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334 lines
12 KiB
334 lines
12 KiB
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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# 全局变量
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img_path = "" # 用于存储图像路径
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src = None # 用于存储已选择的图像
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X = None # 用于存储第一张图像
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Y = None # 用于存储第二张图像
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img_label = None # 用于存储显示选择的图片的标签
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edge = None # 用于存储处理后的图像
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ThreWin = None # 用于阈值化处理结果窗口
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VergeWin = None # 用于边缘检测结果窗口
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LineWin = None # 用于线条变化检测结果窗口
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def select_image(root):
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"""
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选择图像文件并显示在主窗口中
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"""
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global img_path, src, img_label, edge
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# 弹出文件选择对话框，选择图像文件
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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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# 使用 cv2.imdecode 加载图像，处理中文路径
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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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# 将图像从 BGR 转换为 RGB
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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，如果不存在则创建新的 Label
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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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# 使用 PIL 加载并缩放图像以适应标签大小
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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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"""
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显示已选择的图像
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"""
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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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"""
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动态调整图像大小以适应窗口大小
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"""
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img_aspect = img.shape[1] / img.shape[0] # 计算图像宽高比
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new_aspect = event.width / event.height # 计算新窗口的宽高比
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# 根据宽高比调整图像大小
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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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# 调整图像大小并更新显示
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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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"""
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保存处理后的图像
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"""
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global edge
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# 弹出文件保存对话框
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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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def threshold(root):
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"""
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对图像进行阈值化处理并显示结果
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"""
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global src, ThreWin, 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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# 转变图像为灰度图
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gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
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# TRIANGLE 自适应阈值
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ret, TRIANGLE_img = cv2.threshold(gray, 0, 255, cv2.THRESH_TRIANGLE)
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# OTSU 自适应阈值
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ret, OTSU_img = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
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# TRUNC 截断阈值(200)
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ret, TRUNC_img = cv2.threshold(gray, 200, 255, cv2.THRESH_TRUNC)
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# TOZERO 归零阈值(100)
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ret, TOZERO__img = cv2.threshold(gray, 100, 255, cv2.THRESH_TOZERO)
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# 将处理后的图像拼接在一起
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combined = np.hstack((TRIANGLE_img, OTSU_img, TRUNC_img, TOZERO__img))
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# 更新 edge 变量
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edge = Image.fromarray(combined)
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# 创建 Toplevel 窗口用于显示处理结果
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try:
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ThreWin.destroy()
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except Exception as e:
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print("NVM")
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finally:
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ThreWin = Toplevel()
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ThreWin.attributes('-topmost', True)
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ThreWin.geometry("720x300")
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ThreWin.resizable(True, True) # 可缩放
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ThreWin.title("阈值化结果")
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# 显示图像
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LabelPic = tk.Label(ThreWin, 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(ThreWin, 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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def verge(root):
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"""
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对图像进行边缘检测并显示结果
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"""
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global src, VergeWin, 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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# 转变图像为灰度图
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grayImage = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
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# 1. Roberts 算子
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kernelx = np.array([[-1, 0], [0, 1]], dtype=int)
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kernely = np.array([[0, -1], [1, 0]], dtype=int)
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x = cv2.filter2D(grayImage, cv2.CV_16S, kernelx)
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y = cv2.filter2D(grayImage, cv2.CV_16S, kernely)
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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Roberts = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
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# 2. Sobel 算子
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x = cv2.Sobel(grayImage, cv2.CV_16S, 1, 0)
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y = cv2.Sobel(grayImage, cv2.CV_16S, 0, 1)
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
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# 3. 拉普拉斯算法 & 高斯滤波
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gray = cv2.GaussianBlur(grayImage, (5, 5), 0, 0)
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dst = cv2.Laplacian(gray, cv2.CV_16S, ksize=3)
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Laplacian = cv2.convertScaleAbs(dst)
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# 4. LoG 边缘算子 & 边缘扩充 & 高斯滤波
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gray = cv2.copyMakeBorder(grayImage, 2, 2, 2, 2, borderType=cv2.BORDER_REPLICATE)
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image = cv2.GaussianBlur(gray, (3, 3), 0, 0)
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#使用Numpy定义LoG算子
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m1 = np.array(
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[[0, 0, -1, 0, 0], [0, -1, -2, -1, 0], [-1, -2, 16, -2, -1], [0, -1, -2, -1, 0], [0, 0, -1, 0, 0]])
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image1 = np.zeros(image.shape)
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rows = image.shape[0]
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cols = image.shape[1]
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for i in range(2, rows - 2):
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for j in range(2, cols - 2):
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image1[i, j] = np.sum((m1 * image[i - 2:i + 3, j - 2:j + 3]))
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Log = cv2.convertScaleAbs(image1)
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# 5. Canny 边缘检测
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image = cv2.GaussianBlur(grayImage, (3, 3), 0)
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gradx = cv2.Sobel(image, cv2.CV_16SC1, 1, 0)
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grady = cv2.Sobel(image, cv2.CV_16SC1, 0, 1)
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edge_output = cv2.Canny(gradx, grady, 50, 150)
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# 调整大小以匹配原始图像大小
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Roberts = cv2.resize(Roberts, (grayImage.shape[1], grayImage.shape[0]))
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Sobel = cv2.resize(Sobel, (grayImage.shape[1], grayImage.shape[0]))
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Laplacian = cv2.resize(Laplacian, (grayImage.shape[1], grayImage.shape[0]))
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Log = cv2.resize(Log, (grayImage.shape[1], grayImage.shape[0]))
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edge_output = cv2.resize(edge_output, (grayImage.shape[1], grayImage.shape[0]))
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# 将结果水平堆叠在一起
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combined = np.hstack((Roberts, Sobel, Laplacian, Log, edge_output))
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# 更新 edge 变量为 PIL.Image 对象
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edge = Image.fromarray(combined)
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# 创建 Toplevel 窗口显示边缘检测结果
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try:
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VergeWin.destroy()
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except Exception as e:
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print("NVM")
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finally:
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VergeWin = Toplevel()
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VergeWin.attributes('-topmost', True)
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VergeWin.geometry("720x300")
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VergeWin.resizable(True, True) # 可缩放
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VergeWin.title("边缘检测结果")
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# 显示图像
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LabelPic = tk.Label(VergeWin, 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(VergeWin, 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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def line_chan(root):
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"""
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检测图像中的线条变化并显示结果
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"""
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global src, LineWin, 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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# 使用高斯模糊和 Canny 边缘检测处理图像
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img = cv2.GaussianBlur(src, (3, 3), 0)
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edges = cv2.Canny(img, 50, 150, apertureSize=3)
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# 使用 HoughLines 算法检测直线
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lines = cv2.HoughLines(edges, 1, np.pi / 2, 118)
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result = img.copy()
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for i_line in lines:
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for line in i_line:
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rho = line[0]
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theta = line[1]
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if (theta < (np.pi / 4.)) or (theta > (3. * np.pi / 4.0)): # 垂直直线
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pt1 = (int(rho / np.cos(theta)), 0)
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pt2 = (int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta)), result.shape[0])
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cv2.line(result, pt1, pt2, (0, 0, 255))
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else:
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pt1 = (0, int(rho / np.sin(theta)))
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pt2 = (result.shape[1], int((rho - result.shape[1] * np.cos(theta)) / np.sin(theta)))
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cv2.line(result, pt1, pt2, (0, 0, 255), 1)
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# 使用 HoughLinesP 算法检测直线段
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minLineLength = 200
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maxLineGap = 15
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linesP = cv2.HoughLinesP(edges, 1, np.pi / 180, 80, minLineLength, maxLineGap)
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result_P = img.copy()
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for i_P in linesP:
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for x1, y1, x2, y2 in i_P:
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cv2.line(result_P, (x1, y1), (x2, y2), (0, 255, 0), 3)
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# 将结果水平堆叠在一起
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combined = np.hstack((result, result_P))
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# 更新 edge 变量为 PIL.Image 对象
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edge = Image.fromarray(result)
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# 创建 Toplevel 窗口显示线条变化检测结果
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try:
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LineWin.destroy()
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except Exception as e:
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print("NVM")
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finally:
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LineWin = Toplevel()
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LineWin.attributes('-topmost', True)
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LineWin.geometry("720x300")
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LineWin.resizable(True, True) # 可缩放
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LineWin.title("线条变化检测结果")
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# 显示图像
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LabelPic = tk.Label(LineWin, text="IMG", width=720, height=240)
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image = ImageTk.PhotoImage(Image.fromarray(cv2.cvtColor(combined, cv2.COLOR_BGR2RGB)))
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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(LineWin, 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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