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# In case this module is invoked from other modules, e.g., preprocessing
from pathlib import Path
import sys
sys.path.append(str(Path.cwd() / "annotation"))
import json
import os
import argparse
from sklearn.model_selection import train_test_split
from datatypes import VideoAnnotation, Metadata
from annotate import dump_json
from utils import get_metadata, filter_video
from typing import List
if __name__ == "__main__":
parser = argparse.ArgumentParser(
prog = 'train_test.py',
description='Annotate video dataset with JSON format'
)
parser.add_argument(
'--folders',
type = str,
nargs = '+',
required = True,
help = "List of folder paths to video data"
)
parser.add_argument(
'--train_size',
type=float,
default=0.8,
help='Proportion of the dataset for training'
)
parser.add_argument(
'--output_train_file',
type=str,
default='data/EnTube_train.json',
help='Output JSON file for training'
)
parser.add_argument(
'--output_test_file',
type=str,
default='data/EnTube_test.json',
help='Output JSON file for testing'
)
parser.add_argument(
'--max_duration',
type=int,
help='Maximum duration of video in seconds'
)
parser.add_argument(
'--random_state',
type=int,
default=42,
help='Random seed for train-test split'
)
args = parser.parse_args()
folder_paths: List[str] = args.folders
metadata: Metadata = get_metadata(folder_paths)
# split metadata into 3 submetadata corresponding to 3 labels
metadata_label = {0: [], 1: [], 2: []}
for video, label in metadata:
metadata_label[int(label)].append((video, label))
train = []
test = []
for label, videos in metadata_label.items():
train_l, test_l = train_test_split(
videos,
train_size=args.train_size,
random_state=args.random_state
)
print(f'Label {label}: {len(train_l)} training videos, {len(test_l)} testing videos')
train.extend(train_l)
test.extend(test_l)
json_train: List[VideoAnnotation] = dump_json(train, filter_video, **vars(args))
json_test: List[VideoAnnotation] = dump_json(test, filter_video, **vars(args))
with open(args.output_train_file, 'w') as f:
json.dump(json_train, f, indent=4)
print(f"Training data saved to {args.output_train_file}")
with open(args.output_test_file, 'w') as f:
json.dump(json_test, f, indent=4)
print(f"Testing data saved to {args.output_test_file}") |