keras+resnet实现车牌识别

from PIL import ImageFont,Image,ImageDraw
import cv2
import numpy as np
import os
from math import *
#创建 生成车牌图像数据 的类
index = {"京": 0, "沪": 1, "津": 2, "渝": 3, "冀": 4, "晋": 5, "蒙": 6, "辽": 7, "吉": 8, "黑": 9, "苏": 10, "浙": 11, "皖": 12,
         "闽": 13, "赣": 14, "鲁": 15, "豫": 16, "鄂": 17, "湘": 18, "粤": 19, "桂": 20, "琼": 21, "川": 22, "贵": 23, "云": 24,
         "藏": 25, "陕": 26, "甘": 27, "青": 28, "宁": 29, "新": 30, "0": 31, "1": 32, "2": 33, "3": 34, "4": 35, "5": 36,
         "6": 37, "7": 38, "8": 39, "9": 40, "A": 41, "B": 42, "C": 43, "D": 44, "E": 45, "F": 46, "G": 47, "H": 48,
         "J": 49, "K": 50, "L": 51, "M": 52, "N": 53, "P": 54, "Q": 55, "R": 56, "S": 57, "T": 58, "U": 59, "V": 60,
         "W": 61, "X": 62, "Y": 63, "Z": 64}
 
chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
             "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "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"
             ]
def r(val):
    return int(np.random.random()*val)
def GenCh(f,val):#生成中文字符，f是中文字体对象，
    img=Image.new(‘RGB‘,(45,70),(255,255,255))#创建中文字符区域的全白画布，中文一般要方正一些，所以画布设置大一点，等下再resize
    draw=ImageDraw.Draw(img)#对画布创建画画对象
    draw.text((0,3),val,(0,0,0),font=f)#画画对象画出规定字体的黑色的规定val【0】文字,在左上角位置是（0，3）的点开始
    img =  img.resize((23,70))
    A=np.array(img)#图画转换成array格式
    return A
def GenCh1(f,val):#生成英文和数字字符，f是中文字体对象，
    img=Image.new(‘RGB‘,(23,70),(255,255,255))
    draw=ImageDraw.Draw(img)
    draw.text((0,2),val,(0,0,0),font=f)#画画对象在左上角（0，2）出开始画出val，颜色全黑，字体是f
    A=np.array(img)
    return A
def rot(img,angle,shape,max_angle):#透视畸变 
    size_o=[shape[1],shape[0]]#cv读的（h,w)换成[w,h]
    size=(shape[1]+int(shape[0]*cos((float(max_angle)/180)*3.14)),shape[0])#【变化后的w，h】
    interval=abs(int(sin(float(angle)/180)*3.14)*shape[0])#h变换的绝对值
    pts1=np.float32([[0,0],[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]])#(00,0h,w0,wh)
    if (angle>0):
        pts2=np.float32([[interval,0],[0,size[1]],[size[0],0],[size[0]-interval,size_o[1]]])
    else:
        pts2 = np.float32([[0,0],[interval,size[1]  ],[size[0]-interval,0  ],[size[0],size_o[1]]])
    M=cv2.getPerspectiveTransform(pts1,pts2)#根据两幅图的四个坐标点计算透视矩阵
    dst=cv2.warpPerspective(img,M,size)#img再M矩阵的变化下生成size大小的变化图
    return dst
def rotRandrom(img,factor,shape):#仿射畸变
    pts1=np.float32([[0,0],[0,shape[0]],[shape[1],0],[shape[1],shape[0]]])#00,0h,w0,wh
    pts2=np.float32([[r(factor),r(factor)],[r(factor),shape[0]-r(factor)],[shape[1]-factor,r(factor)],[shape[1]-r(factor),shape[0]-r(factor)]])
    M=cv2.getPerspectiveTransform(pts1,pts2)
    dst=cv2.warpPerspective(img,M,shape)
    return dst
def tfactor(img):#添加饱和度光照的噪声
    hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
    hsv[:,:,0] = hsv[:,:,0]*(0.8+ np.random.random()*0.2)
    hsv[:,:,1] = hsv[:,:,1]*(0.3+ np.random.random()*0.7)
    hsv[:,:,2] = hsv[:,:,2]*(0.2+ np.random.random()*0.8)
    img=cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR)
    return img
def random_envirment(img,data_set):#添加自然环境噪声
    index=r(len(data_set))
    env=cv2.imread(data_set[index])#随机读出一张环境噪声图片
    env=cv2.resize(env,(img.shape[1],img.shape[0]))#改变环境噪声图片的大下
    bak=(img==0)
    bak=bak.astype(np.uint8)*255#图片的黑色部分变成白色部分
    inv=cv2.bitwise_and(bak,env)#图片的非黑部分和噪声求and，
    img=cv2.bitwise_or(inv,img)
    return img
def AddGauss(img,level):#添加高斯模糊
    return cv2.blur(img,(level*2+1,level*2+1))
def AddNoiseSingleChannel(single):#高斯噪声
    diff=255-single.max()
    noise=np.random.normal(0,1+r(6),single.shape)
    noise = (noise - noise.min())/(noise.max()-noise.min())
    noise= diff*noise
    noise= noise.astype(np.uint8)
    dst = single + noise
    return dst
def addNoise(img,sdev = 0.5,avg=10):#每个通道随机添加高斯噪声
    img[:,:,0] =  AddNoiseSingleChannel(img[:,:,0])
    img[:,:,1] =  AddNoiseSingleChannel(img[:,:,1])
    img[:,:,2] =  AddNoiseSingleChannel(img[:,:,2])
    return img
class GenPlate:#生成车牌图像数据 的类
    def __init__(self,fontCh,fontEng,NoPlates):
        self.fontC=ImageFont.truetype(fontCh,43,0)#创建中文字体对象，fontCh是字体文件地址，43是字体大小,规定文字字体
        self.fontE=ImageFont.truetype(fontEng,60,0)
        self.img=np.array(Image.new(‘RGB‘,(226,70),(255,255,255)))#创建（226，70）大小的全白图像，并转换成array格式
        self.bg=cv2.resize(cv2.imread(‘./input_data/images/template.bmp‘),(226,70))#读取出背景模板图
        self.smu=cv2.imread(‘./input_data/images/smu2.jpg‘)#********************************
        self.noplates_path=[]
        for parent,parent_folder,filenames in os.walk(NoPlates):#NoPlates的上级目录，NoPlates的子目录（没有），NoPlates的子文件
            for filename in filenames:
                path=parent+‘/‘+filename
                self.noplates_path.append(path)#环境噪声图片
    def draw(self,val):
        offset=2
        self.img[0:70,offset+8:offset+8+23]=GenCh(self.fontC,val[0])#再self.img画布上画出中文字
        self.img[0:79,offset+8+23+6:offset+8+23+6+23]=GenCh1(self.fontE,val[1])#英文字
        for i in range(5):#画出5个数字
            base=offset+8+23+6+23+17+ i*23+ i*6
            self.img[0:70,base:base+23]=GenCh1(self.fontE,val[i+2])
        return self.img#画出背景白色，文字黑色的文字内容
    def generate(self,text):
        if len(text)==7:
            fg=self.draw(text)#调用draw函数，画出这7个字符)
            fg=cv2.bitwise_not(fg)#图像的array按位取反：黑色背景，白色文字
            com=cv2.bitwise_or(fg,self.bg)#再与背景图片取或，则生成背景是背景图片，前景是白色文字的图片array格式
            com=rot(com,r(60)-30,com.shape,30)#透视畸变效果(img,angle,shape,max_angle)
            com = rotRandrom(com,10,(com.shape[1],com.shape[0]))#仿射畸变
            com = tfactor(com)#添加饱和度光照的噪声
            com = random_envirment(com,self.noplates_path)#环境图片的噪声
            com = AddGauss(com, 1+r(4))#高斯模糊
            com = addNoise(com)#高斯噪声
            return com
    def genPlateString(self,pos,val):#随机生成（中文 英文 数字*5）的字符
        #pos!=-1时，读取出val值就是车牌值************************pos=-1时，val随机，主要是生成车牌
        plateStr=‘‘
        if (pos!=-1):#
            plateStr+=val#读出你想要生成的车牌号
        else:#随机生成车牌号
            for cpos in range(7):
                if cpos==0:
                    plateStr+=chars[r(31)]
                elif cpos==1:
                    plateStr+=chars[41+r(24)]
                else:
                    plateStr+=chars[31+r(10)]
        return plateStr
    def genBatch(self,batchSize,size):#生成batch——size个车牌数据
        for i in range(batchSize):
            plateStr=self.genPlateString(-1,-1)
            print(plateStr)
            img=self.generate(plateStr)
            
            img=cv2.resize(img,size)
            filename=str(plateStr)+‘.jpg‘
            cv2.imencode(‘.jpg‘,img)[1].tofile(filename)#此处解决中文名乱码
if __name__==‘__main__‘:
    G=GenPlate("./input_data/font/platech.ttf",‘./input_data/font/platechar.ttf‘,"./input_data/NoPlates")
    G.genBatch(10,(224,224))