当前位置：首页 > web >正文

YOLOv5-入门篇笔记

web 2025/7/15 4:06:46

1.创建环境

conda create -n yolvo5 python=3.8

去pytorch.org下载1.8.2的版本。

pip --default-timeout=1688 install torch==1.8.2 torchvision==0.9.2 torchaudio==0.8.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cu111

github上下载yolov5的zip

pip --default-timeout=1688 install -r requirements.txt

通过网盘分享的文件：yolov5
链接: https://pan.baidu.com/s/1oigw2mPo9uVoX4hWXmiGWA?pwd=npue 提取码: npue

安装jupyter

pip install pywinpty==2.0.5
pip --default-timeout=1688 install jupyterlab

安装标注工具

pip install labelimg
pip install pyqt5 sip

2.模型检测

weights

训练好的模型文件

python detect.py --weights yolov5x.pt

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

iou-thres

基于torch.hub的检测方法

import torchmodel = torch.hub.load("./", "yolov5s", source = "local")img = "./data/images/zidane.jpg"results = model(img)results.show()

3.数据集构建

用labelimg

存储格式改成yolov

a上一张图

d下一张图

w创建框

4.模型训练

创建

分别存放没标注的和标注的文件

把labels里的classes移到和images、labels同一目录

看detect.py文件中的weight和data参数

5.Pyside6可视化界面

pip install Pyside6

在可视化界面弄好前端后，在vscode中下载pyside6插件后。将ui文件转换成py

import cv2
import sys
import torch
from PySide6.QtWidgets import QMainWindow, QApplication, QFileDialog
from PySide6.QtGui import QPixmap, QImage
from PySide6.QtCore import QTimerfrom main_window_ui import Ui_MainWindowdef convert2QImage(img):height, width, channel = img.shapereturn QImage(img, width, height, width * channel, QImage.Format_RGB888)class MainWindow(QMainWindow, Ui_MainWindow):def __init__(self):super(MainWindow, self).__init__()self.model = torch.hub.load('./',  # 指向本地克隆的仓库'custom', path='runs/train/exp11/weights/best.pt',source='local')self.setupUi(self)self.timer = QTimer()self.timer.setInterval(100)self.video = Noneself.bind_slots()def image_pred(self, file_path):results = self.model(file_path)image = results.render()[0]return convert2QImage(image)def open_image(self):print("点击了检测文件")self.timer.stop()file_path, _ = QFileDialog.getOpenFileName(self, dir = "./demo_images/images/train", filter = "*.jpg;*.png;*.jpeg")if file_path:pixmap = QPixmap(file_path)qimage = self.image_pred(file_path)self.input.setPixmap(pixmap)self.output.setPixmap(QPixmap.fromImage(qimage))def video_pred(self):ret, frame = self.video.read()if not ret:self.timer.stop()else:frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)self.input.setPixmap(QPixmap.fromImage(convert2QImage(frame)))results = self.model(frame)image = results.render()[0]self.output.setPixmap(QPixmap.fromImage(convert2QImage(image)))def open_video(self):print("点击了检测文件")file_path, _ = QFileDialog.getOpenFileName(self, dir = "./", filter = "*.mp4")if file_path:self.video = cv2.VideoCapture(file_path)self.timer.start()   def bind_slots(self):self.detpic.clicked.connect(self.open_image)self.detvid.clicked.connect(self.open_video)self.timer.timeout.connect(self.video_pred)if __name__ == "__main__":app = QApplication(sys.argv)window = MainWindow()window.show()app.exec()

6.Gradio搭建Web GUI

pip install gradio

import torch
import gradio as grmodel = torch.hub.load('./', 'custom', path='runs/train/exp11/weights/best.pt',source='local')title = "基于Gradio的YOLOv5演示项目"desc = "这是一个基于Gradio的YOLOv5演示项目,非常简洁，非常方便!"base_conf, base_iou = 0.25, 0.45def det_image(img, conf, iou):model.conf = confmodel.iou = ioureturn model(img).render()[0]
gr.Interface(inputs = ["image", gr.Slider(minimum = 0, maximum = 1, value = base_conf, interactive=True), gr.Slider(minimum = 0, maximum = 1, value = base_iou, interactive=True)],outputs = ["image"],fn = det_image,title = title,description = desc,live = True,examples = [["./demo_images/images/train/demo_images30.jpg", base_conf, base_iou], ["./demo_images/images/train/demo_images120.jpg", base_conf, base_iou]]).launch()

查看全文

http://www.xdnf.cn/news/10459.html