| """Run local SAM3 on a saved RGB image and write a binary mask. |
| |
| This script is intentionally small and dependency-isolated: Task-E/Isaac can |
| call it from the SAM3 virtualenv without installing SAM3 into the Isaac env. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| import sys |
| from pathlib import Path |
|
|
| import numpy as np |
| from PIL import Image |
|
|
|
|
| PROMPT_INPAINT = Path("/home/ubuntu/Documents/01Proj/sam3d_gs/submodule/Prompt-Inpaint") |
| if not PROMPT_INPAINT.exists(): |
| raise SystemExit(f"Prompt-Inpaint not found: {PROMPT_INPAINT}") |
| sys.path.insert(0, str(PROMPT_INPAINT)) |
|
|
| from src.sam3_predictor import SAM3Predictor |
|
|
|
|
| def _bbox_area(mask: np.ndarray) -> int: |
| ys, xs = np.nonzero(mask) |
| if len(xs) == 0: |
| return 0 |
| return int((xs.max() - xs.min() + 1) * (ys.max() - ys.min() + 1)) |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser(description="Segment one image with local SAM3.") |
| parser.add_argument("--image", required=True, help="Input RGB image path.") |
| parser.add_argument("--out_mask", required=True, help="Output .npy binary mask path.") |
| parser.add_argument("--out_meta", default=None, help="Optional JSON metadata output.") |
| parser.add_argument("--out_candidates", default=None, help="Optional .npz with all candidate masks.") |
| parser.add_argument("--prompt", action="append", required=True, help="Text prompt. Can repeat.") |
| parser.add_argument("--threshold", type=float, default=0.35) |
| parser.add_argument("--mask_threshold", type=float, default=0.5) |
| parser.add_argument( |
| "--model", |
| default="/home/ubuntu/Documents/01Proj/sam3d_gs/submodule/Prompt-Inpaint/checkpoints/sam3.pt", |
| ) |
| args = parser.parse_args() |
|
|
| image = np.asarray(Image.open(args.image).convert("RGB")) |
| predictor = SAM3Predictor( |
| model_id=args.model, |
| device="cuda", |
| threshold=args.threshold, |
| mask_threshold=args.mask_threshold, |
| ) |
| predictor.set_image(image) |
|
|
| detections = [] |
| for prompt in args.prompt: |
| detections.extend(predictor.detect(prompt)) |
|
|
| candidates = [] |
| image_area = image.shape[0] * image.shape[1] |
| for det in detections: |
| if det.mask is None: |
| continue |
| mask = det.mask.astype(bool) |
| area = int(mask.sum()) |
| if area < 40 or area > int(image_area * 0.35): |
| continue |
| candidates.append( |
| { |
| "prompt": det.label, |
| "score": float(det.score), |
| "bbox": [int(v) for v in det.bbox], |
| "area": area, |
| "bbox_area": _bbox_area(mask), |
| "mask": mask, |
| } |
| ) |
|
|
| if not candidates: |
| raise SystemExit("SAM3 produced no usable mask") |
|
|
| |
| |
| best = max(candidates, key=lambda c: (c["score"], -c["bbox_area"])) |
| out_mask = Path(args.out_mask) |
| out_mask.parent.mkdir(parents=True, exist_ok=True) |
| np.save(out_mask, best["mask"].astype(np.bool_)) |
|
|
| if args.out_meta: |
| meta = {k: v for k, v in best.items() if k != "mask"} |
| meta["num_candidates"] = len(candidates) |
| Path(args.out_meta).parent.mkdir(parents=True, exist_ok=True) |
| Path(args.out_meta).write_text(json.dumps(meta, indent=2), encoding="utf-8") |
|
|
| if args.out_candidates: |
| cand_path = Path(args.out_candidates) |
| cand_path.parent.mkdir(parents=True, exist_ok=True) |
| masks = np.stack([c["mask"].astype(np.bool_) for c in candidates], axis=0) |
| metas = [ |
| {k: v for k, v in c.items() if k != "mask"} |
| for c in candidates |
| ] |
| np.savez_compressed(cand_path, masks=masks, metas=json.dumps(metas)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
