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import tkinter as tk
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from tkinter import filedialog, messagebox
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from tkinter import Toplevel
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from PIL import Image, ImageTk
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import numpy as np
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import cv2
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import os
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img_path = "" # 全局变量，用于存储图像路径
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src = None # 全局变量，用于存储已选择的图像
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img_label = None # 全局变量，用于存储显示选择的图片的标签
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edge = None
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FreqsharWin = 0
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AirsharWin = 0
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def select_image(root):
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global img_path, src, img_label, edge
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img_path = filedialog.askopenfilename(filetypes=[("Image files", "*.jpg;*.png;*.jpeg;*.bmp")])
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if img_path:
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# 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
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img_path_fixed = os.path.normpath(img_path)
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# 图像输入
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src_temp = cv2.imdecode(np.fromfile(img_path_fixed, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
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if src_temp is None:
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messagebox.showerror("错误", "无法读取图片，请选择有效的图片路径")
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return
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src = cv2.cvtColor(src_temp, cv2.COLOR_BGR2RGB)
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# 检查 img_label 是否存在且有效
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if img_label is None or not img_label.winfo_exists():
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img_label = tk.Label(root)
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img_label.pack(side=tk.TOP, pady=10)
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img = Image.open(img_path)
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img.thumbnail((160, 160))
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img_tk = ImageTk.PhotoImage(img)
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img_label.configure(image=img_tk)
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img_label.image = img_tk
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# 定义 edge 变量为 PIL.Image 对象，以便稍后保存
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edge = Image.fromarray(src)
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else:
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messagebox.showerror("错误", "没有选择图片路径")
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def select_image(root):
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global img_path, src, img_label
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img_path = filedialog.askopenfilename(filetypes=[("Image files", "*.jpg;*.png;*.jpeg;*.bmp")])
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if img_path:
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# 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
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img_path_fixed = os.path.normpath(img_path)
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# 图像输入
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src_temp = cv2.imdecode(np.fromfile(img_path_fixed, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
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if src_temp is None:
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messagebox.showerror("错误", "无法读取图片，请选择有效的图片路径")
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return
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src = cv2.cvtColor(src_temp, cv2.COLOR_BGR2RGB)
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# 检查 img_label 是否存在且有效
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if img_label is None or not img_label.winfo_exists():
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img_label = tk.Label(root)
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img_label.pack(side=tk.TOP, pady=10)
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img = Image.open(img_path)
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img.thumbnail((160, 160))
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img_tk = ImageTk.PhotoImage(img)
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img_label.configure(image=img_tk)
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img_label.image = img_tk
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src = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
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else:
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messagebox.showerror("错误", "没有选择图片路径")
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def show_selected_image(root):
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global img_label
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img_label = tk.Label(root)
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img_label.pack(side=tk.TOP, pady=10)
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img = Image.open(img_path)
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img.thumbnail((200, 200))
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img_tk = ImageTk.PhotoImage(img)
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img_label.configure(image=img_tk)
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img_label.image = img_tk
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def changeSize(event, img, LabelPic):
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img_aspect = img.shape[1] / img.shape[0]
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new_aspect = event.width / event.height
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if new_aspect > img_aspect:
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new_width = int(event.height * img_aspect)
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new_height = event.height
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else:
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new_width = event.width
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new_height = int(event.width / img_aspect)
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resized_image = cv2.resize(img, (new_width, new_height))
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image1 = ImageTk.PhotoImage(Image.fromarray(resized_image))
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LabelPic.image = image1
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LabelPic['image'] = image1
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def savefile():
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global edge
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filename = filedialog.asksaveasfilename(defaultextension=".jpg", filetypes=[("JPEG files", "*.jpg"), ("PNG files", "*.png"), ("BMP files", "*.bmp")])
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if not filename:
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return
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# 确保 edge 变量已定义
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if edge is not None:
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try:
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edge.save(filename)
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messagebox.showinfo("保存成功", "图片保存成功！")
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except Exception as e:
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messagebox.showerror("保存失败", f"无法保存图片: {e}")
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else:
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messagebox.showerror("保存失败", "没有图像可保存")
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#频域锐化
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def freq_shar(root):
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global src, FreqsharWin, edge
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# 判断是否已经选取图片
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if src is None:
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messagebox.showerror("错误", "没有选择图片！")
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return
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# 理想高通滤波
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def Ideal_HighPassFilter(rows, cols, crow, ccol, D0=40):
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# 创建空白图像以存储滤波结果
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Ideal_HighPass = np.zeros((rows, cols), np.uint8)
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# 计算理想高通滤波器
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for i in range(rows):
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for j in range(cols):
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D = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2)
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if D >= D0:
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Ideal_HighPass[i, j] = 255
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# 应用滤波器到频域表示
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mask = Ideal_HighPass[:, :, np.newaxis]
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fshift = dft_shift * mask
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# 逆傅里叶变换以获得处理后的图像
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f_ishift = np.fft.ifftshift(fshift)
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img_back = cv2.idft(f_ishift)
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img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
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# 归一化图像到0-255
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cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX)
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img_back = np.uint8(img_back)
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return img_back
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#Butterworth高通滤波器
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def ButterWorth_HighPassFilter(rows, cols, crow, ccol, D0=40, n=2):
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# 创建空白图像以存储滤波结果
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ButterWorth_HighPass = np.zeros((rows, cols), np.uint8)
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# 计算 Butterworth 高通滤波器
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for i in range(rows):
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for j in range(cols):
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D = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2)
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if D == 0:
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ButterWorth_HighPass[i, j] = 0 # 如果 D = 0，直接赋值为 0，避免除以零错误
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else:
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ButterWorth_HighPass[i, j] = 255 / (1 + (D0 / D) ** (2 * n))
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# 应用滤波器到频域表示
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mask = ButterWorth_HighPass[:, :, np.newaxis]
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fshift = dft_shift * mask
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# 逆傅里叶变换以获得处理后的图像
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f_ishift = np.fft.ifftshift(fshift)
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img_back = cv2.idft(f_ishift)
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img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
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# 归一化图像到0-255
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cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX)
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img_back = np.uint8(img_back)
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return img_back
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#Gauss高通滤波器
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def Gauss_HighPassFilter(rows, cols, crow, ccol, D0=40):
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# 创建空白图像以存储滤波结果
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Gauss_HighPass = np.zeros((rows, cols), np.uint8)
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# 计算 Gauss 高通滤波器
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for i in range(rows):
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for j in range(cols):
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D = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2)
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Gauss_HighPass[i, j] = 255 * (1 - np.exp(-0.5 * (D ** 2) / (D0 ** 2)))
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# 应用滤波器到频域表示
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mask = Gauss_HighPass[:, :, np.newaxis]
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fshift = dft_shift * mask
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# 逆傅里叶变换以获得平滑后的图像
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f_ishift = np.fft.ifftshift(fshift)
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img_back = cv2.idft(f_ishift)
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img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
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# 归一化图像到0-255
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cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX)
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img_back = np.uint8(img_back)
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return img_back
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# 读取灰度图像
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im = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
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# 获取图像尺寸
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rows, cols = im.shape
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crow, ccol = rows // 2, cols // 2 # 中心位置
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# 获取图像的频域表示
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dft = cv2.dft(np.float32(im), flags=cv2.DFT_COMPLEX_OUTPUT)
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dft_shift = np.fft.fftshift(dft)
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# 理想高通滤波器
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Ideal_HighPass = Ideal_HighPassFilter(rows, cols, crow, ccol)
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# 巴特沃斯高通滤波器
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ButterWorth_HighPass = ButterWorth_HighPassFilter(rows, cols, crow, ccol)
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# 高斯高通滤波器
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Gauss_HighPass = Gauss_HighPassFilter(rows, cols, crow, ccol)
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combined = np.hstack((Ideal_HighPass, ButterWorth_HighPass, Gauss_HighPass))
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# 更新 edge 变量
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edge = Image.fromarray(combined)
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# 创建Toplevel窗口
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try:
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FreqsharWin.destroy()
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except Exception as e:
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print("NVM")
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finally:
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FreqsharWin = Toplevel()
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FreqsharWin.attributes('-topmost', True)
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FreqsharWin.geometry("720x300")
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FreqsharWin.resizable(True, True) # 可缩放
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FreqsharWin.title("频域锐化结果")
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# 显示图像
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LabelPic = tk.Label(FreqsharWin, text="IMG", width=720, height=240)
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image = ImageTk.PhotoImage(Image.fromarray(combined))
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LabelPic.image = image
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LabelPic['image'] = image
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LabelPic.bind('<Configure>', lambda event: changeSize(event, combined, LabelPic))
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LabelPic.pack(fill=tk.BOTH, expand=tk.YES)
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# 添加保存按钮
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btn_save = tk.Button(FreqsharWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
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command=savefile)
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btn_save.pack(pady=10)
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return
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#空域锐化
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def air_shar(root):
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global src, AirsharWin, edge
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# 判断是否已经选取图片
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if src is None:
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messagebox.showerror("错误", "没有选择图片！")
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return
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# 定义Roberts边缘检测算子
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def Roberts(Image_In, height, width):
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# 创建输出图像
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Roberts = np.zeros((height - 1, width - 1), dtype=np.uint8)
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# 进行Roberts边缘检测
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for i in range(height - 1):
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for j in range(width - 1):
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# 计算Roberts响应
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tmp = abs(int(Image_In[i + 1, j + 1]) - int(Image_In[i, j])) + abs(
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int(Image_In[i + 1, j]) - int(Image_In[i, j + 1]))
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tmp = max(0, min(255, tmp)) # 确保结果在0到255之间
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Roberts[i, j] = tmp
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return Roberts
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# 定义Sobel边缘检测算子
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def Sobel(Image_In, height, width):
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# 创建输出图像
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Image_Sobel = np.zeros((height - 2, width - 2), dtype=np.uint8)
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# 进行Sobel边缘检测
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for i in range(1, height - 1):
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for j in range(1, width - 1):
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# 使用Sobel算子计算水平和垂直方向的梯度
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tmp1 = abs(-int(Image_In[i - 1, j - 1]) - 2 * int(Image_In[i - 1, j]) - int(Image_In[i - 1, j + 1]) +
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int(Image_In[i + 1, j - 1]) + 2 * int(Image_In[i + 1, j]) + int(Image_In[i + 1, j + 1]))
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tmp2 = abs(-int(Image_In[i - 1, j - 1]) - 2 * int(Image_In[i, j - 1]) - int(Image_In[i + 1, j - 1]) +
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int(Image_In[i - 1, j + 1]) + 2 * int(Image_In[i, j + 1]) + int(Image_In[i + 1, j + 1]))
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tmp = tmp1 + tmp2
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tmp = max(0, min(255, tmp)) # 确保结果在0到255之间
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Image_Sobel[i - 1, j - 1] = tmp
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return Image_Sobel
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# 定义Prewitt边缘检测算子
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def Prewitt(Image_In, height, width):
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# 创建输出图像
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Image_Prewitt = np.zeros((height - 2, width - 2), dtype=np.uint8)
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# 进行Prewitt边缘检测
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for i in range(1, height - 1):
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for j in range(1, width - 1):
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# 使用Prewitt算子计算水平和垂直方向的梯度
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tmp1 = abs(-int(Image_In[i - 1, j - 1]) - int(Image_In[i - 1, j]) - int(Image_In[i - 1, j + 1]) +
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int(Image_In[i + 1, j - 1]) + int(Image_In[i + 1, j]) + int(Image_In[i + 1, j + 1]))
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tmp2 = abs(-int(Image_In[i - 1, j - 1]) - int(Image_In[i, j - 1]) - int(Image_In[i + 1, j - 1]) +
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int(Image_In[i - 1, j + 1]) + int(Image_In[i, j + 1]) + int(Image_In[i + 1, j + 1]))
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tmp = tmp1 + tmp2
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tmp = max(0, min(255, tmp)) # 确保结果在0到255之间
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Image_Prewitt[i - 1, j - 1] = tmp
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return Image_Prewitt
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# 将彩色图像转换为灰度图像
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im = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
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# 获取图像的尺寸
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height, width = im.shape
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# 使用各种算子进行边缘检测
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Rob = Roberts(im, height, width) # Roberts算子
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Sob = Sobel(im, height, width) # Sobel算子
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Pre = Prewitt(im, height, width) # Prewitt算子
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# 找出最小的尺寸，以便进行裁剪使得结果图像尺寸一致
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min_height = min(Rob.shape[0], Sob.shape[0], Pre.shape[0])
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min_width = min(Rob.shape[1], Sob.shape[1], Pre.shape[1])
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# 对所有结果进行裁剪，使它们的尺寸一致
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Rob = Rob[:min_height, :min_width]
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Sob = Sob[:min_height, :min_width]
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Pre = Pre[:min_height, :min_width]
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# 将三种边缘检测结果水平拼接成一张图像
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combined = np.hstack((Rob, Sob, Pre))
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# 更新 edge 变量，这里假设 edge 是用来存储结果图像的变量
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# 创建Toplevel窗口用于显示结果
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try:
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AirsharWin.destroy()
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except Exception as e:
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print("NVM")
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finally:
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AirsharWin = Toplevel()
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AirsharWin.attributes('-topmost', True)
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AirsharWin.geometry("720x300")
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AirsharWin.resizable(True, True) # 可缩放
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AirsharWin.title("空域锐化结果")
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# 显示图像
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LabelPic = tk.Label(AirsharWin, text="IMG", width=720, height=240)
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image = ImageTk.PhotoImage(Image.fromarray(combined))
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LabelPic.image = image
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LabelPic['image'] = image
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# 配置LabelPic以自适应窗口大小
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LabelPic.bind('<Configure>', lambda event: changeSize(event, combined, LabelPic))
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LabelPic.pack(fill=tk.BOTH, expand=tk.YES)
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# 添加保存按钮
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btn_save = tk.Button(AirsharWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
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command=savefile)
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btn_save.pack(pady=10)
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return
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