parent
6c1bb98f70
commit
4dc8b82e85
@ -1,336 +1,326 @@
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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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FreqsmoWin = None
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AirsmoWin = None
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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 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((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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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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#频域平滑
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def freq_smo(root):
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global src, FreqsmoWin, 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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# 理想低通滤波器
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def Ideal_LowPassFilter(rows, cols, crow, ccol, D0=20):
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# 创建一个与输入图像大小相同的空白图像
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Ideal_LowPass = np.zeros((rows, cols), dtype=np.uint8)
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# 创建理想低通滤波器
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for i in range(rows):
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for j in range(cols):
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x = i - crow
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y = j - ccol
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D = np.sqrt(x**2 + y**2)
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if D <= D0:
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Ideal_LowPass[i, j] = 255
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# 应用滤波器到频域表示
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mask = Ideal_LowPass[:, :, 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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# 布特沃斯低通滤波器
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def ButterWorth_LowPassFilter(rows, cols, crow, ccol, D0=20, n=2):
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# 创建一个与输入图像大小相同的空白图像
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ButterWorth_LowPass = np.zeros((rows, cols), dtype=np.uint8)
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# 创建巴特沃斯低通滤波器
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for i in range(rows):
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for j in range(cols):
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x = i - crow
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y = j - ccol
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D = np.sqrt(x ** 2 + y ** 2)
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ButterWorth_LowPass[i, j] = 255 / (1 + (D / D0) ** (2 * n))
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# 应用滤波器到频域表示
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mask = ButterWorth_LowPass[:, :, 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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# 高斯低通滤波器
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def Gauss_LowPassFilter(rows, cols, crow, ccol, D0=20):
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# 创建一个与输入图像大小相同的空白图像
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Gauss_LowPass = np.zeros((rows, cols), dtype=np.uint8)
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# 创建高斯低通滤波器
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for i in range(rows):
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for j in range(cols):
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x = i - crow
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y = j - ccol
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D = np.sqrt(x ** 2 + y ** 2)
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Gauss_LowPass[i, j] = 255 * 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_LowPass[:, :, 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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# 获取图像的尺寸
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rows, cols = im.shape
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crow, ccol = rows // 2, cols // 2
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# 理想低通滤波器
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Ideal_LowPass = Ideal_LowPassFilter(rows, cols, crow, ccol)
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# 巴特沃斯低通滤波器
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ButterWorth_LowPass = ButterWorth_LowPassFilter(rows, cols, crow, ccol)
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# 高斯低通滤波器
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Gauss_LowPass = Gauss_LowPassFilter(rows, cols, crow, ccol)
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combined = np.hstack((Ideal_LowPass, ButterWorth_LowPass, Gauss_LowPass))
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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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FreqsmoWin.destroy()
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except Exception as e:
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print("NVM")
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finally:
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FreqsmoWin = Toplevel()
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FreqsmoWin.attributes('-topmost', True)
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FreqsmoWin.geometry("720x300")
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FreqsmoWin.resizable(True, True) # 可缩放
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FreqsmoWin.title("频域平滑结果")
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# 显示图像
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LabelPic = tk.Label(FreqsmoWin, 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(FreqsmoWin, 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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#空域平滑
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def air_smo(root):
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global src, AirsmoWin, 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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# 均值平滑滤波
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def mean_filter(image, height, width):
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# 创建空白图像以存储滤波结果
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filtered_image = np.zeros((height - 2, width - 2), dtype=np.uint8)
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# 执行3x3均值滤波
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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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tmp = (int(image[i - 1, j - 1]) + int(image[i - 1, j]) + int(image[i - 1, j + 1]) +
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int(image[i, j - 1]) + int(image[i, j]) + int(image[i, j + 1]) +
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int(image[i + 1, j - 1]) + int(image[i + 1, j]) + int(image[i + 1, j + 1])) // 9
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filtered_image[i - 1, j - 1] = tmp
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return filtered_image
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# 中值平滑滤波
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def median_filter(image, height, width):
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# 创建空白图像以存储滤波结果
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filtered_image = np.zeros((height - 2, width - 2), dtype=np.uint8)
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# 执行3x3中值滤波
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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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# 取3x3邻域
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region = [
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image[i - 1, j - 1], image[i - 1, j], image[i - 1, j + 1],
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image[i, j - 1], image[i, j], image[i, j + 1],
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image[i + 1, j - 1], image[i + 1, j], image[i + 1, j + 1]
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]
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# 计算中值
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filtered_image[i - 1, j - 1] = np.median(region)
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return filtered_image
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# 5x5 中值平滑滤波
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def med_filter_5x5(image, height, width):
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# 创建空白图像以存储滤波结果
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filtered_image = np.zeros((height - 4, width - 4), dtype=np.uint8)
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# 执行5x5中值滤波
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for i in range(2, height - 2):
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for j in range(2, width - 2):
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# 取5x5邻域的所有值
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neighbors = [
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image[i - 2, j - 2], image[i - 2, j - 1], image[i - 2, j], image[i - 2, j + 1], image[i - 2, j + 2],
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image[i - 1, j - 2], image[i - 1, j - 1], image[i - 1, j], image[i - 1, j + 1], image[i - 1, j + 2],
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image[i, j - 2], image[i, j - 1], image[i, j], image[i, j + 1], image[i, j + 2],
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image[i + 1, j - 2], image[i + 1, j - 1], image[i + 1, j], image[i + 1, j + 1], image[i + 1, j + 2],
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image[i + 2, j - 2], image[i + 2, j - 1], image[i + 2, j], image[i + 2, j + 1], image[i + 2, j + 2]
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]
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# 计算中值
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filtered_image[i - 2, j - 2] = np.median(neighbors)
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return filtered_image
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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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height, width = im.shape
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# 邻域平均
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mean = mean_filter(im, height, width)
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# 中值滤波3x3
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median = median_filter(im, height, width)
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# 中值滤波5x5
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med = med_filter_5x5(im, height, width)
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min_height = min(mean.shape[0], median.shape[0], med.shape[0])
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min_width = min(mean.shape[1], median.shape[1], med.shape[1])
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mean_cropped = mean[:min_height, :min_width]
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median_cropped = median[:min_height, :min_width]
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med_cropped = med[:min_height, :min_width]
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combined = np.hstack((mean_cropped, median_cropped, med_cropped))
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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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AirsmoWin.destroy()
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except Exception as e:
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print("NVM")
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finally:
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AirsmoWin = Toplevel()
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AirsmoWin.attributes('-topmost', True)
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AirsmoWin.geometry("720x300")
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AirsmoWin.resizable(True, True) # 可缩放
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AirsmoWin.title("空域平滑结果")
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# 显示图像
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LabelPic = tk.Label(AirsmoWin, 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(AirsmoWin, 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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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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FreqsmoWin = None
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AirsmoWin = None
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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 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)
|
||||
|
||||
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('<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(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('<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(AirsmoWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
|
||||
command=savefile)
|
||||
btn_save.pack(pady=10)
|
||||
|
||||
return
|
||||
Loading…
Reference in new issue