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car_main.py
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from car import car_test
'''
主函数，识别图片的同时，将识别出的文字分类，看是否属于店铺名
'''
from paddleocr import PaddleOCR
from lstm import test
import os
from paddleocr import PaddleOCR
from tools.infer.utility import draw_ocr
from PIL import Image
import pandas as pd
import wx
import re
import wx.xrc
from car.video import video_frame as vf
#图形开始
app = wx.App()
window = wx.Frame(None, title = u"实时识别状态展示", size = (969,150),style = wx.DEFAULT_FRAME_STYLE|wx.TAB_TRAVERSAL)
window.SetSizeHints( wx.DefaultSize, wx.DefaultSize )
gbSizer3 = wx.GridBagSizer( 0, 0 )
gbSizer3.SetFlexibleDirection( wx.BOTH )
gbSizer3.SetNonFlexibleGrowMode( wx.FLEX_GROWMODE_SPECIFIED )
window.m_staticText10 = wx.StaticText( window, wx.ID_ANY, u"正在识别：", wx.DefaultPosition, wx.DefaultSize, 0 )
window.m_staticText10.Wrap( -1 )
gbSizer3.Add( window.m_staticText10, wx.GBPosition( 0, 0 ), wx.GBSpan( 1, 1 ), wx.ALL, 5 )
window.m_staticText11 = wx.StaticText( window, wx.ID_ANY, u"图片名字", wx.DefaultPosition, wx.DefaultSize, 0 )
window.m_staticText11.Wrap( -1 )
window.m_staticText11.SetForegroundColour( wx.Colour( 255, 0, 0 ) )
window.m_staticText11.SetMinSize( wx.Size( 800,30 ) )
gbSizer3.Add( window.m_staticText11, wx.GBPosition( 0, 1 ), wx.GBSpan( 1, 1 ), wx.ALL, 5 )
window.m_staticText12 = wx.StaticText( window, wx.ID_ANY, u"进度：", wx.DefaultPosition, wx.DefaultSize, 0 )
window.m_staticText12.Wrap( -1 )
gbSizer3.Add( window.m_staticText12, wx.GBPosition( 1, 0 ), wx.GBSpan( 1, 1 ), wx.ALL, 5 )
window.m_staticText13 = wx.StaticText( window, wx.ID_ANY, u"正在识别的图片：", wx.DefaultPosition, wx.DefaultSize, 0 )
window.m_staticText13.Wrap( -1 )
gbSizer3.Add( window.m_staticText13, wx.GBPosition( 2, 0 ), wx.GBSpan( 1, 1 ), wx.ALL, 5 )
window.m_gauge1 = wx.Gauge( window, wx.ID_ANY, 100, wx.DefaultPosition, wx.DefaultSize, wx.GA_HORIZONTAL )
window.m_gauge1.SetValue( 0 )
window.m_gauge1.SetMinSize( wx.Size( 800,-1 ) )
gbSizer3.Add( window.m_gauge1, wx.GBPosition( 1, 1 ), wx.GBSpan( 1, 1 ), wx.ALL, 5 )
window.SetSizer( gbSizer3 )
window.Layout()
window.Centre( wx.BOTH )
#图形结束
def pre_save(img_path,save_path,csv_path,m_filePicker11=None):
cechu = []
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
i = 0
names =[]
#abels = []
imgs=[]
one_qian_pic_path = ""
one_hou_pic_path = ""
#存放所有的图片路径
n_qian_pic_path = []
n_hou_pic_path = []
j=0
stR = ""
# 启动窗口
window.Show(True)
jindu_len = len(os.listdir(img_path)) + 2
print("共："+str(jindu_len-2))
window.m_gauge1.SetRange(jindu_len)
dangqian_jindu = 1
n=0
aaa = []
for img in os.listdir(img_path):
n+=1
window.m_staticText11.SetLabel("正在识别："+img_path+'/'+img)
print("当前正在识别"+str(dangqian_jindu)+"-->"+"正在识别："+img_path+'/'+img)
window.m_gauge1.SetValue(dangqian_jindu)
dangqian_jindu = dangqian_jindu + 1
#动态显示进度条
if dangqian_jindu==jindu_len:
window.m_gauge1.SetValue(dangqian_jindu)
print(img_path+'/'+img)
i += 1
result = ocr.ocr(img_path+'/'+img, cls=True)
image = Image.open(img_path+'/'+img).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
# im_show.show()
# im_show.save是保存识别后的图片
im_show.save(save_path+"/"+img)
#存储所有的图片路径
n_qian_pic_path.append(img_path+'/'+img)
n_hou_pic_path.append(save_path+"/"+img)
for line in result:
if line[1][0] =="":
continue
#print("+++",line)
if len(re.findall("[\u4e00-\u9fa5]",str(line[1][0]))) > 2:
continue
if len(str(line[1][0])) < 6:
continue
car_board = one_pred(str(line[1][0]))
aaa.append(car_board)
#print("***",car_board)
if j==0:
one_hou_pic_path = save_path+"/"+img
one_qian_pic_path = img_path+'/'+img
j = j + 1
if str(result) == "None":
continue
dataframe = pd.DataFrame({'图片名': imgs, '车牌号': names})
if csv_path[-3:]=="xls":
dataframe.to_excel(csv_path, index=True)
elif csv_path[-3:]=="csv":
dataframe.to_csv(csv_path, index=True,sep=',')
else:
print("请选择csv或者xls文件")
n = 0
for i in aaa:
if i != None:
if str(i) not in set(cechu):
cechu.append(str(i))
# n += 1
# for i in aaa:
# if i != None:
# if str(i) not in cechu:
# stR = stR + str(i) + "\n"
# n +=1
for p in set(cechu):
print(p)
#print(stR)
stR = stR # + "共识别出的车牌数为：" + str(n)
print("共识别出的车牌数为：" ,len(set(cechu)))
dangqian_jindu = dangqian_jindu + 1
window.m_gauge1.SetValue(dangqian_jindu)
# print("当前正在识别" + str(dangqian_jindu))
# print("+++",cechu)
# print(stR)
return stR,one_qian_pic_path,one_hou_pic_path,n_qian_pic_path,n_hou_pic_path
#图片拼接为视频
import os
import cv2
from PIL import Image
# 图片合成视频
def picvideo(pthoto_path,save_path):
# path = r'C:\Users\Administrator\Desktop\1\huaixiao\\'#文件路径
filelist = os.listdir(pthoto_path) # 获取该目录下的所有文件名
# print(filelist)
# print(pthoto_path+'/'+filelist[1])
size = Image.open(pthoto_path+'/'+filelist[0]).size
# print(size)
'''
fps:
帧率：1秒钟有n张图片写进去[控制一张图片停留5秒钟，那就是帧率为1，重复播放这张图片5次]
如果文件夹下有50张 534*300的图片，这里设置1秒钟播放5张，那么这个视频的时长就是10秒
'''
fps = 7
# size = (591,705) #图片的分辨率片
# time=len(filelist)/fps
# file_path = save_path+'/' + str(int(time)) + ".mp4" # 导出路径
n=len(filelist)
file_path = save_path + "/car_after.mp4"
fourcc = cv2.VideoWriter_fourcc('D', 'I', 'V', 'X') # 不同视频编码对应不同视频格式（例：'I','4','2','0' 对应avi格式）
video = cv2.VideoWriter(file_path, fourcc, fps, size)
for item in filelist:
#if item.endswith('.jpg'): # 判断图片后缀是否是.png
item = pthoto_path + '/' + item
img = cv2.imread(item) # 使用opencv读取图像，直接返回numpy.ndarray 对象，通道顺序为BGR ，注意是BGR，通道值默认范围0-255。
video.write(img) # 把图片写进视频
video.release() # 释放
def one_pred(text):
a = text[0]
province = ["京","沪","津","渝","鲁","冀","晋","蒙","辽","吉","黑","苏","浙","皖","闽","赣","豫","湘","鄂","粤","桂","琼","川","贵","云","藏","陕","甘","青","宁","新","港","澳","台"]
if str(a) in province:
# print(a)
c = car_test.pre(text)
if c == "车牌":
print("\n识别出车牌号为：",text)
return text
else:
return 0
# 视频地址
video_path=r'img/car_befor.mp4'
# 视频分帧的图片地址
photo_path=r'img\video_img'
# 识别后图片的地址
save_path=r'img\video_save'
# 保存scv数据文件的地址
csv_path=r'img\chepai12.csv'
# 视频分帧
vf.fenzhen(video_path,photo_path)
# 预测+保存数据
pre_save(photo_path,save_path,csv_path)
# 视频合成
video_path = r"img"
picvideo(save_path,video_path)