第十四周周总结
2020.05.23
车牌模板匹配
将之前切割的车牌字符图篇一张一张的与已知数据集进行对比，获取相似度，取相似度最高的图篇标签为该车牌字符
# 导入所需模块
import cv2
from matplotlib import pyplot as plt
import os
def chepai_shibie():
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']
resultChepai = ''
# 读取一个文件夹下的所有图片，输入参数是文件名
def read_directory(directory_name):
referImg_list = []
for filename in os.listdir(directory_name):
# print(filename) # 仅仅是为了测试
# img = cv2.imread(directory_name + "/" + filename)
referImg_list.append(directory_name + "/" + filename)
return referImg_list
# 匹配中文
c_words = []
for i in range(34, 64):
c_word = read_directory('refer1/' + template[i])
c_words.append(c_word)
# 读取一个车牌字符
img = cv2.imread('chepai/test2_1.jpg')
# plt_show0(img)
# 灰度处理，二值化
# 高斯去噪
image = cv2.GaussianBlur(img, (1, 1), 0)
# plt_show(image)
# 灰度处理
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# plt_show(gray_image)
# 自适应阈值处理
ret, image_ = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
# plt_show(image_)
import numpy as np
best_score = []
for c_word in c_words:
# print('c_word:',c_word)
score = []
for word in c_word:
# print('word:', word)
# print(word)
# fromfile()函数读回数据时需要用户指定元素类型，并对数组的形状进行适当的修改
template_img = cv2.imdecode(np.fromfile(word, dtype=np.uint8), 1)
# template_img = cv2.imread(word)
# print(template_img)
# cv_show('template_img',template_img)
# template_img = np.float32(template_img)
# plt_show0(template_img)
# print(word)
template_img = cv2.cvtColor(template_img, cv2.COLOR_RGB2GRAY)
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))
result = cv2.matchTemplate(image, template_img, cv2.TM_CCOEFF) # matchTemplate可更改参数 ↓↓↓ 匹配方法
score.append(result[0][0])
best_score.append(max(score))
print(best_score)
print(max(best_score))
print(best_score.index(max(best_score)))
flag1 = 34 + best_score.index(max(best_score))
# print(template[34])
print(template[int(flag1)]) # 车牌第一次汉字
resultChepai += template[int(flag1)]
'''
score = []
best_template = None
best_refer = None
for refer in referImg_list:
# refer = refer[0]
# print(refer)
template = cv2.imread(refer)
template = cv2.GaussianBlur(template, (3, 3), 0)
# 灰度处理
template = cv2.cvtColor(template, cv2.COLOR_RGB2GRAY)
# 自适应阈值处理
ret, template = cv2.threshold(template, 0, 255, cv2.THRESH_OTSU)
height, width = template.shape
image = image_.copy()
image = cv2.resize(image, (width, height)) # 和模板一致
# plt_show(image)
# TM_SQDIFF TM_CCOEFF
# TM_SQDIFF：计算平方不同，计算出来的值越小，越相关
# TM_CCORR：计算相关性，计算出来的值越大，越相关
# TM_CCOEFF：计算相关系数，计算出来的值越大，越相关
# TM_SQDIFF_NORMED：计算归一化平方不同，计算出来的值越接近0，越相关
# TM_CCORR_NORMED：计算归一化相关性，计算出来的值越接近1，越相关
# TM_CCOEFF_NORMED：计算归一化相关系数，计算出来的值越接近1，越相关
result = cv2.matchTemplate(image, template, cv2.TM_CCOEFF)
# print(score)
if score != [] and result[0][0] > max(score):
best_template = None
best_refer = None
best_template = template
best_refer = refer
score.append(result[0][0])
print(score)
plt_show(best_template)
print(max(score))
print(best_refer)
'''
# 读取一个车牌字符
img = cv2.imread('chepai/test2_2.jpg')
# plt_show0(img)
# 灰度处理，二值化
# 高斯去噪
image = cv2.GaussianBlur(img, (3, 3), 0)
# 灰度处理
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# plt_show(gray_image)
# 自适应阈值处理
ret, image_ = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
# plt_show(image_)
# 字母模板列表
# 匹配中文
c_words = []
for i in range(10, 34):
c_word = read_directory('./refer1/' + template[i])
c_words.append(c_word)
import numpy as np
best_score = []
for c_word in c_words:
score = []
for word in c_word:
# fromfile()函数读回数据时需要用户指定元素类型，并对数组的形状进行适当的修改
template_img = cv2.imdecode(np.fromfile(word, dtype=np.uint8), 1)
template_img = cv2.cvtColor(template_img, cv2.COLOR_RGB2GRAY)
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))
result = cv2.matchTemplate(image, template_img, cv2.TM_CCOEFF)
score.append(result[0][0])
best_score.append(max(score))
print(best_score)
print(max(best_score))
print(best_score.index(max(best_score)))
flag2 = 10 + int(best_score.index(max(best_score)))
print(template[int(flag2)])
resultChepai += template[int(flag2)]
chepais = ['test2_3', 'test2_4', 'test2_5', 'test2_6', 'test2_7']
for chepai in chepais:
# 读取一个车牌字符
img = cv2.imread('chepai/' + chepai + '.jpg')
# plt_show0(img)
# 灰度处理，二值化
# 高斯去噪
image = cv2.GaussianBlur(img, (3, 3), 0)
# 灰度处理
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# plt_show(gray_image)
# 自适应阈值处理
ret, image_ = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
# plt_show(image_)
# 字母数字模板列表
c_words = []
for i in range(0, 34):
c_word = read_directory('refer1/' + template[i])
c_words.append(c_word)
import numpy as np
best_score = []
for c_word in c_words:
score = []
for word in c_word:
# fromfile()函数读回数据时需要用户指定元素类型，并对数组的形状进行适当的修改
template_img = cv2.imdecode(np.fromfile(word, dtype=np.uint8), 1)
template_img = cv2.cvtColor(template_img, cv2.COLOR_RGB2GRAY)
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))
result = cv2.matchTemplate(image, template_img, cv2.TM_CCOEFF)
score.append(result[0][0])
best_score.append(max(score))
print(best_score)
print(max(best_score))
flag3 = 0 + best_score.index(max(best_score))
print(template[int(flag3)])
resultChepai += template[int(flag3)]
print('车牌识别结果为:', resultChepai
