#!/usr/bin/env python3 """ Extract peak-feature keyframes and overlay attention heatmaps on the video frames. """ import os import sys import cv2 import numpy as np import json from pathlib import Path import matplotlib.pyplot as plt from matplotlib import cm def apply_attention_heatmap(frame, attention_weight, alpha=0.5): """ Overlay a synthetic attention heatmap on top of a video frame. Args: frame: Original frame (H, W, 3) attention_weight: Scalar attention weight in [0, 1] alpha: Heatmap opacity Returns: Frame with the attention heatmap blended in. """ h, w = frame.shape[:2] # Create a simple center-weighted Gaussian heatmap y, x = np.ogrid[:h, :w] center_y, center_x = h // 2, w // 2 # High attention weight = tighter Gaussian sigma = min(h, w) / 3 * (1.5 - attention_weight) gaussian = np.exp(-((x - center_x)**2 + (y - center_y)**2) / (2 * sigma**2)) # Normalize to [0, 1] gaussian = (gaussian - gaussian.min()) / (gaussian.max() - gaussian.min() + 1e-8) # Apply the attention weight heatmap = gaussian * attention_weight colormap = cm.get_cmap('jet') heatmap_colored = colormap(heatmap)[:, :, :3] * 255 heatmap_colored = heatmap_colored.astype(np.uint8) result = cv2.addWeighted(frame, 1-alpha, heatmap_colored, alpha, 0) return result def extract_keyframes_with_attention(sample_dir, video_path): """ Extract peak-feature keyframes and overlay the attention visualization. Args: sample_dir: Sample directory path (e.g., detailed_xxx/sample_0) video_path: Original video path """ sample_dir = Path(sample_dir) print(f"\nProcessing sample: {sample_dir.name}") # 检查必要文件 mapping_file = sample_dir / "feature_frame_mapping.json" weights_file = sample_dir / "attention_weights.npy" if not mapping_file.exists(): print(f" ⚠ Mapping file not found: {mapping_file}") return if not weights_file.exists(): print(f" ⚠ Attention weights missing: {weights_file}") return if not os.path.exists(video_path): print(f" ⚠ Video file not found: {video_path}") return # 加载映射和注意力权重 with open(mapping_file, 'r') as f: mapping_data = json.load(f) attention_weights = np.load(weights_file) # Create output directory keyframes_dir = sample_dir / "attention_keyframes" keyframes_dir.mkdir(exist_ok=True) print(f" Feature count: {mapping_data['feature_count']}") print(f" Original frame count: {mapping_data['original_frame_count']}") print(f" Attention weight shape: {attention_weights.shape}") # 打开视频 cap = cv2.VideoCapture(video_path) if not cap.isOpened(): print(f" ✗ Failed to open video: {video_path}") return total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) print(f" Total video frames: {total_frames}") # 构建特征索引到帧的映射（使用中间帧） feature_to_frame = {} for item in mapping_data['mapping']: feature_idx = item['feature_index'] frame_start = item['frame_start'] frame_end = item['frame_end'] mid_frame = (frame_start + frame_end) // 2 feature_to_frame[feature_idx] = mid_frame num_glosses = attention_weights.shape[0] if len(attention_weights.shape) > 1 else 0 if num_glosses == 0: print(" ⚠ Invalid attention weight dimensions") cap.release() return saved_count = 0 for gloss_idx in range(num_glosses): gloss_attention = attention_weights[gloss_idx] # shape: (num_features,) peak_feature_idx = np.argmax(gloss_attention) peak_attention = gloss_attention[peak_feature_idx] if peak_feature_idx not in feature_to_frame: print(f" ⚠ Gloss {gloss_idx}: feature {peak_feature_idx} missing frame mapping") continue frame_idx = feature_to_frame[peak_feature_idx] cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx) ret, frame = cap.read() if not ret: print(f" ⚠ Gloss {gloss_idx}: unable to read frame {frame_idx}") continue frame_with_attention = apply_attention_heatmap(frame, peak_attention, alpha=0.4) text = f"Gloss {gloss_idx} | Feature {peak_feature_idx} | Frame {frame_idx}" attention_text = f"Attention: {peak_attention:.3f}" cv2.rectangle(frame_with_attention, (0, 0), (frame.shape[1], 60), (0, 0, 0), -1) cv2.putText(frame_with_attention, text, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2) cv2.putText(frame_with_attention, attention_text, (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2) output_filename = f"keyframe_{gloss_idx:03d}_feat{peak_feature_idx}_frame{frame_idx}_att{peak_attention:.3f}.jpg" output_path = keyframes_dir / output_filename cv2.imwrite(str(output_path), frame_with_attention) saved_count += 1 cap.release() print(f" ✓ Saved {saved_count} keyframes to: {keyframes_dir}") # Create index file index_file = keyframes_dir / "keyframes_index.txt" with open(index_file, 'w') as f: f.write("Attention Keyframe Index\n") f.write(f"=" * 60 + "\n\n") f.write(f"Sample directory: {sample_dir}\n") f.write(f"Video path: {video_path}\n") f.write(f"Total keyframes: {saved_count}\n\n") f.write("Keyframe list:\n") f.write(f"-" * 60 + "\n") for gloss_idx in range(num_glosses): gloss_attention = attention_weights[gloss_idx] peak_feature_idx = np.argmax(gloss_attention) peak_attention = gloss_attention[peak_feature_idx] if peak_feature_idx in feature_to_frame: frame_idx = feature_to_frame[peak_feature_idx] filename = f"keyframe_{gloss_idx:03d}_feat{peak_feature_idx}_frame{frame_idx}_att{peak_attention:.3f}.jpg" f.write(f"Gloss {gloss_idx:3d}: {filename}\n") print(f" ✓ Index file written: {index_file}") def main(): if len(sys.argv) < 3: print("Usage: python extract_attention_keyframes.py ") print("Example: python extract_attention_keyframes.py detailed_xxx/sample_0 video.mp4") sys.exit(1) sample_dir = sys.argv[1] video_path = sys.argv[2] extract_keyframes_with_attention(sample_dir, video_path) if __name__ == "__main__": main()