From 4dc8b82e853554da579bc34c5749fc11aa1dbe8b Mon Sep 17 00:00:00 2001 From: pos97em56 <10225101485@ecnu.stu.edu.cn> Date: Wed, 3 Jul 2024 20:17:12 +0800 Subject: [PATCH] Update image_smooth.py --- basic/image_smooth.py | 660 +++++++++++++++++++++--------------------- 1 file changed, 325 insertions(+), 335 deletions(-) diff --git a/basic/image_smooth.py b/basic/image_smooth.py index 02e5f83..4ce7b29 100644 --- a/basic/image_smooth.py +++ b/basic/image_smooth.py @@ -1,336 +1,326 @@ -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 - -img_path = "" # 全局变量，用于存储图像路径 -src = None # 全局变量，用于存储已选择的图像 -img_label = None # 全局变量，用于存储显示选择的图片的标签 -edge = None - -FreqsmoWin = None -AirsmoWin = None - -def select_image(root): - global img_path, src, img_label, edge - - 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) - - # 图像输入 - 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 - src = cv2.cvtColor(src_temp, cv2.COLOR_BGR2RGB) - - # 检查 img_label 是否存在且有效 - if img_label is None or not img_label.winfo_exists(): - 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 - - # 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): - 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): - img_aspect = img.shape[1] / img.shape[0] - new_aspect = event.width / event.height - - 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) - - resized_image = cv2.resize(img, (new_width, new_height)) - image1 = ImageTk.PhotoImage(Image.fromarray(resized_image)) - LabelPic.image = image1 - LabelPic['image'] = image1 - -def savefile(): - global edge - - 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") - -#频域平滑 -def freq_smo(root): - global src, FreqsmoWin, 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 - - # 理想低通滤波器 - def Ideal_LowPassFilter(rows, cols, crow, ccol, D0=20): - # 创建一个与输入图像大小相同的空白图像 - Ideal_LowPass = np.zeros((rows, cols), dtype=np.uint8) - # 创建理想低通滤波器 - for i in range(rows): - for j in range(cols): - x = i - crow - y = j - ccol - D = np.sqrt(x**2 + y**2) - if D <= D0: - Ideal_LowPass[i, j] = 255 - # 应用滤波器到频域表示 - mask = Ideal_LowPass[:, :, np.newaxis] - fshift = dft_shift * mask - # 逆傅里叶变换以获得平滑后的图像 - f_ishift = np.fft.ifftshift(fshift) - img_back = cv2.idft(f_ishift) - img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1]) - # 归一化图像到0-255 - cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX) - img_back = np.uint8(img_back) - - return img_back - - # 布特沃斯低通滤波器 - def ButterWorth_LowPassFilter(rows, cols, crow, ccol, D0=20, n=2): - # 创建一个与输入图像大小相同的空白图像 - ButterWorth_LowPass = np.zeros((rows, cols), dtype=np.uint8) - # 创建巴特沃斯低通滤波器 - for i in range(rows): - for j in range(cols): - x = i - crow - y = j - ccol - D = np.sqrt(x ** 2 + y ** 2) - ButterWorth_LowPass[i, j] = 255 / (1 + (D / D0) ** (2 * n)) - # 应用滤波器到频域表示 - mask = ButterWorth_LowPass[:, :, np.newaxis] - fshift = dft_shift * mask - # 逆傅里叶变换以获得平滑后的图像 - f_ishift = np.fft.ifftshift(fshift) - img_back = cv2.idft(f_ishift) - img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1]) - # 归一化图像到0-255 - cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX) - img_back = np.uint8(img_back) - - return img_back - - # 高斯低通滤波器 - def Gauss_LowPassFilter(rows, cols, crow, ccol, D0=20): - # 创建一个与输入图像大小相同的空白图像 - Gauss_LowPass = np.zeros((rows, cols), dtype=np.uint8) - # 创建高斯低通滤波器 - for i in range(rows): - for j in range(cols): - x = i - crow - y = j - ccol - D = np.sqrt(x ** 2 + y ** 2) - Gauss_LowPass[i, j] = 255 * np.exp(-0.5 * (D ** 2) / (D0 ** 2)) - # 应用滤波器到频域表示 - mask = Gauss_LowPass[:, :, np.newaxis] - fshift = dft_shift * mask - # 逆傅里叶变换以获得平滑后的图像 - f_ishift = np.fft.ifftshift(fshift) - img_back = cv2.idft(f_ishift) - img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1]) - # 归一化图像到0-255 - cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX) - img_back = np.uint8(img_back) - - return img_back - - # 读取灰度图像 - im = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) - - # 获取图像的频域表示 - dft = cv2.dft(np.float32(im), flags=cv2.DFT_COMPLEX_OUTPUT) - dft_shift = np.fft.fftshift(dft) - - # 获取图像的尺寸 - rows, cols = im.shape - crow, ccol = rows // 2, cols // 2 - - # 理想低通滤波器 - Ideal_LowPass = Ideal_LowPassFilter(rows, cols, crow, ccol) - # 巴特沃斯低通滤波器 - ButterWorth_LowPass = ButterWorth_LowPassFilter(rows, cols, crow, ccol) - # 高斯低通滤波器 - Gauss_LowPass = Gauss_LowPassFilter(rows, cols, crow, ccol) - - combined = np.hstack((Ideal_LowPass, ButterWorth_LowPass, Gauss_LowPass)) - # 更新 edge 变量 - edge = Image.fromarray(combined) - - # 创建Toplevel窗口 - try: - FreqsmoWin.destroy() - except Exception as e: - print("NVM") - finally: - FreqsmoWin = Toplevel() - FreqsmoWin.attributes('-topmost', True) - FreqsmoWin.geometry("720x300") - FreqsmoWin.resizable(True, True) # 可缩放 - FreqsmoWin.title("频域平滑结果") - - # 显示图像 - LabelPic = tk.Label(FreqsmoWin, 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(FreqsmoWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20, - command=savefile) - btn_save.pack(pady=10) - - return - -#空域平滑 -def air_smo(root): - global src, AirsmoWin, 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 - - # 均值平滑滤波 - def mean_filter(image, height, width): - # 创建空白图像以存储滤波结果 - filtered_image = np.zeros((height - 2, width - 2), dtype=np.uint8) - - # 执行3x3均值滤波 - for i in range(1, height - 1): - for j in range(1, width - 1): - tmp = (int(image[i - 1, j - 1]) + int(image[i - 1, j]) + int(image[i - 1, j + 1]) + - int(image[i, j - 1]) + int(image[i, j]) + int(image[i, j + 1]) + - int(image[i + 1, j - 1]) + int(image[i + 1, j]) + int(image[i + 1, j + 1])) // 9 - filtered_image[i - 1, j - 1] = tmp - - return filtered_image - - # 中值平滑滤波 - def median_filter(image, height, width): - # 创建空白图像以存储滤波结果 - filtered_image = np.zeros((height - 2, width - 2), dtype=np.uint8) - - # 执行3x3中值滤波 - for i in range(1, height - 1): - for j in range(1, width - 1): - # 取3x3邻域 - region = [ - image[i - 1, j - 1], image[i - 1, j], image[i - 1, j + 1], - image[i, j - 1], image[i, j], image[i, j + 1], - image[i + 1, j - 1], image[i + 1, j], image[i + 1, j + 1] - ] - # 计算中值 - filtered_image[i - 1, j - 1] = np.median(region) - - return filtered_image - - # 5x5 中值平滑滤波 - def med_filter_5x5(image, height, width): - # 创建空白图像以存储滤波结果 - filtered_image = np.zeros((height - 4, width - 4), dtype=np.uint8) - - # 执行5x5中值滤波 - for i in range(2, height - 2): - for j in range(2, width - 2): - # 取5x5邻域的所有值 - neighbors = [ - image[i - 2, j - 2], image[i - 2, j - 1], image[i - 2, j], image[i - 2, j + 1], image[i - 2, j + 2], - image[i - 1, j - 2], image[i - 1, j - 1], image[i - 1, j], image[i - 1, j + 1], image[i - 1, j + 2], - image[i, j - 2], image[i, j - 1], image[i, j], image[i, j + 1], image[i, j + 2], - image[i + 1, j - 2], image[i + 1, j - 1], image[i + 1, j], image[i + 1, j + 1], image[i + 1, j + 2], - image[i + 2, j - 2], image[i + 2, j - 1], image[i + 2, j], image[i + 2, j + 1], image[i + 2, j + 2] - ] - # 计算中值 - filtered_image[i - 2, j - 2] = np.median(neighbors) - - return filtered_image - - # 读取灰度图像 - im = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) - - # 获取图像尺寸 - height, width = im.shape - - # 邻域平均 - mean = mean_filter(im, height, width) - # 中值滤波3x3 - median = median_filter(im, height, width) - # 中值滤波5x5 - med = med_filter_5x5(im, height, width) - - min_height = min(mean.shape[0], median.shape[0], med.shape[0]) - min_width = min(mean.shape[1], median.shape[1], med.shape[1]) - - mean_cropped = mean[:min_height, :min_width] - median_cropped = median[:min_height, :min_width] - med_cropped = med[:min_height, :min_width] - - combined = np.hstack((mean_cropped, median_cropped, med_cropped)) - # 更新 edge 变量 - edge = Image.fromarray(combined) - - # 创建Toplevel窗口 - try: - AirsmoWin.destroy() - except Exception as e: - print("NVM") - finally: - AirsmoWin = Toplevel() - AirsmoWin.attributes('-topmost', True) - AirsmoWin.geometry("720x300") - AirsmoWin.resizable(True, True) # 可缩放 - AirsmoWin.title("空域平滑结果") - - # 显示图像 - LabelPic = tk.Label(AirsmoWin, 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(AirsmoWin, 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 + +img_path = "" # 全局变量，用于存储图像路径 +src = None # 全局变量，用于存储已选择的图像 +img_label = None # 全局变量，用于存储显示选择的图片的标签 +edge = None + +FreqsmoWin = None +AirsmoWin = None + +def select_image(root): + global img_path, src, img_label, edge + + 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) + + # 图像输入 + 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 + src = cv2.cvtColor(src_temp, cv2.COLOR_BGR2RGB) + + # 检查 img_label 是否存在且有效 + if img_label is None or not img_label.winfo_exists(): + 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 + + # 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): + img_aspect = img.shape[1] / img.shape[0] + new_aspect = event.width / event.height + + 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) + + resized_image = cv2.resize(img, (new_width, new_height)) + image1 = ImageTk.PhotoImage(Image.fromarray(resized_image)) + LabelPic.image = image1 + LabelPic['image'] = image1 + +def savefile(): + global edge + + 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") + +#频域平滑 +def freq_smo(root): + global src, FreqsmoWin, 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 + + # 理想低通滤波器 + def Ideal_LowPassFilter(rows, cols, crow, ccol, D0=20): + # 创建一个与输入图像大小相同的空白图像 + Ideal_LowPass = np.zeros((rows, cols), dtype=np.uint8) + # 创建理想低通滤波器 + for i in range(rows): + for j in range(cols): + x = i - crow + y = j - ccol + D = np.sqrt(x**2 + y**2) + if D <= D0: + Ideal_LowPass[i, j] = 255 + # 应用滤波器到频域表示 + mask = Ideal_LowPass[:, :, np.newaxis] + fshift = dft_shift * mask + # 逆傅里叶变换以获得平滑后的图像 + f_ishift = np.fft.ifftshift(fshift) + img_back = cv2.idft(f_ishift) + img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1]) + # 归一化图像到0-255 + cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX) + img_back = np.uint8(img_back) + + return img_back + + # 布特沃斯低通滤波器 + def ButterWorth_LowPassFilter(rows, cols, crow, ccol, D0=20, n=2): + # 创建一个与输入图像大小相同的空白图像 + ButterWorth_LowPass = np.zeros((rows, cols), dtype=np.uint8) + # 创建巴特沃斯低通滤波器 + for i in range(rows): + for j in range(cols): + x = i - crow + y = j - ccol + D = np.sqrt(x ** 2 + y ** 2) + ButterWorth_LowPass[i, j] = 255 / (1 + (D / D0) ** (2 * n)) + # 应用滤波器到频域表示 + mask = ButterWorth_LowPass[:, :, np.newaxis] + fshift = dft_shift * mask + # 逆傅里叶变换以获得平滑后的图像 + f_ishift = np.fft.ifftshift(fshift) + img_back = cv2.idft(f_ishift) + img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1]) + # 归一化图像到0-255 + cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX) + img_back = np.uint8(img_back) + + return img_back + + # 高斯低通滤波器 + def Gauss_LowPassFilter(rows, cols, crow, ccol, D0=20): + # 创建一个与输入图像大小相同的空白图像 + Gauss_LowPass = np.zeros((rows, cols), dtype=np.uint8) + # 创建高斯低通滤波器 + for i in range(rows): + for j in range(cols): + x = i - crow + y = j - ccol + D = np.sqrt(x ** 2 + y ** 2) + Gauss_LowPass[i, j] = 255 * np.exp(-0.5 * (D ** 2) / (D0 ** 2)) + # 应用滤波器到频域表示 + mask = Gauss_LowPass[:, :, np.newaxis] + fshift = dft_shift * mask + # 逆傅里叶变换以获得平滑后的图像 + f_ishift = np.fft.ifftshift(fshift) + img_back = cv2.idft(f_ishift) + img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1]) + # 归一化图像到0-255 + cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX) + img_back = np.uint8(img_back) + + return img_back + + # 读取灰度图像 + im = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) + + # 获取图像的频域表示 + dft = cv2.dft(np.float32(im), flags=cv2.DFT_COMPLEX_OUTPUT) + dft_shift = np.fft.fftshift(dft) + + # 获取图像的尺寸 + rows, cols = im.shape + crow, ccol = rows // 2, cols // 2 + + # 理想低通滤波器 + Ideal_LowPass = Ideal_LowPassFilter(rows, cols, crow, ccol) + # 巴特沃斯低通滤波器 + ButterWorth_LowPass = ButterWorth_LowPassFilter(rows, cols, crow, ccol) + # 高斯低通滤波器 + Gauss_LowPass = Gauss_LowPassFilter(rows, cols, crow, ccol) + + combined = np.hstack((Ideal_LowPass, ButterWorth_LowPass, Gauss_LowPass)) + # 更新 edge 变量 + edge = Image.fromarray(combined) + + # 创建Toplevel窗口 + try: + FreqsmoWin.destroy() + except Exception as e: + print("NVM") + finally: + FreqsmoWin = Toplevel() + FreqsmoWin.attributes('-topmost', True) + FreqsmoWin.geometry("720x300") + FreqsmoWin.resizable(True, True) # 可缩放 + FreqsmoWin.title("频域平滑结果") + + # 显示图像 + LabelPic = tk.Label(FreqsmoWin, 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(FreqsmoWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20, + command=savefile) + btn_save.pack(pady=10) + + return + +#空域平滑 +def air_smo(root): + global src, AirsmoWin, 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 + + # 均值平滑滤波 + def mean_filter(image, height, width): + # 创建空白图像以存储滤波结果 + filtered_image = np.zeros((height - 2, width - 2), dtype=np.uint8) + + # 执行3x3均值滤波 + for i in range(1, height - 1): + for j in range(1, width - 1): + tmp = (int(image[i - 1, j - 1]) + int(image[i - 1, j]) + int(image[i - 1, j + 1]) + + int(image[i, j - 1]) + int(image[i, j]) + int(image[i, j + 1]) + + int(image[i + 1, j - 1]) + int(image[i + 1, j]) + int(image[i + 1, j + 1])) // 9 + filtered_image[i - 1, j - 1] = tmp + + return filtered_image + + # 中值平滑滤波 + def median_filter(image, height, width): + # 创建空白图像以存储滤波结果 + filtered_image = np.zeros((height - 2, width - 2), dtype=np.uint8) + + # 执行3x3中值滤波 + for i in range(1, height - 1): + for j in range(1, width - 1): + # 取3x3邻域 + region = [ + image[i - 1, j - 1], image[i - 1, j], image[i - 1, j + 1], + image[i, j - 1], image[i, j], image[i, j + 1], + image[i + 1, j - 1], image[i + 1, j], image[i + 1, j + 1] + ] + # 计算中值 + filtered_image[i - 1, j - 1] = np.median(region) + + return filtered_image + + # 5x5 中值平滑滤波 + def med_filter_5x5(image, height, width): + # 创建空白图像以存储滤波结果 + filtered_image = np.zeros((height - 4, width - 4), dtype=np.uint8) + + # 执行5x5中值滤波 + for i in range(2, height - 2): + for j in range(2, width - 2): + # 取5x5邻域的所有值 + neighbors = [ + image[i - 2, j - 2], image[i - 2, j - 1], image[i - 2, j], image[i - 2, j + 1], image[i - 2, j + 2], + image[i - 1, j - 2], image[i - 1, j - 1], image[i - 1, j], image[i - 1, j + 1], image[i - 1, j + 2], + image[i, j - 2], image[i, j - 1], image[i, j], image[i, j + 1], image[i, j + 2], + image[i + 1, j - 2], image[i + 1, j - 1], image[i + 1, j], image[i + 1, j + 1], image[i + 1, j + 2], + image[i + 2, j - 2], image[i + 2, j - 1], image[i + 2, j], image[i + 2, j + 1], image[i + 2, j + 2] + ] + # 计算中值 + filtered_image[i - 2, j - 2] = np.median(neighbors) + + return filtered_image + + # 读取灰度图像 + im = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) + + # 获取图像尺寸 + height, width = im.shape + + # 邻域平均 + mean = mean_filter(im, height, width) + # 中值滤波3x3 + median = median_filter(im, height, width) + # 中值滤波5x5 + med = med_filter_5x5(im, height, width) + + min_height = min(mean.shape[0], median.shape[0], med.shape[0]) + min_width = min(mean.shape[1], median.shape[1], med.shape[1]) + + mean_cropped = mean[:min_height, :min_width] + median_cropped = median[:min_height, :min_width] + med_cropped = med[:min_height, :min_width] + + combined = np.hstack((mean_cropped, median_cropped, med_cropped)) + # 更新 edge 变量 + edge = Image.fromarray(combined) + + # 创建Toplevel窗口 + try: + AirsmoWin.destroy() + except Exception as e: + print("NVM") + finally: + AirsmoWin = Toplevel() + AirsmoWin.attributes('-topmost', True) + AirsmoWin.geometry("720x300") + AirsmoWin.resizable(True, True) # 可缩放 + AirsmoWin.title("空域平滑结果") + + # 显示图像 + LabelPic = tk.Label(AirsmoWin, 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(AirsmoWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20, + command=savefile) + btn_save.pack(pady=10) + return \ No newline at end of file