接上篇车牌识别系列，Tensorflow生成车牌数据(一)，实际上上篇生成的数据仅仅是一个字符串序列，加上对应的label，使用Pygame或者其他库生成图片后，并不具备真实感觉的车牌。考虑到实际车牌拍照出来的效果，我们必须添加些环境噪声，模糊处理，畸变等，让最终生成的车牌看起来更加接近真实图片拍下来的效果。并且加入背景图片，生成车牌字符串list和label，存为图片格式，批量生成。
先来看看效果：
上图即为生成的车牌数据，可以看到已经很接近真实图片风格了，有清晰的有模糊的，有比较方正的，也有一些比较倾斜，生成完大量的车牌样张后就可以进行车牌识别了。下一小节将会讲如何用端对端的CNN进行车牌识别，不需要通过传统的ocr先对字符进行分割处理后再识别。
源码如下：
#coding=utf-8
"""
genPlate.py:生成随机车牌
"""
__author__ = "Huxiaoman&Gavin"
__copyright__ = "Copyright (c) 2018 "
import PIL
from PIL import ImageFont
from PIL import Image
from PIL import ImageDraw
import cv2
import numpy as np
import os
from math import *
import sys
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 AddSmudginess(img, Smu):
rows = r(Smu.shape[0] - 50)
cols = r(Smu.shape[1] - 50)
adder = Smu[rows:rows + 50, cols:cols + 50]
adder = cv2.resize(adder, (50, 50))
#adder = cv2.bitwise_not(adder)
img = cv2.resize(img,(50,50))
img = cv2.bitwise_not(img)
img = cv2.bitwise_and(adder, img)
img = cv2.bitwise_not(img)
return img
def rot(img,angel,shape,max_angel):
"""
添加透视畸变
"""
size_o = [shape[1],shape[0]]
size = (shape[1]+ int(shape[0]*cos((float(max_angel )/180) * 3.14)),shape[0])
interval = abs( int( sin((float(angel) /180) * 3.14)* shape[0]))
pts1 = np.float32([[0,0],[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]])
if(angel>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)
dst = cv2.warpPerspective(img,M,size)
return dst
def rotRandrom(img, factor, size):
"""
添加放射畸变
img 输入图像
factor 畸变的参数
size 为图片的目标尺寸
"""
shape = size
pts1 = np.float32([[0, 0], [0, shape[0]], [shape[1], 0], [shape[1], shape[0]]])
pts2 = np.float32([[r(factor), r(factor)], [ r(factor), shape[0] - r(factor)], [shape[1] - r(factor), r(factor)],
[shape[1] - r(factor), shape[0] - r(factor)]])
M = cv2.getPerspectiveTransform(pts1, pts2)
dst = cv2.warpPerspective(img, M, size)
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):
"""
添加自然环境的噪声
"""
index=r(len(data_set))
env = cv2.imread(data_set[index])
env = cv2.resize(env,(img.shape[1],img.shape[0]))
bak = (img==0)
bak = bak.astype(np.uint8)*255
inv = cv2.bitwise_and(bak,env)
img = cv2.bitwise_or(inv,img)
return img
def GenCh(f,val):
"""
生成中文字符
"""
img=Image.new("RGB", (45,70),(255,255,255))
draw = ImageDraw.Draw(img)
draw.text((0, 3),val,(0,0,0),font=f)
img = img.resize((23,70))
A = np.array(img)
return A
def GenCh1(f,val):
"""
生成英文字符
"""
img=Image.new("RGB", (23,70),(255,255,255))
draw = ImageDraw.Draw(img)
draw.text((0, 2),val,(0,0,0),font=f) # val.decode('utf-8')
A = np.array(img)
return A
def AddGauss(img, level):
"""
添加高斯模糊
"""
return cv2.blur(img, (level * 2 + 1, level * 2 + 1))
def r(val):
return int(np.random.random() * val)
def AddNoiseSingleChannel(single):
"""
添加高斯噪声
"""
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):
img[:,:,0] = AddNoiseSingleChannel(img[:,:,0])
img[:,:,1] = AddNoiseSingleChannel(img[:,:,1])
img[:,:,2] = AddNoiseSingleChannel(img[:,:,2])
return img
class GenPlate:
def __init__(self,fontCh,fontEng,NoPlates):
self.fontC = ImageFont.truetype(fontCh,43,0)
self.fontE = ImageFont.truetype(fontEng,60,0)
self.img=np.array(Image.new("RGB", (226,70),(255,255,255)))
self.bg = cv2.resize(cv2.imread("./images/template.bmp"),(226,70))
self.smu = cv2.imread("./images/smu2.jpg")
self.noplates_path = []
for parent,parent_folder,filenames in os.walk(NoPlates):
for filename in filenames:
path = parent+"/"+filename
self.noplates_path.append(path)
def draw(self,val):
offset= 2
self.img[0:70,offset+8:offset+8+23]= GenCh(self.fontC,val[0])
self.img[0:70,offset+8+23+6:offset+8+23+6+23]= GenCh1(self.fontE,val[1])
for i in range(5):
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
def generate(self,text):
if len(text) == 7:
fg = self.draw(text) #.decode(encoding="utf-8")
fg = cv2.bitwise_not(fg)
com = cv2.bitwise_or(fg,self.bg)
com = rot(com,r(60)-30,com.shape,30)
com = rotRandrom(com,10,(com.shape[1],com.shape[0]))
com = tfactor(com)
com = random_envirment(com,self.noplates_path)
com = AddGauss(com, 1+r(4))
com = addNoise(com)
return com
def genPlateString(self,pos,val):
'''
生成车牌String,存为图片
生成车牌list,存为label
'''
plateStr = ""
plateList=[]
box = [0,0,0,0,0,0,0]
if(pos!=-1):
box[pos]=1
for unit,cpos in zip(box,range(len(box))):
if unit == 1:
plateStr += val
#print plateStr
plateList.append(val)
else:
if cpos == 0:
plateStr += chars[r(31)]
plateList.append(plateStr)
elif cpos == 1:
plateStr += chars[41+r(24)]
plateList.append(plateStr)
else:
plateStr += chars[31 + r(34)]
plateList.append(plateStr)
plate = [plateList[0]]
b = [plateList[i][-1] for i in range(len(plateList))]
plate.extend(b[1:7])
return plateStr,plate
# 将生成的车牌图片写入文件夹，对应的label写入label.txt
def genBatch(self, batchSize,pos,charRange, outputPath,size):
if (not os.path.exists(outputPath)):
os.mkdir(outputPath)
outfile = open('label.txt','w')
for i in range(batchSize):
plateStr,plate = G.genPlateString(-1,-1)
print(plateStr,plate)
# print('len of %s:%d' %(plateStr,len(plateStr)))
img = G.generate(plateStr)
img = cv2.resize(img,size)
cv2.imwrite(outputPath + "/" + str(i).zfill(2) + ".jpg", img)
outfile.write(str(plate)+"\n")
G = GenPlate("./font/platech.ttf",'./font/platechar.ttf',"./NoPlates")
if __name__=='__main__':
G.genBatch(int(sys.argv[1]),2,range(31,65),sys.argv[2],(272,72))
参考github：https://github.com/huxiaoman7/mxnet-cnn-plate-recognition