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