opencv-046-二值图像的联通组件寻找

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知识点

连通组件标记算法介绍
连接组件标记算法(connected component labeling algorithm)是图像分析中最常用的算法之一，算法的实质是扫描二值图像的每个像素点，对于像素值相同的而且相互连通分为相同的组(group),最终得到图像中所有的像素连通组件。扫描的方式可以是从上到下，从左到右，对于一幅有N个像素的图像来说，最大连通组件个数为N/2。扫描是基于每个像素单位，OpenCV中进行连通组件扫码调用的时候必须保证背景像素是黑色、前景像素是白色。最常见的连通组件扫码有如下两类算法：

  1. 一步法，基于图的搜索算法
  2. 两步法、基于扫描与等价类合并算法

API

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int cv::connectedComponents(
    InputArray 	image, // 输入二值图像，黑色背景
    OutputArray 	labels, // 输出的标记图像，背景index=0
    int 	connectivity = 8, // 连通域，默认是8连通
    int 	ltype = CV_32S // 输出的labels类型，默认是CV_32S
)

代码（c++,python）

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#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

RNG rng(12345);

void connected_component_demo(Mat &image);

/*
 * 二值图像的连通组件标记
 */
int main() {
    Mat src = imread("../images/rice.png");
    if (src.empty()) {
        cout << "could not load image.." << endl;
    }
    imshow("input", src);

    connected_component_demo(src);

    waitKey(0);
    return 0;
}

void connected_component_demo(Mat &image) {
    // extract labels
    Mat gray, binary;
    GaussianBlur(image, image, Size(3, 3), 0);
    cvtColor(image, gray, COLOR_BGR2GRAY);
    threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU);
    Mat labels = Mat::zeros(image.size(), CV_32S);
    int num_labels = connectedComponents(binary, labels, 8, CV_32S);
    cout << "total labels : " << num_labels - 1 << endl;
    vector<Vec3b> colors(num_labels);

    // 背景颜色
    colors[0] = Vec3b(0, 0, 0);

    // 目标颜色
    for (int i = 1; i < num_labels; ++i) {
        colors[i] = Vec3b(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256));
    }

    // 给结果着色
    Mat dst = Mat::zeros(image.size(), image.type());
    for (int row = 0; row < image.rows; ++row) {
        for (int col = 0; col < image.cols; ++col) {
            int label = labels.at<int>(row, col);
            if (label == 0) continue;
            dst.at<Vec3b>(row, col) = colors[label];
        }
    }

    imshow("result", dst);
}
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import cv2 as cv
import numpy as np


def connected_components_demo(src):
    src = cv.GaussianBlur(src, (3, 3), 0)
    gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
    ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
    cv.imshow("binary", binary)

    output = cv.connectedComponents(binary, connectivity=8, ltype=cv.CV_32S)
    num_labels = output[0]
    labels = output[1]
    colors = []
    for i in range(num_labels):
        b = np.random.randint(0, 256)
        g = np.random.randint(0, 256)
        r = np.random.randint(0, 256)
        colors.append((b, g, r))

    colors[0] = (0, 0, 0)
    h, w = gray.shape
    image = np.zeros((h, w, 3), dtype=np.uint8)
    for row in range(h):
        for col in range(w):
            image[row, col] = colors[labels[row, col]]

    cv.imshow("colored labels", image)
    cv.imwrite("D:/labels.png", image)
    print("total rice : ", num_labels - 1)


src = cv.imread("D:/images/rice.png")
h, w = src.shape[:2]
connected_components_demo(src)
cv.waitKey(0)
cv.destroyAllWindows()

结果

代码地址

github