opencv实现车牌识别
作者：墙缝里的草
这篇文章主要为大家详细介绍了opencv实现车牌识别，文中示例代码介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们可以参考一下
本文实例为大家分享了opencv实现车牌识别的具体代码，供大家参考，具体内容如下
（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----------------以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。