opencv车牌识别
墙缝里的草 人气:0(1)提取车牌位置,将车牌从图中分割出来;
(2)车牌字符的分割;
(3)通过模版匹配识别字符;
(4)将结果绘制在图片上显示出来。
import cv2 from matplotlib import pyplot as plt import os import numpy as np # plt显示彩色图片 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() # 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_RGB2GRAY) return gray_image # 读取待检测图片 origin_image = cv2.imread('img。png') # 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“苏”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) contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) words = [] word_images = [] 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) words = sorted(words,key=lambda s:s[0],reverse=False) i = 0 for word in words: 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): 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) image_ = image.copy() height, width = image_.shape template_img = cv2.resize(template_img, (width, height)) 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() result = template_matching(word_images_) print(result) print( "".join(result)) # 未完结----------------
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