prima/datasets/split_acinoset.py

"""
PRIMA: Boosting Animal Mesh Recovery with Biological Priors and Test-Time Adaptation

Official implementation of the paper:
"PRIMA: Boosting Animal Mesh Recovery with Biological Priors and Test-Time Adaptation"
by Xiaohang Yu, Ti Wang, and Mackenzie Weygandt Mathis
Licensed under a modified MIT license
"""

"""
Split acinoset multiview_mapping.json into train and test sets (7:3 ratio).

Usage:
    python split_acinoset.py \
        --input_json /path/to/multiview_mapping.json \
        --output_dir /path/to/output \
        --train_ratio 0.7 \
        --seed 42
"""

import argparse
import json
import random
from pathlib import Path
from collections import defaultdict

# ------------------------------------------------------------------
# EDIT THIS to point to your dataset root (see examples above).
# All paths below are relative to this directory.
# ------------------------------------------------------------------
BASE_DIR = Path("datasets")


def split_multiview_data(input_json, output_dir, train_ratio=0.7, seed=42):
    """
    Split multiview mapping data into train and test sets.


    Args:
        input_json: Path to multiview_mapping.json
        output_dir: Directory to save train.json and test.json
        train_ratio: Ratio of training data (default 0.7 for 70%%)
        train_ratio: Ratio of training data (default 0.7 for 70%%)
        seed: Random seed for reproducibility
    """
    # Set random seed
    random.seed(seed)

    # Load data
    print(f"Loading data from {input_json}...")
    with open(input_json, 'r') as f:
        data = json.load(f)

    # Initialize train and test splits
    train_data = defaultdict(dict)
    test_data = defaultdict(dict)

    # Process each behavior
    for behavior, frames in data.items():
        print(f"\nProcessing behavior: {behavior}")

        # Get all frame indices
        frame_indices = list(frames.keys())
        total_frames = len(frame_indices)

        # Shuffle frame indices
        random.shuffle(frame_indices)

        # Calculate split point
        train_size = int(total_frames * train_ratio)

        # Split frames
        train_frames = frame_indices[:train_size]
        test_frames = frame_indices[train_size:]

        print(f"  Total frames: {total_frames}")
        print(f"  Train frames: {len(train_frames)}")
        print(f"  Test frames: {len(test_frames)}")

        # Assign to train and test
        for frame_idx in train_frames:
            train_data[behavior][frame_idx] = frames[frame_idx]

        for frame_idx in test_frames:
            test_data[behavior][frame_idx] = frames[frame_idx]

    # Save train and test splits
    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    train_json = output_dir / "train.json"
    test_json = output_dir / "test.json"

    print(f"\nSaving train data to {train_json}...")
    with open(train_json, 'w') as f:
        json.dump(dict(train_data), f, indent=4)

    print(f"Saving test data to {test_json}...")
    with open(test_json, 'w') as f:
        json.dump(dict(test_data), f, indent=4)

    # Print summary
    print("\n" + "="*50)
    print("Summary:")
    print("="*50)

    total_train_frames = sum(len(frames) for frames in train_data.values())
    total_test_frames = sum(len(frames) for frames in test_data.values())
    total_frames = total_train_frames + total_test_frames

    print(f"Total frames: {total_frames}")
    print(f"Train frames: {total_train_frames} ({total_train_frames/total_frames*100:.1f}%%)")
    print(f"Test frames: {total_test_frames} ({total_test_frames/total_frames*100:.1f}%%)")
    print("\nPer behavior:")
    for behavior in train_data.keys():
        train_count = len(train_data[behavior])
        test_count = len(test_data[behavior])
        total_count = train_count + test_count
        print(f"  {behavior}: train={train_count}, test={test_count}, total={total_count}")

    print("\nDone!")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Split multiview_mapping.json into train/test sets (default 7:3)."
    )
    parser.add_argument(
        "--input_json", type=str,
        default="datasets/acinoset/multiview_mapping.json",
        help="Path to multiview_mapping.json (default: datasets/acinoset/multiview_mapping.json)."
    )
    parser.add_argument(
        "--output_dir", type=str,
        default="datasets/acinoset",
        help="Directory to save train.json and test.json (default: datasets/acinoset)."
    )
    parser.add_argument(
        "--train_ratio", type=float, default=0.7,
        help="Fraction of data for training (default: 0.7)."
    )
    parser.add_argument(
        "--seed", type=int, default=42,
        help="Random seed for reproducibility (default: 42)."
    )
    args = parser.parse_args()

    split_multiview_data(
        input_json=args.input_json,
        output_dir=args.output_dir,
        train_ratio=args.train_ratio,
        seed=args.seed,
    )