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文章目录

1 导入相关模块2 相关功能函数定义2.1 彩色图片显示函数（plt_show0）2.2 灰度图片显示函数（plt_show）2.3 图像去噪函数（gray_guss）

2 图像预处理2.1 图片读取2.2 高斯去噪2.3 边缘检测

2.4 阈值化3 车牌定位3.1 区域选择3.2 形态学操作3.3 轮廓检测

4 车牌字符分割4.1 高斯去噪4.2 阈值化4.3 膨胀操作4.4 车牌号排序4.5 分割效果

5 模板匹配5.1 准备模板5.2 匹配结果5.3 匹配效果展示

6完整代码

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

<code>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')code>

plt.show()

2.3 图像去噪函数（gray_guss）

<code>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

<code>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

<code># 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

<code>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

<code># 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

<code>#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

<code>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')code>

plt.show()

运行结果：

5 模板匹配

<code># 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

<code># 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')code>

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')code>

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)