一，原圖和效果圖

二，程式碼

//#########################產生隨機顏色#########################
cv::Scalar icvprGetRandomColor()
{
	uchar r = 255 * (rand() / (1.0 + RAND_MAX));
	uchar g = 255 * (rand() / (1.0 + RAND_MAX));
	uchar b = 255 * (rand() / (1.0 + RAND_MAX));
	return cv::Scalar(b,g,r);
}
//#########################產生隨機顏色#########################

//########################種子填充法）#########################
void ConnectedCountBySeedFill(const cv::Mat& _binImg,cv::Mat& _lableImg,int &iConnectedAreaCount)
{
  //拓寬1個畫素的原因是：如果連通域在邊緣，執行此函式會異常崩潰，所以需要在周圍加一圈0值，確保連通域不在邊上
	//==========影象周圍拓寬1個畫素============================================
	int top,bottom;      //【新增邊界後的影象尺寸】
	int leftImage,rightImage;
	int borderType = BORDER_CONSTANT; //BORDER_REPLICATE
	//【初始化引數】
	top = (int)(1); bottom = (int)(1);
	leftImage = (int)(1); rightImage = (int)(1);
	Mat _binImg2,_binImg3;
	_binImg.copyTo(_binImg2);
		//初始化引數value
		Scalar value(0); //填充值
		//建立影象邊界
		copyMakeBorder(_binImg2,_binImg3,top,bottom,leftImage,rightImage,borderType,value);

	//==========影象周圍拓寬1個畫素============================================

	// connected component analysis (4-component) 
	// use seed filling algorithm 
	// 1. begin with a foreground pixel and push its foreground neighbors into a stack; 
	// 2. pop the top pixel on the stack and label it with the same label until the stack is empty 
	//  
	// foreground pixel: _binImg(x,y) = 1 
	// background pixel: _binImg(x,y) = 0 

	if (_binImg3.empty() ||
		_binImg3.type() != CV_8UC1)
	{
		return;
	}

	_lableImg.release();
	_binImg3.convertTo(_lableImg,CV_32SC1);
	int icount = 0;
	int label = 1; // start by 2 

	int rows = _binImg3.rows - 1;
	int cols = _binImg3.cols - 1;
	for (int i = 1; i < rows - 1; i++)
	{
		int* data = _lableImg.ptr<int>(i);  //取一行資料
		for (int j = 1; j < cols - 1; j++)
		{
			if (data[j] == 1)  //畫素不為0
			{
				std::stack<std::pair<int,int>> neighborPixels;   //新建一個棧
				neighborPixels.push(std::pair<int,int>(i,j));   // 畫素座標: <i,j> ，以該畫素為起點，尋找連通域 
				++label; // 開始一個新標籤，各連通域區別的標誌
				while (!neighborPixels.empty())
				{
					// 獲取堆疊中的頂部畫素並使用相同的標籤對其進行標記
					std::pair<int,int> curPixel = neighborPixels.top();
					int curX = curPixel.first;
					int curY = curPixel.second;
					_lableImg.at<int>(curX,curY) = label; //對影象對應位置的點進行標記

					// 彈出頂部畫素  （頂部畫素出棧）
					neighborPixels.pop();

						// 加入8鄰域點
						if (_lableImg.at<int>(curX,curY - 1) == 1)
						{// 左點 
							neighborPixels.push(std::pair<int,int>(curX,curY - 1)); //左邊點入棧
						}

						if (_lableImg.at<int>(curX,curY + 1) == 1)
						{// 右點 
							neighborPixels.push(std::pair<int,curY + 1)); //右邊點入棧
						}

						if (_lableImg.at<int>(curX - 1,curY) == 1)
						{// 上點 
							neighborPixels.push(std::pair<int,int>(curX - 1,curY)); //上邊點入棧
						}

						if (_lableImg.at<int>(curX + 1,curY) == 1)
						{// 下點 
							neighborPixels.push(std::pair<int,int>(curX + 1,curY)); //下邊點入棧
						}
						//===============補充到8連通域======================================================
						if (_lableImg.at<int>(curX - 1,curY - 1) == 1)
						{// 左上點 
							neighborPixels.push(std::pair<int,curY - 1)); //左上點入棧
						}

						if (_lableImg.at<int>(curX - 1,curY + 1) == 1)
						{// 右上點 
							neighborPixels.push(std::pair<int,curY + 1)); //右上點入棧
						}

						if (_lableImg.at<int>(curX + 1,curY - 1) == 1)
						{// 左下點 
							neighborPixels.push(std::pair<int,curY - 1)); //左下點入棧
						}

						if (_lableImg.at<int>(curX + 1,curY + 1) == 1)
						{// 右下點 
							neighborPixels.push(std::pair<int,curY + 1)); //右下點入棧
						}
					//===============補充到8連通域======================================================
				}
			}
		}
	}
	iConnectedAreaCount = label - 1; //因為label從2開始計數的
	int a = 0;
}
###########################################################
//#############新增顏色#####################################
Mat PaintColor(Mat src,int iConnectedAreaCount)
{
	int rows = src.rows;
	int cols = src.cols;

	//cv::Scalar(b,r);
	std::map<int,cv::Scalar> colors;
	for (int n = 1; n <= iConnectedAreaCount + 1; n++)
	{
		colors[n] = icvprGetRandomColor(); //根據不同標誌位產生隨機顏色

		cv::Scalar color = colors[n];
		int a = color[0];
		int b = color[1];
		int c = color[2];
		int d = 0;
	}

	Mat dst2(rows,cols,CV_8UC3);
	dst2 = cv::Scalar::all(0);
	for (int i = 0; i < rows; i++)
	{
		for (int j = 0; j < cols; j++)
		{
			int value = src.at<int>(i,j);
			if (value>1)
			{
				cv::Scalar color = colors[value];
				int a = color[0];
				int b = color[1];
				int c = color[2];
				dst2.at<Vec3b>(i,j)[0] = color[0];
				dst2.at<Vec3b>(i,j)[1] = color[1];
				dst2.at<Vec3b>(i,j)[2] = color[2];
			}
		}
	}
	return dst2;
}
//#############新增顏色##################################

//########呼叫##########################################
  Mat binImage = cv::imread("D:\\sxl\\處理圖片\\testImages\\22.jpg",0);
	threshold(binImage,binImage,50,1,CV_THRESH_BINARY_INV);

	// 連通域標記 
	Mat labelImg;
	int iConnectedAreaCount = 0; //連通域個數
	ConnectedCountBySeedFill(binImage,labelImg,iConnectedAreaCount);//針對黑底白字
	int a=iConnectedAreaCount;
	
	// 顯示結果
	Mat dstColor2=PaintColor(labelImg,iConnectedAreaCount);
	imshow("colorImg",dstColor2);

	Mat grayImg;
	labelImg *= 10;
	labelImg.convertTo(grayImg,CV_8UC1);
	imshow("labelImg",grayImg);

	waitKey(0);
//########呼叫##########################################

補充知識：Opencv快速獲取連通域

對於ndarray資料中的連通域查詢，opencv提供了介面，非常方便。

import cv2
import numpy as np

img = np.array([
  [0,255,],[0,0],255],[255,0]
],dtype=np.uint8)

num,labels = cv2.connectedComponents(img)
labels_dict = {i:[] for i in range(1,num+1)}
height,width = img.shape
for h in range(height):
  for w in range(width):
    if labels[h][w] in labels_dict:
      labels_dict[labels[h][w]].append([h,w])

cv2.connectedComponents()函式返回查詢到的連通域個數和對應的label。

上面程式碼返回連通域個數為4（包含值為0區域，可通過lables過濾）,labels結果如圖所示：

以上這篇使用OpenCV獲取圖片連通域數量,並用不同顏色標記函就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。