Metadata-Version: 2.4
Name: anime_seg
Version: 0.3.2
Summary: Anime Character Segmentation with DINOv2
Home-page: https://github.com/suzukimain/AnimeSeg
Author: suzukimain
Author-email: gt13579552@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: torch>=2.0.0
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: Pillow
Requires-Dist: transformers
Requires-Dist: huggingface_hub
Requires-Dist: safetensors
Requires-Dist: peft
Requires-Dist: opencv-python
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# AnimeSeg

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Anime Character Segmentation using Mask2Former and DINOv2 + U-Net++ with LoRA fine-tuning.
Also integrates Background Removal via [anime-segmentation](https://github.com/SkyTNT/anime-segmentation).

## sample image

    sample image

## Installation

```bash
pip install anime_seg
```

## Usage

```python
from anime_seg import AnimeSegPipeline
pipe = AnimeSegPipeline.from_mask2former().to("cuda")
mask = pipe("path/to/image.jpg")
mask.save("output.png")

# Background Removal (powered by anime-segmentation)
bg_pipe = AnimeSegPipeline.from_bg_remover().to("cuda")
no_bg_img = bg_pipe("path/to/image.jpg")
no_bg_img.save("no_bg_output.png")
```

`AnimeSegPipeline()` default constructor is deprecated. Use `from_mask2former()`, `from_dinoV2()`, or `from_bg_remover()`.

## Optional: output size

```python
# Same as input size (default)
mask_same = pipe("path/to/image.jpg")

# Fixed output size
mask_fixed = pipe("path/to/image.jpg", width=1024, height=1024)

# Width/height can be specified independently
mask_w = pipe("path/to/image.jpg", width=1024)
mask_h = pipe("path/to/image.jpg", height=1024)
```

## Advanced Usage

```python
# Load specific file from HF repo
pipe = AnimeSegPipeline.from_mask2former(
    repo_id="suzukimain/AnimeSeg",
    filename="models/anime_seg_mask2former_v3.safetensors"
).to(device="cuda")

# DINOv2 backend
pipe_dino = AnimeSegPipeline.from_dinoV2(
    filename="models/anime_seg_dinov2_v2.safetensors"
).to("cuda")

# Use PIL Image
from PIL import Image
img = Image.open("image.jpg")
mask = pipe(img)

# Background Removal (powered by anime-segmentation)
bg_pipe = AnimeSegPipeline.from_bg_remover().to("cuda")
no_bg_img = bg_pipe("path/to/image.jpg")
no_bg_img.save("no_bg_output.png")
```

## Model Files

Models should follow the naming convention:
```
models/anime_seg_{architecture}_v{version}.safetensors
```

Example:
- `models/anime_seg_dinov2_v2.safetensors`
- `models/anime_seg_mask2former_v3.safetensors`

Resolution order:
1. `models/model_config.json`
2. fallback scan by `models/anime_seg_{architecture}_v{max_version}.{ext}`

## Segmentation Classes and Mask Colors

Default `from_mask2former()` returns **12 classes**:

| ID | Class Key | RGB | Color |
|---:|---|---|---|
| 0 | background | (0, 0, 0) | Black |
| 1 | skin | (255, 220, 180) | Pale Orange |
| 2 | face | (100, 150, 255) | Blue |
| 3 | hair_main | (255, 0, 0) | Red |
| 4 | left_eye | (0, 255, 255) | Cyan |
| 5 | right_eye | (255, 255, 0) | Yellow |
| 6 | left_eyebrow | (150, 255, 0) | Yellow Green |
| 7 | right_eyebrow | (0, 255, 100) | Emerald Green |
| 8 | nose | (255, 140, 0) | Dark Orange |
| 9 | mouth | (255, 0, 150) | Magenta Pink |
| 10 | clothes | (180, 0, 255) | Purple |
| 11 | accessory | (128, 128, 0) | Olive |

`from_dinoV2()` returns **13 classes** (includes `unknown` as ID 12).

## DINOv2 Compatibility Note

Earlier versions primarily used DINOv2. Current recommendation is `from_mask2former()`, while `from_dinoV2()` remains for compatibility.
