From 6c1bb98f7072e799bfdf2e17434ab43f913997e0 Mon Sep 17 00:00:00 2001 From: pos97em56 <10225101485@ecnu.stu.edu.cn> Date: Wed, 3 Jul 2024 20:16:44 +0800 Subject: [PATCH] Update image_sharp.py --- basic/image_sharp.py | 705 ++++++++++++++++++++----------------------- 1 file changed, 333 insertions(+), 372 deletions(-) diff --git a/basic/image_sharp.py b/basic/image_sharp.py index de67fe9..3c879ff 100644 --- a/basic/image_sharp.py +++ b/basic/image_sharp.py @@ -1,372 +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 - -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 - -FreqsharWin = 0 -AirsharWin = 0 - -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 select_image(root): - global img_path, src, img_label - - 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 - src = cv2.cvtColor(src, cv2.COLOR_BGR2RGB) - 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((200, 200)) - 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") - -#频域锐化 -def freq_shar(root): - global src, FreqsharWin, 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 - - # 理想高通滤波 - def Ideal_HighPassFilter(rows, cols, crow, ccol, D0=40): - # 创建空白图像以存储滤波结果 - Ideal_HighPass = np.zeros((rows, cols), np.uint8) - # 计算理想高通滤波器 - for i in range(rows): - for j in range(cols): - D = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2) - if D >= D0: - Ideal_HighPass[i, j] = 255 - # 应用滤波器到频域表示 - mask = Ideal_HighPass[:, :, np.newaxis] - fshift = dft_shift * mask - # 逆傅里叶变换以获得处理后的图像 - 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 - - #Butterworth高通滤波器 - def ButterWorth_HighPassFilter(rows, cols, crow, ccol, D0=40, n=2): - # 创建空白图像以存储滤波结果 - ButterWorth_HighPass = np.zeros((rows, cols), np.uint8) - - # 计算 Butterworth 高通滤波器 - for i in range(rows): - for j in range(cols): - D = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2) - if D == 0: - ButterWorth_HighPass[i, j] = 0 # 如果 D = 0，直接赋值为 0，避免除以零错误 - else: - ButterWorth_HighPass[i, j] = 255 / (1 + (D0 / D) ** (2 * n)) - - # 应用滤波器到频域表示 - mask = ButterWorth_HighPass[:, :, np.newaxis] - fshift = dft_shift * mask - - # 逆傅里叶变换以获得处理后的图像 - 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 - - #Gauss高通滤波器 - def Gauss_HighPassFilter(rows, cols, crow, ccol, D0=40): - # 创建空白图像以存储滤波结果 - Gauss_HighPass = np.zeros((rows, cols), np.uint8) - - # 计算 Gauss 高通滤波器 - for i in range(rows): - for j in range(cols): - D = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2) - Gauss_HighPass[i, j] = 255 * (1 - np.exp(-0.5 * (D ** 2) / (D0 ** 2))) - - # 应用滤波器到频域表示 - mask = Gauss_HighPass[:, :, 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) - - # 获取图像尺寸 - rows, cols = im.shape - crow, ccol = rows // 2, cols // 2 # 中心位置 - - # 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) - - # 理想高通滤波器 - Ideal_HighPass = Ideal_HighPassFilter(rows, cols, crow, ccol) - # 巴特沃斯高通滤波器 - ButterWorth_HighPass = ButterWorth_HighPassFilter(rows, cols, crow, ccol) - # 高斯高通滤波器 - Gauss_HighPass = Gauss_HighPassFilter(rows, cols, crow, ccol) - - combined = np.hstack((Ideal_HighPass, ButterWorth_HighPass, Gauss_HighPass)) - - # 更新 edge 变量 - edge = Image.fromarray(combined) - - # 创建Toplevel窗口 - try: - FreqsharWin.destroy() - except Exception as e: - print("NVM") - finally: - FreqsharWin = Toplevel() - FreqsharWin.attributes('-topmost', True) - FreqsharWin.geometry("720x300") - FreqsharWin.resizable(True, True) # 可缩放 - FreqsharWin.title("频域锐化结果") - - # 显示图像 - LabelPic = tk.Label(FreqsharWin, 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(FreqsharWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20, - command=savefile) - btn_save.pack(pady=10) - - return - -#空域锐化 -def air_shar(root): - global src, AirsharWin, 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 - - # 定义Roberts边缘检测算子 - def Roberts(Image_In, height, width): - # 创建输出图像 - Roberts = np.zeros((height - 1, width - 1), dtype=np.uint8) - - # 进行Roberts边缘检测 - for i in range(height - 1): - for j in range(width - 1): - # 计算Roberts响应 - tmp = abs(int(Image_In[i + 1, j + 1]) - int(Image_In[i, j])) + abs( - int(Image_In[i + 1, j]) - int(Image_In[i, j + 1])) - tmp = max(0, min(255, tmp)) # 确保结果在0到255之间 - Roberts[i, j] = tmp - - return Roberts - - # 定义Sobel边缘检测算子 - def Sobel(Image_In, height, width): - # 创建输出图像 - Image_Sobel = np.zeros((height - 2, width - 2), dtype=np.uint8) - - # 进行Sobel边缘检测 - for i in range(1, height - 1): - for j in range(1, width - 1): - # 使用Sobel算子计算水平和垂直方向的梯度 - tmp1 = abs(-int(Image_In[i - 1, j - 1]) - 2 * int(Image_In[i - 1, j]) - int(Image_In[i - 1, j + 1]) + - int(Image_In[i + 1, j - 1]) + 2 * int(Image_In[i + 1, j]) + int(Image_In[i + 1, j + 1])) - tmp2 = abs(-int(Image_In[i - 1, j - 1]) - 2 * int(Image_In[i, j - 1]) - int(Image_In[i + 1, j - 1]) + - int(Image_In[i - 1, j + 1]) + 2 * int(Image_In[i, j + 1]) + int(Image_In[i + 1, j + 1])) - tmp = tmp1 + tmp2 - tmp = max(0, min(255, tmp)) # 确保结果在0到255之间 - Image_Sobel[i - 1, j - 1] = tmp - - return Image_Sobel - - # 定义Prewitt边缘检测算子 - def Prewitt(Image_In, height, width): - # 创建输出图像 - Image_Prewitt = np.zeros((height - 2, width - 2), dtype=np.uint8) - - # 进行Prewitt边缘检测 - for i in range(1, height - 1): - for j in range(1, width - 1): - # 使用Prewitt算子计算水平和垂直方向的梯度 - tmp1 = abs(-int(Image_In[i - 1, j - 1]) - int(Image_In[i - 1, j]) - int(Image_In[i - 1, j + 1]) + - int(Image_In[i + 1, j - 1]) + int(Image_In[i + 1, j]) + int(Image_In[i + 1, j + 1])) - tmp2 = abs(-int(Image_In[i - 1, j - 1]) - int(Image_In[i, j - 1]) - int(Image_In[i + 1, j - 1]) + - int(Image_In[i - 1, j + 1]) + int(Image_In[i, j + 1]) + int(Image_In[i + 1, j + 1])) - tmp = tmp1 + tmp2 - tmp = max(0, min(255, tmp)) # 确保结果在0到255之间 - Image_Prewitt[i - 1, j - 1] = tmp - - return Image_Prewitt - - # 将彩色图像转换为灰度图像 - im = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) - - # 获取图像的尺寸 - height, width = im.shape - - # 使用各种算子进行边缘检测 - Rob = Roberts(im, height, width) # Roberts算子 - Sob = Sobel(im, height, width) # Sobel算子 - Pre = Prewitt(im, height, width) # Prewitt算子 - - # 找出最小的尺寸，以便进行裁剪使得结果图像尺寸一致 - min_height = min(Rob.shape[0], Sob.shape[0], Pre.shape[0]) - min_width = min(Rob.shape[1], Sob.shape[1], Pre.shape[1]) - - # 对所有结果进行裁剪，使它们的尺寸一致 - Rob = Rob[:min_height, :min_width] - Sob = Sob[:min_height, :min_width] - Pre = Pre[:min_height, :min_width] - - # 将三种边缘检测结果水平拼接成一张图像 - combined = np.hstack((Rob, Sob, Pre)) - - # 更新 edge 变量，这里假设 edge 是用来存储结果图像的变量 - - # 创建Toplevel窗口用于显示结果 - try: - AirsharWin.destroy() - except Exception as e: - print("NVM") - finally: - AirsharWin = Toplevel() - AirsharWin.attributes('-topmost', True) - AirsharWin.geometry("720x300") - AirsharWin.resizable(True, True) # 可缩放 - AirsharWin.title("空域锐化结果") - - # 显示图像 - LabelPic = tk.Label(AirsharWin, text="IMG", width=720, height=240) - image = ImageTk.PhotoImage(Image.fromarray(combined)) - LabelPic.image = image - LabelPic['image'] = image - - # 配置LabelPic以自适应窗口大小 - 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(AirsharWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20, - command=savefile) - btn_save.pack(pady=10) - - return +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 + +FreqsharWin = 0 +AirsharWin = 0 + +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") + +#频域锐化 +def freq_shar(root): + global src, FreqsharWin, 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 + + # 理想高通滤波 + def Ideal_HighPassFilter(rows, cols, crow, ccol, D0=40): + # 创建空白图像以存储滤波结果 + Ideal_HighPass = np.zeros((rows, cols), np.uint8) + # 计算理想高通滤波器 + for i in range(rows): + for j in range(cols): + D = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2) + if D >= D0: + Ideal_HighPass[i, j] = 255 + # 应用滤波器到频域表示 + mask = Ideal_HighPass[:, :, np.newaxis] + fshift = dft_shift * mask + # 逆傅里叶变换以获得处理后的图像 + 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 + + #Butterworth高通滤波器 + def ButterWorth_HighPassFilter(rows, cols, crow, ccol, D0=40, n=2): + # 创建空白图像以存储滤波结果 + ButterWorth_HighPass = np.zeros((rows, cols), np.uint8) + + # 计算 Butterworth 高通滤波器 + for i in range(rows): + for j in range(cols): + D = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2) + if D == 0: + ButterWorth_HighPass[i, j] = 0 # 如果 D = 0，直接赋值为 0，避免除以零错误 + else: + ButterWorth_HighPass[i, j] = 255 / (1 + (D0 / D) ** (2 * n)) + + # 应用滤波器到频域表示 + mask = ButterWorth_HighPass[:, :, np.newaxis] + fshift = dft_shift * mask + + # 逆傅里叶变换以获得处理后的图像 + 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 + + #Gauss高通滤波器 + def Gauss_HighPassFilter(rows, cols, crow, ccol, D0=40): + # 创建空白图像以存储滤波结果 + Gauss_HighPass = np.zeros((rows, cols), np.uint8) + + # 计算 Gauss 高通滤波器 + for i in range(rows): + for j in range(cols): + D = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2) + Gauss_HighPass[i, j] = 255 * (1 - np.exp(-0.5 * (D ** 2) / (D0 ** 2))) + + # 应用滤波器到频域表示 + mask = Gauss_HighPass[:, :, 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) + + # 获取图像尺寸 + rows, cols = im.shape + crow, ccol = rows // 2, cols // 2 # 中心位置 + + # 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) + + # 理想高通滤波器 + Ideal_HighPass = Ideal_HighPassFilter(rows, cols, crow, ccol) + # 巴特沃斯高通滤波器 + ButterWorth_HighPass = ButterWorth_HighPassFilter(rows, cols, crow, ccol) + # 高斯高通滤波器 + Gauss_HighPass = Gauss_HighPassFilter(rows, cols, crow, ccol) + + combined = np.hstack((Ideal_HighPass, ButterWorth_HighPass, Gauss_HighPass)) + + # 更新 edge 变量 + edge = Image.fromarray(combined) + + # 创建Toplevel窗口 + try: + FreqsharWin.destroy() + except Exception as e: + print("NVM") + finally: + FreqsharWin = Toplevel() + FreqsharWin.attributes('-topmost', True) + FreqsharWin.geometry("720x300") + FreqsharWin.resizable(True, True) # 可缩放 + FreqsharWin.title("频域锐化结果") + + # 显示图像 + LabelPic = tk.Label(FreqsharWin, 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(FreqsharWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20, + command=savefile) + btn_save.pack(pady=10) + + return + +#空域锐化 +def air_shar(root): + global src, AirsharWin, 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 + + # 定义Roberts边缘检测算子 + def Roberts(Image_In, height, width): + # 创建输出图像 + Roberts = np.zeros((height - 1, width - 1), dtype=np.uint8) + + # 进行Roberts边缘检测 + for i in range(height - 1): + for j in range(width - 1): + # 计算Roberts响应 + tmp = abs(int(Image_In[i + 1, j + 1]) - int(Image_In[i, j])) + abs( + int(Image_In[i + 1, j]) - int(Image_In[i, j + 1])) + tmp = max(0, min(255, tmp)) # 确保结果在0到255之间 + Roberts[i, j] = tmp + + return Roberts + + # 定义Sobel边缘检测算子 + def Sobel(Image_In, height, width): + # 创建输出图像 + Image_Sobel = np.zeros((height - 2, width - 2), dtype=np.uint8) + + # 进行Sobel边缘检测 + for i in range(1, height - 1): + for j in range(1, width - 1): + # 使用Sobel算子计算水平和垂直方向的梯度 + tmp1 = abs(-int(Image_In[i - 1, j - 1]) - 2 * int(Image_In[i - 1, j]) - int(Image_In[i - 1, j + 1]) + + int(Image_In[i + 1, j - 1]) + 2 * int(Image_In[i + 1, j]) + int(Image_In[i + 1, j + 1])) + tmp2 = abs(-int(Image_In[i - 1, j - 1]) - 2 * int(Image_In[i, j - 1]) - int(Image_In[i + 1, j - 1]) + + int(Image_In[i - 1, j + 1]) + 2 * int(Image_In[i, j + 1]) + int(Image_In[i + 1, j + 1])) + tmp = tmp1 + tmp2 + tmp = max(0, min(255, tmp)) # 确保结果在0到255之间 + Image_Sobel[i - 1, j - 1] = tmp + + return Image_Sobel + + # 定义Prewitt边缘检测算子 + def Prewitt(Image_In, height, width): + # 创建输出图像 + Image_Prewitt = np.zeros((height - 2, width - 2), dtype=np.uint8) + + # 进行Prewitt边缘检测 + for i in range(1, height - 1): + for j in range(1, width - 1): + # 使用Prewitt算子计算水平和垂直方向的梯度 + tmp1 = abs(-int(Image_In[i - 1, j - 1]) - int(Image_In[i - 1, j]) - int(Image_In[i - 1, j + 1]) + + int(Image_In[i + 1, j - 1]) + int(Image_In[i + 1, j]) + int(Image_In[i + 1, j + 1])) + tmp2 = abs(-int(Image_In[i - 1, j - 1]) - int(Image_In[i, j - 1]) - int(Image_In[i + 1, j - 1]) + + int(Image_In[i - 1, j + 1]) + int(Image_In[i, j + 1]) + int(Image_In[i + 1, j + 1])) + tmp = tmp1 + tmp2 + tmp = max(0, min(255, tmp)) # 确保结果在0到255之间 + Image_Prewitt[i - 1, j - 1] = tmp + + return Image_Prewitt + + # 将彩色图像转换为灰度图像 + im = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) + + # 获取图像的尺寸 + height, width = im.shape + + # 使用各种算子进行边缘检测 + Rob = Roberts(im, height, width) # Roberts算子 + Sob = Sobel(im, height, width) # Sobel算子 + Pre = Prewitt(im, height, width) # Prewitt算子 + + # 找出最小的尺寸，以便进行裁剪使得结果图像尺寸一致 + min_height = min(Rob.shape[0], Sob.shape[0], Pre.shape[0]) + min_width = min(Rob.shape[1], Sob.shape[1], Pre.shape[1]) + + # 对所有结果进行裁剪，使它们的尺寸一致 + Rob = Rob[:min_height, :min_width] + Sob = Sob[:min_height, :min_width] + Pre = Pre[:min_height, :min_width] + + # 将三种边缘检测结果水平拼接成一张图像 + combined = np.hstack((Rob, Sob, Pre)) + + # 更新 edge 变量，这里假设 edge 是用来存储结果图像的变量 + + # 创建Toplevel窗口用于显示结果 + try: + AirsharWin.destroy() + except Exception as e: + print("NVM") + finally: + AirsharWin = Toplevel() + AirsharWin.attributes('-topmost', True) + AirsharWin.geometry("720x300") + AirsharWin.resizable(True, True) # 可缩放 + AirsharWin.title("空域锐化结果") + + # 显示图像 + LabelPic = tk.Label(AirsharWin, text="IMG", width=720, height=240) + image = ImageTk.PhotoImage(Image.fromarray(combined)) + LabelPic.image = image + LabelPic['image'] = image + + # 配置LabelPic以自适应窗口大小 + 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(AirsharWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20, + command=savefile) + btn_save.pack(pady=10) + + return