python车牌识别系统开源代码_Python+Tensorflow+CNN实现
车牌识别的⽰例代码
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
python新手代码示例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 = w("RGB", (45, 70), (255, 255, 255))
draw = ImageDraw.Draw(img)
<((0, 3), val, (0, 0, 0), font=f)
img = size((23, 70))
A = np.array(img)
return A
def GenCh1(f, val):
"""
⽣成英⽂字符
"""
img =w("RGB", (23, 70), (255, 255, 255))
draw = ImageDraw.Draw(img)
<((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 = al(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 = uetype(fontCh, 43, 0)
self.fontE = uetype(fontEng, 60, 0)
self.img = np.w("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
for parent, parent_folder, filenames in os.walk(NoPlates):
for filename in filenames:
path = parent + "\\" + filename
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, plates_path)
com = AddGauss(com, 1+r(4))
com = addNoise(com)
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