File size: 7,965 Bytes
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import csv
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Any
import cv2
import numpy as np
from datasets import Dataset
from dataset_upload.helpers import (
create_hf_trajectory,
create_output_directory,
generate_unique_id,
load_sentence_transformer_model,
)
@dataclass
class EpicClip:
participant_id: str
video_id: str
narration_id: str
start_frame: int
stop_frame: int
narration: str
def _read_epic_csv(csv_path: Path) -> list[EpicClip]:
clips: list[EpicClip] = []
with open(csv_path, "r") as f:
reader = csv.DictReader(f)
for row in reader:
try:
clips.append(
EpicClip(
participant_id=row["participant_id"].strip(),
video_id=row["video_id"].strip(),
start_frame=int(row["start_frame"]),
stop_frame=int(row["stop_frame"]),
narration=row["narration"].strip(),
narration_id=row["narration_id"].strip(),
)
)
except Exception:
continue
return clips
def _video_path_for_clip(dataset_path: Path, clip: EpicClip) -> Path:
# video_id maps to video basename (without .MP4). Participant folder contains videos/ with .MP4
# Example: <dataset_path>/P01/videos/<video_id>.MP4
return dataset_path / clip.participant_id / "videos" / f"{clip.video_id}.MP4"
def _read_video_segment(video_path: Path, start_frame: int, stop_frame: int) -> np.ndarray:
cap = cv2.VideoCapture(str(video_path))
if not cap.isOpened():
raise FileNotFoundError(f"Cannot open video: {video_path}")
total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
start = max(0, min(start_frame, total - 1))
end = max(start + 1, min(stop_frame, total))
# Seek to start
cap.set(cv2.CAP_PROP_POS_FRAMES, start)
frames: list[np.ndarray] = []
idx = start
while idx < end:
ok, frame_bgr = cap.read()
if not ok:
break
frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
frames.append(frame_rgb)
idx += 1
cap.release()
return np.asarray(frames, dtype=np.uint8)
def _process_single_epic_clip(args: tuple[Any, ...]) -> dict | None:
(
clip,
dataset_name,
root,
output_dir,
max_frames,
fps,
shortest_edge_size,
center_crop,
lang_vec,
) = args
video_path = _video_path_for_clip(root, clip) # derive from output_dir -> dataset root
if not video_path.exists():
return None
# skip anything > 1000
if clip.stop_frame - clip.start_frame > 1000:
print("Skipping clip because it's too long, length is", clip.stop_frame - clip.start_frame)
return None
frames = _read_video_segment(video_path, clip.start_frame, clip.stop_frame)
if frames.size == 0:
return None
traj = {
"id": generate_unique_id(),
"task": clip.narration,
"frames": frames,
"is_robot": False,
"quality_label": "successful",
"preference_group_id": None,
"preference_rank": None,
}
out_dir = os.path.join(output_dir, dataset_name.lower(), clip.participant_id)
os.makedirs(out_dir, exist_ok=True)
out_video = os.path.join(out_dir, f"{clip.narration_id}.mp4")
entry = create_hf_trajectory(
traj_dict=traj,
video_path=out_video,
lang_vector=lang_vec,
max_frames=max_frames,
dataset_name=dataset_name,
use_video=True,
fps=fps,
shortest_edge_size=shortest_edge_size,
center_crop=center_crop,
)
if entry:
entry["frames"] = os.path.relpath(out_video, output_dir)
return entry
def convert_epic_dataset_to_hf(
dataset_path: str,
dataset_name: str,
output_dir: str,
max_trajectories: int | None = None,
max_frames: int = 64,
fps: int = 10,
num_workers: int = -1,
shortest_edge_size: int = 240,
center_crop: bool = False,
) -> Dataset:
"""Convert EPIC-KITCHENS to HF format by writing videos directly (H2R/OXE-style)."""
create_output_directory(output_dir)
root = Path(dataset_path)
csv_path = root / "EPIC_100_train.csv"
if not csv_path.exists():
raise FileNotFoundError(f"EPIC_100_train.csv not found at {csv_path}")
clips = _read_epic_csv(csv_path)
if not clips:
return Dataset.from_dict({
"id": [],
"task": [],
"lang_vector": [],
"data_source": [],
"frames": [],
"is_robot": [],
"quality_label": [],
"preference_group_id": [],
"preference_rank": [],
})
# Language model and cache
lang_model = load_sentence_transformer_model()
lang_cache: dict[str, Any] = {}
# Determine workers
if num_workers == -1:
try:
from multiprocessing import cpu_count as _cpu_count
num_workers = min(_cpu_count(), 8)
except Exception:
num_workers = 1
elif num_workers == 0:
num_workers = 1
batch_size = 6
entries: list[dict] = []
produced = 0
max_limit = float("inf") if (max_trajectories is None or max_trajectories == -1) else int(max_trajectories)
file_batch: list[EpicClip] = []
vec_batch: list[np.ndarray] = []
from tqdm import tqdm
for idx, clip in tqdm(enumerate(clips), desc="iterating through EPIC-KITCHENS Clips", total=len(clips)):
if produced >= max_limit:
break
# Precompute language vector
if clip.narration not in lang_cache:
lang_cache[clip.narration] = lang_model.encode(clip.narration)
lang_vec = lang_cache[clip.narration]
file_batch.append(clip)
vec_batch.append(lang_vec)
if len(file_batch) >= batch_size or idx + 1 == len(clips):
worker_args = [
(
clip,
dataset_name,
root,
output_dir,
max_frames,
fps,
shortest_edge_size,
center_crop,
vec,
)
for clip, vec in zip(file_batch, vec_batch)
]
if num_workers == 1:
for args in worker_args:
entry = _process_single_epic_clip(args)
if entry:
entries.append(entry)
produced += 1
if produced >= max_limit:
break
else:
from multiprocessing import Pool
from tqdm import tqdm
with Pool(processes=num_workers) as pool:
results = list(
tqdm(
pool.imap_unordered(_process_single_epic_clip, worker_args),
total=len(worker_args),
desc=f"Processing EPIC clips (workers={num_workers})",
)
)
for entry in results:
if entry:
entries.append(entry)
produced += 1
if produced >= max_limit:
break
file_batch = []
vec_batch = []
if not entries:
return Dataset.from_dict({
"id": [],
"task": [],
"lang_vector": [],
"data_source": [],
"frames": [],
"is_robot": [],
"quality_label": [],
"preference_group_id": [],
"preference_rank": [],
})
return Dataset.from_list(entries)
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