SignX/eval/regenerate_visualizations.py

#!/usr/bin/env python3
"""
Regenerate visualization assets (using the latest attention_analysis.py).

Usage:
    python regenerate_visualizations.py <detailed_prediction_dir> <video_path>

Example:
    python regenerate_visualizations.py detailed_prediction_20251226_161117 ./eval/tiny_test_data/videos/632051.mp4
"""

import sys
import os
from pathlib import Path

# 添加项目根目录到path
SCRIPT_DIR = Path(__file__).parent.parent
sys.path.insert(0, str(SCRIPT_DIR))

from eval.attention_analysis import AttentionAnalyzer
import numpy as np


def regenerate_sample_visualizations(sample_dir, video_path):
    """Regenerate every visualization asset for a single sample directory."""
    sample_dir = Path(sample_dir)

    if not sample_dir.exists():
        print(f"Error: sample directory not found: {sample_dir}")
        return False

    # 加载数据
    attn_file = sample_dir / "attention_weights.npy"
    trans_file = sample_dir / "translation.txt"

    if not attn_file.exists() or not trans_file.exists():
        print(f"  Skipping {sample_dir.name}: required files are missing")
        return False

    # 读取数据
    attention_weights = np.load(attn_file)
    with open(trans_file, 'r') as f:
        lines = f.readlines()
        # Prefer the translation following the "Clean:" line
        translation = None
        for line in lines:
            if line.startswith('Clean:'):
                translation = line.replace('Clean:', '').strip()
                break
        if translation is None:
            translation = lines[0].strip()  # fallback

    # Determine feature count (video_frames)
    if len(attention_weights.shape) == 4:
        video_frames = attention_weights.shape[3]
    elif len(attention_weights.shape) == 3:
        video_frames = attention_weights.shape[2]
    else:
        video_frames = attention_weights.shape[1]

    print(f"  Sample: {sample_dir.name}")
    print(f"    Attention shape: {attention_weights.shape}")
    print(f"    Translation: {translation}")
    print(f"    Features: {video_frames}")

    # 创建分析器
    analyzer = AttentionAnalyzer(
        attentions=attention_weights,
        translation=translation,
        video_frames=video_frames,
        video_path=str(video_path) if video_path else None
    )

    # Regenerate frame_alignment.png (with original-frame layer)
    print("    Regenerating frame_alignment.png...")
    analyzer.plot_frame_alignment(sample_dir / "frame_alignment.png")

    # Regenerate gloss_to_frames.png (feature index overlay)
    if video_path and Path(video_path).exists():
        print("    Regenerating gloss_to_frames.png...")
        try:
            analyzer.generate_gloss_to_frames_visualization(sample_dir / "gloss_to_frames.png")
        except Exception as e:
            print(f"      Warning: failed to create gloss_to_frames.png: {e}")

    return True


def main():
    if len(sys.argv) < 2:
        print("Usage: python regenerate_visualizations.py <detailed_prediction_dir> [<video_path>]")
        print("\nExample:")
        print("  python regenerate_visualizations.py detailed_prediction_20251226_161117 ./eval/tiny_test_data/videos/632051.mp4")
        sys.exit(1)

    pred_dir = Path(sys.argv[1])
    video_path = Path(sys.argv[2]) if len(sys.argv) > 2 else None

    if not pred_dir.exists():
        print(f"Error: detailed prediction directory not found: {pred_dir}")
        sys.exit(1)

    if video_path and not video_path.exists():
        print(f"Warning: video file not found, disabling video overlays: {video_path}")
        video_path = None

    print("Regenerating visualizations:")
    print(f"  Detailed prediction dir: {pred_dir}")
    print(f"  Video path: {video_path if video_path else 'N/A'}")
    print()

    # 处理所有样本
    success_count = 0
    for sample_dir in sorted([d for d in pred_dir.iterdir() if d.is_dir()]):
        if regenerate_sample_visualizations(sample_dir, video_path):
            success_count += 1

    print(f"\n✓ Done! Successfully processed {success_count} sample(s)")


if __name__ == "__main__":
    main()