import os import shutil import subprocess import av import torch import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel from utils.util import get_fps, read_frames, save_videos_from_pil from utils.preprocess_video import * from PIL import Image import numpy as np import json def ensure_dir(directory): if os.path.exists(directory): print(f"Directory already exists: {directory}") else: os.makedirs(directory) print(f"Created directory: {directory}") return directory # [previous helper functions remain the same] def get_video_dimensions(video_path): cmd = [ 'ffprobe', '-v', 'error', '-select_streams', 'v:0', '-show_entries', 'stream=width,height', '-of', 'csv=p=0', video_path ] result = subprocess.run(cmd, capture_output=True, text=True) width, height = map(int, result.stdout.strip().split(',')) return width, height def compile_frames_to_video(frame_dir, output_path, fps=30): """Compile frames into a video using H.264 codec.""" cmd = [ 'ffmpeg', '-y', '-f', 'image2', '-r', str(fps), '-i', f'{frame_dir}/%08d.jpg', '-c:v', 'libx264', '-preset', 'medium', '-crf', '18', '-pix_fmt', 'yuv420p', output_path ] subprocess.run(cmd, check=True) print(f"Successfully compiled video: {output_path}") def preprocess_videos(video_dir, dataset_name, square_crop=False, fps=24, quality_preset="medium", target_resolution=None): """ Preprocess all videos with optional square cropping, customizable FPS, quality, and resolution. Args: video_dir (str): Directory containing input videos dataset_name (str): Name of the dataset square_crop (bool): Whether to crop videos to 1:1 aspect ratio (default: True) fps (int): Target frames per second (default: 30) quality_preset (str): Quality preset - "high", "medium", "low", "ultra_low" (default: "medium") target_resolution (int): Target resolution for the shorter side (e.g., 512, 256). None for original """ result_dir = ensure_dir(f"../output/{dataset_name}_results") # 质量设置 quality_settings = { "high": {"qscale": "2", "crf": "18"}, # 高质量 "medium": {"qscale": "5", "crf": "23"}, # 中等质量 "low": {"qscale": "10", "crf": "28"}, # 低质量 "ultra_low": {"qscale": "15", "crf": "35"} # 超低质量 } current_quality = quality_settings.get(quality_preset, quality_settings["medium"]) for video_file in os.listdir(video_dir): if not video_file.endswith(".mp4"): continue video_name = os.path.splitext(video_file)[0] video_full_path = os.path.join(video_dir, video_file) folder_path = f"{result_dir}/{video_name}" frame_path = f"{folder_path}/crop_frame" output_video_path = f"{folder_path}/crop_original_video.mp4" # Skip if already processed if os.path.exists(frame_path) and os.listdir(frame_path) and os.path.exists(output_video_path): crop_status = "cropped" if square_crop else "original" print(f"{crop_status.capitalize()} frames and video already exist for {video_name}. Skipping preprocessing.") continue try: # Create output directory os.makedirs(frame_path, exist_ok=True) # Get video dimensions width, height = get_video_dimensions(video_full_path) # 构建视频滤镜 filters = [] if square_crop: # Calculate crop dimensions if width < height: crop_size = width x_offset = 0 y_offset = (height - width) // 2 else: crop_size = height x_offset = (width - height) // 2 y_offset = 0 filters.append(f'crop={crop_size}:{crop_size}:{x_offset}:{y_offset}') # 添加分辨率缩放 if target_resolution: if square_crop: # 方形裁剪后直接缩放到目标分辨率 filters.append(f'scale={target_resolution}:{target_resolution}') else: # 保持宽高比缩放 filters.append(f'scale=-2:{target_resolution}:force_original_aspect_ratio=decrease') # 添加帧率 filters.append(f'fps={fps}/1') # 组合所有滤镜 filter_complex = ','.join(filters) # 提取帧的命令 cmd = [ 'ffmpeg', '-i', video_full_path, '-vf', filter_complex, '-f', 'image2', '-qscale', current_quality["qscale"], # 使用可调节的质量 f'{frame_path}/%08d.jpg' ] resolution_info = f" (Resolution: {target_resolution})" if target_resolution else "" crop_info = "with square cropping" if square_crop else "without cropping" print(f"Processing {video_file} {crop_info} (FPS: {fps}, Quality: {quality_preset}{resolution_info})") subprocess.run(cmd, check=True) print(f"Successfully extracted frames for {video_file}") # Compile frames back into a video with optimized settings compile_frames_to_video_optimized(frame_path, output_video_path, fps, quality_preset) except Exception as e: print(f"Error preprocessing {video_file}: {str(e)}") continue def compile_frames_to_video_optimized(frame_dir, output_path, fps=30, quality_preset="medium"): """Compile frames into a video with optimized quality settings.""" # 质量设置 - CRF值（越高质量越低，文件越小） quality_crf = { "high": "18", "medium": "23", "low": "28", "ultra_low": "35" } crf_value = quality_crf.get(quality_preset, "23") cmd = [ 'ffmpeg', '-y', '-f', 'image2', '-r', str(fps), '-i', f'{frame_dir}/%08d.jpg', '-c:v', 'libx264', '-preset', 'medium', # 可以改为 'fast' 加速编码 '-crf', crf_value, '-pix_fmt', 'yuv420p', output_path ] subprocess.run(cmd, check=True) print(f"Successfully compiled optimized video: {output_path} (Quality: {quality_preset})") # 使用示例： # 1. 保持原分辨率，降低质量 # preprocess_videos(video_dir, dataset_name, square_crop=True, fps=30, quality_preset="low") # 2. 降低分辨率到512x512（方形裁剪） # preprocess_videos(video_dir, dataset_name, square_crop=True, fps=30, quality_preset="medium", target_resolution=512) # 3. 极度压缩：低分辨率 + 超低质量 # preprocess_videos(video_dir, dataset_name, square_crop=True, fps=30, quality_preset="ultra_low", target_resolution=256) # 4. 不裁剪，但缩放到较小尺寸 # preprocess_videos(video_dir, dataset_name, square_crop=False, fps=30, quality_preset="low", target_resolution=480) def process_npz_files(input_folder_path, output_folder_path): """ Process all NPZ files in the specified folder and generate the required output format. Args: input_folder_path (str): Path to the folder containing NPZ files output_folder_path (str): Path where output files will be saved """ # Get all NPZ files in the folder npz_files = sorted([f for f in os.listdir(input_folder_path) if f.endswith('.npz')]) total_frames = len(npz_files) output = [] for idx, npz_file in enumerate(npz_files): file_path = os.path.join(input_folder_path, npz_file) data = np.load(file_path, allow_pickle=True) # Process bodies data bodies = data['bodies'] body_scores = data['body_scores'][0] # Process hands data hands = data['hands'] hands_scores = data['hands_scores'] # Process faces data faces = data['faces'][0] faces_scores = data['faces_scores'][0] # Convert coordinates to strings with space separation frame_data = [] # Add body coordinates and scores for i in range(bodies.shape[0]): frame_data.extend([f"{bodies[i][0]:.8f}", f"{bodies[i][1]:.8f}"]) for score in body_scores: frame_data.append(f"{score:.8f}") # Add hand coordinates and scores for hand in hands: for point in hand: frame_data.extend([f"{point[0]:.8f}", f"{point[1]:.8f}"]) for hand_score in hands_scores: frame_data.extend([f"{score:.8f}" for score in hand_score]) # Add face coordinates and scores for point in faces: frame_data.extend([f"{point[0]:.8f}", f"{point[1]:.8f}"]) for score in faces_scores: frame_data.append(f"{score:.8f}") # Add frame count frame_count = idx / (total_frames - 1) if total_frames > 1 else 0 frame_data.append(f"{frame_count:.8f}") # 验证这一帧的数据点数是否为385 if len(frame_data) != 385: print(f"Warning: Frame {idx} in {input_folder_path} has {len(frame_data)} values instead of 385") continue # 跳过这一帧 # Join all data with spaces output.append(" ".join(frame_data)) return " ".join(output) + "\n"