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原文链接：实战 | YOLOv8自定义数据集训练实现手势识别 (标注+训练+预测 保姆级教程)

0 导  读

    本文将手把手教你用YoloV8训练自己的数据集并实现手势识别。

1 安装环境

【1】安装torch, torchvision对应版本，这里先下载好，直接安装

<code>pip install torch-1.13.1+cu116-cp38-cp38-win_amd64.whl

pip install torchvision-0.14.1+cu116-cp38-cp38-win_amd64.whl

安装好后可以查看是否安装成功，上面安装的gpu版本，查看指令与结果：

<code>import torch

print(torch.__version__)

print(torch.cuda.is_available())

【2】安装ultralytics

pip install ultralytics

【3】下载YoloV8预训练模型：GitHub - ultralytics/ultralytics: NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite

本文使用YOLOv8n，直接下载第一个即可

【4】运行demo测试安装是否成功：

<code>from ultralytics import YOLO

# Load a model

model = YOLO('yolov8n.pt') # pretrained YOLOv8n model

# Run batched inference on a list of images

results = model(['1.jpg', '2.jpg']) # return a list of Results objects

# Process results list

for result in results:

boxes = result.boxes # Boxes object for bounding box outputs

masks = result.masks # Masks object for segmentation masks outputs

keypoints = result.keypoints # Keypoints object for pose outputs

probs = result.probs # Probs object for classification outputs

result.show() # display to screen

result.save(filename='result.jpg') # save to diskcode>

2 标注/制作数据集

<code># -*- coding: utf-8 -*-

import cv2

cap = cv2.VideoCapture(0)

flag = 0

if(cap.isOpened()): #视频打开成功

flag = 1

else:

flag = 0

print('open cam failed!')

if(flag==1):

while(True):

cv2.namedWindow("frame")

ret,frame = cap.read()#读取一帧

if ret==False: #读取帧失败

break

cv2.imshow("frame", frame)

if cv2.waitKey(50)&0xFF ==27: #按下Esc键退出

cv2.imwrite("1.jpg",frame)

break

cap.release()

cv2.destroyAllWindows()

本文使用共3种手势1，2，5，三种手势各300张，大家可以根据实际情况增减样本数量。

【2】标注样本

    标注工具使用labelimg即可，直接pip安装：

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

安装完成后，命令行直接输入labelimg，回车即可打开labelimg，数据集类型切换成YOLO，然后依次完成标注即可。

【3】标注划分

    标注好之后，使用下面的脚本划分训练集、验证集，注意设置正确的图片和txt路径：

<code>

# -*- coding: utf-8 -*-

import os

import random

import shutil

# 设置文件路径和划分比例

root_path = "./voc_yolo/"

image_dir = "./JPEGImages/"

label_dir = "./Annotations/"

train_ratio = 0.7

val_ratio = 0.2

test_ratio = 0.1

# 创建训练集、验证集和测试集目录

os.makedirs("images/train", exist_ok=True)

os.makedirs("images/val", exist_ok=True)

os.makedirs("images/test", exist_ok=True)

os.makedirs("labels/train", exist_ok=True)

os.makedirs("labels/val", exist_ok=True)

os.makedirs("labels/test", exist_ok=True)

# 获取所有图像文件名

image_files = os.listdir(image_dir)

total_images = len(image_files)

random.shuffle(image_files)

# 计算划分数量

train_count = int(total_images * train_ratio)

val_count = int(total_images * val_ratio)

test_count = total_images - train_count - val_count

# 划分训练集

train_images = image_files[:train_count]

for image_file in train_images:

label_file = image_file[:image_file.rfind(".")] + ".txt"

shutil.copy(os.path.join(image_dir, image_file), "images/train/")

shutil.copy(os.path.join(label_dir, label_file), "labels/train/")

# 划分验证集

val_images = image_files[train_count:train_count+val_count]

for image_file in val_images:

label_file = image_file[:image_file.rfind(".")] + ".txt"

shutil.copy(os.path.join(image_dir, image_file), "images/val/")

shutil.copy(os.path.join(label_dir, label_file), "labels/val/")

# 划分测试集

test_images = image_files[train_count+val_count:]

for image_file in test_images:

label_file = image_file[:image_file.rfind(".")] + ".txt"

shutil.copy(os.path.join(image_dir, image_file), "images/test/")

shutil.copy(os.path.join(label_dir, label_file), "labels/test/")

# 生成训练集图片路径txt文件

with open("train.txt", "w") as file:

file.write("\n".join([root_path + "images/train/" + image_file for image_file in train_images]))

# 生成验证集图片路径txt文件

with open("val.txt", "w") as file:

file.write("\n".join([root_path + "images/val/" + image_file for image_file in val_images]))

# 生成测试集图片路径txt文件

with open("test.txt", "w") as file:

file.write("\n".join([root_path + "images/test/" + image_file for image_file in test_images]))

print("数据划分完成！")

接着会生成划分好的数据集如下：

打开images文件夹：

打开images下的train文件夹：

打开labels下的train文件夹：

3 训练与预测

【1】开始训练

    训练脚本如下：

<code>from ultralytics import YOLO

# Load a model

model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)

results = model.train(data='hand.yaml', epochs=30, imgsz=640, device=[0],code>

workers=0,lr0=0.001,batch=8,amp=False)

    hand.yaml内容如下，注意修改自己的数据集路径即可：

# Ultralytics YOLO 🚀, AGPL-3.0 license

# COCO8 dataset (first 8 images from COCO train2017) by Ultralytics

# Documentation: https://docs.ultralytics.com/datasets/detect/coco8/

# Example usage: yolo train data=coco8.yaml

# parent

# ├── ultralytics

# └── datasets

# └── coco8 ← downloads here (1 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: E:/Practice/DeepLearning/Yolo_Test/dataset/hand # dataset root dir

train: E:/Practice/DeepLearning/Yolo_Test/dataset/hand/images/train # train images (relative to 'path') 4 images

val: E:/Practice/DeepLearning/Yolo_Test/dataset/hand/images/val # val images (relative to 'path') 4 images

test: # test images (optional)

# Classes

names:

0: hand-1

1: hand-2

2: hand-5

# Download script/URL (optional)

# download: https://ultralytics.com/assets/coco8.zip

CPU训练将device=[0]改为device='cpu'即可

训练完成后再runs/detect/train文件夹下生成如下内容：

    在weights文件夹下生成两个模型文件，直接使用best.pt即可。

【2】预测推理

    预测脚本如下：

<code>from ultralytics import YOLO

# Load a model

model = YOLO('best.pt') # pretrained YOLOv8n model

# Run batched inference on a list of images

results = model(['1 (1).jpg', '1 (2).jpg', '1 (3).jpg']) # return a list of Results objects

# Process results list

for result in results:

boxes = result.boxes # Boxes object for bounding box outputs

masks = result.masks # Masks object for segmentation masks outputs

keypoints = result.keypoints # Keypoints object for pose outputs

probs = result.probs # Probs object for classification outputs

result.show() # display to screen

result.save(filename='result.jpg') # save to diskcode>

    预测结果：

—THE END—

THE END!

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