erode

Arithmetic operations/bitwise.cpp

@@ -0,0 +1,44 @@
+#include <opencv2/opencv.hpp>
+#include <iostream>
+
+using namespace std;
+using namespace cv;
+
+int main()
+{
+    string path1 = "../img/2.jpg";
+    string path2 = "../img/3.jpg";
+
+    Mat img1 = imread(path1);
+    Mat img2 = imread(path2);
+
+    if (img1.empty() || img2.empty())
+    {
+        cout << "无法读取图片，请检查路径！" << endl;
+        return -1;
+    }
+
+    if (img1.size() != img2.size())
+    {
+        cout << "尺寸不一致，正在自动调整 img2 的尺寸..." << endl;
+        // 将 img2 缩放到与 img1 相同的尺寸
+        resize(img2, img2, img1.size());
+    }
+
+    Mat result;
+// bitwise_and()	按位与操作	掩码操作、图像分割
+// bitwise_or()	按位或操作	图像叠加
+// bitwise_not()	按位取反操作	图像反色
+// bitwise_xor()	按位异或操作	图像差异检测
+    bitwise_and(img1, img2, result);
+
+    imshow("Original Image 1", img1);
+    imshow("Original Image 2", img2);
+    imshow("Multiply Result", result);
+
+    // 等待按键后关闭窗口
+    waitKey(0);
+    destroyAllWindows();
+
+    return 0;
+}

Arithmetic operations/multiply.cpp

@@ -0,0 +1,40 @@
+#include <opencv2/opencv.hpp>
+#include <iostream>
+
+using namespace std;
+using namespace cv;
+
+int main()
+{
+    string path1 = "../img/2.jpg";
+    string path2 = "../img/3.jpg";
+
+    Mat img1 = imread(path1);
+    Mat img2 = imread(path2);
+
+    if (img1.empty() || img2.empty())
+    {
+        cout << "无法读取图片，请检查路径！" << endl;
+        return -1;
+    }
+
+    if (img1.size() != img2.size())
+    {
+        cout << "尺寸不一致，正在自动调整 img2 的尺寸..." << endl;
+        // 将 img2 缩放到与 img1 相同的尺寸
+        resize(img2, img2, img1.size());
+    }
+
+    Mat result;
+    multiply(img1, img2, result);
+
+    imshow("Original Image 1", img1);
+    imshow("Original Image 2", img2);
+    imshow("Multiply Result", result);
+
+    // 等待按键后关闭窗口
+    waitKey(0);
+    destroyAllWindows();
+
+    return 0;
+}

Arithmetic operations/plus.cpp

@@ -13,12 +13,14 @@ int main()
     Mat img1 = imread(path1);
     Mat img2 = imread(path2);
 
-    if (img1.empty() || img2.empty()) {
+    if (img1.empty() || img2.empty())
+    {
         cout << "无法读取图片，请检查路径！" << endl;
         return -1;
     }
 
-    if (img1.size() != img2.size()) {
+    if (img1.size() != img2.size())
+    {
         cout << "尺寸不一致，正在自动调整 img2 的尺寸..." << endl;
         // 将 img2 缩放到与 img1 相同的尺寸
         resize(img2, img2, img1.size());

Arithmetic operations/subtract.cpp

@@ -13,12 +13,14 @@ int main()
     Mat img1 = imread(path1);
     Mat img2 = imread(path2);
 
-    if (img1.empty() || img2.empty()) {
+    if (img1.empty() || img2.empty())
+    {
         cout << "无法读取图片，请检查路径！" << endl;
         return -1;
     }
 
-    if (img1.size() != img2.size()) {
+    if (img1.size() != img2.size())
+    {
         cout << "尺寸不一致，正在自动调整 img2 的尺寸..." << endl;
         // 将 img2 缩放到与 img1 相同的尺寸
         resize(img2, img2, img1.size());

Blur/bilateral.cpp

@@ -0,0 +1,14 @@
+#include <opencv2/opencv.hpp>
+
+int main() {
+    cv::Mat src = cv::imread("image.jpg");
+    cv::Mat dst;
+
+    // 参数：输入, 输出, 邻域直径, 颜色空间标准差, 坐标空间标准差
+    // 颜色标准差越大，表示越宽的颜色范围会被混合
+    cv::bilateralFilter(src, dst, 9, 75, 75);
+
+    cv::imshow("Bilateral Filter", dst);
+    cv::waitKey(0);
+    return 0;
+}

Blur/blur.cpp

@@ -0,0 +1,13 @@
+#include <opencv2/opencv.hpp>
+
+int main() {
+    cv::Mat src = cv::imread("../img/2.jpg");
+    cv::Mat dst;
+
+    // 参数：输入, 输出, 卷积核大小 Size(w, h)
+    cv::blur(src, dst, cv::Size(5, 5));
+
+    cv::imshow("Mean Blur", dst);
+    cv::waitKey(0);
+    return 0;
+}

Blur/gauss.cpp

@@ -0,0 +1,14 @@
+#include <opencv2/opencv.hpp>
+
+int main() {
+    cv::Mat src = cv::imread("image.jpg");
+    cv::Mat dst;
+
+    // 参数：输入, 输出, 核大小, sigmaX(标准差), sigmaY
+    // 如果 sigmaY 为 0，则默认与 sigmaX 相等
+    cv::GaussianBlur(src, dst, cv::Size(5, 5), 0);
+
+    cv::imshow("Gaussian Blur", dst);
+    cv::waitKey(0);
+    return 0;
+}

Blur/median.cpp

@@ -0,0 +1,13 @@
+#include <opencv2/opencv.hpp>
+
+int main() {
+    cv::Mat src = cv::imread("image.jpg");
+    cv::Mat dst;
+
+    // 参数：输入, 输出, 核大小(必须是大于1的奇数整数，如3, 5, 7)
+    cv::medianBlur(src, dst, 5);
+
+    cv::imshow("Median Blur", dst);
+    cv::waitKey(0);
+    return 0;
+}

Morphology/erode.cpp

@@ -0,0 +1,34 @@
+#include <opencv2/opencv.hpp>
+#include <iostream>
+
+using namespace std;
+using namespace cv;
+
+int main() {
+    // 1. 读取图像 (0 表示以灰度模式读取)
+    Mat src = imread("../img/1.png", IMREAD_GRAYSCALE);
+
+    if (src.empty()) {
+        cout << "无法加载图像，请检查文件路径！" << endl;
+        return -1;
+    }
+
+    // 2. 定义结构元素 (Kernel)
+    // 使用 getStructuringElement 创建一个 5x5 的矩形结构元素
+    Mat kernel = getStructuringElement(MORPH_RECT, Size(5, 5));
+
+    // 3. 执行腐蚀操作
+    Mat eroded_image;
+    // 参数：输入, 输出, 结构元素, 锚点(默认-1,-1), 迭代次数
+    erode(src, eroded_image, kernel, Point(-1, -1), 1);
+
+    // 4. 显示结果
+    imshow("Original Image", src);
+    imshow("Eroded Image", eroded_image);
+
+    // 等待按键后关闭窗口
+    waitKey(0);
+    destroyAllWindows();
+
+    return 0;
+}

Threshold/adaptive.cpp

@@ -0,0 +1,15 @@
+#include <opencv2/opencv.hpp>
+
+int main() {
+    cv::Mat img = cv::imread("../img/2.jpg", cv::IMREAD_GRAYSCALE);
+    if (img.empty()) return -1;
+
+    cv::Mat dst;
+    // 参数依次为：输入, 输出, 最大值, 自适应方法, 阈值类型, 邻域大小(须为奇数), 常数C
+    cv::adaptiveThreshold(img, dst, 255, cv::ADAPTIVE_THRESH_MEAN_C, 
+                          cv::THRESH_BINARY, 11, 2);
+
+    cv::imshow("Adaptive Threshold", dst);
+    cv::waitKey(0);
+    return 0;
+}

Threshold/auto.cpp

@@ -0,0 +1,18 @@
+#include <opencv2/opencv.hpp>
+#include <iostream>
+
+int main() {
+    cv::Mat img = cv::imread("../img/2.jpg", cv::IMREAD_GRAYSCALE);
+    if (img.empty()) return -1;
+
+    cv::Mat dst;
+    // 注意：使用 THRESH_OTSU 时，输入的 thresh 参数 (0) 会被忽略
+    // 它是通过 “标志位组合” 来实现的：cv::THRESH_BINARY | cv::THRESH_OTSU
+    double otsuThresh = cv::threshold(img, dst, 0, 255, cv::THRESH_BINARY | cv::THRESH_OTSU);
+
+    std::cout << "Otsu 自动确定的阈值是: " << otsuThresh << std::endl;
+
+    cv::imshow("Otsu Threshold", dst);
+    cv::waitKey(0);
+    return 0;
+}

Threshold/simple.cpp

@@ -0,0 +1,20 @@
+#include <opencv2/opencv.hpp>
+#include <iostream>
+
+int main() {
+    // 读取灰度图
+    cv::Mat img = cv::imread("../img/2.jpg", cv::IMREAD_GRAYSCALE);
+    if (img.empty()) return -1;
+
+    cv::Mat dst;
+    double thresh = 127;
+    double maxVal = 255;
+
+    // 简单阈值处理：二进制阈值
+    // 返回值是处理时使用的阈值
+    double ret = cv::threshold(img, dst, thresh, maxVal, cv::THRESH_BINARY);
+
+    cv::imshow("Simple Threshold", dst);
+    cv::waitKey(0);
+    return 0;
+}