函数介绍

输入必须是单通道图像，最好是二值图

int  cv::connectedComponents (
    cv::InputArrayn image,                // input 8-bit single-channel 二值图
    cv::OutputArray labels,               // output label map
    int             connectivity = 8,     // 4- or 8-connected components
    int             ltype        = CV_32S // Output label type (CV_32S or CV_16U)
    );

int  cv::connectedComponentsWithStats (
    cv::InputArrayn image,                // input 8-bit single-channel 二值图
    cv::OutputArray labels,               // output label map
    cv::OutputArray stats,                // N x 5 matrix (CV_32S) of statistics: [x1, y1, width, height, area] 分别是连通域外接矩形和区域面积
    cv::OutputArray centroids,            // Nx2 CV_64F matrix of centroids: [cx0, cy0] 质心坐标
    int             connectivity = 8,     // 4- or 8-connected components
    int             ltype        = CV_32S // Output label type (CV_32S or CV_16U)
    );

C++ 代码示例

• 参数类型：

num int
labels     = Mat   w × h        CV_32S
stats      = Mat   num × 5      CV_32S
centroids  = Mat   num × 2      CV64F

• 获取随机色

vector<cv::Vec3b> getColors(int num)
{
    int step = 256 / num;
    vector<int> px;
    for (int i = 0; i < 256; i += step)
        px.push_back(i);

    vector<cv::Vec3b> colors(num);
    for (int j = 0; j < 3; j++)
    {
        random_shuffle(px.begin(), px.end());
        for (int i = 0; i < num; i++)
        {
            colors[i][j] = px[i];
        }
    }
    return colors;
}

• 给各个连通域上色： 只需要用到标签矩阵 labels

cv::Mat labels, stats, centroids;  //labels CV_32S; stats CV_32S; centroids CV64F;
num = cv::connectedComponentsWithStats(img, labels, stats, centroids);
auto colors = getColors(num);
cv:: Mat drawing= cv::Mat(img.size(), CV_8UC3, cv::Scalar(255, 255, 255));
for (int i = 0; i < img.size().height; i++) {
    for (int j = 0; j < img.size().width; j++) {
        index = labels.at<int>(i, j);
        drawing.at<cv::Vec3b>(i, j) = colors[index];
    }
}

• 过滤连通域，获取目标区域的标签： 只需要用到状态矩阵 stats （和质心坐标矩阵 centroids）

cv::Mat labels, stats, centroids;  //labels CV_32S; stats CV_32S; centroids CV64F;
num = cv::connectedComponentsWithStats(img, labels, stats, centroids);
int x0, y0, x1, y1, w, h;
std::cout << "符合规则的区域label: "<< std::endl;
for(int k = 0; k < num; k++){
	x0 = centroids.at<double>(k, 0);
	y0 = centroids.at<double>(k, 1);
    x1 = stats.at<int>(k, 0);  
    y1= stats.at<int>(k, 1);
    w = stats.at<int>(k, 2);
    h = stats.at<int>(k, 3);
    printf("Comp %2d: (%3d×%3d) from (%3d,%3d) 质心(%3d,%3d)\n", k, w, h, x1, y1, x0, y0);
    if(条件1 or 条件2 or ...) continue;
    std::cout << k << ", ";
}
std::cout << std::endl;

Python 代码示例

• 获取随机色

def getColors(n):
    colors = np.zeros((n, 3))
    colors[:, 0] = np.random.permutation(np.linspace(0, 256, n))
    colors[:, 1] = np.random.permutation(colors[:, 0])
    colors[:, 2] = np.random.permutation(colors[:, 1])
    return colors

• 给各个连通域上色： 只需要用到标签矩阵 labels

ccNum, labels, stats, centroids = cv.connectedComponentsWithStats(img)
colors = getColors(ccNum)
dst = np.ones((img.shape[0], img.shape[1], 3), dtype=np.uint8) * 255
for i in range(ccNum):
    dst[labels == i] = colors[i]

• 过滤连通域，获取目标区域的标签： 只需要用到状态矩阵 stats （和质心坐标矩阵 centroids）

ccNum, labels, stats, centroids = cv.connectedComponentsWithStats(img)
colors = getColors(ccNum)
print("符合规则的区域label: ")
for i in range(ccNum):
    x1, y1, width, height, count = stats[i]
    x0, y0 = centroids[i]  # 质心坐标
    if 条件1 or 条件2 or ...: 
    	continue
    print("符合规则的区域label: ")
    dst[labels == i] = colors[i];  // 可以随手上色

参考文章: jsxyhelu: OpenCV中的新函数connectedComponentsWithStats使用