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README.md

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+# EPIC-KITCHENS-100 Extracted Clips
+
+## About
+
+Dataset of 37455 video clips (24GB) extracted from videos in the EPIC-KITCHENS-100 dataset,
+more precisely the extension part not contained in EPIC-KITCHENS-55. For details,
+see https://lightly.ai/blog. (TODO: Update the link when the blogpost is published.)
+
+The `clips` folder contains one video for every narration from action annotations stored
+as a file named `{narration_id}.mp4`. The videos have been downscaled an compressed for easier
+manipulation, the extraction script is `cut_clips.py`. The annotation files are preserved
+in `epic-kitchens-100-annotations/` folder.
+
+**Note:** The extracted clips have been sorted into folders by participants
+
+## Citation
+
+The credit is due to the original authors of the EPIC-KITCHENS-100 dataset: https://epic-kitchens.github.io
+
+```
+@ARTICLE{Damen2021PAMI,
+   title={The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines},
+   author={Damen, Dima and Doughty, Hazel and Farinella, Giovanni Maria  and Fidler, Sanja and 
+           Furnari, Antonino and Kazakos, Evangelos and Moltisanti, Davide and Munro, Jonathan 
+           and Perrett, Toby and Price, Will and Wray, Michael},
+   journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
+   year={2021},
+   volume={43},
+   number={11},
+   pages={4125-4141},
+   doi={10.1109/TPAMI.2020.2991965}
+} 
+```

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cut_clips.py

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+"""Cut short action clips from full EPIC-KITCHENS videos using annotations."""
+
+import argparse
+import subprocess
+import sys
+from pathlib import Path
+
+import pandas as pd
+from tqdm import tqdm
+
+ANNOTATIONS_DIR = Path("epic-kitchens-100-annotations")
+DATA_DIR = Path("EPIC-KITCHENS-100")
+CLIPS_DIR = Path("clips")
+
+
+def load_annotations(video_ids: list[str] | None) -> pd.DataFrame:
+    train = pd.read_csv(ANNOTATIONS_DIR / "EPIC_100_train.csv")
+    val = pd.read_csv(ANNOTATIONS_DIR / "EPIC_100_validation.csv")
+    annotations = pd.concat([train, val], ignore_index=True)
+
+    if not video_ids:
+        return annotations
+
+    filtered = annotations[annotations["video_id"].isin(video_ids)]
+
+    found_ids = set(filtered["video_id"].unique())
+    for vid in video_ids:
+        if vid not in found_ids:
+            print(f"WARNING: No annotations found for video {vid}")
+
+    return filtered
+
+
+def video_path(participant_id: str, video_id: str) -> Path:
+    return DATA_DIR / participant_id / "videos" / f"{video_id}.MP4"
+
+
+def build_ffmpeg_cmd(row: pd.Series) -> list[str]:
+    input_path = video_path(row["participant_id"], row["video_id"])
+    output_path = CLIPS_DIR / row["participant_id"] / f"{row['narration_id']}.mp4"
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    return [
+        "ffmpeg",
+        "-ss", str(row["start_timestamp"]),
+        "-to", str(row["stop_timestamp"]),
+        "-i", str(input_path),
+        "-vf", "scale=854:480",
+        "-c:v", "libx264", "-crf", "23", "-preset", "medium",
+        "-c:a", "aac", "-b:a", "128k",
+        "-movflags", "+faststart",
+        "-y", str(output_path),
+    ]
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Cut action clips from EPIC-KITCHENS videos")
+    parser.add_argument("video_ids", nargs="*", help="Video IDs to process (e.g. P04_26 P03_11). If omitted, process all.")
+    parser.add_argument("--dry-run", action="store_true", help="Print ffmpeg commands without executing")
+    parser.add_argument("--force", action="store_true", help="Reprocess clips even if they already exist")
+    args = parser.parse_args()
+
+    annotations = load_annotations(args.video_ids or None)
+    if annotations.empty:
+        print("No annotations found. Exiting.")
+        sys.exit(1)
+
+    CLIPS_DIR.mkdir(exist_ok=True)
+
+    created = 0
+    skipped = 0
+    failed = 0
+
+    rows = list(annotations.iterrows())
+    progress = tqdm(rows, unit="clip")
+    for _, row in progress:
+        narration_id = row["narration_id"]
+        progress.set_description(narration_id)
+
+        input_file = video_path(row["participant_id"], row["video_id"])
+        if not input_file.exists():
+            tqdm.write(f"WARNING: Source video not found: {input_file}, skipping {narration_id}")
+            failed += 1
+            continue
+
+        cmd = build_ffmpeg_cmd(row)
+
+        if args.dry_run:
+            tqdm.write(" ".join(cmd))
+            continue
+
+        output_path = CLIPS_DIR / f"{narration_id}.mp4"
+        if output_path.exists() and not args.force:
+            skipped += 1
+            continue
+
+        result = subprocess.run(cmd, capture_output=True, text=True)
+        if result.returncode != 0:
+            tqdm.write(f"ERROR: ffmpeg failed for {narration_id}: {result.stderr.splitlines()[-1] if result.stderr else 'unknown error'}")
+            failed += 1
+        else:
+            created += 1
+
+    if not args.dry_run:
+        print(f"\nDone: {created} created, {skipped} skipped, {failed} failed")
+
+
+if __name__ == "__main__":
+    main()
+

license.txt

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+All files in this dataset are copyright by us and published under the 
+Creative Commons Attribution-NonCommerial 4.0 International License, found 
+at https://creativecommons.org/licenses/by-nc/4.0/.
+This means that you must give appropriate credit, provide a link to the license,
+and indicate if changes were made. You may do so in any reasonable manner,
+but not in any way that suggests the licensor endorses you or your use. You
+may not use the material for commercial purposes.