diff --git a/basic/image_split.py b/basic/image_split.py
new file mode 100644
index 0000000..b078e05
--- /dev/null
+++ b/basic/image_split.py
@@ -0,0 +1,333 @@
+import tkinter as tk
+from tkinter import filedialog, messagebox
+from tkinter import Toplevel
+from PIL import Image, ImageTk
+import numpy as np
+import cv2
+import os
+
+# 全局变量
+img_path = ""  # 用于存储图像路径
+src = None  # 用于存储已选择的图像
+X = None  # 用于存储第一张图像
+Y = None  # 用于存储第二张图像
+img_label = None  # 用于存储显示选择的图片的标签
+edge = None  # 用于存储处理后的图像
+
+ThreWin = None  # 用于阈值化处理结果窗口
+VergeWin = None  # 用于边缘检测结果窗口
+LineWin = None  # 用于线条变化检测结果窗口
+
+def select_image(root):
+    """
+    选择图像文件并显示在主窗口中
+    """
+    global img_path, src, img_label, edge
+
+    # 弹出文件选择对话框，选择图像文件
+    img_path = filedialog.askopenfilename(filetypes=[("Image files", "*.jpg;*.png;*.jpeg;*.bmp")])
+    if img_path:
+        # 确保路径中的反斜杠正确处理，并使用 UTF-8 编码处理中文路径
+        img_path_fixed = os.path.normpath(img_path)
+
+        # 使用 cv2.imdecode 加载图像，处理中文路径
+        src_temp = cv2.imdecode(np.fromfile(img_path_fixed, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
+        if src_temp is None:
+            messagebox.showerror("错误", "无法读取图片，请选择有效的图片路径")
+            return
+        # 将图像从 BGR 转换为 RGB
+        src = cv2.cvtColor(src_temp, cv2.COLOR_BGR2RGB)
+
+        # 检查 img_label 是否存在且有效，如果不存在则创建新的 Label
+        if img_label is None or not img_label.winfo_exists():
+            img_label = tk.Label(root)
+            img_label.pack(side=tk.TOP, pady=10)
+
+        # 使用 PIL 加载并缩放图像以适应标签大小
+        img = Image.open(img_path)
+        img.thumbnail((160, 160))
+        img_tk = ImageTk.PhotoImage(img)
+        img_label.configure(image=img_tk)
+        img_label.image = img_tk
+
+        # 定义 edge 变量为 PIL.Image 对象，以便稍后保存
+        edge = Image.fromarray(src)
+    else:
+        messagebox.showerror("错误", "没有选择图片路径")
+
+def show_selected_image(root):
+    """
+    显示已选择的图像
+    """
+    global img_label
+    img_label = tk.Label(root)
+    img_label.pack(side=tk.TOP, pady=10)
+    img = Image.open(img_path)
+    img.thumbnail((160, 160))
+    img_tk = ImageTk.PhotoImage(img)
+    img_label.configure(image=img_tk)
+    img_label.image = img_tk
+
+def changeSize(event, img, LabelPic):
+    """
+    动态调整图像大小以适应窗口大小
+    """
+    img_aspect = img.shape[1] / img.shape[0]  # 计算图像宽高比
+    new_aspect = event.width / event.height  # 计算新窗口的宽高比
+
+    # 根据宽高比调整图像大小
+    if new_aspect > img_aspect:
+        new_width = int(event.height * img_aspect)
+        new_height = event.height
+    else:
+        new_width = event.width
+        new_height = int(event.width / img_aspect)
+
+    # 调整图像大小并更新显示
+    resized_image = cv2.resize(img, (new_width, new_height))
+    image1 = ImageTk.PhotoImage(Image.fromarray(resized_image))
+    LabelPic.image = image1
+    LabelPic['image'] = image1
+
+def savefile():
+    """
+    保存处理后的图像
+    """
+    global edge
+
+    # 弹出文件保存对话框
+    filename = filedialog.asksaveasfilename(defaultextension=".jpg", filetypes=[("JPEG files", "*.jpg"), ("PNG files", "*.png"), ("BMP files", "*.bmp")])
+    if not filename:
+        return
+    # 确保 edge 变量已定义
+    if edge is not None:
+        try:
+            edge.save(filename)
+            messagebox.showinfo("保存成功", "图片保存成功！")
+        except Exception as e:
+            messagebox.showerror("保存失败", f"无法保存图片: {e}")
+    else:
+        messagebox.showerror("保存失败", "没有图像可保存")
+
+def threshold(root):
+    """
+    对图像进行阈值化处理并显示结果
+    """
+    global src, ThreWin, edge
+
+    # 判断是否已经选取图片
+    if src is None:
+        messagebox.showerror("错误", "没有选择图片！")
+        return
+
+    # 转变图像为灰度图
+    gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
+
+    # TRIANGLE 自适应阈值
+    ret, TRIANGLE_img = cv2.threshold(gray, 0, 255, cv2.THRESH_TRIANGLE)
+    # OTSU 自适应阈值
+    ret, OTSU_img = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
+    # TRUNC 截断阈值(200)
+    ret, TRUNC_img = cv2.threshold(gray, 200, 255, cv2.THRESH_TRUNC)
+    # TOZERO 归零阈值(100)
+    ret, TOZERO__img = cv2.threshold(gray, 100, 255, cv2.THRESH_TOZERO)
+
+    # 将处理后的图像拼接在一起
+    combined = np.hstack((TRIANGLE_img, OTSU_img, TRUNC_img, TOZERO__img))
+
+    # 更新 edge 变量
+    edge = Image.fromarray(combined)
+
+    # 创建 Toplevel 窗口用于显示处理结果
+    try:
+        ThreWin.destroy()
+    except Exception as e:
+        print("NVM")
+    finally:
+        ThreWin = Toplevel()
+        ThreWin.attributes('-topmost', True)
+    ThreWin.geometry("720x300")
+    ThreWin.resizable(True, True)  # 可缩放
+    ThreWin.title("阈值化结果")
+
+    # 显示图像
+    LabelPic = tk.Label(ThreWin, text="IMG", width=720, height=240)
+    image = ImageTk.PhotoImage(Image.fromarray(combined))
+    LabelPic.image = image
+    LabelPic['image'] = image
+
+    LabelPic.bind('<Configure>', lambda event: changeSize(event, combined, LabelPic))
+    LabelPic.pack(fill=tk.BOTH, expand=tk.YES)
+
+    # 添加保存按钮
+    btn_save = tk.Button(ThreWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
+                         command=savefile)
+    btn_save.pack(pady=10)
+
+def verge(root):
+    """
+    对图像进行边缘检测并显示结果
+    """
+    global src, VergeWin, edge
+
+    # 判断是否已经选取图片
+    if src is None:
+        messagebox.showerror("错误", "没有选择图片！")
+        return
+
+    # 转变图像为灰度图
+    grayImage = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
+
+    # 1. Roberts 算子
+    kernelx = np.array([[-1, 0], [0, 1]], dtype=int)
+    kernely = np.array([[0, -1], [1, 0]], dtype=int)
+    x = cv2.filter2D(grayImage, cv2.CV_16S, kernelx)
+    y = cv2.filter2D(grayImage, cv2.CV_16S, kernely)
+    absX = cv2.convertScaleAbs(x)
+    absY = cv2.convertScaleAbs(y)
+    Roberts = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+    # 2. Sobel 算子
+    x = cv2.Sobel(grayImage, cv2.CV_16S, 1, 0)
+    y = cv2.Sobel(grayImage, cv2.CV_16S, 0, 1)
+    absX = cv2.convertScaleAbs(x)
+    absY = cv2.convertScaleAbs(y)
+    Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+    # 3. 拉普拉斯算法 & 高斯滤波
+    gray = cv2.GaussianBlur(grayImage, (5, 5), 0, 0)
+    dst = cv2.Laplacian(gray, cv2.CV_16S, ksize=3)
+    Laplacian = cv2.convertScaleAbs(dst)
+
+    # 4. LoG 边缘算子 & 边缘扩充 & 高斯滤波
+    gray = cv2.copyMakeBorder(grayImage, 2, 2, 2, 2, borderType=cv2.BORDER_REPLICATE)
+    image = cv2.GaussianBlur(gray, (3, 3), 0, 0)
+    #使用Numpy定义LoG算子
+    m1 = np.array(
+        [[0, 0, -1, 0, 0], [0, -1, -2, -1, 0], [-1, -2, 16, -2, -1], [0, -1, -2, -1, 0], [0, 0, -1, 0, 0]])
+    image1 = np.zeros(image.shape)
+    rows = image.shape[0]
+    cols = image.shape[1]
+    for i in range(2, rows - 2):
+        for j in range(2, cols - 2):
+            image1[i, j] = np.sum((m1 * image[i - 2:i + 3, j - 2:j + 3]))
+
+    Log = cv2.convertScaleAbs(image1)
+
+    # 5. Canny 边缘检测
+    image = cv2.GaussianBlur(grayImage, (3, 3), 0)
+    gradx = cv2.Sobel(image, cv2.CV_16SC1, 1, 0)
+    grady = cv2.Sobel(image, cv2.CV_16SC1, 0, 1)
+    edge_output = cv2.Canny(gradx, grady, 50, 150)
+
+    # 调整大小以匹配原始图像大小
+    Roberts = cv2.resize(Roberts, (grayImage.shape[1], grayImage.shape[0]))
+    Sobel = cv2.resize(Sobel, (grayImage.shape[1], grayImage.shape[0]))
+    Laplacian = cv2.resize(Laplacian, (grayImage.shape[1], grayImage.shape[0]))
+    Log = cv2.resize(Log, (grayImage.shape[1], grayImage.shape[0]))
+    edge_output = cv2.resize(edge_output, (grayImage.shape[1], grayImage.shape[0]))
+
+    # 将结果水平堆叠在一起
+    combined = np.hstack((Roberts, Sobel, Laplacian, Log, edge_output))
+
+    # 更新 edge 变量为 PIL.Image 对象
+    edge = Image.fromarray(combined)
+
+    # 创建 Toplevel 窗口显示边缘检测结果
+    try:
+        VergeWin.destroy()
+    except Exception as e:
+        print("NVM")
+    finally:
+        VergeWin = Toplevel()
+        VergeWin.attributes('-topmost', True)
+    VergeWin.geometry("720x300")
+    VergeWin.resizable(True, True)  # 可缩放
+    VergeWin.title("边缘检测结果")
+
+    # 显示图像
+    LabelPic = tk.Label(VergeWin, text="IMG", width=720, height=240)
+    image = ImageTk.PhotoImage(Image.fromarray(combined))
+    LabelPic.image = image
+    LabelPic['image'] = image
+
+    LabelPic.bind('<Configure>', lambda event: changeSize(event, combined, LabelPic))
+    LabelPic.pack(fill=tk.BOTH, expand=tk.YES)
+
+    # 添加保存按钮
+    btn_save = tk.Button(VergeWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
+                         command=savefile)
+    btn_save.pack(pady=10)
+
+
+def line_chan(root):
+    """
+    检测图像中的线条变化并显示结果
+    """
+    global src, LineWin, edge
+
+    # 判断是否已经选取图片
+    if src is None:
+        messagebox.showerror("错误", "没有选择图片！")
+        return
+
+    # 使用高斯模糊和 Canny 边缘检测处理图像
+    img = cv2.GaussianBlur(src, (3, 3), 0)
+    edges = cv2.Canny(img, 50, 150, apertureSize=3)
+
+    # 使用 HoughLines 算法检测直线
+    lines = cv2.HoughLines(edges, 1, np.pi / 2, 118)
+    result = img.copy()
+    for i_line in lines:
+        for line in i_line:
+            rho = line[0]
+            theta = line[1]
+            if (theta < (np.pi / 4.)) or (theta > (3. * np.pi / 4.0)):  # 垂直直线
+                pt1 = (int(rho / np.cos(theta)), 0)
+                pt2 = (int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta)), result.shape[0])
+                cv2.line(result, pt1, pt2, (0, 0, 255))
+            else:
+                pt1 = (0, int(rho / np.sin(theta)))
+                pt2 = (result.shape[1], int((rho - result.shape[1] * np.cos(theta)) / np.sin(theta)))
+                cv2.line(result, pt1, pt2, (0, 0, 255), 1)
+
+    # 使用 HoughLinesP 算法检测直线段
+    minLineLength = 200
+    maxLineGap = 15
+    linesP = cv2.HoughLinesP(edges, 1, np.pi / 180, 80, minLineLength, maxLineGap)
+    result_P = img.copy()
+    for i_P in linesP:
+        for x1, y1, x2, y2 in i_P:
+            cv2.line(result_P, (x1, y1), (x2, y2), (0, 255, 0), 3)
+
+    # 将结果水平堆叠在一起
+    combined = np.hstack((result, result_P))
+
+    # 更新 edge 变量为 PIL.Image 对象
+    edge = Image.fromarray(result)
+
+    # 创建 Toplevel 窗口显示线条变化检测结果
+    try:
+        LineWin.destroy()
+    except Exception as e:
+        print("NVM")
+    finally:
+        LineWin = Toplevel()
+        LineWin.attributes('-topmost', True)
+    LineWin.geometry("720x300")
+    LineWin.resizable(True, True)  # 可缩放
+    LineWin.title("线条变化检测结果")
+
+    # 显示图像
+    LabelPic = tk.Label(LineWin, text="IMG", width=720, height=240)
+    image = ImageTk.PhotoImage(Image.fromarray(cv2.cvtColor(combined, cv2.COLOR_BGR2RGB)))
+    LabelPic.image = image
+    LabelPic['image'] = image
+
+    LabelPic.bind('<Configure>', lambda event: changeSize(event, combined, LabelPic))
+    LabelPic.pack(fill=tk.BOTH, expand=tk.YES)
+
+    # 添加保存按钮
+    btn_save = tk.Button(LineWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
+                         command=savefile)
+    btn_save.pack(pady=10)