模型检测

关键参数

weights:训练好的模型文件

image-20230803192833654

source: 检测的目标,可以是单张图片、文件夹、屏幕或者摄像头等

image-20230803192903522

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

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

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激活环境

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python -m ipykernel install --name yolov5

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新建Jupyter文件

hub_detect.ipynb

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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

image-20230803200936030

数据集构建

视频放在data02文件夹下

抽取视频帧

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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

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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

image-20230803225027177

  • 设置保存路径

image-20230803225658417

  • 点击下图的标志,将保存格式改为YOLO

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  • 开启自动保存

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  • 开始标注,鼠标右键点击Create RectBox

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  • 框出两个人物,并命名

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  • 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

image-20230803232028650

模型训练

调整文件命名

命名严格一致,不能改

image-20230803232326057

文件调整

image-20230803232806766

image-20230803232740636

设置yaml文件

  1. 复制coco128.yaml文件,并命名为bvn.yaml

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  1. 修改bvn.yaml

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  1. 修改train.py

文件名改为bvn

workers设置为1

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  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

image-20230804010009222

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

image-20230804000941294

1. 更改虚拟内存

  1. 进入高级系统设置,应该都会进,就不说过程了

image-20230804002758867

  1. 设置虚拟内存大小

image-20230804003012617

2. 减小占用内容大小

  1. 新建一个fixNvPe.py程序
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# 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 )
  1. 安装pefile
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pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pefile

image-20230804001137635

  1. 终端运行fixNvPe.py文件
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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

image-20230804001547119

RuntimeError: CUDA out of memory.

解决方式:

换小模型

image-20230804004234793

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

image-20230804004739957

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

解决方式:

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pip install -i https://pypi.tuna.tsinghua.edu.cn/simple Pillow==9.5

查看日志

终端输入tensorboard --logdir runs

image-20230804010221749

训练效果检测

命令行输入以下命令

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python detect.py  --weights runs/train/exp/weights/best.pt --source data02/BVN.mp4 --view-img

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