import os import cv2 import numpy as np import librosa import matplotlib.pyplot as plt from tqdm import tqdm from librosa import feature as audio import sys """ Structure of the AVLips dataset: AVLips ├── 0_real ├── 1_fake └── wav ├── 0_real └── 1_fake """ ############ Custom parameter ############## N_EXTRACT = 10 # number of extracted windows from video WINDOW_LEN = 5 # frames of each window MAX_SAMPLE = 0 # maximum number of videos to process (0 means no limit) ############################################ audio_root = "./AVLips/wav" video_root = "./AVLips" output_root = "./datasets/AVLips" # 确保临时目录存在 os.makedirs("./temp", exist_ok=True) os.makedirs(output_root, exist_ok=True) def get_spectrogram(audio_file, output_path="./temp/mel.png"): """ Generate mel-spectrogram from audio file Args: audio_file: path to audio file output_path: path to save spectrogram image """ try: data, sr = librosa.load(audio_file, sr=16000) mel = librosa.power_to_db(audio.melspectrogram(y=data, sr=sr), ref=np.min) plt.imsave(output_path, mel) return True except Exception as e: print(f"Error generating spectrogram for {audio_file}: {str(e)}") return False def run(): labels = [(0, "0_real"), (1, "1_fake")] for label, dataset_name in labels: # Create output directory os.makedirs(f"{output_root}/{dataset_name}", exist_ok=True) root = f"{video_root}/{dataset_name}" if not os.path.exists(root): print(f"Warning: {root} does not exist, skipping...") continue video_list = os.listdir(root) print(f"\nHandling {dataset_name}... (Total: {len(video_list)} videos)") # Limit number of samples if MAX_SAMPLE > 0 if MAX_SAMPLE > 0: video_list = video_list[:MAX_SAMPLE] print(f"Limiting to {MAX_SAMPLE} videos") # 断点续传：检查已处理的文件 output_dir = f"{output_root}/{dataset_name}" processed_files = set() if os.path.exists(output_dir): # 获取已处理的所有输出文件 for f in os.listdir(output_dir): if f.endswith('.png'): # 提取视频文件名（去掉 _group.png 后缀） parts = f.rsplit('_', 1) if len(parts) == 2 and parts[1].startswith('0') and parts[1].endswith('.png'): processed_files.add(parts[0] + '.mp4') print(f" - Already processed: {len(processed_files)} videos") # 过滤掉已处理的视频 video_list = [v for v in video_list if v not in processed_files] print(f" - Remaining to process: {len(video_list)} videos") processed_count = 0 error_count = 0 skip_count = 0 for j in tqdm(range(len(video_list)), desc=dataset_name): v = video_list[j] # Check if video file exists video_path = f"{root}/{v}" if not os.path.exists(video_path): print(f"\nWarning: Video file not found: {video_path}") skip_count += 1 continue # Load video video_capture = cv2.VideoCapture(video_path) if not video_capture.isOpened(): print(f"\nError: Cannot open video {video_path}") error_count += 1 continue fps = video_capture.get(cv2.CAP_PROP_FPS) frame_count = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT)) # Skip if video is too short if frame_count < WINDOW_LEN: print(f"\nWarning: Video {v} has only {frame_count} frames (need {WINDOW_LEN}), skipping...") video_capture.release() skip_count += 1 continue # Select N_EXTRACT starting points from frames # Ensure we don't go beyond frame_count - WINDOW_LEN max_start = frame_count - WINDOW_LEN if max_start <= 0: print(f"\nWarning: Video {v} is too short, skipping...") video_capture.release() error_count += 1 continue frame_idx = np.linspace( 0, max_start, N_EXTRACT, endpoint=True, ).astype(int).tolist() frame_idx.sort() # Selected frames frame_sequence = [ i for num in frame_idx for i in range(num, num + WINDOW_LEN) ] frame_list = [] current_frame = 0 # Read frames while current_frame <= frame_sequence[-1]: ret, frame = video_capture.read() if not ret: print(f"\nWarning: Error reading frame {current_frame} from {v}") break if current_frame in frame_sequence: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame_list.append(cv2.resize(frame, (500, 500))) current_frame += 1 video_capture.release() # Check if we got all frames if len(frame_list) != len(frame_sequence): print(f"\nWarning: Could not read all frames from {v} ({len(frame_list)}/{len(frame_sequence)}), skipping...") skip_count += 1 continue # Load audio name = os.path.splitext(v)[0] audio_path = f"{audio_root}/{dataset_name}/{name}.wav" if not os.path.exists(audio_path): print(f"\nWarning: Audio file not found for {v}: {audio_path}") skip_count += 1 continue # Generate spectrogram if not get_spectrogram(audio_path): print(f"\nWarning: Could not generate spectrogram for {v}, skipping...") skip_count += 1 continue # Load spectrogram mel = plt.imread("./temp/mel.png") * 255 # load spectrogram (int) mel = mel.astype(np.uint8) # Calculate mapping from video frames to spectrogram time axis mapping = mel.shape[1] / frame_count # Process each window group = 0 for i in range(0, len(frame_list), WINDOW_LEN): idx = i // WINDOW_LEN try: begin = int(np.round(frame_sequence[i] * mapping)) end = int(np.round((frame_sequence[i] + WINDOW_LEN) * mapping)) # Ensure bounds are valid begin = max(0, begin) end = min(mel.shape[1], end) if end <= begin: print(f"\nWarning: Invalid spectrogram bounds for {name}, skipping window {group}") continue # Extract and resize spectrogram for this window sub_mel = cv2.resize( mel[:, begin:end], (500 * WINDOW_LEN, 500) ) # Concatenate frames horizontally x = np.concatenate(frame_list[i:i + WINDOW_LEN], axis=1) # Concatenate spectrogram (top) and frames (bottom) x = np.concatenate((sub_mel[:, :, :3], x[:, :, :3]), axis=0) # Save output image output_path = f"{output_root}/{dataset_name}/{name}_{group}.png" plt.imsave(output_path, x) group += 1 except Exception as e: print(f"\nError processing window {group} for {name}: {str(e)}") continue processed_count += 1 # Clean up temp file periodically if processed_count % 100 == 0: if os.path.exists("./temp/mel.png"): os.remove("./temp/mel.png") print(f"\n{dataset_name}:") print(f" - Processed: {processed_count} videos") print(f" - Skipped: {skip_count} videos") print(f" - Errors: {error_count} videos") if __name__ == "__main__": print("="*50) print("AVLips Preprocessing Script") print("="*50) print(f"Parameters:") print(f" - N_EXTRACT: {N_EXTRACT} windows per video") print(f" - WINDOW_LEN: {WINDOW_LEN} frames per window") print(f" - MAX_SAMPLE: {MAX_SAMPLE} (0 = no limit)") print(f" - Video root: {video_root}") print(f" - Audio root: {audio_root}") print(f" - Output root: {output_root}") print("="*50) # Create necessary directories if not os.path.exists(output_root): os.makedirs(output_root, exist_ok=True) if not os.path.exists("./temp"): os.makedirs("./temp", exist_ok=True) run() print("\n" + "="*50) print("Processing complete!") print("="*50)