File size: 2,023 Bytes
670fa55 | 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | import os
import json
from pathlib import Path
from PIL import Image
data_dir = Path("data/animations")
output_dir = Path("data/shots")
# create output directory if it doesn't exist
output_dir.mkdir(parents=True, exist_ok=True)
# Find all .webp files in data/animations
webp_files = sorted(data_dir.glob("sample-*.webp"))
for webp_file in webp_files:
# Extract NNN from filename
base_name = webp_file.stem # sample-NNN
nnn = base_name.split("-")[1]
hf_json_path = data_dir / f"sample-{nnn}.hf.json"
if not hf_json_path.exists():
print(f"Missing {hf_json_path}, skipping {webp_file}")
continue
# Load human feedback json
with open(hf_json_path, "r") as f:
hf_data = json.load(f)
# Open the .webp image
img = Image.open(webp_file)
# Get scene change indices
indices = hf_data.get("scene_change_indices", [])
if not indices:
print(f"No scene_change_indices in {hf_json_path}, skipping.")
continue
# Build split frame ranges
ranges = []
prev = 0
for idx in indices:
ranges.append((prev, idx))
prev = idx + 1
# Add final range to end of frames
total_frames = getattr(img, 'n_frames', 1)
ranges.append((prev, total_frames - 1))
for i, (start, end) in enumerate(ranges):
frames = []
durations = []
for frame_idx in range(start, end + 1):
img.seek(frame_idx)
frames.append(img.copy())
# Get duration for each frame
duration = img.info.get('duration', 40) # default to 40ms if missing
durations.append(duration)
if frames:
out_path = output_dir / f"sample-{nnn}-{i}.webp"
if len(frames) == 1:
frames[0].save(out_path, format="WEBP", duration=durations[0])
else:
frames[0].save(out_path, format="WEBP", save_all=True, append_images=frames[1:], duration=durations)
print(f"Saved {out_path} ({len(frames)} frames)")
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