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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+1if 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)continueif 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)continueelse: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)continuereturn resultsword_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+1if 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)continueif 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)continueelse: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)continuereturn resultsword_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)