Add create reward visualization (#3155)

* Add create reward visualization and multimodal analysis tool

* add example for creating progress video for sarm

* nit

* precommit

* refactor: address review comments on create_progress_videos.py

- Add shebang and Apache 2.0 license header
- Replace hardcoded absolute OUTPUT_DIR with relative default (./progress_videos)
- Add argparse CLI (--repo-id, --episode, --camera-key, --output-dir, --gif)
- Wrap entrypoint in def main()
- Replace all print() with logging
- Use logging.error/warning instead of traceback.print_exc
- Release VideoCapture via try/finally; consolidate triple-open into single seek
- Eliminate intermediate clip file: seek directly via CAP_PROP_POS_MSEC
- Make MP4 the default output, GIF opt-in via --gif flag
- Add return types to all functions
- Add Args/Returns docstrings
- Use descriptive variable names throughout

Made-with: Cursor

* refactor: move create_progress_videos.py to examples/dataset/ for consistency

Made-with: Cursor

* refactor: address PR review comments on create_progress_videos.py

- Replace Unicode ellipsis and multiplication sign with ASCII equivalents
- Fix step numbering from 1-5 to 1-4 (only 4 actual steps)
- Move frame_width reading into convert_mp4_to_gif
- Remove unused text_height variable

Made-with: Cursor

examples/dataset/create_progress_videos.py

@@ -0,0 +1,680 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Create MP4 (or GIF) videos with sarm_progress overlay for specified episodes.
+
+Downloads datasets from HuggingFace, seeks directly into the episode segment
+of the source video, draws a progress line on each frame, and writes the result.
+
+Usage:
+    python examples/dataset/create_progress_videos.py \
+        --repo-id lerobot-data-collection/level2_final_quality3 \
+        --episode 1100
+
+    python examples/dataset/create_progress_videos.py \
+        --repo-id lerobot-data-collection/level2_final_quality3 \
+        --episode 1100 \
+        --camera-key observation.images.top \
+        --output-dir ./my_videos \
+        --gif
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import subprocess
+from pathlib import Path
+
+import cv2
+import numpy as np
+import pandas as pd
+from huggingface_hub import snapshot_download
+
+GRAPH_Y_TOP_FRAC = 0.01
+GRAPH_Y_BOT_FRAC = 0.99
+LINE_THICKNESS = 3
+SHADOW_THICKNESS = 6
+REF_ALPHA = 0.45
+FILL_ALPHA = 0.55
+SCORE_FONT_SCALE = 0.8
+TASK_FONT_SCALE = 0.55
+
+
+def download_episode_metadata(repo_id: str, episode: int) -> Path:
+    """Download only the metadata and sarm_progress files for a dataset.
+
+    Args:
+        repo_id: HuggingFace dataset repository ID.
+        episode: Episode index (used for logging only; all meta is fetched).
+
+    Returns:
+        Local cache path for the downloaded snapshot.
+    """
+    logging.info("[1/4] Downloading metadata for %s (episode %d) ...", repo_id, episode)
+    local_path = Path(
+        snapshot_download(
+            repo_id=repo_id,
+            repo_type="dataset",
+            allow_patterns=["meta/**", "sarm_progress.parquet"],
+            ignore_patterns=["*.mp4"],
+        )
+    )
+    return local_path
+
+
+def load_episode_meta(local_path: Path, episode: int, camera_key: str | None) -> dict:
+    """Read info.json and episode parquet to resolve fps, video path, and timestamps.
+
+    Args:
+        local_path: Local cache directory containing meta/.
+        episode: Episode index to look up.
+        camera_key: Camera observation key (e.g. "observation.images.base").
+            If None, the first available video key is used.
+
+    Returns:
+        Dict with keys: fps, camera, video_rel, chunk_index, file_index,
+        from_ts, to_ts, task_name.
+    """
+    info = json.loads((local_path / "meta" / "info.json").read_text())
+    fps = info["fps"]
+    features = info["features"]
+
+    video_keys = [k for k, v in features.items() if v.get("dtype") == "video"]
+    if not video_keys:
+        raise RuntimeError("No video keys found in dataset features")
+
+    if camera_key is not None:
+        if camera_key not in video_keys:
+            raise RuntimeError(f"camera_key='{camera_key}' not found. Available: {video_keys}")
+        selected_camera = camera_key
+    else:
+        selected_camera = video_keys[0]
+    logging.info("   fps=%d  camera='%s'  all_cams=%s", fps, selected_camera, video_keys)
+
+    episode_rows = []
+    for parquet_file in sorted((local_path / "meta" / "episodes").glob("**/*.parquet")):
+        episode_rows.append(pd.read_parquet(parquet_file))
+    episode_df = pd.concat(episode_rows, ignore_index=True)
+    row = episode_df[episode_df["episode_index"] == episode]
+    if row.empty:
+        raise RuntimeError(f"Episode {episode} not found in episode metadata")
+    row = row.iloc[0]
+
+    chunk_col = f"videos/{selected_camera}/chunk_index"
+    file_col = f"videos/{selected_camera}/file_index"
+    ts_from_col = f"videos/{selected_camera}/from_timestamp"
+    ts_to_col = f"videos/{selected_camera}/to_timestamp"
+
+    if chunk_col not in row.index:
+        chunk_col = f"{selected_camera}/chunk_index"
+        file_col = f"{selected_camera}/file_index"
+        ts_from_col = f"{selected_camera}/from_timestamp"
+        ts_to_col = f"{selected_camera}/to_timestamp"
+    if chunk_col not in row.index:
+        raise RuntimeError(
+            f"Cannot find video metadata columns for {selected_camera}.\nAvailable: {list(row.index)}"
+        )
+
+    chunk_index = int(row[chunk_col])
+    file_index = int(row[file_col])
+    from_timestamp = float(row[ts_from_col])
+    to_timestamp = float(row[ts_to_col])
+
+    video_template = info.get(
+        "video_path", "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4"
+    )
+    video_rel = video_template.format(
+        video_key=selected_camera,
+        chunk_index=chunk_index,
+        file_index=file_index,
+    )
+
+    task_name = _resolve_task_name(row, local_path)
+
+    return {
+        "fps": fps,
+        "camera": selected_camera,
+        "video_rel": video_rel,
+        "chunk_index": chunk_index,
+        "file_index": file_index,
+        "from_ts": from_timestamp,
+        "to_ts": to_timestamp,
+        "task_name": task_name,
+    }
+
+
+def _resolve_task_name(row: pd.Series, local_path: Path) -> str:
+    """Best-effort extraction of the task name for an episode row.
+
+    Args:
+        row: Single-episode row from the episodes parquet.
+        local_path: Dataset cache root.
+
+    Returns:
+        Task name string, or empty string if unavailable.
+    """
+    try:
+        if "tasks" in row.index and row["tasks"] is not None:
+            tasks_val = row["tasks"]
+            if isinstance(tasks_val, (list, tuple, np.ndarray)) and len(tasks_val) > 0:
+                return str(tasks_val[0])
+            return str(tasks_val).strip("[]'")
+
+        tasks_parquet = local_path / "meta" / "tasks.parquet"
+        if tasks_parquet.exists():
+            tasks_df = pd.read_parquet(tasks_parquet)
+            task_idx = int(row.get("task_index", 0)) if "task_index" in row.index else 0
+            match = tasks_df[tasks_df["task_index"] == task_idx]
+            if not match.empty:
+                return str(match.index[0])
+    except Exception as exc:
+        logging.warning("Could not load task name: %s", exc)
+    return ""
+
+
+def download_video_file(repo_id: str, local_path: Path, video_rel: str) -> Path:
+    """Download the specific video file if not already cached.
+
+    Args:
+        repo_id: HuggingFace dataset repository ID.
+        local_path: Local cache directory.
+        video_rel: Relative path to the video file within the dataset.
+
+    Returns:
+        Absolute path to the downloaded video file.
+    """
+    video_path = local_path / video_rel
+    if video_path.exists():
+        logging.info("   Video already cached: %s", video_path)
+        return video_path
+    logging.info("[2/4] Downloading video file %s ...", video_rel)
+    snapshot_download(
+        repo_id=repo_id,
+        repo_type="dataset",
+        local_dir=str(local_path),
+        allow_patterns=[video_rel],
+    )
+    if not video_path.exists():
+        raise RuntimeError(f"Video not found after download: {video_path}")
+    return video_path
+
+
+def load_progress_data(local_path: Path, episode: int) -> np.ndarray | None:
+    """Load sarm_progress values for an episode.
+
+    Args:
+        local_path: Dataset cache root.
+        episode: Episode index.
+
+    Returns:
+        Sorted (N, 2) array of (frame_index, progress), or None if unavailable.
+    """
+    parquet_path = local_path / "sarm_progress.parquet"
+    if not parquet_path.exists():
+        logging.warning("sarm_progress.parquet not found")
+        return None
+    df = pd.read_parquet(parquet_path)
+    logging.info("   sarm_progress.parquet columns: %s", list(df.columns))
+    episode_df = df[df["episode_index"] == episode].copy()
+    if episode_df.empty:
+        logging.warning("No sarm_progress rows for episode %d", episode)
+        return None
+    episode_df = episode_df.sort_values("frame_index")
+
+    if "progress_dense" in episode_df.columns and episode_df["progress_dense"].notna().any():
+        progress_column = "progress_dense"
+    elif "progress_sparse" in episode_df.columns:
+        progress_column = "progress_sparse"
+    else:
+        progress_columns = [c for c in episode_df.columns if "progress" in c.lower()]
+        if not progress_columns:
+            return None
+        progress_column = progress_columns[0]
+
+    logging.info("   Using progress column: '%s'", progress_column)
+    return episode_df[["frame_index", progress_column]].rename(columns={progress_column: "progress"}).values
+
+
+def _precompute_pixel_coords(
+    progress_data: np.ndarray,
+    num_frames: int,
+    frame_width: int,
+    frame_height: int,
+) -> np.ndarray:
+    """Map progress samples to pixel coordinates for overlay drawing.
+
+    Args:
+        progress_data: (N, 2) array of (frame_index, progress).
+        num_frames: Total number of video frames.
+        frame_width: Video width in pixels.
+        frame_height: Video height in pixels.
+
+    Returns:
+        (N, 2) array of (x, y) pixel coordinates.
+    """
+    frame_indices = progress_data[:, 0].astype(float)
+    progress_values = np.clip(progress_data[:, 1].astype(float), 0.0, 1.0)
+
+    y_top = int(frame_height * GRAPH_Y_TOP_FRAC)
+    y_bot = int(frame_height * GRAPH_Y_BOT_FRAC)
+    graph_height = y_bot - y_top
+
+    x_coords = (frame_indices / (num_frames - 1) * (frame_width - 1)).astype(int)
+    y_coords = (y_bot - progress_values * graph_height).astype(int)
+
+    return np.stack([x_coords, y_coords], axis=1)
+
+
+def _progress_color(normalized_position: float) -> tuple[int, int, int]:
+    """Interpolate BGR color from red to green based on position in [0, 1].
+
+    Args:
+        normalized_position: Value in [0, 1] indicating how far along the episode.
+
+    Returns:
+        BGR color tuple.
+    """
+    red = int(255 * (1.0 - normalized_position))
+    green = int(255 * normalized_position)
+    return (0, green, red)
+
+
+def _prerender_fill_polygon(
+    pixel_coords: np.ndarray,
+    frame_width: int,
+    frame_height: int,
+) -> np.ndarray:
+    """Pre-render the grey fill polygon under the progress curve as a BGRA image.
+
+    Args:
+        pixel_coords: (N, 2) array of (x, y) pixel coordinates.
+        frame_width: Video width in pixels.
+        frame_height: Video height in pixels.
+
+    Returns:
+        BGRA image array of shape (frame_height, frame_width, 4).
+    """
+    y_bot = int(frame_height * GRAPH_Y_BOT_FRAC)
+    fill_image = np.zeros((frame_height, frame_width, 4), dtype=np.uint8)
+    polygon = np.concatenate(
+        [
+            pixel_coords,
+            [[pixel_coords[-1][0], y_bot], [pixel_coords[0][0], y_bot]],
+        ],
+        axis=0,
+    ).astype(np.int32)
+    cv2.fillPoly(fill_image, [polygon], color=(128, 128, 128, int(255 * FILL_ALPHA)))
+    return fill_image
+
+
+def _alpha_composite_region(base: np.ndarray, overlay_bgra: np.ndarray, x_limit: int) -> None:
+    """Blend BGRA overlay onto BGR base in-place, up to x_limit columns.
+
+    Args:
+        base: BGR frame to draw on (modified in-place).
+        overlay_bgra: BGRA overlay image.
+        x_limit: Only blend columns [0, x_limit).
+    """
+    if x_limit <= 0:
+        return
+    region_base = base[:, :x_limit]
+    region_overlay = overlay_bgra[:, :x_limit]
+    alpha = region_overlay[:, :, 3:4].astype(np.float32) / 255.0
+    region_base[:] = np.clip(
+        region_overlay[:, :, :3].astype(np.float32) * alpha + region_base.astype(np.float32) * (1.0 - alpha),
+        0,
+        255,
+    ).astype(np.uint8)
+
+
+def _draw_text_outlined(
+    frame: np.ndarray,
+    text: str,
+    position: tuple[int, int],
+    font_scale: float,
+    thickness: int = 1,
+) -> None:
+    """Draw white text with a dark outline for readability on any background.
+
+    Args:
+        frame: BGR image to draw on (modified in-place).
+        text: String to render.
+        position: (x, y) bottom-left corner of the text.
+        font_scale: OpenCV font scale.
+        thickness: Text stroke thickness.
+    """
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    cv2.putText(frame, text, position, font, font_scale, (0, 0, 0), thickness + 2, cv2.LINE_AA)
+    cv2.putText(frame, text, position, font, font_scale, (255, 255, 255), thickness, cv2.LINE_AA)
+
+
+def composite_progress_video(
+    video_path: Path,
+    from_timestamp: float,
+    to_timestamp: float,
+    progress_data: np.ndarray,
+    output_path: Path,
+    fps: float,
+    task_name: str = "",
+) -> Path:
+    """Read episode frames by seeking into the source video, draw progress overlay, write output.
+
+    Uses cv2.CAP_PROP_POS_MSEC to seek directly into the source video,
+    eliminating the need for an intermediate clip file.
+
+    Args:
+        video_path: Path to the full source video file.
+        from_timestamp: Start timestamp of the episode in seconds.
+        to_timestamp: End timestamp of the episode in seconds.
+        progress_data: (N, 2) array of (frame_index, progress).
+        output_path: Path to write the output MP4.
+        fps: Frames per second for the output video.
+        task_name: Optional task name to display at the top of the video.
+
+    Returns:
+        Path to the written output file (MP4).
+    """
+    capture = cv2.VideoCapture(str(video_path))
+    try:
+        capture.set(cv2.CAP_PROP_POS_MSEC, from_timestamp * 1000)
+
+        frame_width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
+        frame_height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        duration_seconds = to_timestamp - from_timestamp
+        num_frames = int(round(duration_seconds * fps))
+
+        logging.info(
+            "   Video: %dx%d, %d frames @ %.1f fps (%.2fs)",
+            frame_width,
+            frame_height,
+            num_frames,
+            fps,
+            duration_seconds,
+        )
+
+        pixel_coords = _precompute_pixel_coords(progress_data, num_frames, frame_width, frame_height)
+        y_ref = int(frame_height * GRAPH_Y_TOP_FRAC)
+
+        fill_image = _prerender_fill_polygon(pixel_coords, frame_width, frame_height)
+
+        ref_line_image = np.zeros((frame_height, frame_width, 4), dtype=np.uint8)
+        cv2.line(
+            ref_line_image,
+            (0, y_ref),
+            (frame_width - 1, y_ref),
+            (200, 200, 200, int(255 * REF_ALPHA)),
+            1,
+            cv2.LINE_AA,
+        )
+
+        frame_indices = progress_data[:, 0].astype(int)
+        progress_values = progress_data[:, 1].astype(float)
+
+        logging.info("[3/4] Compositing %d frames ...", num_frames)
+        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        writer = cv2.VideoWriter(str(output_path), fourcc, fps, (frame_width, frame_height))
+
+        for frame_idx in range(num_frames):
+            ret, frame = capture.read()
+            if not ret:
+                break
+
+            drawn_count = int(np.searchsorted(frame_indices, frame_idx, side="right"))
+            x_current = (
+                int(pixel_coords[min(drawn_count, len(pixel_coords)) - 1][0]) + 1 if drawn_count > 0 else 0
+            )
+
+            _alpha_composite_region(frame, ref_line_image, frame_width)
+            _alpha_composite_region(frame, fill_image, x_current)
+
+            if drawn_count >= 2:
+                time_position = (drawn_count - 1) / max(len(progress_values) - 1, 1)
+                line_color = _progress_color(time_position)
+                points = pixel_coords[:drawn_count].reshape(-1, 1, 2).astype(np.int32)
+                cv2.polylines(
+                    frame,
+                    [points],
+                    isClosed=False,
+                    color=(255, 255, 255),
+                    thickness=SHADOW_THICKNESS,
+                    lineType=cv2.LINE_AA,
+                )
+                cv2.polylines(
+                    frame,
+                    [points],
+                    isClosed=False,
+                    color=line_color,
+                    thickness=LINE_THICKNESS,
+                    lineType=cv2.LINE_AA,
+                )
+
+            if drawn_count > 0:
+                score = float(progress_values[min(drawn_count, len(progress_values)) - 1])
+                score_text = f"{score:.2f}"
+                (text_width, _), _ = cv2.getTextSize(
+                    score_text, cv2.FONT_HERSHEY_SIMPLEX, SCORE_FONT_SCALE, 2
+                )
+                score_x = frame_width - text_width - 12
+                score_y = frame_height - 12
+                time_position = (drawn_count - 1) / max(len(progress_values) - 1, 1)
+                score_color = _progress_color(time_position)
+                cv2.putText(
+                    frame,
+                    score_text,
+                    (score_x, score_y),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    SCORE_FONT_SCALE,
+                    (0, 0, 0),
+                    4,
+                    cv2.LINE_AA,
+                )
+                cv2.putText(
+                    frame,
+                    score_text,
+                    (score_x, score_y),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    SCORE_FONT_SCALE,
+                    score_color,
+                    2,
+                    cv2.LINE_AA,
+                )
+
+            if task_name:
+                (text_width, _), _ = cv2.getTextSize(task_name, cv2.FONT_HERSHEY_SIMPLEX, TASK_FONT_SCALE, 1)
+                task_x = max((frame_width - text_width) // 2, 4)
+                _draw_text_outlined(frame, task_name, (task_x, 22), TASK_FONT_SCALE)
+
+            writer.write(frame)
+            if frame_idx % 100 == 0:
+                logging.info("   Frame %d/%d ...", frame_idx, num_frames)
+
+        writer.release()
+    finally:
+        capture.release()
+
+    logging.info("   MP4 written: %s", output_path)
+    return output_path
+
+
+def convert_mp4_to_gif(mp4_path: Path) -> Path:
+    """Convert an MP4 to an optimized GIF using ffmpeg palette generation.
+
+    Args:
+        mp4_path: Path to the source MP4 file.
+
+    Returns:
+        Path to the generated GIF file.
+    """
+    capture = cv2.VideoCapture(str(mp4_path))
+    frame_width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
+    capture.release()
+
+    gif_path = mp4_path.with_suffix(".gif")
+    palette_path = mp4_path.parent / "_palette.png"
+
+    logging.info("[4/4] Converting to GIF ...")
+    result_palette = subprocess.run(  # nosec B607
+        [
+            "ffmpeg",
+            "-y",
+            "-i",
+            str(mp4_path),
+            "-vf",
+            f"fps=10,scale={frame_width}:-1:flags=lanczos,palettegen=max_colors=128:stats_mode=diff",
+            "-update",
+            "1",
+            str(palette_path),
+        ],
+        capture_output=True,
+        text=True,
+    )
+    if result_palette.returncode != 0:
+        logging.warning("palettegen failed:\n%s", result_palette.stderr[-500:])
+
+    result_gif = subprocess.run(  # nosec B607
+        [
+            "ffmpeg",
+            "-y",
+            "-i",
+            str(mp4_path),
+            "-i",
+            str(palette_path),
+            "-filter_complex",
+            f"fps=10,scale={frame_width}:-1:flags=lanczos[v];[v][1:v]paletteuse=dither=bayer:bayer_scale=3",
+            str(gif_path),
+        ],
+        capture_output=True,
+        text=True,
+    )
+    if result_gif.returncode != 0:
+        logging.warning("GIF encode failed:\n%s", result_gif.stderr[-500:])
+
+    palette_path.unlink(missing_ok=True)
+    logging.info("   GIF written: %s", gif_path)
+    return gif_path
+
+
+def process_dataset(
+    repo_id: str,
+    episode: int,
+    camera_key: str | None,
+    output_dir: Path,
+    create_gif: bool = False,
+) -> Path | None:
+    """Full pipeline: download, extract metadata, composite progress, write output.
+
+    Args:
+        repo_id: HuggingFace dataset repository ID.
+        episode: Episode index.
+        camera_key: Camera key to use, or None for auto-selection.
+        output_dir: Directory to write output files.
+        create_gif: If True, also generate a GIF from the MP4.
+
+    Returns:
+        Path to the final output file, or None on failure.
+    """
+    safe_name = repo_id.replace("/", "_")
+    logging.info("Processing: %s  |  episode %d", repo_id, episode)
+
+    local_path = download_episode_metadata(repo_id, episode)
+    logging.info("   Local cache: %s", local_path)
+
+    episode_meta = load_episode_meta(local_path, episode, camera_key)
+    logging.info("   Episode meta: %s", episode_meta)
+
+    video_path = download_video_file(repo_id, local_path, episode_meta["video_rel"])
+
+    progress_data = load_progress_data(local_path, episode)
+    if progress_data is None:
+        logging.error("Could not load sarm_progress data. Skipping overlay.")
+        return None
+
+    logging.info("   Progress frames: %d", len(progress_data))
+
+    output_path = output_dir / f"{safe_name}_ep{episode}_progress.mp4"
+    final_path = composite_progress_video(
+        video_path=video_path,
+        from_timestamp=episode_meta["from_ts"],
+        to_timestamp=episode_meta["to_ts"],
+        progress_data=progress_data,
+        output_path=output_path,
+        fps=episode_meta["fps"],
+        task_name=episode_meta.get("task_name", ""),
+    )
+
+    if create_gif:
+        final_path = convert_mp4_to_gif(final_path)
+
+    logging.info("Done: %s", final_path)
+    return final_path
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Create MP4/GIF videos with sarm_progress overlay for dataset episodes."
+    )
+    parser.add_argument(
+        "--repo-id",
+        type=str,
+        required=True,
+        help="HuggingFace dataset repository ID (e.g. 'lerobot-data-collection/level2_final_quality3').",
+    )
+    parser.add_argument(
+        "--episode",
+        type=int,
+        required=True,
+        help="Episode index to visualize.",
+    )
+    parser.add_argument(
+        "--camera-key",
+        type=str,
+        default=None,
+        help="Camera observation key (e.g. 'observation.images.base'). Auto-selects first camera if omitted.",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("progress_videos"),
+        help="Directory to write output files (default: ./progress_videos).",
+    )
+    parser.add_argument(
+        "--gif",
+        action="store_true",
+        help="Also generate a GIF from the MP4 output.",
+    )
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
+
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    result = process_dataset(
+        repo_id=args.repo_id,
+        episode=args.episode,
+        camera_key=args.camera_key,
+        output_dir=args.output_dir,
+        create_gif=args.gif,
+    )
+
+    if result:
+        logging.info("Output: %s", result)
+
+
+if __name__ == "__main__":
+    main()