""" Cut video clips from downloaded videos. Example. D=/work/piyush/from_nfs2/datasets/Charades video_dir=$D/Charades_v1_480/ EPIC S=/datasets/EpicKitchens-100/ D=/work/piyush/from_nfs2/datasets/EPIC-Kitchens-100/cut_clips csv=$D/../epic-kitchens-100-annotations/EPIC_100_train_with_id.csv python shared/scripts/cut_clips.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 """ import os from os.path import join, exists from subprocess import call import time import numpy as np import pandas as pd from tqdm import tqdm import shared.utils.io as io import shared.utils.log as log from video_language.datasets.charades import get_paths, load_main_csv def time_float_to_str(time_in_seconds): import datetime # Calculate hours, minutes, seconds, and milliseconds hours, remainder = divmod(time_in_seconds, 3600) minutes, seconds_with_ms = divmod(remainder, 60) seconds, milliseconds = divmod(int(seconds_with_ms * 1000), 1000) # Create a timedelta object time_delta = datetime.timedelta(hours=hours, minutes=minutes, seconds=seconds, milliseconds=milliseconds) # Format the time as HH:MM:SS.mmm formatted_time = str(time_delta) return formatted_time 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, ) args = parser.parse_args() # Make cut_dir os.makedirs(args.cut_dir, exist_ok=True) # Load 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), \ f"CSV file must contain columns {args.video_id_key}, {args.start_time_key}, and {args.end_time_key}." # Filter CSV 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}." # Only keep the rows in df that match on key with df_filter keep_values = df_filter[key].unique() df = df[df[key].isin(keep_values)] print(">>> Filtered CSV file with shape", df.shape) # 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() 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) # Custom filter # df = df[df.split == "validation"] # print(">>> Filtered videos for", df.shape[0], "rows.") if args.debug: args.verbose = True # Cut videos ext = args.ext iterator = tqdm(range(len(df)), desc="Cutting clips") for i in iterator: row = df.iloc[i].to_dict() f = row["video_path"] v, s, e = row[args.video_id_key], row[args.start_time_key], row[args.end_time_key] s = float(s) e = float(e) if args.no_round_times: clip_filename = f"{v}_{s}_{e}.{ext}" else: clip_filename = f"{v}_{np.round(s, 1)}_{np.round(e, 1)}.{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: continue # bring s in HH:MM:SS.mmm format with milliseconds s = time_float_to_str(s) e = time_float_to_str(e) # # bring s in HH:MM:SS. format # s = time.strftime("%H:%M:%S", time.gmtime(s)) # e = time.strftime("%H:%M:%S", time.gmtime(e)) # ffmpeg code # ffmpeg_source = "/users/piyush/install/ffmpeg-06092024/ffmpeg-7.0.2-i686-static/ffmpeg" ffmpeg_source = " /users/piyush/install/ffmpeg/ffmpeg-7.0.2-i686-static/ffmpeg" # print("FFMpeg version: ", call(f"{ffmpeg_source} -version", shell=True)) # use ffmpeg to cut the clip + change spatial resolution to have max height # NOTE: also changes spatial resolution to have max width as 480 command = f"{ffmpeg_source} -i {f} -ss {s} -to {e} -strict -2 -c:v libx264 "\ f"-pix_fmt yuv420p -c:a copy"\ " -vf 'scale=480:-1' "\ f"{clip_filepath} "\ f"-y -format {ext}" if not args.verbose: command += " -loglevel quiet" else: print(">>> Cutting clip", clip_filepath) call(command, shell=True) if args.debug: print(command) break print(">>> Number of cut files:", len(os.listdir(args.cut_dir)))