File size: 4,111 Bytes
cbc5e69 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | #!/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()
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