车牌识别的N种办法

2024-07-16 08:55| 来源: 网络整理| 查看: 265

一、车牌识别简介

随着科学技术的发展，人工智能技术在我们的生活中的应用越来越广泛，人脸识别、车牌识别、目标识别等众多场景已经落地应用，给我们的生活品质得到很大的提升，办事效率大大提高，同时也节约了大量的劳动力。

今天我们来讲一讲车牌识别这个任务，车牌识别技术经过多年的发展，技术路线也呈现多样化，我们来介绍一下其中的两种：

第一种，单个字符识别

首先，在地面上有传感器感应区域，当有车辆经过时自动进行拍照，然后对拍的照片进行预处理，变成灰度图像，去除噪声等，把一些干扰因素去掉，同时降低图像的大小，便于后期进行边缘提取；

其次，利用边缘提取技术，比如canny算子、sobel算子，把图像轮廓提取出来，根据车牌长宽比大概为1:2.5左右把大部分不疑似的去掉；然后，再利用图像灰度值在水平方向和竖直方向上的投影，将字符切割出来；

最后，利用模板匹配或者是已经训练好的深度学习模型进行识别，单独把每个字符识别后进行串联得到整张车牌号码。

这种方法的瓶颈在于怎么样把每一个字符准确地切割出来，这涉及到很多图像处理技术，比如高斯模糊、图像锐化、图像膨胀、伽马变化、仿射变换等等，目前对于英文字符和数字的识别准确度在96%左右，汉字识别的准确度在95%左右。

在这里插入图片描述第二种办法，整张车牌识别

首先，与第一种车牌识别一样，利用传感器进行车辆感应，把整辆车进行拍照；

其次，利用训练好的车牌检测算法，比如YOLO算法或者其他的目标检测算法把车牌检测出来。为了提升车牌识别的准确度，可以利用图像预处理技术对图像进行处理，降低干扰项；

最后，利用已经训练好的深度学习模型对整张车牌进行识别。比如LPRNet、Darknet等

二、车牌识别技术实现

## 第一种方法 ——利用 pytesseract 进行识别

pytesseract是一个OCR识别工具，具体的 pytesseract 安装可百度安装，直接使用这个工具进行识别得到的准确度还有点低，达不到理想的效果，需要利用车牌数据进行训练才可以使用。

import cv2 as cv from PIL import Image import pytesseract as tess def recoginse_text(image): """ 步骤： 1、灰度，二值化处理 2、形态学操作去噪 3、识别 :param image: :return: """ # 灰度二值化 gray = cv.cvtColor(image,cv.COLOR_BGR2GRAY) # 如果是白底黑字建议 _INV ret,binary = cv.threshold(gray,0,255,cv.THRESH_BINARY_INV| cv.THRESH_OTSU) # 形态学操作 (根据需要设置参数（1，2）) kernel = cv.getStructuringElement(cv.MORPH_RECT,(1,2)) #去除横向细线 morph1 = cv.morphologyEx(binary,cv.MORPH_OPEN,kernel) kernel = cv.getStructuringElement(cv.MORPH_RECT, (2, 1)) #去除纵向细线 morph2 = cv.morphologyEx(morph1,cv.MORPH_OPEN,kernel) cv.imshow("Morph",morph2) # 黑底白字取非，变为白底黑字（便于pytesseract 识别） cv.bitwise_not(morph2,morph2) textImage = Image.fromarray(morph2) # 图片转文字 text=tess.image_to_string(textImage) print("识别结果：%s"%text) def main(): # 读取需要识别的数字字母图片，并显示读到的原图 src = cv.imread(r'C:\Users\lenovo\Desktop\00A8CX87_5.jpg') cv.imshow("src",src) # 识别 recoginse_text(src) cv.waitKey(0) cv.destroyAllWindows() if __name__=="__main__": main()

## 第二种识别办法——PaddleOCR

PaddleOCR 是百度paddlepaddle下的OCR模块，可以用它来识别车牌，直接下载预训练模型进行识别，对于数字和子母都得到不错的准确率，但是针对中文识别的效果并不理想，需要自己的数据集进行再训练。

要想利用这个模型进行训练车牌识别，需要大量的车牌数据，人工收集不仅耗费时间长，要涉及每个省份的车牌需要比较大的成本。

另外的办法是利用图像处理技术进行数据生成，加上图像增强技术来制作数据集，这将在我们的第三章识别方法中讲到。

from paddleocr import PaddleOCR import os # ocr = PaddleOCR(use_gpu=False, use_angle_cls=True, lang="ch") ocr = PaddleOCR(use_gpu=True, use_angle_cls=True, lang="ch") path=r'E:\车牌数据' results=[] for file in os.listdir(path): img_path =os.path.join(path,file) result = ocr.ocr(img_path, cls=True) if len(result)>0: results.append([img_path,result[0][1][0]]) else: results.append([img_path])

将结果导出到Excel文件夹

import pandas as pd resultss=pd.DataFrame(results) resultss.to_excel(r'C:\Users\lenovo\Desktop\11.xlsx')

## 第三种办法 ——LPRNet算法由于要收集满足深度学习的数据需要花费大量的时间，有一种办法是通过数据生成的办法生成大量的模拟数据集。

紧接着，我们可以利用LPRNet算法进行车牌识别，从GitHub上下载有tensorflow、torch两个版本。

第一步，生成蓝色车牌数据集第二步，根据算法模型要求将图像名称修改并放到指定的文件夹中第三步，模型训练和测试

## (1) 蓝色车牌生成

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)) #白色 # img = Image.new("RGB", (45, 70), (0, 0, 0)) #黑色 draw = ImageDraw.Draw(img) # draw.text((0, 3), val, (0, 0, 0), font=f) 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))#白色 # img =Image.new("RGB", (23, 70), (0, 0, 0))#黑色 draw = ImageDraw.Draw(img) draw.text((0, 2), val, (0,125,125), 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.img = np.array(Image.new("RGB", (226, 70),(0, 0, 0))) self.bg = cv.resize(cv.imread(r"C:\Users\lenovo\Desktop\Plate_Recognition-LPRnet-master\Plate_Recognition-LPRnet-master\generate_picture\images\blue.bmp"), (226, 70)) # template.bmp:车牌背景图 self.smu = cv.imread(r"E:\AI_projects\vehicle license plate recognition\car_recognition\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(30)-10, com.shape,30) com = rotRandrom(com, 5, (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(r'C:\Users\lenovo\Desktop\Plate_Recognition-LPRnet-master\Plate_Recognition-LPRnet-master\label_blue.txt', 'w', encoding='utf-8') for i in range(batchsize): plateStr, plate = G.genPlateString(-1, -1) img = G.generate(plateStr) img = cv.resize(img, size) content = "".join(plate) cv.imwrite(outputPath + "\\" + str(i).zfill(2) + ".jpg", img) outfile.write("./train/"+str(i).zfill(2)+".jpg"+'\t'+content +"\n") G = GenPlate(r"E:\AI_projects\vehicle license plate recognition\car_recognition\data\font\platech.ttf", r'E:\AI_projects\vehicle license plate recognition\car_recognition\data\font\platechar.ttf', r"E:\AI_projects\vehicle license plate recognition\car_recognition\data\NoPlates") G.genBatch(1000, r'C:\Users\lenovo\Desktop\Plate_Recognition-LPRnet-master\Plate_Recognition-LPRnet-master\test_in_blue', (272, 72))

需要生成多少张车牌数据可通过修改最后一行代码的数字即可

## (2) 生产黄色车牌

生成车牌的步骤是把字符写到纯净的背景图片上，蓝色的车牌是把白色的字写到蓝色底的车牌上，可以直接把字符写到底牌上去，但是黄色底的车牌不能简单把黑色字写上去，写上去的结果是字符被隐藏起来，需要特别处理。

正确的做法是：第一步，构造一张车牌大小的纯黑色车牌，往上面写白色字符第二步，将黑白色进行反转，把这牌的字符和背景进行翻转第三步，将一张没有字符的黄色底牌和反转后的车牌进行结合，然后经过高斯模糊、改变光线、旋转角度等各种随机组合模拟出于生活中拍摄处理的图像。

#黄色车牌生成 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)) #白色 # img = Image.new("RGB", (45, 70), (0, 0, 0)) #黑色 draw = ImageDraw.Draw(img) # draw.text((0, 3), val, (0, 0, 0), font=f) 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))#白色 # img =Image.new("RGB", (23, 70), (0, 0, 0))#黑色 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.img = np.array(Image.new("RGB", (226, 70),(0, 0, 0))) self.bg = cv.resize(cv.imread(r"C:\Users\lenovo\Desktop\Plate_Recognition-LPRnet-master\Plate_Recognition-LPRnet-master\generate_picture\images\black.jpg"), (226, 70)) # template.bmp:车牌背景图 self.smu = cv.imread(r"E:\AI_projects\vehicle license plate recognition\car_recognition\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(r'C:\Users\lenovo\Desktop\Plate_Recognition-LPRnet-master\Plate_Recognition-LPRnet-master\label_yellow.txt', 'w', encoding='utf-8') for i in range(batchsize): plateStr, plate = G.genPlateString(-1, -1) img = G.generate(plateStr) img = cv.resize(img, size) content = "".join(plate) cv.imwrite(outputPath + "\\" + str(i).zfill(2) + ".jpg", img) outfile.write("./train/"+str(i).zfill(2)+".jpg"+'\t'+content +"\n") G = GenPlate(r"E:\AI_projects\vehicle license plate recognition\car_recognition\data\font\platech.ttf", r'E:\AI_projects\vehicle license plate recognition\car_recognition\data\font\platechar.ttf', r"E:\AI_projects\vehicle license plate recognition\car_recognition\data\NoPlates") G.genBatch(1000, r'C:\Users\lenovo\Desktop\Plate_Recognition-LPRnet-master\Plate_Recognition-LPRnet-master\test_in_yellow', (272, 72))

字符和背景反转

#将车牌黑色反转 import cv2 import random 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 sample_cp=cv.resize(cv2.imread(r"C:\Users\lenovo\Desktop\Plate_Recognition-LPRnet-master\Plate_Recognition-LPRnet-master\generate_picture\images\yellow.bmp"), (272, 72)) path=r'C:\Users\lenovo\Desktop\Plate_Recognition-LPRnet-master\Plate_Recognition-LPRnet-master\test_in_yellow' for file in os.listdir(path): if file.endswith('.jpg'): image=os.path.join(path,file) img=cv2.imread(image) for i in range(72): for j in range(272): if img[i][j][0]>50: img[i][j][0]=0 #黑色 img[i][j][1]=0 img[i][j][2]=0 else: img[i][j][0]=255 #白色 img[i][j][1]=255 img[i][j][2]=255 for i in range(72): for j in range(272): if img[i][j][0]

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