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Faster clip cutting script generated by Claude.
S=/datasets/EpicKitchens-100/
D=/work/piyush/from_nfs2/datasets/EPIC-Kitchens-100/cut_clips
csv=$D/../epic-kitchens-100-annotations/EPIC_100_train_with_id.csv
python shared/scripts/cut_clips_fast.py --csv $csv --video_id_key path_id --start_time_key start_sec --end_time_key stop_sec --video_dir $S/ --cut_dir $D/ --ext MP4 --max_workers 4
"""
import os
from os.path import join, exists
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import pandas as pd
from tqdm import tqdm
from moviepy.editor import VideoFileClip
from moviepy.video.fx.resize import resize
def time_float_to_str(time_in_seconds):
import datetime
hours, remainder = divmod(time_in_seconds, 3600)
minutes, seconds_with_ms = divmod(remainder, 60)
seconds, milliseconds = divmod(int(seconds_with_ms * 1000), 1000)
time_delta = datetime.timedelta(hours=hours, minutes=minutes, seconds=seconds, milliseconds=milliseconds)
return str(time_delta)
def process_video(row, args):
"""Process a single video clip"""
try:
f = row["video_path"]
v, s, e = row[args.video_id_key], float(row[args.start_time_key]), float(row[args.end_time_key])
if args.no_round_times:
clip_filename = f"{v}_{s}_{e}.{args.ext}"
else:
clip_filename = f"{v}_{np.round(s, 1)}_{np.round(e, 1)}.{args.ext}"
clip_filepath = join(args.cut_dir, clip_filename)
os.makedirs(os.path.dirname(clip_filepath), exist_ok=True)
if os.path.exists(clip_filepath) and not args.overwrite:
return None
# Load video and extract clip
with VideoFileClip(f) as video:
# Calculate target width maintaining aspect ratio with max height 480
aspect_ratio = video.w / video.h
target_height = 480
target_width = int(target_height * aspect_ratio)
# Extract and resize clip
clip = video.subclip(s, e)
clip = clip.resize(width=target_width, height=target_height)
# Write clip with optimized settings
clip.write_videofile(
clip_filepath,
codec='libx264',
audio_codec='aac',
preset='faster', # Faster encoding
threads=2, # Use multiple threads for encoding
logger=None if not args.verbose else None
)
return clip_filepath
except Exception as e:
if args.verbose:
print(f"Error processing {row[args.video_id_key]}: {str(e)}")
return None
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--csv", type=str, required=True,
help="Path to CSV file containing video IDs and timestamps",
)
parser.add_argument(
"--video_id_key", type=str, default="video_id",
)
parser.add_argument(
"--start_time_key", type=str, default="start_time",
)
parser.add_argument(
"--end_time_key", type=str, default="end_time",
)
parser.add_argument(
"--video_dir", type=str, required=True,
help="Path to directory containing downloaded videos",
)
parser.add_argument(
"--cut_dir", type=str, required=True,
help="Path to directory where cut videos will be saved",
)
parser.add_argument(
"--overwrite", action="store_true",
help="Whether to overwrite existing cut videos",
)
parser.add_argument(
"--verbose", action="store_true",
)
parser.add_argument(
"--no_round_times", action="store_true",
help="Whether to round start and end times to nearest second in filenames",
)
parser.add_argument(
"--debug", action="store_true",
)
parser.add_argument(
"--ext", type=str, default="mp4",
)
parser.add_argument(
"--si", type=int, default=0,
)
parser.add_argument(
"--ei", type=int, default=None,
)
parser.add_argument(
"--filter_csv", type=str, default=None, required=False,
)
parser.add_argument(
"--filter_key", type=str, default=None, required=False,
)
parser.add_argument(
"--max_workers", type=int, default=4,
help="Number of parallel workers for processing videos",
)
args = parser.parse_args()
# Make cut_dir
os.makedirs(args.cut_dir, exist_ok=True)
# Load and filter CSV
assert os.path.exists(args.csv), f"CSV file {args.csv} does not exist."
df = pd.read_csv(args.csv)
print(">>> Loaded CSV file with shape", df.shape)
assert {args.video_id_key, args.start_time_key, args.end_time_key}.issubset(df.columns)
# Filter CSV if needed
if args.filter_csv is not None:
path = args.filter_csv
assert os.path.exists(path), f"CSV file {path} does not exist."
key = args.filter_key
df_filter = pd.read_csv(path)
assert key in df_filter.columns, f"CSV file must contain column {key}."
keep_values = df_filter[key].unique()
df = df[df[key].isin(keep_values)]
print(">>> Filtered CSV file with shape", df.shape)
# Apply index slicing
si = args.si
ei = args.ei if args.ei is not None else len(df)
df = df.iloc[si:ei]
print("Start index:", si, "End index:", ei)
# More efficient way to add video path
print(">>> Adding video paths to dataframe")
video_ids = df[args.video_id_key].unique()
video_paths = [join(args.video_dir, f"{video_id}.{args.ext}") for video_id in video_ids]
video_id_to_path = {video_id: path for video_id, path in zip(video_ids, video_paths)}
df["video_path"] = df[args.video_id_key].map(video_id_to_path)
# df = df[df["video_path"].apply(exists)]
df['check_video'] = df['video_path'].apply(exists)
df = df[df['check_video']]
del df['check_video']
print(">>> Found videos for", df.shape[0], "rows.")
# # Filter out videos that don't exist
# df["video_path"] = df[args.video_id_key].apply(
# lambda video_id: join(args.video_dir, f"{video_id}.{args.ext}"),
# )
# df["check_video"] = df["video_path"].apply(exists)
# df = df[df["check_video"]]
# del df["check_video"]
# print(">>> Found videos for", df.shape[0], "rows.")
if len(df) == 0:
print(">>> No videos to cut.")
exit()
if args.debug:
args.verbose = True
# Process only one video in debug mode
process_video(df.iloc[0], args)
else:
# Process videos in parallel
with ThreadPoolExecutor(max_workers=args.max_workers) as executor:
futures = [executor.submit(process_video, row, args)
for _, row in df.iterrows()]
# Show progress bar
with tqdm(total=len(futures), desc="Cutting clips") as pbar:
for future in as_completed(futures):
result = future.result()
pbar.update(1)
print(">>> Number of cut files:", len(os.listdir(args.cut_dir))) |