40c40695dd
- check_all_episodes.py：检查各个episode是否有重复帧。 - check_specific_frames.py：检查前几帧是否位于正确初始位置。 - generate_dataset_videos.py：根据hdf5生成视频
325 lines
11 KiB
Python
325 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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将 HDF5 数据集转换为视频，用于可视化检查
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功能：
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1. 将单个 episode 转换为视频
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2. 对比多个 episode 的视频
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3. 放慢播放速度便于观察
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"""
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import os
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import h5py
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import glob
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import cv2
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import numpy as np
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def episode_to_video(episode_file, output_path, camera='top', fps=30, slow_factor=1):
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"""
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将单个 episode 转换为视频
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Args:
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episode_file: HDF5 文件路径
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output_path: 输出视频路径
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camera: 要使用的相机名称
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fps: 帧率
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slow_factor: 慢放倍数（1=正常，2=半速）
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"""
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try:
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with h5py.File(episode_file, 'r') as f:
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# 读取图像序列
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img_path = f'/observations/images/{camera}'
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if img_path not in f:
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print(f" ❌ 相机 {camera} 不存在")
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return False
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images = f[img_path][:] # shape: (T, H, W, C)
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qpos = f['/observations/qpos'][:]
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actions = f['/action'][:]
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total_frames = len(images)
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height, width = images.shape[1], images.shape[2]
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# 创建视频写入器
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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actual_fps = fps // slow_factor
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out = cv2.VideoWriter(output_path, fourcc, actual_fps, (width, height))
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# 逐帧写入
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for i in range(total_frames):
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frame = images[i].astype(np.uint8)
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# 在图像上添加信息
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info_text = [
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f"Episode: {os.path.basename(episode_file).replace('.hdf5', '')}",
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f"Frame: {i}/{total_frames}",
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f"qpos[0:3]: [{qpos[i, 0]:.2f}, {qpos[i, 1]:.2f}, {qpos[i, 2]:.2f}]",
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]
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for j, text in enumerate(info_text):
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cv2.putText(frame, text, (10, 30 + j*30),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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out.write(frame)
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out.release()
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print(f" ✅ 保存: {output_path}")
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print(f" 帧数: {total_frames}, 尺寸: {width}x{height}, FPS: {actual_fps}")
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return True
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except Exception as e:
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print(f" ❌ 错误: {e}")
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return False
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def generate_all_videos(camera='top', num_episodes=5, slow_factor=1):
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"""生成前 N 个 episode 的视频"""
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dataset_dir = "roboimi/demos/dataset/sim_transfer"
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episode_files = sorted(glob.glob(os.path.join(dataset_dir, "episode_*.hdf5")))
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if len(episode_files) == 0:
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print(f"❌ 没有找到数据文件: {dataset_dir}")
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return
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# 创建输出目录
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output_dir = '/tmp/dataset_videos'
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os.makedirs(output_dir, exist_ok=True)
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print(f"找到 {len(episode_files)} 个 episode 文件")
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print(f"将生成前 {min(num_episodes, len(episode_files))} 个 episode 的视频\n")
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# 生成视频
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for i in range(min(num_episodes, len(episode_files))):
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ep_file = episode_files[i]
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ep_name = os.path.basename(ep_file).replace('.hdf5', '')
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output_path = f"{output_dir}/{ep_name}_{camera}.mp4"
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print(f"[{i+1}/{min(num_episodes, len(episode_files))}] {ep_name}")
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episode_to_video(ep_file, output_path, camera=camera, slow_factor=slow_factor)
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print()
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print(f"✅ 所有视频已保存到: {output_dir}")
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print(f"\n播放方法:")
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print(f" # 播放单个视频")
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print(f" vlc {output_dir}/*.mp4")
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print(f" ")
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print(f" # 或用文件管理器")
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print(f" nautilus {output_dir}")
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def generate_multi_camera_video(episode_idx=0, slow_factor=1):
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"""生成包含多个相机的视频（分屏显示）"""
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dataset_dir = "roboimi/demos/dataset/sim_transfer"
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episode_files = sorted(glob.glob(os.path.join(dataset_dir, "episode_*.hdf5")))
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if episode_idx >= len(episode_files):
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print(f"❌ Episode {episode_idx} 不存在")
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return
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ep_file = episode_files[episode_idx]
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try:
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with h5py.File(ep_file, 'r') as f:
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# 获取所有相机
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cameras = []
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for key in f.keys():
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if 'images' in key:
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for cam_name in f[key].keys():
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if cam_name not in cameras:
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cameras.append(cam_name)
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print(f"Episode {episode_idx} 的相机: {cameras}")
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# 读取所有相机的图像
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all_images = {}
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for cam in cameras:
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img_path = f'/observations/images/{cam}'
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if img_path in f:
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all_images[cam] = f[img_path][:]
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if not all_images:
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print("❌ 没有找到图像数据")
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return
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# 获取第一个相机的尺寸
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first_cam = list(all_images.keys())[0]
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total_frames = len(all_images[first_cam])
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height, width = all_images[first_cam].shape[1], all_images[first_cam].shape[2]
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# 创建多相机布局
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num_cams = len(all_images)
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cols = min(2, num_cams)
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rows = (num_cams + cols - 1) // cols
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canvas_width = width * cols
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canvas_height = height * rows
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# 创建视频写入器
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output_path = f'/tmp/dataset_videos/episode_{episode_idx}_all_cameras.mp4'
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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out = cv2.VideoWriter(output_path, fourcc, 30 // slow_factor, (canvas_width, canvas_height))
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# 逐帧合成
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for i in range(total_frames):
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canvas = np.zeros((canvas_height, canvas_width, 3), dtype=np.uint8)
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for cam_idx, cam_name in enumerate(all_images.keys()):
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img = all_images[cam_name][i]
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# 计算在画布上的位置
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row = cam_idx // cols
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col = cam_idx % cols
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y_start = row * height
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y_end = y_start + height
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x_start = col * width
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x_end = x_start + width
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# 调整大小（如果需要）
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if img.shape[:2] != (height, width):
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img = cv2.resize(img, (width, height))
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# 放到画布上
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canvas[y_start:y_end, x_start:x_end] = img
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# 添加相机名称
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cv2.putText(canvas, cam_name, (x_start + 10, y_start + 30),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
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# 添加帧信息
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cv2.putText(canvas, f"Frame: {i}/{total_frames}", (10, canvas_height - 10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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out.write(canvas)
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out.release()
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print(f"✅ 保存多相机视频: {output_path}")
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except Exception as e:
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print(f"❌ 错误: {e}")
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def compare_episodes(camera='top', slow_factor=2):
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"""并排对比多个 episode 的视频"""
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dataset_dir = "roboimi/demos/dataset/sim_transfer"
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episode_files = sorted(glob.glob(os.path.join(dataset_dir, "episode_*.hdf5")))
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# 选择要对比的 episode
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episodes_to_compare = [0, 1, 2, 3, 4] # 对比前 5 个
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print(f"对比 Episodes: {episodes_to_compare}")
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# 读取所有 episode 的数据
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all_data = []
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for ep_idx in episodes_to_compare:
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if ep_idx >= len(episode_files):
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continue
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try:
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with h5py.File(episode_files[ep_idx], 'r') as f:
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img_path = f'/observations/images/{camera}'
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if img_path in f:
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all_data.append({
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'idx': ep_idx,
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'images': f[img_path][:],
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'qpos': f['/observations/qpos'][:]
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})
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except:
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pass
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if len(all_data) == 0:
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print("❌ 没有数据")
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return
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# 获取参数
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first_data = all_data[0]
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height, width = first_data['images'].shape[1], first_data['images'].shape[2]
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total_frames = min([d['images'].shape[0] for d in all_data])
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# 创建并排布局
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num_compare = len(all_data)
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canvas_width = width * num_compare
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canvas_height = height
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# 创建视频
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output_path = f'/tmp/dataset_videos/compare_{camera}.mp4'
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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out = cv2.VideoWriter(output_path, fourcc, 30 // slow_factor, (canvas_width, canvas_height))
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print(f"生成对比视频，共 {total_frames} 帧...")
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# 逐帧对比
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for i in range(total_frames):
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canvas = np.zeros((canvas_height, canvas_width, 3), dtype=np.uint8)
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for j, data in enumerate(all_data):
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img = data['images'][i]
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qpos = data['qpos'][i]
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# 调整大小（如果需要）
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if img.shape[:2] != (height, width):
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img = cv2.resize(img, (width, height))
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# 放到画布上
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x_start = j * width
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x_end = x_start + width
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canvas[:, x_start:x_end] = img
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# 添加信息
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ep_name = f"Ep {data['idx']}"
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cv2.putText(canvas, ep_name, (x_start + 10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)
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cv2.putText(canvas, f"qpos[0:3]: [{qpos[0]:.2f}, {qpos[1]:.2f}, {qpos[2]:.2f}]",
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(x_start + 10, height - 10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
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# 添加帧号
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cv2.putText(canvas, f"Frame: {i}/{total_frames}", (10, canvas_height - 30),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
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out.write(canvas)
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if i % 100 == 0:
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print(f" 进度: {i}/{total_frames}")
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out.release()
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print(f"✅ 保存对比视频: {output_path}")
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if __name__ == "__main__":
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import sys
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print("="*60)
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print("数据集视频生成工具")
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print("="*60)
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if len(sys.argv) > 1:
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command = sys.argv[1]
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if command == 'compare':
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# 对比多个 episode
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camera = sys.argv[2] if len(sys.argv) > 2 else 'top'
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compare_episodes(camera=camera, slow_factor=2)
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elif command == 'multi':
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# 多相机视频
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ep_idx = int(sys.argv[2]) if len(sys.argv) > 2 else 0
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generate_multi_camera_video(episode_idx=ep_idx, slow_factor=1)
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else:
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print("未知命令")
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else:
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# 默认：生成前 5 个 episode 的视频
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print("\n生成前 5 个 episode 的视频（top 相机，慢放 2x）...")
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print("="*60 + "\n")
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generate_all_videos(camera='top', num_episodes=5, slow_factor=2)
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print("\n" + "="*60)
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print("其他用法:")
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print(" python generate_dataset_videos.py compare top # 对比多个 episode")
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print(" python generate_dataset_videos.py multi 0 # 多相机视频")
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print("="*60)
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