# 導(dǎo)入相關(guān)包
# 導(dǎo)入必要的包
import argparse
import cv2

# 解析構(gòu)建的參數(shù)解析器
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="path to input image")
ap.add_argument("-c", "--connectivity", type=int, default=4, help="connectivity for connected analysis")
args = vars(ap.parse_args())  # 將參數(shù)轉(zhuǎn)為字典格式

# 加載輸入圖像，將其轉(zhuǎn)換為灰度，并對(duì)其進(jìn)行閾值處理
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

output = cv2.connectedComponentsWithStats(thresh, args["connectivity"], cv2.CV_32S)
(numLabels, labels, stats, centroids) = output

# 遍歷每個(gè)連通分量
for i in range(0, numLabels):
    # 0表示的是背景連通分量，忽略
    if i == 0:
        text = "examining component {}/{} (background)".format(
            i + 1, numLabels)
    # otherwise, we are examining an actual connected component
    else:
        text = "examining component {}/{}".format(i + 1, numLabels)
    # 打印當(dāng)前的狀態(tài)信息
    print("[INFO] {}".format(text))
    # 提取當(dāng)前標(biāo)簽的連通分量統(tǒng)計(jì)信息和質(zhì)心
    x = stats[i, cv2.CC_STAT_LEFT]
    y = stats[i, cv2.CC_STAT_TOP]
    w = stats[i, cv2.CC_STAT_WIDTH]
    h = stats[i, cv2.CC_STAT_HEIGHT]
    area = stats[i, cv2.CC_STAT_AREA]
    (cX, cY) = centroids[i]

    # 可視化邊界框和當(dāng)前連通分量的質(zhì)心
    # clone原始圖，在圖上畫(huà)當(dāng)前連通分量的邊界框以及質(zhì)心
    output = image.copy()
    cv2.rectangle(output, (x, y), (x + w, y + h), (0, 255, 0), 3)
    cv2.circle(output, (int(cX), int(cY)), 4, (0, 0, 255), -1)

    # 創(chuàng)建掩碼
    componentMask = (labels == i).astype("uint8") * 255
    # 顯示輸出圖像和掩碼
    cv2.imshow("Output", output)
    cv2.imshow("Connected Component", componentMask)
    cv2.waitKey(0)

# 導(dǎo)入必要的包
import argparse
import cv2

# 解析構(gòu)建的參數(shù)解析器
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default="plate.jpg", help="path to input image")
ap.add_argument("-c", "--connectivity", type=int, default=4, help="connectivity for connected analysis")
args = vars(ap.parse_args())  # 將參數(shù)轉(zhuǎn)為字典格式

# 加載輸入圖像，將其轉(zhuǎn)換為灰度，并對(duì)其進(jìn)行閾值處理
image = cv2.imread(args["image"])
cv2.imshow("src", image)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv2.imshow("threshold", thresh)

# 對(duì)閾值化后的圖像應(yīng)用連通分量分析
output = cv2.connectedComponentsWithStats(thresh, args["connectivity"], cv2.CV_32S)
(numLabels, labels, stats, centroids) = output

# 遍歷每個(gè)連通分量
for i in range(0, numLabels):
    # 0表示的是背景連通分量，忽略
    if i == 0:
        text = "examining component {}/{} (background)".format(
            i + 1, numLabels)
    # otherwise, we are examining an actual connected component
    else:
        text = "examining component {}/{}".format(i + 1, numLabels)
    # 打印當(dāng)前的狀態(tài)信息
    print("[INFO] {}".format(text))
    # 提取當(dāng)前標(biāo)簽的連通分量統(tǒng)計(jì)信息和質(zhì)心
    x = stats[i, cv2.CC_STAT_LEFT]
    y = stats[i, cv2.CC_STAT_TOP]
    w = stats[i, cv2.CC_STAT_WIDTH]
    h = stats[i, cv2.CC_STAT_HEIGHT]
    area = stats[i, cv2.CC_STAT_AREA]
    (cX, cY) = centroids[i]

    # 可視化邊界框和當(dāng)前連通分量的質(zhì)心
    # clone原始圖，在圖上畫(huà)當(dāng)前連通分量的邊界框以及質(zhì)心
    output = image.copy()
    cv2.rectangle(output, (x, y), (x + w, y + h), (0, 255, 0), 3)
    cv2.circle(output, (int(cX), int(cY)), 4, (0, 0, 255), -1)

    # 創(chuàng)建掩碼
    componentMask = (labels == i).astype("uint8") * 255
    # 顯示輸出圖像和掩碼
    cv2.imshow("Output", output)
    cv2.imshow("Connected Component", componentMask)
    cv2.waitKey(0)

import numpy as np
import argparse
import cv2

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default="plate.jpg", help="path to image")
ap.add_argument("-c", "--connectivity", type=int, default=4, help="connectivity for connected component analysis")

args = vars(ap.parse_args())

# 加載圖像，轉(zhuǎn)為灰度，二值化
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY)

# 應(yīng)用連通分量分析
output = cv2.connectedComponentsWithStats(thresh, connectivity=args["connectivity"], ltype=cv2.CV_32S)
(numLabels, labels, stats, centriods) = output

mask = np.zeros(gray.shape, dtype="uint8")

for i in range(1, numLabels):  # 忽略背景
    x = stats[i, cv2.CC_STAT_LEFT]  # [i, 0]
    y = stats[i, cv2.CC_STAT_TOP]  # [i, 1]
    w = stats[i, cv2.CC_STAT_WIDTH]  # [i, 2]
    h = stats[i, cv2.CC_STAT_HEIGHT]  # [i, 3]
    area = stats[i, cv2.CC_STAT_AREA]  # [i, 4]
    # 確保寬高以及面積既不太大也不太小
    keepWidth = w > 50 and w < 500
    keepHeight = h > 150 and h < 650
    keepArea = area > 500 and area < 25000
    # 我使用print語(yǔ)句顯示每個(gè)連接組件的寬度、高度和面積，
    # 同時(shí)將它們單獨(dú)顯示在屏幕上。我記錄了車(chē)牌字符的寬度、高度和面積，并找到了它們的最小/最大值，
    # 對(duì)于您自己的應(yīng)用程序也應(yīng)該這樣做。

    if all((keepWidth, keepHeight, keepArea)):
        print("[INFO] keep connected component '{}'".format(i))
        componentMask = (labels == i).astype("uint8") * 255
        mask = cv2.bitwise_or(mask, componentMask)

cv2.imshow("Image", image)
cv2.imshow("Chracters", mask)
cv2.waitKey(0)

#include <opencv2/core/utility.hpp>
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
using namespace cv;
using namespace std;
Mat img;
int threshval = 100;
static void on_trackbar(int, void*)
{
    Mat bw = threshval < 128 ? (img < threshval) : (img > threshval);
    Mat labelImage(img.size(), CV_32S);
    int nLabels = connectedComponents(bw, labelImage, 8);
    std::vector<Vec3b> colors(nLabels);
    colors[0] = Vec3b(0, 0, 0);//background
    for(int label = 1; label < nLabels; ++label){
        colors[label] = Vec3b( (rand()&255), (rand()&255), (rand()&255) );
    }
    Mat dst(img.size(), CV_8UC3);
    for(int r = 0; r < dst.rows; ++r){
        for(int c = 0; c < dst.cols; ++c){
            int label = labelImage.at<int>(r, c);
            Vec3b &pixel = dst.at<Vec3b>(r, c);
            pixel = colors[label];
         }
     }
    imshow( "Connected Components", dst );
}
int main( int argc, const char** argv )
{
    CommandLineParser parser(argc, argv, "{@image|stuff.jpg|image for converting to a grayscale}");
    parser.about("\nThis program demonstrates connected components and use of the trackbar\n");
    parser.printMessage();
    cout << "\nThe image is converted to grayscale and displayed, another image has a trackbar\n"
            "that controls thresholding and thereby the extracted contours which are drawn in color\n";
    String inputImage = parser.get<string>(0);
    img = imread(samples::findFile(inputImage), IMREAD_GRAYSCALE);
    if(img.empty())
    {
        cout << "Could not read input image file: " << inputImage << endl;
        return EXIT_FAILURE;
    }
    imshow( "Image", img );
    namedWindow( "Connected Components", WINDOW_AUTOSIZE);
    createTrackbar( "Threshold", "Connected Components", &threshval, 255, on_trackbar );
    on_trackbar(threshval, 0);
    waitKey(0);
    return EXIT_SUCCESS;
}