Metadata-Version: 2.1
Name: castle-ai
Version: 0.0.1
Summary: Distinguish behavioral clusters Toolbox
Maintainer: Raiso Liu
Maintainer-email: rainsoon717@gmail.com
License: AGPL-3.0 license
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: tqdm
Requires-Dist: torch
Requires-Dist: av
Requires-Dist: opencv-python
Requires-Dist: torchvision
Requires-Dist: matplotlib

# CASTLE

CASTLE integrates the strengths of visual foundation models trained on large datasets possess open-world visual concepts, including Segment Anything (SA), DeAOT, and DINOv2, for one-shot image segmentation and unsupervised visual feature extraction. Furthermore, CASTLE employs unsupervised multi-stage and/or multi-layer UMAP clustering algorithms to distinguish behavioral clusters with unique temporal variability. 

# Install
```
git clone https://github.com/RaisoLiu/castle-animal.git
cd castle-animal
python install .
```

# Example

## Image segmentation
```python
1+1
```

## Video objects segmentation
```python
from castle import generate_aot

tracker = generate_aot(ckpt_path, MODEL, DEVICE)
tracker.add_reference_frame(frame, mask, num_object)


new_mask = 
```

## Visual latent extractioin
```python
1+1
```

## UMAP & HDBSCAN analysis
```python
1+1
```
