Instructions to use jamiewjm/sam-tp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use jamiewjm/sam-tp with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(jamiewjm/sam-tp) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): predictor.set_image(<your_image>) masks, _, _ = predictor.predict(<input_prompts>)# Use SAM2 with videos import torch from sam2.sam2_video_predictor import SAM2VideoPredictor predictor = SAM2VideoPredictor.from_pretrained(jamiewjm/sam-tp) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): state = predictor.init_state(<your_video>) # add new prompts and instantly get the output on the same frame frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>): # propagate the prompts to get masklets throughout the video for frame_idx, object_ids, masks in predictor.propagate_in_video(state): ... - Notebooks
- Google Colab
- Kaggle
File size: 724 Bytes
9f848c5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | from datasets import load_dataset
from pathlib import Path
from PIL import Image
REPO = "jamiewjm/sam-tp" # change to your dataset repo id
ds_imgs = load_dataset(
"imagefolder",
data_dir=".",
data_files={"image": f"hf://datasets/{REPO}/images/**"},
split="train",
)
ds_msks = load_dataset(
"imagefolder",
data_dir=".",
data_files={"mask": f"hf://datasets/{REPO}/annotations/**"},
split="train",
)
mask_index = {Path(r["image"]["path"]).name: r["image"]["path"] for r in ds_msks}
row = ds_imgs[0]
img_path = Path(row["image"]["path"])
msk_path = Path(mask_index[img_path.name])
print("Image:", img_path)
print("Mask: ", msk_path)
Image.open(img_path).show()
Image.open(msk_path).show()
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