基于YOLOv8的车牌检测与识别(CCPD2020数据集)

news2024/10/6 8:34:46

前言

本篇博客主要记录在autodl服务器中基于yolov8实现车牌检测与识别，以下记录实现全过程~

yolov8源码：GitHub - ultralytics/ultralytics: NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite

一、环境配置

1、第一步:配置yolov8环境

使用云服务器autoDL

二、数据集准备

数据集下载地址：https://github.com/detectRecog/CCPD

1、数据集介绍

CCPD2020数据集是一个大型的、多样化的、经过仔细标注的中国城市车牌开源数据集。CCPD数据集主要分为CCPD2019数据集和CCPD2020(CCPD-Green)数据集。CCPD2019数据集车牌类型仅有普通车牌(蓝色车牌)，CCPD2020数据集车牌类型仅有新能源车牌(绿色车牌)。CCPD数据集没有专门的标注文件，每张图片的文件名就是该图像对于的数据标注。

数据集图片命名规则：

025-95_113-154&383_386&473-386&473_177&454_154&383_363&402-0_0_22_27_27_33_16-37-15.jpg


1. 025：车牌区域占整个画面的比例；
2. 95_113： 车牌水平和垂直角度, 水平95°, 竖直113°
3. 154&383_386&473：标注框左上、右下坐标，左上(154, 383), 右下(386, 473)
4. 86&473_177&454_154&383_363&402：标注框四个角点坐标，顺序为右下、左下、左上、右上
5. 0_0_22_27_27_33_16：车牌号码映射关系如下: 第一个0为省份 对应省份字典provinces中的’皖’,；第二个0是该车所在地的地市一级代码，对应地市一级代码字典alphabets的’A’；后5位为字母和文字, 查看车牌号ads字典，如22为Y，27为3，33为9，16为S，最终车牌号码为皖AY339S

省份：[“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”, “藏”, “陕”, “甘”, “青”, “宁”,
“新”]

地市：[‘A’, ‘B’, ‘C’, ‘D’, ‘E’, ‘F’, ‘G’, ‘H’, ‘J’, ‘K’, ‘L’, ‘M’, ‘N’, ‘P’, ‘Q’,
‘R’, ‘S’, ‘T’, ‘U’, ‘V’, ‘W’,‘X’, ‘Y’, ‘Z’]

车牌字典：[‘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’]

CCPD2019数据集包含将近30万张图片、图片尺寸为720x1160x3，共包含8种类型图片，每种类型、数量及类型说明如下表：

2、数据集制作

第一步：新建datasets目录，将数据集上传

第二步：解压数据集，根据以下目录结构制作训练集、验证集和测试集：

代码如下：

import shutil
import cv2
import os

def txt_translate(path, txt_path):
    print(path)
    print(txt_path)
    for filename in os.listdir(path):
        # print(filename)

        list1 = filename.split("-", 3)  # 第一次分割，以减号'-'做分割
        subname = list1[2]
        list2 = filename.split(".", 1)
        subname1 = list2[1]
        if subname1 == 'txt':
            continue
        lt, rb = subname.split("_", 1)  # 第二次分割，以下划线'_'做分割
        lx, ly = lt.split("&", 1)
        rx, ry = rb.split("&", 1)
        width = int(rx) - int(lx)
        height = int(ry) - int(ly)  # bounding box的宽和高
        cx = float(lx) + width / 2
        cy = float(ly) + height / 2  # bounding box中心点

        img = cv2.imread(path + filename)
        if img is None:  # 自动删除失效图片（下载过程有的图片会存在无法读取的情况）
            print(path + filename)
            os.remove(path + filename)
            continue
        width = width / img.shape[1]
        height = height / img.shape[0]
        cx = cx / img.shape[1]
        cy = cy / img.shape[0]

        txtname = filename.split(".", 1)
        txtfile = txt_path + txtname[0] + ".txt"
        # 绿牌是第0类，蓝牌是第1类
        with open(txtfile, "w") as f:
            f.write(str(0) + " " + str(cx) + " " + str(cy) + " " + str(width) + " " + str(height))


if __name__ == '__main__':
    # det图片存储地址
    trainDir = r"/root/ultralytics/datasets/CCPD2020/ccpd_green/train/"
    validDir = r"/root/ultralytics/datasets/CCPD2020/ccpd_green/val/"
    testDir = r"/root/ultralytics/datasets/CCPD2020/ccpd_green/test/"
    # det txt存储地址
    train_txt_path = r"/root/ultralytics/datasets/platedata/labels/train/"
    val_txt_path = r"/root/ultralytics/datasets/platedata/labels/val/"
    test_txt_path = r"/root/ultralytics/datasets/platedata/labels/test/"
    txt_translate(trainDir, train_txt_path)
    txt_translate(validDir, val_txt_path)
    txt_translate(testDir, test_txt_path)

制作之后文件夹结构：

3、配置文件修改

第一步：在/root/ultralytics/ultralytics/cfg/datasets下复制COCO128数据集，重命名为CCPD2020.yaml

更改内容如下：

# Ultralytics YOLO 🚀, AGPL-3.0 license
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
# Example usage: yolo train data=coco128.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── coco128  ← downloads here (7 MB)


# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
#path: ../datasets/coco128  # dataset root dir
train: /root/ultralytics/datasets/platedata/images/train  # train images (relative to 'path') 128 images
val: /root/ultralytics/datasets/platedata/images/val  # val images (relative to 'path') 128 images
test: /root/ultralytics/datasets/platedata/images/test  # test images (optional)

# Classes
names:
  0: license_plate



# Download script/URL (optional)
#download: https://ultralytics.com/assets/coco128.zip

第二步：将/root/ultralytics/ultralytics/cfg/models/v8/yolov8.yaml下的nc改为1