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[深度学习]CCPD 车牌数据集介绍 - CCPD2020 数据集

最编程 2024-06-08 16:21:23
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CCPD2020数据集采集方法应该和CCPD2019数据集类似。CCPD2020仅仅有新能源车牌图片,包含不同亮度,不同倾斜角度,不同天气环境下的车牌。CCPD2020中的图像被拆分为train/val/test数据集,train/val/test数据集中图片数分别为5769/1001/5006。CCPD2020数据集(数据大小865.7MB)下载地址:

  • 谷歌云盘
  • 百度云盘(代码:ol3j)

CCPD2020中部分图片如下图所示:

CCPD数据集标注处理

CCPD数据集没有专门的标注文件,每张图像的文件名就是该图像对应的数据标注。例如图片3061158854166666665-97_100-159&434_586&578-558&578_173&523_159&434_586&474-0_0_3_24_33_32_28_30-64-233.jpg的文件名可以由分割符'-'分为多个部分:

  1. 3061158854166666665为区域(这个值可能有问题,可以不管);
  2. 97_100对应车牌的两个倾斜角度-水平倾斜角和垂直倾斜角, 水平倾斜97度, 竖直倾斜100度。水平倾斜度是车牌与水平线之间的夹角。二维旋转后,垂直倾斜角为车牌左边界线与水平线的夹角。CCPD数据集中这个参数标注可能不那么准,这个指标具体参考了论文Hough Transform and Its Application in Vehicle License Plate Tilt Correction
  3. 159&434_586&578对应边界框左上角和右下角坐标:左上(159, 434), 右下(586, 578);
  4. 558&578_173&523_159&434_586&474对应车牌四个顶点坐标(右下角开始顺时针排列):右下(558, 578),左下(173, 523),左上(159, 434),右上(586, 474);
  5. 0_0_3_24_33_32_28_30为车牌号码(第一位为省份缩写),在CCPD2019中这个参数为7位,CCPD2020中为8位,有对应的关系表;
  6. 64为亮度,数值越大车牌越亮(可能不准确,仅供参考);
  7. 233为模糊度,数值越小车牌越模糊(可能不准确,仅供参考)。

对于每张图片的标注信息直接字符分割即可。一个展示CCPD数据集单张图片标注的Python代码如下。

# -*- coding: utf-8 -*-
"""
Created on Wed Jun  9 18:24:10 2021

@author: luohenyueji
"""

from PIL import Image, ImageDraw, ImageFont
import os


provincelist = [
    "皖", "沪", "津", "渝", "冀",
    "晋", "蒙", "辽", "吉", "黑",
    "苏", "浙", "京", "闽", "赣",
    "鲁", "豫", "鄂", "湘", "粤",
    "桂", "琼", "川", "贵", "云",
    "西", "陕", "甘", "青", "宁",
    "新"]

wordlist = [
    "A", "B", "C", "D", "E",
    "F", "G", "H", "J", "K",
    "L", "M", "N", "P", "Q",
    "R", "S", "T", "U", "V",
    "W", "X", "Y", "Z", "0",
    "1", "2", "3", "4", "5",
    "6", "7", "8", "9"]

# --- 绘制边界框


def DrawBox(im, box):
    draw = ImageDraw.Draw(im)
    draw.rectangle([tuple(box[0]), tuple(box[1])],  outline="#FFFFFF", width=3)

# --- 绘制四个关键点


def DrawPoint(im, points):

    draw = ImageDraw.Draw(im)

    for p in points:
        center = (p[0], p[1])
        radius = 5
        right = (center[0]+radius, center[1]+radius)
        left = (center[0]-radius, center[1]-radius)
        draw.ellipse((left, right), fill="#FF0000")

# --- 绘制车牌


def DrawLabel(im, label):
    draw = ImageDraw.Draw(im)
   # draw.multiline_text((30,30), label.encode("utf-8"), fill="#FFFFFF")
    font = ImageFont.truetype('simsun.ttc', 64)
    draw.text((30, 30), label, font=font)

# --- 图片可视化


def ImgShow(imgpath, box, points, label):
    # 打开图片
    im = Image.open(imgpath)
    DrawBox(im, box)
    DrawPoint(im, points)
    DrawLabel(im, label)
    # 显示图片
    im.show()
    im.save('result.jpg')


def main():
    # 图像路径
    imgpath = 'ccpd_green/val/0136360677083-95_103-255&434_432&512-432&512_267&494_255&434_424&449-0_0_3_25_30_24_24_32-98-218.jpg'

    # 图像名
    imgname = os.path.basename(imgpath).split('.')[0]

    # 根据图像名分割标注
    _, _, box, points, label, brightness, blurriness = imgname.split('-')

    # --- 边界框信息
    box = box.split('_')
    box = [list(map(int, i.split('&'))) for i in box]

    # --- 关键点信息
    points = points.split('_')
    points = [list(map(int, i.split('&'))) for i in points]
    # 将关键点的顺序变为从左上顺时针开始
    points = points[-2:]+points[:2]

    # --- 读取车牌号
    label = label.split('_')
    # 省份缩写
    province = provincelist[int(label[0])]
    # 车牌信息
    words = [wordlist[int(i)] for i in label[1:]]
    # 车牌号
    label = province+''.join(words)

    # --- 图片可视化
    ImgShow(imgpath, box, points, label)


main()

上面的代码读取了CCPD中的一张图片,并绘制了其车牌的边界框,关键点,车牌名。结果如下所示

类型 图片
原图
标注展示图

原文地址:https://www.cnblogs.com/luohenyueji/p/16992925.html

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