一、项目概述

本次项目目标是实现对自动生成的带有各种噪声的车牌识别。在噪声干扰情况下，车牌字符分割较困难，此次车牌识别是将车牌7个字符同时训练，字符包括31个省份简称、10个阿拉伯数字、24个英文字母（'O'和'I'除外），共有65个类别，7个字符使用单独的loss函数进行训练。
（运行环境：tensorflow1.14.0-GPU版）

二、生成车牌数据集

import os
import cv2 as cv
import numpy as np
from math import *
from PIL import ImageFont
from PIL import Image
from PIL import ImageDraw


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):
  """
  模糊处理
  :param img: 输入图像
  :param Smu: 模糊图像
  :return: 添加模糊后的图像
  """
  rows = r(Smu.shape[0] - 50)
  cols = r(Smu.shape[1] - 50)
  adder = Smu[rows:rows + 50, cols:cols + 50]
  adder = cv.resize(adder, (50, 50))
  img = cv.resize(img,(50,50))
  img = cv.bitwise_not(img)
  img = cv.bitwise_and(adder, img)
  img = cv.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 = cv.getPerspectiveTransform(pts1, pts2)
  dst = cv.warpPerspective(img, M, size)
  return dst


def rotRandrom(img, factor, size):
  """
  添加放射畸变
  :param img: 输入图像
  :param factor: 畸变的参数
  :param size: 图片目标尺寸
  :return: 放射畸变后的图像
  """
  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 = cv.getPerspectiveTransform(pts1, pts2)
  dst = cv.warpPerspective(img, M, size)
  return dst


def tfactor(img):
  """
  添加饱和度光照的噪声
  """
  hsv = cv.cvtColor(img,cv.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 = cv.cvtColor(hsv, cv.COLOR_HSV2BGR)
  return img


def random_envirment(img, noplate_bg):
  """
  添加自然环境的噪声, noplate_bg为不含车牌的背景图
  """
  bg_index = r(len(noplate_bg))
  env = cv.imread(noplate_bg[bg_index])
  env = cv.resize(env, (img.shape[1], img.shape[0]))
  bak = (img == 0)
  bak = bak.astype(np.uint8) * 255
  inv = cv.bitwise_and(bak, env)
  img = cv.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 cv.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.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 = cv.resize(cv.imread("data\\images\\template.bmp"), (226, 70))  # template.bmp:车牌背景图
    self.smu = cv.imread("data\\images\\smu2.jpg")  # smu2.jpg:模糊图像
    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 = cv.bitwise_not(fg)
      com = cv.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

  @staticmethod
  def genPlateString(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
        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

  @staticmethod
  def genBatch(batchsize, outputPath, size):
    """
    将生成的车牌图片写入文件夹，对应的label写入label.txt
    :param batchsize: 批次大小
    :param outputPath: 输出图像的保存路径
    :param size: 输出图像的尺寸
    :return: None
    """
    if not os.path.exists(outputPath):
      os.mkdir(outputPath)
    outfile = open('data\\plate\\label.txt', 'w', encoding='utf-8')
    for i in range(batchsize):
      plateStr, plate = G.genPlateString(-1, -1)
      # print(plateStr, plate)
      img = G.generate(plateStr)
      img = cv.resize(img, size)
      cv.imwrite(outputPath + "\\" + str(i).zfill(2) + ".jpg", img)
      outfile.write(str(plate) + "\n")


if __name__ == '__main__':
  G = GenPlate("data\\font\\platech.ttf", 'data\\font\\platechar.ttf', "data\\NoPlates")
  G.genBatch(101, 'data\\plate', (272, 72))