.end());

	int max = *max0 > *max1 ? *max0:*max1;
	max = max > *max2 ? max : *max2;
	cout << max<<endl;

	Point p[3][256];
  //生成坐标点，并绘制
	for (int i = 0; i < 3; i++)
	{
			if (i == 0)
			{
				for (int j = 0; j < 256; j++)
				{
					p[i][j].x = j*3;
					p[i][j].y = 800 - int(num0[j] * (700.0 / max));
				}
				for (int j = 1; j < 256; j++)
				{
					line(colorimg, p[i][j - 1], p[i][j], Scalar(255, 0, 0));
				}
			}
			else if (i == 1)
			{
				for (int j = 0; j < 256; j++)
				{
					p[i][j].x = j * 3;
					p[i][j].y = 800 - int(num1[j] * (700.0 / max));
				}
				for (int j = 1; j < 256; j++)
				{
					line(colorimg, p[i][j - 1], p[i][j], Scalar(0, 255, 0));
				}
			}
			else if(i==2)
			{
				for (int j = 0; j < 256; j++)
				{
					p[i][j].x = j * 3;
					p[i][j].y = 800 - int(num2[j] * (700.0 / max));
				}
				for (int j = 1; j < 256; j++)
				{
					line(colorimg, p[i][j - 1], p[i][j], Scalar(0, 0, 255));
				}
			}
	}

	namedWindow("color直方图", WINDOW_AUTOSIZE);
	imshow("color直方图", colorimg);

}

灰色图形直方图均衡化

/*均衡化*/
void mEqual()
{
    string src = "../img/tiger.jpg";
    Mat img = imread(src, IMREAD_GRAYSCALE);
    namedWindow("oequal", WINDOW_NORMAL);
    cvResizeWindow("oequal", 480, 319);
    imshow("oequal", img);
    Mat afimg = img.clone();
    //计算图像每个灰度级的像素数
    vector<int> n(256);
    //cout << img << endl;
    for (int i = 0; i < img.rows; i++)
    {
    	//uchar* p = img.ptr<uchar>(i);
    	for (int j = 0; j < img.cols; j++)
    	{
    		int index = img.at<uchar>(i, j);
    		n[index]++;
    	}
    }
    vector<double> pr(256);
    int pix_total = img.rows*img.cols;//图形像素总数
    //归一化后,计算概率
    for (int i = 0; i < 256; i++)
    {
    	pr[i] = (float)n[i] / pix_total;
    }
    //计算skT
    vector<double> skt(256);
    for (int i = 0; i < 256; i++)
    {
    	if (i == 0)
    	{
    		skt[i] = pr[i];
    	}
    	else
    	{
    		skt[i] = skt[i-1]+pr[i];
    	}
    }
    //计算sk
    vector<int> sk(256);
    for (int i = 0; i < 256; i++)
    {
    	sk[i] = int(255*skt[i]+0.5);
    }

    vector < double > ps(256);
    //计算ps
    for (int i = 0; i < 256; i++)
    {
    	ps[sk[i]] += pr[i];
    }

    //计算均衡化后每一灰度级的像素数
    vector<int> res(256);
    for (int i = 0; i < 256; i++)
    {
    	res[i] = (int)(ps[i] * pix_total);
    	cout << res[i] << endl;
    }
    //绘制
    Mat hist = Mat::zeros(600, 800, CV_8UC3);
    auto Max = max_element(res.begin(),res.end());
    //绘制坐标系
    Point o = Point(0, 800);
    Point x = Point(500, 700);
    Point y = Point(0, 0);

    //x轴
    line(hist, o, x, Scalar(255, 255, 255));
    //y轴
    line(hist, o, y, Scalar(255, 255, 255));

    Point pts[256];//灰度点
    //生成坐标点
    for (int i = 0; i < 256; i++)
    {
    	pts[i].x = i * 3;
    	pts[i].y = 800-int(res[i] * (700.0 / *Max));
    	//cout << pts[i].x << "," << pts[i].y << "," << ps[i] << endl;
    }
    //绘制线
    for (int i = 0; i < 256; i++)
    {
    	line(hist, Point(3*i,800), pts[i], Scalar(0, 0, 205));
    }
    //均衡化后图
    for (int i = 0; i < img.rows; i++)
    {
    	for (int j = 0; j < img.cols; j++)
    	{
    		afimg.at<uchar>(i,j)=sk[img.at<uchar>(i,j)];
    	}
    }
    namedWindow("equal图", WINDOW_AUTOSIZE);
    imshow("equal图", afimg);

    namedWindow("equal直方图", WINDOW_AUTOSIZE);
    imshow("equal直方图", hist);

}

编辑日期 2019.5.16

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