该篇文章将以实战形式演示利用Python结合Opencv实现车牌识别，全程涉及图像预处理、车牌定位、车牌分割、通过模板匹配识别结果输出。该项目对于智能交通、车辆管理等领域具有实际应用价值。通过自动识别车牌号码，可以实现车辆追踪、违章查询、停车场管理等功能，提高交通管理的效率和准确性。可用于车牌识别技术学习。
技术要点：
- OpenCV：用于图像处理和计算机视觉任务。
- Python：作为编程语言，具有简单易学、资源丰富等优点。
- 图像处理技术：如灰度化、噪声去除、边缘检测、形态学操作、透视变换等。
1 导入相关模块
import cv2 from matplotlib import pyplot as plt import os import numpy as np from PIL import ImageFont, ImageDraw, Image
2 相关功能函数定义
2.1 彩色图片显示函数（plt_show0）
def plt_show0(img): b,g,r = cv2.split(img) img = cv2.merge([r, g, b]) plt.imshow(img) plt.show()
cv2与plt的图像通道不同：cv2为[b,g,r];plt为[r, g, b]
2.2 灰度图片显示函数（plt_show）
def plt_show(img): plt.imshow(img,cmap='gray') plt.show()
2.3 图像去噪函数（gray_guss）
def gray_guss(image): image = cv2.GaussianBlur(image, (3, 3), 0) gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) return gray_image
此处演示使用高斯模糊去噪。
cv2.GaussianBlur参数说明：
src：输入图像，可以是任意数量的通道，这些通道可以独立处理，但深度应为CV_8U、CV_16U、CV_16S、CV_32F或CV_64F。ksize：高斯核的大小，必须是正奇数，例如 (3, 3)、(5, 5) 等。如果ksize的值为零，那么它会根据sigmaX和sigmaY的值来计算。sigmaX：X 方向上的高斯核标准偏差。dst：输出图像，大小和类型与src相同。sigmaY：Y 方向上的高斯核标准偏差，如果sigmaY是零，那么它会与sigmaX的值相同。如果sigmaY是负数，那么它会从ksize.width和ksize.height计算得出。borderType：像素外插法，有默认值。
2 图像预处理
2.1 图片读取
origin_image = cv2.imread('D:/image/car3.jpg')
此处演示识别车牌原图：
2.2 高斯去噪
origin_image = cv2.imread('D:/image/car3.jpg') # 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() gray_image = gray_guss(image)
2.3 边缘检测
Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0) absX = cv2.convertScaleAbs(Sobel_x) image = absX
x方向上的边缘检测（增强边缘信息）。
2.4 阈值化
# 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)
运行结果：
3 车牌定位
3.1 区域选择
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)
从图像中提取对表达和描绘区域形状有意义的图像分量。
运行结果：
3.2 形态学操作
# 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)
使用膨胀和腐蚀操作来突出车牌区域。
运行结果：
3.3 轮廓检测
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)) and (weight < (height * 4.5)): image = origin_image[y:y + height, x:x + weight] plt_show(image)
4 车牌字符分割
4.1 高斯去噪
# 图像去噪灰度处理 gray_image = gray_guss(image)
4.2 阈值化
ret, image = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU) plt_show(image)
运行结果：
4.3 膨胀操作
#膨胀操作 kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 4)) image = cv2.dilate(image, kernel) plt_show(image)
运行结果：
4.4 车牌号排序
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] * 5.5)) and (word[2] > 10): i = i+1 if word[2] < 15: splite_image = image[word[1]:word[1] + word[3], word[0]-word[2]:word[0] + word[2]*2] else: splite_image = image[word[1]:word[1] + word[3], word[0]:word[0] + word[2]] word_images.append(splite_image) print(i) print(words)
运行结果：
1 2 3 4 5 6 7 [[2, 0, 7, 70], [12, 6, 30, 55], [15, 7, 7, 9], [46, 6, 32, 55], [83, 30, 9, 9], [96, 7, 32, 55], [132, 8, 32, 55], [167, 8, 30, 54], [202, 62, 7, 6], [203, 7, 30, 55], [245, 7, 12, 54], [266, 0, 12, 70]]
4.5 分割效果
for i,j in enumerate(word_images): plt.subplot(1,7,i+1) plt.imshow(word_images[i],cmap='gray') plt.show()
运行结果：
5 模板匹配
5.1 准备模板
# 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('D:/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('D:/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('D:/refer1/'+ template[i]) eng_num_words_list.append(word) return eng_num_words_list eng_num_words_list = get_eng_num_words_list()
此处需提前准备各类字符模板。
5.2 匹配结果
# 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('D:/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) print( "".join(result))
运行结果：
['渝', 'B', 'F', 'U', '8', '7', '1'] 渝BFU871
“”.join(result)函数将列表转换为拼接好的字符串，方便结果显示
5.3 匹配效果展示
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) print( "".join(result)) plt_show0(bk_img)
运行结果：
6完整代码
# 导入所需模块 import cv2 from matplotlib import pyplot as plt import os import numpy as np from PIL import ImageFont, ImageDraw, Image # plt显示彩色图片 def plt_show0(img): 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('D:/image/car3.jpg') # 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)) and (weight < (height * 4.5)): image = origin_image[y:y + height, x:x + weight] plt_show(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 kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 4)) 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] * 5.5)) and (word[2] > 10): i = i+1 if word[2] < 15: splite_image = image[word[1]:word[1] + word[3], word[0]-word[2]:word[0] + word[2]*2] else: 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('D:/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('D:/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('D:/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)) 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) print( "".join(result)) plt_show0(bk_img)
