76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
"""Depth Anything V2 mask generation script.
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Usage:
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python tools/depth_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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Foreground = white (255), background = black (0), via Otsu threshold on depth map.
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Requires: torch, transformers, opencv-python, Pillow
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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 cv2
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import numpy as np
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from PIL import Image
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from transformers import pipeline
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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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pipe = pipeline(
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"depth-estimation",
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model="depth-anything/Depth-Anything-V2-Large-hf",
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device=device,
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)
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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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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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pil_img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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result = pipe(pil_img)
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depth = np.array(result["depth"]) # float32 array
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# Normalise to 0–255
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d_min, d_max = depth.min(), depth.max()
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if d_max > d_min:
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depth_u8 = ((depth - d_min) / (d_max - d_min) * 255).astype(np.uint8)
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else:
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depth_u8 = np.zeros_like(depth, dtype=np.uint8)
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# Otsu threshold: closer objects (higher depth value) = foreground
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_, mask = cv2.threshold(depth_u8, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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out_path = os.path.join(args.output, f"frame_{idx:04d}.png")
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cv2.imwrite(out_path, mask)
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idx += 1
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print(f"frame {idx}/{total}", flush=True)
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cap.release()
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print("done", flush=True)
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if __name__ == "__main__":
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main()
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