OpenCV實現邊緣檢測（實踐篇一）

前言

上篇基礎篇已經實現opencv環境開發的配置以及圖像加載和顯示，下面這篇是基於圖像邊緣檢測實踐，這裏同樣使用C++語言，其實我個人更擅長python，使用python實現會更加簡潔，話不多說了，直接上代碼。。。

opencv實現邊緣檢測demo

#include <opencv2/core.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/opencv.hpp>
#include <iostream>
#include <string>
using namespace cv;
using namespace std;

int main(int argc, char** argv)
{
    String imageName("1.png"); // 圖片路徑

    Mat src,src_blur, src_gray;
    src = imread(imageName); // 讀取文件
    if (src.empty()) {
        return -1;
    }
    imshow("原圖", src);
    GaussianBlur(src, src_blur,Size(15,15),0);//高斯濾波，降噪
    cvtColor(src_blur, src_gray, COLOR_BGR2GRAY);//圖像轉換爲灰度

    Mat bw;
    adaptiveThreshold(~src_gray, bw, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 15, -2);
    //imshow("bw", bw);

    Mat horizontal = bw.clone();
    Mat vertical = bw.clone();
    //在水平軸上指定大小
    int horizontalsize = horizontal.cols / 30;
    //創建結構元素，用於通過形態運算提取水平線
    Mat horizontalStructure = getStructuringElement(MORPH_RECT, Size(horizontalsize, 1));

    erode(horizontal, horizontal, horizontalStructure, Point(-1, -1));
    dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1));
    imshow("horizontal", horizontal);

    //在垂直軸上指定大小
    int verticalsize = vertical.rows / 30;
    Mat verticalStructure = getStructuringElement(MORPH_RECT, Size(1, verticalsize));
    erode(vertical, vertical, verticalStructure, Point(-1, -1));
    dilate(vertical, vertical, verticalStructure, Point(-1, -1));
    imshow("vertical",vertical);
    //獲取反向垂直圖像
    bitwise_not(vertical, vertical);
    //imshow("vertical_bit",vertical);

    //根據邏輯提取邊緣和平滑圖像處理
    // Step 1 提取邊緣
    Mat edges;
    adaptiveThreshold(bw, edges, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 3, -2);
    imshow("edges", edges);

    // Step 2  擴張
    Mat kernel = Mat::ones(3, 3, CV_8UC1);
    dilate(edges, edges, kernel);
    //imshow("dilate", edges);

    // Step 3 模糊
    Mat smooth;
    vertical.copyTo(smooth);

    // Step 4 模糊平滑圖片
    blur(smooth, smooth, Size(5, 5));
    smooth.copyTo(vertical, edges);
    //imshow("smooth", vertical);
         
    waitKey(0); 
    return 0;
}

程序運行效果如下：

檢測識別效果還算可以，還有待改進，使用OpenCV函數 Canny來實現的話效果更好一些。