Ethanfel
eca49caee9
Use SAM2VideoPredictor.from_pretrained() instead of the checkpoint-based build_sam2_video_predictor() which doesn't accept HuggingFace model IDs. Threshold out_mask_logits at 0.0 and squeeze shape before converting to binary PNG. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
"""SAM2 mask generation script.
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Usage:
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python tools/sam_masks.py --input video.mp4 --output masks_dir/
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Outputs one binary PNG per frame: frame_0000.png, frame_0001.png, …
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Uses center of first frame as positive point prompt, propagates across all frames.
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Requires: torch, segment-anything-2, opencv-python
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"""
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import argparse
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import os
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import sys
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import tempfile
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import cv2
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import numpy as np
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--input", required=True)
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parser.add_argument("--output", required=True)
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args = parser.parse_args()
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os.makedirs(args.output, exist_ok=True)
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import torch
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"Using device: {device}", flush=True)
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# Extract frames to temp directory (SAM2 video predictor needs image files)
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with tempfile.TemporaryDirectory() as frame_dir:
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cap = cv2.VideoCapture(args.input)
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if not cap.isOpened():
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print(f"ERROR: cannot open {args.input}", file=sys.stderr)
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sys.exit(1)
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total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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idx = 0
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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cv2.imwrite(os.path.join(frame_dir, f"{idx:04d}.jpg"), frame)
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idx += 1
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cap.release()
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print(f"Extracted {idx} frames", flush=True)
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# SAM2: use from_pretrained (SAM2.1+ / HuggingFace integration)
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from sam2.sam2_video_predictor import SAM2VideoPredictor
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predictor = SAM2VideoPredictor.from_pretrained(
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"facebook/sam2-hiera-large"
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).to(device)
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with torch.inference_mode():
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state = predictor.init_state(video_path=frame_dir)
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# Center of first frame as positive point prompt
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cx, cy = width // 2, height // 2
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_, _, _ = predictor.add_new_points_or_box(
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inference_state=state,
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frame_idx=0,
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obj_id=1,
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points=np.array([[cx, cy]], dtype=np.float32),
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labels=np.array([1], dtype=np.int32),
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)
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for frame_idx, obj_ids, out_mask_logits in predictor.propagate_in_video(state):
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# out_mask_logits: (N_objects, 1, H, W) — threshold logits at 0
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mask = (out_mask_logits[0].squeeze().cpu().numpy() > 0.0).astype(np.uint8) * 255
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out_path = os.path.join(args.output, f"frame_{frame_idx:04d}.png")
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cv2.imwrite(out_path, mask)
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print(f"frame {frame_idx + 1}/{total}", flush=True)
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print("done", flush=True)
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if __name__ == "__main__":
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main()
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