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

1、ython车牌识别系统开源代码_Python+Tensorflow+CNN实现车牌识别的例代码、项概述本次项标是实现对动成的带有各种噪声的车牌识别。在噪声扰情况下，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个英字母（O和I除外），共有65个类别，7个字符使单独的loss函数进训练。（运环境：tensorflow1.14.0-GPU版）、成车牌数据集import osimport cv2 as cvimport numpy as npfrom math import *from PIL import ImageFontfrom PIL

2、 import Imagefrom PIL import ImageDrawindex = 京: 0,沪: 1,津: 2,渝: 3,冀: 4,晋: 5,蒙: 6,辽: 7,吉: 8,: 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,: 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:

3、 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: 64chars = 京, 沪, 津, 渝, 冀, 晋, 蒙, 辽, 吉, ,苏, 浙, 皖, 闽, 赣, 鲁, 豫, 鄂, 湘, 粤,桂, 琼, 川, 贵, 云, 藏, 陕, , 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,

4、 U,V, W, X, Y, Zdef AddSmudginess(img, Smu):模糊处理:param img: 输图像:param Smu:模糊图像:return: 添加模糊后的图像rows = r(Smu.shape0 - 50)cols = r(Smu.shape1 - 50)adder = Smurows:rows + 50,cols:cols + 50adder = cv.resize(adder, (50, 50)img = cv.resize(img,(50,50)img = cv.bitwise_not(img)img = cv.bitwise_and(adder, im

5、g)img = cv.bitwise_not(img)return imgdef rot(img, angel, shape, max_angel):添加透视畸变size_o = shape1, shape0size = (shape1+ int(shape0 * cos(float(max_angel ) / 180) * 3.14), shape0)interval = abs(int(sin(float(angel) / 180) * 3.14) * shape0)pts1 = np.float32(0, 0, 0, size_o1, size_o0, 0, size_o0, size_

6、o1)if angel 0:pts2 = np.float32(interval, 0, 0, size1, size0, 0, size0 - interval, size_o1)else:pts2 = np.float32(0, 0, interval, size1, size0 - interval, 0, size0, size_o1)M = cv.getPerspectiveTransform(pts1, pts2)dst = cv.warpPerspective(img, M,size)return dstdef rotRandrom(img, factor, size):添加放射

7、畸变:param img: 输图像:param factor: 畸变的参数:param size: 图标尺:return: 放射畸变后的图像shape = sizepts1 = np.float32(0, 0, 0, shape0, shape1, 0, shape1, shape0)pts2 = np.float32(r(factor), r(factor), r(factor), shape0 - r(factor), shape1 - r(factor), r(factor),shape1 - r(factor), shape0 - r(factor)M = cv.getPerspect

8、iveTransform(pts1, pts2)dst = cv.warpPerspective(img, M,size)return dstdef 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.rand

9、om() * 0.8)img = cv.cvtColor(hsv, cv.COLOR_HSV2BGR)return imgdef random_envirment(img, noplate_bg):添加然环境的噪声,noplate_bg为不含车牌的背景图bg_index = r(len(noplate_bg)env = cv.imread(noplate_bgbg_index)env = cv.resize(env, (img.shape1, img.shape0)bak = (img = 0)bak = bak.astype(np.uint8) * 255inv = cv.bitwise_a

10、nd(bak, env)img = cv.bitwise_or(inv, img)return imgdef GenCh(f, val):成中字符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 Adef GenCh1(f, val):成英字符img =Image.new(RGB, (23, 70), (255, 255,255)d

11、raw = ImageDraw.Draw(img)draw.text(0, 2), val, (0, 0,0), font=f) # val.decode(utf-8)A = np.array(img)return Adef AddGauss(img, level):添加斯模糊return cv.blur(img, (level * 2 + 1,level * 2 + 1)def r(val):return int(np.random.random() * val)def AddNoiseSingleChannel(single):添加斯噪声diff = 255 - single.max()n

12、oise = 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 + noisereturn dstdef addNoise(img): # sdev = 0.5,avg=10img:, :, 0 = AddNoiseSingleChannel(img:, :, 0)img:, :, 1 = AddNoiseSingleChannel(

13、img:, :, 1)img:, :, 2 = AddNoiseSingleChannel(img:, :, 2)return imgclass 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.i

14、mread(dataimagestemplate.bmp), (226, 70) # template.bmp:车牌背景图self.smu = cv.imread(dataimagessmu2.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 + + filenameself.noplates_path.append(path)def draw(self, val):o

15、ffset = 2self.img0:70, offset+8:offset+8+23 = GenCh(self.fontC, val0)self.img0:70, offset+8+23+6:offset+8+23+6+23 = GenCh1(self.fontE, val1)for i in range(5):base = offset + 8 + 23 + 6 + 23 + 17 + i * 23 + i * 6self.img0:70, base:base+23 = GenCh1(self.fontE, vali+2)return self.imgdef generate(self,

16、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.shape1, com.shape0)com = tfactor(com)com = random_envirment(com, self.noplates_path)com = AddGauss(com, 1+r(4

17、)com = addNoise(com)return comstaticmethoddef genPlateString(pos, val):成车牌string，存为图成车牌list，存为labelplateStr = plateList=box = 0, 0,0,0,0,0,0if pos != -1:boxpos = 1for unit, cpos in zip(box, range(len(box):if unit = 1:plateStr += valplateList.append(val)else:if cpos = 0:plateStr += charsr(31)plateLis

18、t.append(plateStr)elif cpos = 1:plateStr += chars41 + r(24)plateList.append(plateStr)else:plateStr += chars31 + r(34)plateList.append(plateStr)plate = plateList0b = plateListi-1 for i in range(len(plateList)plate.extend(b1:7)return plateStr, platestaticmethoddef genBatch(batchsize, outputPath, size):将成的车牌图写件夹，对应的label写label.txt:param batchsize: 批次:param outputPath: 输出图像的保存路径:param size: 输出图像的尺:return: Noneif not os.path.exists(outputPath):os.mkdir(outputPath)outfile = open(dataplatelabel.txt, w, encoding=utf-8)for i in range(batchsize):plateStr, plate = G.genPlate