一.数据集准备

二.标签设置

三.模型训练

四.模型测试

最近尝试了Yolov5训练识别人体，用的是自己尝试做的训练集。见识到Yolo的强大后，决定分享给大家。

一.数据集准备

数据集是从百度图片上下载的，我当然不可能一个一个下载，会累死的。。。

既然会python，就做最好的爬虫~

from fake_useragent import UserAgent
import requests
import re
import uuid
#参考：
#https://blog.csdn.net/weixin_56198196/article/details/120470874
#https://blog.csdn.net/weixin_52971139/article/details/125065788

headers={                            #遇到Forbid spider access就在请求头多加一些东西，试了一下'Accept-Encoding'是关键
    # 'Accept': 'text/plain, */*; q=0.01',
    # 'Accept-Encoding': 'gzip, deflate, br',
    # 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
    # 'Connection': 'keep-alive',
    'Cookie': 'BDqhfp=%E5%8F%B2%E5%8A%AA%E6%AF%94%26%260-10-1undefined%26%263746%26%264; BIDUPSID=5A04241009FD166564DACF4050551F2D; PSTM=1611376447; __yjs_duid=1_2de46e288096c13a7edea3d05a5204421620544039468; BAIDUID=F163DBC1DF098AF604AE753E72659BAA:FG=1; BDUSS=Tczc3RnamRhaUhicm5rfm83V3pkMTBySUd1Z0V4Q25mbXhYdElRemJHdVktVnRoRVFBQUFBJCQAAAAAAQAAAAEAAACR090iR3JpZmZleTUxMQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJhsNGGYbDRhN; BDUSS_BFESS=Tczc3RnamRhaUhicm5rfm83V3pkMTBySUd1Z0V4Q25mbXhYdElRemJHdVktVnRoRVFBQUFBJCQAAAAAAQAAAAEAAACR090iR3JpZmZleTUxMQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJhsNGGYbDRhN; BAIDUID_BFESS=F163DBC1DF098AF604AE753E72659BAA:FG=1; BDORZ=AE84CDB3A529C0F8A2B9DCDD1D18B695; userFrom=cn.bing.com; IMG_WH=573_756; H_WISE_SIDS=110085_178384_179349_181588_182531_183327_183611_183750_184578_185029_185517_185653_185750_186317_186411_187020_187195_187206_187292_187450_187663_187670_187928_8000097_8000100_8000126_8000140_8000150_8000169_8000177_8000185; BDRCVFR[X_XKQks0S63]=mk3SLVN4HKm; firstShowTip=1; cleanHistoryStatus=0; indexPageSugList=%5B%22%E5%8F%B2%E5%8A%AA%E6%AF%94%22%2C%22%E6%B5%B7%E8%B4%BC%E7%8E%8B%22%2C%22%E8%8A%B1%E6%9C%B5%22%2C%22undertale%22%2C%22python%22%2C%22%E8%8A%B1%E7%AE%80%E7%AC%94%E7%94%BB%22%2C%22%E5%90%8C%E5%9E%8B%E4%B8%96%E4%BB%A3%E4%BA%A4%E6%9B%BF%22%2C%22%E6%A8%9F%E7%A7%91%E8%8A%B1%22%2C%22%E6%A8%9F%E7%A7%91%E8%8A%B1%E7%9A%84%E8%A7%A3%E5%89%96%22%5D; BDRCVFR[dG2JNJb_ajR]=mk3SLVN4HKm; ab_sr=1.0.1_MTY1OTI2YjEyMzViNzQyYWVhZjdhZWQxNzc0YjE1NzA2NGEyZmMwZGEwNzRmMWVjZGM3N2IzMDlkYjViZWVlOGYyNTllZDMzZjgwZGMxZWZhOWFiMmEyYjg0NjgyYzgwYjk0Y2QxYWVmM2E1ZTFiZjkyYTNlOGYzMDg1MWVjNjUyODViYzMyZjc2Mjk2OGFmZmZjZTkwNjg3OWI4NjhjZjdiNzJmNTY3NTIyZjg2ODVjMzUzNTExYjhiMjkxZjEx; BDRCVFR[-pGxjrCMryR]=mk3SLVN4HKm',
    'Referer': 'https://image.baidu.com/search/index?tn=baiduimage&ipn=r&ct=201326592&cl=2&lm=-1&st=-1&fm=index&fr=&hs=0&xthttps=111110&sf=1&fmq=&pv=&ic=0&nc=1&z=&se=1&showtab=0&fb=0&width=&height=&face=0&istype=2&ie=utf-8&word=%E5%8F%B2%E5%8A%AA%E6%AF%94&oq=%E5%8F%B2%E5%8A%AA%E6%AF%94&rsp=-1',
    'User-Agent': 'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Mobile Safari/537.36 Edg/93.0.961.52'
}

img_re = re.compile('"thumbURL":"(.*?)"')
img_format = re.compile("f=(.*).*?w")


def file_op(img):
    uuid_str = uuid.uuid4().hex
    tmp_file_name = './arsenal/%s.jpg' % uuid_str
    with open(file=tmp_file_name, mode="wb") as file:
        file.write(img)


def xhr_url(url_xhr, start_num=0, page=5):
    end_num = page*30
    for page_num in range(start_num, end_num, 30):
        resp = requests.get(url=url_xhr+str(page_num), headers=headers)
        if resp.status_code == 200:
            print(resp.text)
            img_url_list = img_re.findall(resp.text)  # 这是个列表形式

            for img_url in img_url_list:
                img_rsp = requests.get(url=img_url, headers=headers)
                file_op(img=img_rsp.content)
        else:
            break
        print("爬取了"+str(page_num)+"张")
    print("内容已经全部爬取")


if __name__ == "__main__":
    org_url = "https://image.baidu.com/search/acjson?tn=resultjson_com&word={text}&pn=".format(text=input("输入你想检索内容:"))
    xhr_url(url_xhr=org_url, start_num=int(input("开始页:")), page=int(input("所需爬取页数:")))

用这样的代码就可以把指定关键词的照片爬取下来，如下图所示是爬取下来的照片。爬取代码位于newdata目录下。

（图源网络侵删）

在上图中，爬取的数据关键词为“路人照片”。

二.标签设置

标签有很多可以打的工具，我采用的是vott，图片和安装包链接如下。

链接：https://pan.baidu.com/s/1CtAiR6ub072WvgfoFkm6Ig?pwd=0kqu
提取码：0kqu
--来自百度网盘超级会员V5的分享

具体使用方法参考：标注工具 VoTT 详细教程_vott使用_清欢守护者的博客-CSDN博客

如果要识别人，就把人的区域都框起来~

最后输出的标签如下：

json内的标签数据

标签数据需要经过如下代码处理成yolov5的可识别数据

import os
import json
import shutil
img_folder_path = "img/"
# 使用os.listdir()函数获取文件夹下的所有文件名
img_file_names = os.listdir(img_folder_path)
lab_folder_path = "label/"
# 使用os.listdir()函数获取文件夹下的所有文件名
lab_file_names = os.listdir(lab_folder_path)
# 打印所有文件名
'''for file_name in img_file_names:
    print(file_name)'''
# 打印所有文件名
data_num=0
outdir_img="outdata/images/"
outdir_lab="outdata/labels/"
for file_name in lab_file_names:
    with open(lab_folder_path+file_name,'r',encoding = 'utf-8') as fp:
        # read()方法将fp(一个支持.read()的文件类对象，包含一个JSON文档)转换成字符串
        data = json.loads(fp.read())

        #print(fp.read())
    for img_file_name in img_file_names:
        if img_file_name[-4:]==".jpg":
            if img_file_name[-18:]==data['asset']['name'][-18:]:
                name="im"+str(data_num)
                data_num+=1
                shutil.copy(img_folder_path+img_file_name,outdir_img+name+".jpg")
                for item in data['regions']:
                    data1=str((item['points'][0]['x']+item['points'][1]['x'])/(2*data['asset']['size']['width']))
                    data2=str((item['points'][0]['y']+item['points'][2]['y'])/(2*data['asset']['size']['height']))
                    data3=str(item['boundingBox']['width']/data['asset']['size']['width'])
                    data4=str(item['boundingBox']['height']/data['asset']['size']['height'])
                    Note=open(outdir_lab+name+".txt",mode='a')
                    Note.write("0 "+data1+" "+data2+" "+data3+" "+data4+"\n")
                    Note.close()

（代码仅供参考，具体以实际情况为准）

上述代码的目录架构：

img
-xx.jpg
-xx.jpg
...
label
-xx.json
-xx.json
...

outdata
-images
-labels

运行后在outdata文件夹会生成转换格式成功的图像和标签

其中，标签的格式如下：

参考yolov5的数据格式：

Train Custom Data - Ultralytics YOLOv8 Docs

三.模型训练

准备好数据集，就可以训练啦~

GitHub - ultralytics/yolov5: YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

下载好YOLOv5后按要求配置环境：

下载COCO128数据集coco128 | Kaggle

将下载好的dataset文件与yolov5文件夹按如下放置：

将coco128.yaml的内容修改：

改成如下：

# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Example usage: python train.py --data coco128.yaml
# parent
# ├── yolov5
# └── 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: images/train2017  # train images (relative to 'path') 128 images
val: images/train2017  # val images (relative to 'path') 128 images
test:  # test images (optional)

# Classes
names:
  0: 0
  # 1: bicycle
  # 2: car
  # 3: motorcycle
  # 4: airplane
  # 5: bus
  # 6: train
  # 7: truck
  # 8: boat
  # 9: traffic light
  # 10: fire hydrant
  # 11: stop sign
  # 12: parking meter
  # 13: bench
  # 14: bird
  # 15: cat
  # 16: dog
  # 17: horse
  # 18: sheep
  # 19: cow
  # 20: elephant
  # 21: bear
  # 22: zebra
  # 23: giraffe
  # 24: backpack
  # 25: umbrella
  # 26: handbag
  # 27: tie
  # 28: suitcase
  # 29: frisbee
  # 30: skis
  # 31: snowboard
  # 32: sports ball
  # 33: kite
  # 34: baseball bat
  # 35: baseball glove
  # 36: skateboard
  # 37: surfboard
  # 38: tennis racket
  # 39: bottle
  # 40: wine glass
  # 41: cup
  # 42: fork
  # 43: knife
  # 44: spoon
  # 45: bowl
  # 46: banana
  # 47: apple
  # 48: sandwich
  # 49: orange
  # 50: broccoli
  # 51: carrot
  # 52: hot dog
  # 53: pizza
  # 54: donut
  # 55: cake
  # 56: chair
  # 57: couch
  # 58: potted plant
  # 59: bed
  # 60: dining table
  # 61: toilet
  # 62: tv
  # 63: laptop
  # 64: mouse
  # 65: remote
  # 66: keyboard
  # 67: cell phone
  # 68: microwave
  # 69: oven
  # 70: toaster
  # 71: sink
  # 72: refrigerator
  # 73: book
  # 74: clock
  # 75: vase
  # 76: scissors
  # 77: teddy bear
  # 78: hair drier
  # 79: toothbrush


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

即将没有用到的种类注释掉。

之后训练：

python train.py --img 640 --epochs 30 --data coco128.yaml --weights yolov5s.pt

其中yolov5s.pt会自动下载，如果进度条没跑完就停止了，则把命令行中的下载网址复制到浏览器下载，并删除原本下载一半的数据。

训练后在runs/train文件夹下寻找训练好的模型best.pt并替换yolov5s.pt

四.模型测试

测试模型的命令行：

python detect.py --weights yolov5s.pt --source       0                               # webcam
                                                     img.jpg                         # image
                                                     vid.mp4                         # video
                                                     screen                          # screenshot
                                                     path/                           # directory
                                                     list.txt                        # list of images
                                                     list.streams                    # list of streams
                                                     'path/*.jpg'                    # glob
                                                     'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                                     'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

测试效果：