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pos97em56 5 months ago
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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
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