python
import cv2
from matplotlib import pyplot as plt
import os
import numpy as np
from PIL import ImageFont, ImageDraw, Image彩色图片显示
python
def plt_show0(img):
b,g,r = cv2.split(img)
img = cv2.merge([r, g, b])
plt.imshow(img)
plt.show()灰度图片显示
python
def plt_show(img):
plt.imshow(img,cmap='gray')
plt.show()图像去噪
python
def gray_guss(image):
image = cv2.GaussianBlur(image, (3, 3), 0)
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
return gray_image图像预处理
读取图像
python
origin_image = cv2.imread("car3.png")高斯去噪
python
# 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)边缘检测
python
Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0)
absX = cv2.convertScaleAbs(Sobel_x)
image = absX阈值化
python
# 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)
车牌定位
区域的选择
python
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)
形态学操作
python
# 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)
轮廓检验
python
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
高斯去噪
python
# 图像去噪灰度处理
gray_image = gray_guss(image)阈值化
python
ret, image = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
plt_show(image)
膨胀操作
python
#膨胀操作
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 4))
image = cv2.dilate(image, kernel)
plt_show(image)
车牌号顺序
python
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)1
2
3
4
5
6
7
[[0, 27, 3, 13], [3, 0, 85, 101], [16, 52, 10, 30], [16, 33, 10, 11], [20, 13, 9, 12], [26, 42, 21, 43], [27, 12, 29, 29], [45, 41, 9, 44], [62, 12, 43, 74], [91, 0, 9, 4], [102, 99, 15, 2], [112, 45, 11, 10], [129, 14, 43, 73], [178, 15, 42, 73], [225, 14, 40, 73], [233, 98, 39, 3], [272, 88, 10, 7], [274, 13, 40, 73], [279, 0, 10, 6], [285, 0, 89, 101], [331, 13, 15, 72]]
分割效果
python
for i,j in enumerate(word_images):
plt.subplot(1,7,i+1)
plt.imshow(word_images[i],cmap='gray')
plt.show()---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-40-97859dfa300a> in <cell line: 1>()
1 for i,j in enumerate(word_images):
----> 2 plt.subplot(1,7,i+1)
3 plt.imshow(word_images[i],cmap='gray')
4 plt.show()
/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py in subplot(*args, **kwargs)
1321
1322 # First, search for an existing subplot with a matching spec.
-> 1323 key = SubplotSpec._from_subplot_args(fig, args)
1324
1325 for ax in fig.axes:
/usr/local/lib/python3.10/dist-packages/matplotlib/gridspec.py in _from_subplot_args(figure, args)
596 else:
597 if not isinstance(num, Integral) or num < 1 or num > rows*cols:
--> 598 raise ValueError(
599 f"num must be an integer with 1 <= num <= {rows*cols}, "
600 f"not {num!r}"
ValueError: num must be an integer with 1 <= num <= 7, not 8
模板准备
colab中解压文件
python
import zipfile
import os
# 假设你上传的ZIP文件名为'example.zip'，并且它位于Colab的当前工作目录中
zip_file_path = 'License Plate Recognition.zip'
# 设置解压的目标目录（例如，解压到当前工作目录下的'extracted_folder'）
extract_dir = 'extracted_folder'
# 如果目标目录已经存在，则删除它（可选）
if os.path.exists(extract_dir):
import shutil
shutil.rmtree(extract_dir)
# 使用zipfile模块解压ZIP文件
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
zip_ref.extractall(extract_dir)
# 现在，'extracted_folder'目录中应该包含了ZIP文件中的所有文件和文件夹
# 验证解压
print(os.listdir(extract_dir))['License Plate Recognition']
python
# 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()---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-4-6b0c5e30ad5b> in <cell line: 22>()
20 chinese_words_list.append(c_word)
21 return chinese_words_list
---> 22 chinese_words_list = get_chinese_words_list()
23
24
<ipython-input-4-6b0c5e30ad5b> in get_chinese_words_list()
17 for i in range(34,64):
18 #将模板存放在字典中
---> 19 c_word = read_directory('D:/refer1/'+ template[i])
20 chinese_words_list.append(c_word)
21 return chinese_words_list
<ipython-input-4-6b0c5e30ad5b> in read_directory(directory_name)
8 def read_directory(directory_name):
9 referImg_list = []
---> 10 for filename in os.listdir(directory_name):
11 referImg_list.append(directory_name + "/" + filename)
12 return referImg_list
FileNotFoundError: [Errno 2] No such file or directory: 'D:/refer1/藏'
python