ADD file via upload

basic/image_sharp.py

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+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('<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(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('<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(AirsharWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
+                         command=savefile)
+    btn_save.pack(pady=10)
+
+    return