diff --git a/basic/image_split.py b/basic/image_split.py index b078e05..81b8610 100644 --- a/basic/image_split.py +++ b/basic/image_split.py @@ -1,333 +1,320 @@ -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('', 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('', 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('', 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) +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 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('', 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('', 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('', 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)