eval/generate_feature_mapping.py

#!/usr/bin/env python
"""
Generate a feature-to-frame mapping file for SignX inference outputs.

Usage:
    python generate_feature_mapping.py <sample_dir> <video_path>

Example:
    python generate_feature_mapping.py detailed_prediction_20251226_155113/sample_000 \\
        eval/tiny_test_data/videos/632051.mp4
"""

import sys
import os
import json
import numpy as np
from pathlib import Path

def generate_feature_mapping(sample_dir, video_path):
    """Create the feature-to-frame mapping JSON for a given sample directory."""
    sample_dir = Path(sample_dir)

    # Check if attention_weights.npy exists
    attn_file = sample_dir / "attention_weights.npy"
    if not attn_file.exists():
        print(f"Error: missing attention_weights.npy: {attn_file}")
        return False

    # Load attention weights to get feature count
    attn_weights = np.load(attn_file)
    # Handle both 2D (time, features) and 3D (time, beam, features) shapes
    if attn_weights.ndim == 2:
        feature_count = attn_weights.shape[1]  # Shape: (time, features) - inference mode
    elif attn_weights.ndim == 3:
        feature_count = attn_weights.shape[2]  # Shape: (time, beam, features) - beam search
    else:
        print(f"Error: unexpected attention_weights shape: {attn_weights.shape}")
        return False

    print(f"Feature count: {feature_count}")

    # Get original frame count from video
    try:
        import cv2
        cap = cv2.VideoCapture(str(video_path))
        if not cap.isOpened():
            print(f"Error: failed to open video file: {video_path}")
            return False

        original_frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        fps = cap.get(cv2.CAP_PROP_FPS)
        cap.release()

        print(f"Original frames: {original_frame_count}, FPS: {fps}")

    except ImportError:
        print("Warning: OpenCV not available, falling back to estimates")
        # Assume 30 fps and approximate the frame count from features
        original_frame_count = feature_count * 3  # default 3x downsampling
        fps = 30.0

    # Calculate uniform mapping: feature i -> frames [start, end]
    frame_mapping = []
    for feat_idx in range(feature_count):
        start_frame = int(feat_idx * original_frame_count / feature_count)
        end_frame = int((feat_idx + 1) * original_frame_count / feature_count)
        frame_mapping.append({
            "feature_index": feat_idx,
            "frame_start": start_frame,
            "frame_end": end_frame,
            "frame_count": end_frame - start_frame
        })

    # Save mapping
    mapping_data = {
        "original_frame_count": original_frame_count,
        "feature_count": feature_count,
        "downsampling_ratio": original_frame_count / feature_count,
        "fps": fps,
        "mapping": frame_mapping
    }

    output_file = sample_dir / "feature_frame_mapping.json"
    with open(output_file, 'w') as f:
        json.dump(mapping_data, f, indent=2)

    print(f"\n✓ Mapping file written: {output_file}")
    print(f"  Original frames: {original_frame_count}")
    print(f"  Feature count: {feature_count}")
    print(f"  Downsampling ratio: {mapping_data['downsampling_ratio']:.2f}x")

    # Print sample mappings
    print("\nSample mappings:")
    for i in range(min(3, len(frame_mapping))):
        mapping = frame_mapping[i]
        print(f"  Feature {mapping['feature_index']}: frames {mapping['frame_start']}-{mapping['frame_end']} "
              f"({mapping['frame_count']} frames)")
    if len(frame_mapping) > 3:
        print("  ...")
        mapping = frame_mapping[-1]
        print(f"  Feature {mapping['feature_index']}: frames {mapping['frame_start']}-{mapping['frame_end']} "
              f"({mapping['frame_count']} frames)")

    return True

if __name__ == "__main__":
    if len(sys.argv) != 3:
        print("Usage: python generate_feature_mapping.py <sample_dir> <video_path>")
        print("\nExample:")
        print("  python generate_feature_mapping.py detailed_prediction_20251226_155113/sample_000 \\")
        print("      eval/tiny_test_data/videos/632051.mp4")
        sys.exit(1)

    sample_dir = sys.argv[1]
    video_path = sys.argv[2]

    if not os.path.exists(sample_dir):
        print(f"Error: sample directory not found: {sample_dir}")
        sys.exit(1)

    if not os.path.exists(video_path):
        print(f"Error: video file not found: {video_path}")
        sys.exit(1)

    success = generate_feature_mapping(sample_dir, video_path)
    sys.exit(0 if success else 1)