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https://github.com/SwimmingLiu/YOLOSHOW

YOLO SHOW - YOLOv5 / YOLOv7 / YOLOv8 / YOLOv9 / RTDETR GUI based on Pyside6
https://github.com/SwimmingLiu/YOLOSHOW

rtdetr yolo-show yologui yolov5 yolov7 yolov8 yolov9

Last synced: 26 days ago
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YOLO SHOW - YOLOv5 / YOLOv7 / YOLOv8 / YOLOv9 / RTDETR GUI based on Pyside6

Host: GitHub
URL: https://github.com/SwimmingLiu/YOLOSHOW
Owner: SwimmingLiu
License: agpl-3.0
Created: 2024-02-18T03:33:48.000Z (4 months ago)
Default Branch: master
Last Pushed: 2024-04-08T03:47:18.000Z (3 months ago)
Last Synced: 2024-04-08T04:38:36.974Z (3 months ago)
Topics: rtdetr, yolo-show, yologui, yolov5, yolov7, yolov8, yolov9
Language: Python
Homepage: https://swimmingliu.cn/posts/diary/yoloshow
Size: 259 MB
Stars: 123
Watchers: 1
Forks: 15
Open Issues: 2
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-yolo-object-detection - JSwimmingLiu/YOLOSHOW - YOLOv10 / YOLOv9 / YOLOv8 / YOLOv7 / YOLOv5 / RTDETR GUI based on Pyside6.[swimmingliu.cn/posts/diary/yoloshow](https://swimmingliu.cn/posts/diary/yoloshow) (Applications)

README

        # YOLOSHOW -  YOLOv5 / YOLOv7 / YOLOv8 / YOLOv9 / YOLOv10 / RTDETR GUI based on Pyside6

## Introduction

***YOLOSHOW*** is a graphical user interface (GUI) application embed with`YOLOv5` `YOLOv7` `YOLOv8` `YOLOv9` `YOLOv10` `RT-DETR` algorithm. 

 
 

  English   |   简体中文

 


![YOLOSHOW-Screen](https://oss.swimmingliu.cn/YOLOSHOW-SCREENSHOT.png)

## Demo Video

`YOLOSHOW v1.x` : [YOLOSHOW-YOLOv9/YOLOv8/YOLOv7/YOLOv5/RTDETR GUI](https://www.bilibili.com/video/BV1BC411x7fW)

`YOLOSHOW v2.x` : [YOLOSHOWv2.0-YOLOv9/YOLOv8/YOLOv7/YOLOv5/RTDETR GUI](https://www.bilibili.com/video/BV1ZD421E7m3)

## Todo List

- [x] Add `YOLOv9` Algorithm

- [x] Adjust User Interface (Menu Bar)

- [x] Complete Rtsp Function

- [x] Support Instance Segmentation （ `YOLOv5` & `YOLOv8` ）

- [x] Add `RT-DETR` Algorithm ( `Ultralytics` repo)

- [x] Add Model Comparison Mode（VS Mode）

- [x] Support Pose Estimation （ `YOLOv8` ）

- [x] Support Http Protocol in Rtsp Function ( Single Mode )

- [x] Support Oriented Bounding Boxes ( `YOLOv8` )

- [x] Add `YOLOv10` Algorithm

- [x] Support Dragging File Input

- [ ] Tracking & Counting ( `Industrialization` )

## Functions

### 1. Support Image / Video / Webcam / Folder (Batch) / IPCam Object Detection

Choose Image / Video / Webcam / Folder (Batch) / IPCam in the menu bar on the left to detect objects.

### 2. Change Models / Hyper Parameters dynamically

When the program is running to detect targets, you can change models / hyper Parameters

1. Support changing model in `YOLOv5` / ` YOLOv7` / `YOLOv8` / `YOLOv9` / `RTDETR` / `YOLOv5-seg` / `YOLOv8-seg` / `YOLOv10` dynamically

2. Support changing `IOU` / `Confidence` / `Delay time ` / `line thickness` dynamically

### 3. Loading Model Automatically

Our program will automatically detect  `pt` files including [YOLOv5 Models](https://github.com/ultralytics/yolov5/releases) /  [YOLOv7 Models](https://github.com/WongKinYiu/yolov7/releases/)  /  [YOLOv8 Models](https://github.com/ultralytics/assets/releases/)  / [YOLOv9 Models](https://github.com/WongKinYiu/yolov9/releases/)  / [YOLOv10 Models](https://github.com/THU-MIG/yolov10/releases/)  that were previously added to the `ptfiles` folder.

If you need add the new `pt` file, please click `Import Model` button in `Settings` box to select your `pt` file. Then our program will put it into  `ptfiles` folder.

**Notice :** 

1. All `pt` files are named including `yolov5` / `yolov7` / `yolov8` / `yolov9` / `yolov10` / `rtdetr` .  (e.g. `yolov8-test.pt`)

2. If it is a `pt` file of  segmentation mode, please name it including `yolov5n-seg` / `yolov8s-seg` .  (e.g. `yolov8n-seg-test.pt`)

3. If it is a `pt` file of  pose estimation mode, please name it including `yolov8n-pose` .  (e.g. `yolov8n-pose-test.pt`)

4. If it is a `pt` file of  oriented bounding box mode, please name it including `yolov8n-obb` .  (e.g. `yolov8n-obb-test.pt`)

### 4. Loading Configures

1.  After startup, the program will automatically loading the last configure parameters.

2.  After closedown, the program will save the changed configure parameters.

### 5. Save Results

If you need Save results, please click `Save Mode` before detection. Then you can save your detection results in selected path.

### 6. Support Object Detection, Instance Segmentation and Pose Estimation 

From ***YOLOSHOW v3.0***，our work supports both Object Detection , Instance Segmentation, Pose Estimation and Oriented Bounding Box. Meanwhile, it also supports task switching between different versions，such as switching from `YOLOv5` Object Detection task to `YOLOv8` Instance Segmentation task.

### 7. Support Model Comparison among Object Detection,  Instance Segmentation, Pose Estimation and Oriented Bounding Box

From ***YOLOSHOW v3.0***，our work supports compare model performance among Object Detection, Instance Segmentation, Pose Estimation and Oriented Bounding Box.

## Preparation

### Experimental environment

```Shell

OS : Windows 11 

CPU : Intel(R) Core(TM) i7-10750H CPU @2.60GHz 2.59 GHz

GPU : NVIDIA GeForce GTX 1660Ti 6GB

```

### 1. Create virtual environment

create a virtual environment equipped with python version 3.9, then activate environment. 

```shell

conda create -n yoloshow python=3.9

conda activate yoloshow

```

### 2. Install Pytorch frame 

```shell

Windows: pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Linux: pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

```

Change other pytorch version in  [![Pytorch](https://img.shields.io/badge/PYtorch-test?style=flat&logo=pytorch&logoColor=white&color=orange)](https://pytorch.org/)

### 3. Install dependency package

Switch the path to the location of the program

```shell

cd {the location of the program}

```

Install dependency package of program 

```shell

pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

pip install "PySide6-Fluent-Widgets[full]" -i https://pypi.tuna.tsinghua.edu.cn/simple

pip install -U Pyside6 -i https://pypi.tuna.tsinghua.edu.cn/simple

```

### 4. Add Font

#### Windows User

Copy all font files `*.ttf` in `fonts` folder into `C:\Windows\Fonts`

#### Linux User

```shell

mkdir -p ~/.local/share/fonts

sudo cp fonts/Shojumaru-Regular.ttf ~/.local/share/fonts/

sudo fc-cache -fv

```

#### MacOS User

The MacBook is so expensive that I cannot afford it, please install `.ttf` by yourself. 😂

### 5. Run Program

```shell

python main.py

```

## Frames

[![Python](https://img.shields.io/badge/python-3776ab?style=for-the-badge&logo=python&logoColor=ffd343)](https://www.python.org/)[![Pytorch](https://img.shields.io/badge/PYtorch-test?style=for-the-badge&logo=pytorch&logoColor=white&color=orange)](https://pytorch.org/)[![Static Badge](https://img.shields.io/badge/Pyside6-test?style=for-the-badge&logo=qt&logoColor=white)](https://doc.qt.io/qtforpython-6/PySide6/QtWidgets/index.html)

## Reference

### YOLO Algorithm

[YOLOv5](https://github.com/ultralytics/yolov5)   [YOLOv7](https://github.com/WongKinYiu/yolov7)  [YOLOv8](https://github.com/ultralytics/ultralytics)  [YOLOv9](https://github.com/WongKinYiu/yolov9)  [YOLOv10](https://github.com/THU-MIG/yolov10)

### YOLO Graphical User Interface

[YOLOSIDE](https://github.com/Jai-wei/YOLOv8-PySide6-GUI)	[PyQt-Fluent-Widgets](https://github.com/zhiyiYo/PyQt-Fluent-Widgets)