semvec/pypi-packages huggingface dataset:
guifrontend 0.1.2
A frontend client for the GUI data collection framework.
Requires Python: >=3.8

# GUI数据采集框架 - 前端

## 功能
- 连接后端WebSocket服务
- 定期截图并发送给后端  
- 执行后端返回的PyAutoGUI代码

## 安装
```bash
pip install -r requirements.txt
```

## 配置
编辑 `config/config.yaml`:
```yaml
backend:
  host: "127.0.0.1"  # 后端IP地址
  port: 8000
frontend:
  id: "frontend_001"
  screenshot_interval: 2.0
```

## 运行
```bash
python run_frontend.py --config config/config.yaml
```

## 通信协议

发送给后端:
```json
{"type": "screenshot", "screenshot": "base64_image"}
```

接收自后端:
```json
{"type": "execute_action", "pyautogui_code": "pyautogui.click(100, 200)"}
```
napari-UniSPAC 1.0.6
A Unified Segmentation framework for Proofreading and Annotation in Connectomics (UniSPAC)!

# napari-UniSPAC
The napari plugin for UniSPAC [A Unified Segmentation framework for Proofreading and Annotation in Connectomics]. UniSPAC provides interactive 3D neuron segmentation. Neuron segmentation, proofreading and tracking can be done with just mouse clicks, which is much more efficient than existing tools.

## Requirements

A system with enough GPU memory and pytorch installed. The size of the GPU memory is related to the size of the vEM image that can be processed. For  `test_roi1_sub_z0-100.tiff` with a shape of 800x800x100, the recommended minimum GPU memory is 12GB.

## Installation

Step 1: install napari via pip:

```shell
pip install -U 'napari[all]'
```

Step 2: install `napari-UniSPAC`

```shell
git clone https://github.com/ddd9898/napari-UniSPAC.git
cd napari-UniSPAC
pip install -e .
```

Step 3: run napari:

```shell
napari
```

You can familiarise yourself with how UniSPAC's napari plugin operates by labeling  `test_roi1_sub_z0-100.tiff`, which is an example of Drosophila vEM images.