YOLOv5入门

news2024/11/28 20:52:38

模型检测

关键参数

weights:训练好的模型文件

source: 检测的目标，可以是单张图片、文件夹、屏幕或者摄像头等

conf-thres: 置信度闯值，越低框越多，越高框越少

iou-thres: IOU闻值，越低框越少，越少框越多

torch.hub检测方法

安装Jupyter

命令行输入以下代码

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple jupyterlab
conda install ipykernel

激活环境

python -m ipykernel install --name yolov5

新建Jupyter文件

hub_detect.ipynb

import torch

#Model
model = torch.hub.load("./","yolov5s",source= "local")

#Images
img = "./data/images/zidane.jpg"

# Inference
results = model(img)

# Results
results.show()

注意运行环境，在下yolov5

数据集构建

视频放在data02文件夹下

抽取视频帧

import cv2
import matplotlib.pyplot as plt
#%%
# 打开视频文件
video = cv2.VideoCapture("./BVN.mp4")
# 读取一帧
ret, frame = video.read()

plt.imshow(frame)

plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

video = cv2.VideoCapture("./BVN.mp4")
num = 0         # 计数器
save_step = 30  # 间隔帧
while True:
    ret, frame = video.read()
    if not ret:
        break
    num += 1
    if num % save_step == 0:
        cv2.imwrite("./images/" + str(num) + ".jpg", frame)

安装标准工具labelimg

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple labelimg

使用labelimg

终端输入labelimg
打开需要标注的文件夹images

设置保存路径

点击下图的标志，将保存格式改为YOLO

开启自动保存

开始标注，鼠标右键点击Create RectBox

框出两个人物，并命名

切换下一张图片，继续标准（快捷键A下一张，D下一张，W创建RectBox）
标准完成后，文件夹内容如图所示

模型训练

调整文件命名

命名严格一致，不能改

文件调整

设置yaml文件

复制coco128.yaml文件，并命名为bvn.yaml

修改bvn.yaml

修改train.py

文件名改为bvn

workers设置为1

运行train.py，训练模型

中间肯能需要下载一写东西，比较慢，慢慢等（可以挂个梯子）

报错，查看下面的报错解决

报错解决

页面文件太小，无法完成操作

训练过程中，发生下图所示的报错，同时pycharm崩溃

1. 更改虚拟内存

进入高级系统设置，应该都会进，就不说过程了

设置虚拟内存大小

2. 减小占用内容大小

新建一个fixNvPe.py程序

# Simple script to disable ASLR and make .nv_fatb sections read-only
# Requires: pefile  ( python -m pip install pefile )
# Usage:  fixNvPe.py --input path/to/*.dll
 
import argparse
import pefile
import glob
import os
import shutil
 
def main(args):
    failures = []
    for file in glob.glob( args.input, recursive=args.recursive ):
        print(f"\n---\nChecking {file}...")
        pe = pefile.PE(file, fast_load=True)
        nvbSect = [ section for section in pe.sections if section.Name.decode().startswith(".nv_fatb")]
        if len(nvbSect) == 1:
            sect = nvbSect[0]
            size = sect.Misc_VirtualSize
            aslr = pe.OPTIONAL_HEADER.IMAGE_DLLCHARACTERISTICS_DYNAMIC_BASE
            writable = 0 != ( sect.Characteristics & pefile.SECTION_CHARACTERISTICS['IMAGE_SCN_MEM_WRITE'] )
            print(f"Found NV FatBin! Size: {size/1024/1024:0.2f}MB  ASLR: {aslr}  Writable: {writable}")
            if (writable or aslr) and size > 0:
                print("- Modifying DLL")
                if args.backup:
                    bakFile = f"{file}_bak"
                    print(f"- Backing up [{file}] -> [{bakFile}]")
                    if os.path.exists( bakFile ):
                        print( f"- Warning: Backup file already exists ({bakFile}), not modifying file! Delete the 'bak' to allow modification")
                        failures.append( file )
                        continue
                    try:
                        shutil.copy2( file, bakFile)
                    except Exception as e:
                        print( f"- Failed to create backup! [{str(e)}], not modifying file!")
                        failures.append( file )
                        continue
                # Disable ASLR for DLL, and disable writing for section
                pe.OPTIONAL_HEADER.DllCharacteristics &= ~pefile.DLL_CHARACTERISTICS['IMAGE_DLLCHARACTERISTICS_DYNAMIC_BASE']
                sect.Characteristics = sect.Characteristics & ~pefile.SECTION_CHARACTERISTICS['IMAGE_SCN_MEM_WRITE']
                try:
                    newFile = f"{file}_mod"
                    print( f"- Writing modified DLL to [{newFile}]")
                    pe.write( newFile )
                    pe.close()
                    print( f"- Moving modified DLL to [{file}]")
                    os.remove( file )
                    shutil.move( newFile, file )
                except Exception as e:
                    print( f"- Failed to write modified DLL! [{str(e)}]")
                    failures.append( file )
                    continue
 
    print("\n\nDone!")
    if len(failures) > 0:
        print("***WARNING**** These files needed modification but failed: ")
        for failure in failures:
            print( f" - {failure}")
 
 
 
 
 
 
 
def parseArgs():
    parser = argparse.ArgumentParser( description="Disable ASLR and make .nv_fatb sections read-only", formatter_class=argparse.ArgumentDefaultsHelpFormatter )
    parser.add_argument('--input', help="Glob to parse", default="*.dll")
    parser.add_argument('--backup', help="Backup modified files", default=True, required=False)
    parser.add_argument('--recursive', '-r', default=False, action='store_true', help="Recurse into subdirectories")
 
    return parser.parse_args()
 
 
###############################
# program entry point
#
if __name__ == "__main__":
    args = parseArgs()
    main( args )

安装pefile

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pefile

终端运行fixNvPe.py文件

python fixNvPe.py --input E:\kaifa\Anaconda3\envs\yolov5\lib\site-packages\torch\lib\cudnn_adv_infer64_8.dll

intput后面的路径，就是报错那里，后面给的路径

出现下图所示表示执行完毕

RuntimeError: CUDA out of memory.

解决方式：

换小模型

AttributeError: ‘FreeTypeFont’ object has no attribute ‘getsize’

这是因为安装了新版本的 Pillow (10)，pip install tf-models-official删除了该getsize 功能，降级到 Pillow 9.5 解决了该问题