# 导入所需模块 import cv2 from matplotlib import pyplot as plt import os import numpy as np import tkinter as tk from tkinter import ttk from PIL import ImageTk, Image from tkinter import filedialog # plt显示彩色图片 def eaowej(file_path): global history # plt显示灰度图片 def plt_show(img): plt.imshow(img, cmap='gray') plt.show() # 图像去噪灰度处理 def gray_guss(image): image = cv2.GaussianBlur(image, (3, 3), 0) gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) return gray_image # 读取待检测图片 origin_image = cv2.imread(file_path) # 复制一张图片，在复制图上进行图像操作，保留原图 image = origin_image.copy() # 图像去噪灰度处理 gray_image = gray_guss(image) # x方向上的边缘检测（增强边缘信息） Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0) absX = cv2.convertScaleAbs(Sobel_x) image = absX # 图像阈值化操作——获得二值化图 ret, image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU) # 显示灰度图像 plt_show(image) # 形态学（从图像中提取对表达和描绘区域形状有意义的图像分量）——闭操作 kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 10)) image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX,iterations = 1) # 显示灰度图像 plt_show(image) # 腐蚀（erode）和膨胀（dilate） kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 1)) kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 20)) #x方向进行闭操作（抑制暗细节） image = cv2.dilate(image, kernelX) image = cv2.erode(image, kernelX) #y方向的开操作 image = cv2.erode(image, kernelY) image = cv2.dilate(image, kernelY) # 中值滤波（去噪） image = cv2.medianBlur(image, 21) # 显示灰度图像 plt_show(image) # 获得轮廓 contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for item in contours: rect = cv2.boundingRect(item) x = rect[0] y = rect[1] weight = rect[2] height = rect[3] # 根据轮廓的形状特点，确定车牌的轮廓位置并截取图像 if (weight > (height * 3.5)) and (weight < (height * 4)): image = origin_image[y:y + height, x:x + weight] plt_show0(image) #车牌字符分割 # 图像去噪灰度处理 gray_image = gray_guss(image) # 图像阈值化操作——获得二值化图 ret, image = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU) plt_show(image) #膨胀操作，使“津”字膨胀为一个近似的整体，为分割做准备 kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)) image = cv2.dilate(image, kernel) plt_show(image) # 查找轮廓 contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) words = [] word_images = [] #对所有轮廓逐一操作 for item in contours: word = [] rect = cv2.boundingRect(item) x = rect[0] y = rect[1] weight = rect[2] height = rect[3] word.append(x) word.append(y) word.append(weight) word.append(height) words.append(word) # 排序，车牌号有顺序。words是一个嵌套列表 words = sorted(words,key=lambda s:s[0],reverse=False) i = 0 #word中存放轮廓的起始点和宽高 for word in words: # 筛选字符的轮廓 if (word[3] > (word[2] * 1.5)) and (word[3] < (word[2] * 3.5)) and (word[2] > 25): i = i+1 splite_image = image[word[1]:word[1] + word[3], word[0]:word[0] + word[2]] word_images.append(splite_image) #print(i) #print(words) for i,j in enumerate(word_images): plt.subplot(1,7,i+1) plt.imshow(word_images[i],cmap='gray') plt.show() #模版匹配 # 准备模板(template[0-9]为数字模板；) template = ['0','1','2','3','4','5','6','7','8','9', 'A','B','C','D','E','F','G','H','J','K', 'L','M','N','P','Q','R','S','T','U','V', 'W','X','Y','Z','藏','川','鄂','甘','赣', '贵','桂','黑','沪','吉','冀','津','晋','京', '辽','鲁','蒙','闽','宁','青','琼','陕','苏', '皖','湘','新','渝','豫','粤','云','浙'] # 读取一个文件夹下的所有图片，输入参数是文件名，返回模板文件地址列表 def read_directory(directory_name): referImg_list = [] for filename in os.listdir(directory_name): referImg_list.append(directory_name + "/" + filename) return referImg_list # 获得中文模板列表（只匹配车牌的第一个字符） def get_chinese_words_list(): chinese_words_list = [] for i in range(34,64): #将模板存放在字典中 c_word = read_directory('./refer1/'+ template[i]) chinese_words_list.append(c_word) return chinese_words_list chinese_words_list = get_chinese_words_list() # 获得英文模板列表（只匹配车牌的第二个字符） def get_eng_words_list(): eng_words_list = [] for i in range(10,34): e_word = read_directory('./refer1/'+ template[i]) eng_words_list.append(e_word) return eng_words_list eng_words_list = get_eng_words_list() # 获得英文和数字模板列表（匹配车牌后面的字符） def get_eng_num_words_list(): eng_num_words_list = [] for i in range(0,34): word = read_directory('./refer1/'+ template[i]) eng_num_words_list.append(word) return eng_num_words_list eng_num_words_list = get_eng_num_words_list() # 读取一个模板地址与图片进行匹配，返回得分 def template_score(template,image): #将模板进行格式转换 template_img=cv2.imdecode(np.fromfile(template,dtype=np.uint8),1) template_img = cv2.cvtColor(template_img, cv2.COLOR_RGB2GRAY) #模板图像阈值化处理——获得黑白图 ret, template_img = cv2.threshold(template_img, 0, 255, cv2.THRESH_OTSU) # height, width = template_img.shape # image_ = image.copy() # image_ = cv2.resize(image_, (width, height)) image_ = image.copy() #获得待检测图片的尺寸 height, width = image_.shape # 将模板resize至与图像一样大小 template_img = cv2.resize(template_img, (width, height)) # 模板匹配，返回匹配得分 result = cv2.matchTemplate(image_, template_img, cv2.TM_CCOEFF) return result[0][0] # 对分割得到的字符逐一匹配 def template_matching(word_images): results = [] for index,word_image in enumerate(word_images): if index==0: best_score = [] for chinese_words in chinese_words_list: score = [] for chinese_word in chinese_words: result = template_score(chinese_word,word_image) score.append(result) best_score.append(max(score)) i = best_score.index(max(best_score)) # print(template[34+i]) r = template[34+i] results.append(r) continue if index==1: best_score = [] for eng_word_list in eng_words_list: score = [] for eng_word in eng_word_list: result = template_score(eng_word,word_image) score.append(result) best_score.append(max(score)) i = best_score.index(max(best_score)) # print(template[10+i]) r = template[10+i] results.append(r) continue else: best_score = [] for eng_num_word_list in eng_num_words_list: score = [] for eng_num_word in eng_num_word_list: result = template_score(eng_num_word,word_image) score.append(result) best_score.append(max(score)) i = best_score.index(max(best_score)) # print(template[i]) r = template[i] results.append(r) continue return results word_images_ = word_images.copy() # 调用函数获得结果 result = template_matching(word_images_) #print(result) # "".join(result)函数将列表转换为拼接好的字符串，方便结果显示 #print( "".join(result)) history.append("".join(result)) from PIL import ImageFont, ImageDraw, Image height,weight = origin_image.shape[0:2] #print(height) #print(weight) image_1 = origin_image.copy() cv2.rectangle(image_1, (int(0.2*weight), int(0.75*height)), (int(weight*0.9), int(height*0.95)), (0, 255, 0), 5) #设置需要显示的字体 fontpath = "font/simsun.ttc" font = ImageFont.truetype(fontpath,64) img_pil = Image.fromarray(image_1) draw = ImageDraw.Draw(img_pil) #绘制文字信息 draw.text((int(0.2*weight)+25, int(0.75*height)), "".join(result), font = font, fill = (255, 255, 0)) bk_img = np.array(img_pil) #print(result) return bk_img root = tk.Tk() root.title("图片导入器") root.geometry("1000x1000") # 用于存储历史记录的列表 history = [] current_image_label = None # 全局变量，用于存储加载的图像 # 显示图像的函数 def show_image(image): global current_image_label # 引用全局变量 if current_image_label is not None: # 如果存在先前的Label，先删除它 current_image_label.pack_forget() # 使用pack_forget()移除组件 pil_image = ImageTk.PhotoImage(image=Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))) lbl_image = tk.Label(root, image=pil_image) lbl_image.image = pil_image # 保持对图像的引用，防止垃圾回收 lbl_image.pack() current_image_label = lbl_image # 更新全局变量，存储新的Label引用 def open_image(): # 弹出文件选择对话框并返回用户选择的文件路径 file_path = filedialog.askopenfilename() return file_path def load_and_show_image(): file_path = open_image() # 调用函数获取文件路径 show_image(eaowej(file_path)) # 传递文件路径给加载图像的函数 # 显示历史记录的函数 def plt_show0(img): # cv2与plt的图像通道不同：cv2为[b,g,r];plt为[r, g, b] b, g, r = cv2.split(img) img = cv2.merge([r, g, b]) plt.imshow(img) plt.show() def show_history(): # 创建一个新的窗口来显示历史记录 history_window = tk.Toplevel(root) history_window.title("历史记录") history_window.geometry("600x400") # 根据需要调整大小 # 创建一个文本框来显示历史记录 txt_history = tk.Text(history_window, height=10, width=50) txt_history.pack(pady=20) # 将历史记录数据添加到文本框中 for record in history: txt_history.insert(tk.END, record + "\n") button_history = tk.Button(root, text="查看历史记录", command=show_history) button_history.pack() # 创建一个按钮，点击时调用 load_and_show_image 函数 button = tk.Button(root, text="导入图片", command=load_and_show_image) button.pack() root.mainloop() print(history)