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