AtlasPatch

import numpy as np
import torch
from pathlib import Path
from PIL import Image
from importlib.resources import files

from atlas_patch.core.config import SegmentationConfig
from atlas_patch.services.segmentation import SAM2SegmentationService
from atlas_patch.core.wsi import WSIFactory

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 1) Config: packaged SAM2 Hiera-T config; leave checkpoint_path=None to auto-download from HF.
cfg_path = Path(files("atlas_patch.configs") / "sam2.1_hiera_t.yaml")
seg_cfg = SegmentationConfig(
    checkpoint_path=None,          # downloads Atlas-Patch/model.pth from Hugging Face
    config_path=cfg_path,
    device=str(device),
    batch_size=1,
    thumbnail_power=1.25,
    thumbnail_max=1024,
    mask_threshold=0.0,
)
segmenter = SAM2SegmentationService(seg_cfg)

# 2) Load a WSI and segment the thumbnail.
wsi = WSIFactory.load("slide.svs")  # backend auto-detected (e.g., openslide)
mask = segmenter.segment_thumbnail(wsi)  # mask.data matches the thumbnail size

# 3) Save the mask.
mask_img = Image.fromarray((mask.data > 0).astype(np.uint8) * 255)
mask_img.save("thumbnail_mask.png")