Python+Tensorflow+CNN实现车牌识别的示例代码_cnn车牌识别

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Python+Tensorflow+CNN实现车牌识别的示例代码实现车牌识别的示例代码

一、项目概述一、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

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