pkl/pkl2zarr.py

# import pickle
# import zarr
# import numpy as np
# import os
# from tqdm import tqdm

# def detect_episode_boundaries(data):
#     """精确检测episode边界"""
#     boundaries = [i for i, d in enumerate(data) if d.get('dones', False)]
#     if not boundaries or boundaries[-1] != len(data)-1:
#         boundaries.append(len(data)-1)
#     return boundaries

# def convert_pkl_to_zarr(pkl_path, zarr_path):
#     # 加载数据
#     with open(pkl_path, 'rb') as f:
#         data = pickle.load(f)
    
#     # 检测真实episode边界
#     episode_ends = detect_episode_boundaries(data)
#     episode_ends = [x + 1 for x in episode_ends]
#     print(f"检测到 {len(episode_ends)} 个episode，边界索引: {episode_ends}")

#     # 初始化zarr存储
#     if os.path.exists(zarr_path):
#         import shutil
#         shutil.rmtree(zarr_path)
#     root = zarr.open_group(zarr_path, mode='w')
#     data_group = root.create_group('data')
#     meta_group = root.create_group('meta')

#     # 数据容器
#     all_actions = []
#     all_states = []
#     all_side_image = []  # side_classifier
#     all_wrist_image = []

#     # 处理每个step（只保留next_observations的第二个视角）
#     for step in tqdm(data, desc="Processing steps"):
#         obs = step['observations']
#         all_side_image.append(obs['side_policy_256'][0])  # (256,256,3)
#         # all_wrist_image.append(obs['wrist_224'][0])   # (128,128,3)
#         all_states.append(obs['state'][0])           # (19,)
#         all_actions.append(step['actions'])
        
        
#     # 转换为numpy数组
#     actions = np.array(all_actions, dtype=np.float64)     
#     states = np.array(all_states, dtype=np.float32) 
#     side_images = np.array(all_side_image, dtype=np.uint8)         # (N,128,128,3)
#     wrist_images = np.array(all_wrist_image, dtype=np.uint8)
    
#     # 存储数据（使用高效压缩）
#     compressor = zarr.Blosc(cname='zstd', clevel=3, shuffle=1)
#     data_group.array('action', actions, chunks=(100,7), compressor=compressor)
#     data_group.array('state', states, chunks=(100,19), compressor=compressor)
#     data_group.array('side_image', side_images, chunks=(100,256,256,3), compressor=compressor)
#     # data_group.array('wrist_image', wrist_images, chunks=(100,224,224,3), compressor=compressor)
#     meta_group.array('episode_ends', np.array(episode_ends, dtype=np.int64))

#     # 验证输出
#     print("\n转换结果验证:")
#     print(f"- action: {actions.shape} {actions.dtype}")
#     print(f"- state: {states.shape} {states.dtype}")
#     print(f"- img: {side_images.shape} {side_images.dtype}")
#     print(f"- img: {wrist_images.shape} {wrist_images.dtype}")
#     print(f"- episode_ends: {len(episode_ends)} points")

# if __name__ == '__main__':
#     convert_pkl_to_zarr(
#         pkl_path='/opt/ts/cfmp/data/lk_data/apple/is_random_False_apple_50_demos_2025-08-11_09-06-10.pkl',
#         zarr_path='data/lk_data_zarr/apple/apple_50_demos_2025-08-11_09-06-10.zarr'
#     )




import pickle
import zarr
import numpy as np
import os
from tqdm import tqdm

def detect_episode_boundaries(data):
    """精确检测episode边界"""
    boundaries = [i for i, d in enumerate(data) if d.get('dones', False)]
    if not boundaries or boundaries[-1] != len(data)-1:
        boundaries.append(len(data)-1)
    return boundaries

# def process_actions(actions, episode_ends):
#     """
#     处理actions：
#     - 保留第0,1,2和第6维，删除中间3,4,5维
#     - 对最后一维的0值，用最近一次出现的1或-1替换（每个episode开头连续0保持不变）
#     """
#     # 保留前三维 + 最后一维 (0,1,2,6)，丢弃中间3-5维
#     actions_new = np.concatenate([actions[:, :3], actions[:, 6:7]], axis=1)  # shape (N,4)

#     last_dim = actions_new[:, -1]

#     start_idx = 0
#     for end_idx in episode_ends:
#         episode_segment = last_dim[start_idx:end_idx]

#         nonzero_idx = np.where(episode_segment != 0)[0]
#         if len(nonzero_idx) == 0:
#             # 整个episode全0，保持不变
#             start_idx = end_idx
#             continue

#         last_val = 0
#         for i in range(len(episode_segment)):
#             if episode_segment[i] != 0:
#                 last_val = episode_segment[i]
#             else:
#                 if i > 0:
#                     episode_segment[i] = last_val

#         last_dim[start_idx:end_idx] = episode_segment
#         start_idx = end_idx

#     actions_new[:, -1] = last_dim
#     return actions_new

# def process_actions(actions, episode_ends, threshold=0.8):
#     """
#     处理 actions：
#     1. 保留第0,1,2和第6维，删除中间3,4,5维
#     2. 对最后一维离散化：>threshold设为1，<-threshold设为-1，其余设为0
#     3. 对最后一维的0值，用最近一次出现的1或-1替换（每个episode开头连续0保持不变）
#     """
#     # 保留前三维 + 最后一维 (0,1,2,6)
#     actions_new = np.concatenate([actions[:, :3], actions[:, 6:7]], axis=1)  # shape (N,4)

#     last_dim = actions_new[:, -1]

#     # Step 1: 按阈值离散化
#     last_dim[last_dim > threshold] = 1
#     last_dim[last_dim < -threshold] = -1
#     mask_mid = (last_dim <= threshold) & (last_dim >= -threshold)
#     last_dim[mask_mid] = 0

#     # Step 2: 按 episode 填充 0
#     start_idx = 0
#     for end_idx in episode_ends:
#         episode_segment = last_dim[start_idx:end_idx]

#         # 找到所有非零位置
#         nonzero_idx = np.where(episode_segment != 0)[0]
#         if len(nonzero_idx) == 0:
#             # 整个 episode 全 0，直接跳过
#             start_idx = end_idx
#             continue

#         last_val = 0
#         for i in range(len(episode_segment)):
#             if episode_segment[i] != 0:
#                 last_val = episode_segment[i]
#             else:
#                 # if i > 0:  # 开头的连续 0 不填充
#                 episode_segment[i] = last_val

#         last_dim[start_idx:end_idx] = episode_segment
#         start_idx = end_idx

#     actions_new[:, -1] = last_dim
#     return actions_new
def process_actions(actions, episode_ends, threshold=0.8):
    # 保留前三维 + 最后一维 (0,1,2,6)
    actions_new = np.concatenate([actions[:, :3], actions[:, 6:7]], axis=1)  # shape (N,4)

    last_dim = actions_new[:, -1]

    # Step 1: 按阈值离散化
    last_dim[last_dim > threshold] = 1
    last_dim[last_dim < -threshold] = -1
    mask_mid = (last_dim <= threshold) & (last_dim >= -threshold)
    last_dim[mask_mid] = 0

    # Step 2: 按 episode 填充 0
    start_idx = 0
    for end_idx in episode_ends:
        episode_segment = last_dim[start_idx:end_idx]

        # 如果全 0，就直接全部改成 1
        if np.all(episode_segment == 0):
            episode_segment[:] = 1
            last_dim[start_idx:end_idx] = episode_segment
            start_idx = end_idx
            continue

        # 开头连续 0 全部改成 1
        first_nonzero_idx = np.argmax(episode_segment != 0) if np.any(episode_segment != 0) else len(episode_segment)
        episode_segment[:first_nonzero_idx] = 1

        # 再做正常的填充
        last_val = episode_segment[0]
        for i in range(1, len(episode_segment)):
            if episode_segment[i] != 0:
                last_val = episode_segment[i]
            else:
                episode_segment[i] = last_val

        last_dim[start_idx:end_idx] = episode_segment
        start_idx = end_idx

    actions_new[:, -1] = last_dim
    return actions_new


def convert_pkl_to_zarr(pkl_path, zarr_path):
    # 加载数据
    with open(pkl_path, 'rb') as f:
        data = pickle.load(f)

    # 检测真实episode边界
    episode_ends = detect_episode_boundaries(data)
    episode_ends = [x + 1 for x in episode_ends]
    print(f"检测到 {len(episode_ends)} 个episode，边界索引: {episode_ends}")

    # 初始化zarr存储
    if os.path.exists(zarr_path):
        import shutil
        shutil.rmtree(zarr_path)
    root = zarr.open_group(zarr_path, mode='w')
    data_group = root.create_group('data')
    meta_group = root.create_group('meta')

    all_actions = []
    all_states = []
    all_side_image = []  # side_classifier
    all_wrist_image = []

    # 处理每个step（只保留next_observations的第二个视角）
    for step in tqdm(data, desc="Processing steps"):
        obs = step['observations']
        all_side_image.append(obs['side_policy_256'][0])  # (256,256,3)
        # all_wrist_image.append(obs['wrist_224'][0])   # (128,128,3)
        all_states.append(obs['state'][0])           # (19,)
        all_actions.append(step['actions'])

    # 转换为numpy数组
    actions = np.array(all_actions, dtype=np.float64)     
    states = np.array(all_states, dtype=np.float32) 
    side_images = np.array(all_side_image, dtype=np.uint8)         # (N,256,256,3)
    wrist_images = np.array(all_wrist_image, dtype=np.uint8)

    # 处理actions
    actions_processed = process_actions(actions, episode_ends)

    # 存储数据（使用高效压缩）
    compressor = zarr.Blosc(cname='zstd', clevel=3, shuffle=1)
    data_group.array('action', actions_processed, chunks=(100,4), compressor=compressor)
    data_group.array('state', states, chunks=(100,19), compressor=compressor)
    data_group.array('side_image', side_images, chunks=(100,256,256,3), compressor=compressor)
    # data_group.array('wrist_image', wrist_images, chunks=(100,224,224,3), compressor=compressor)
    meta_group.array('episode_ends', np.array(episode_ends, dtype=np.int64))

    # 验证输出
    print("\n转换结果验证:")
    print(f"- action: {actions_processed.shape} {actions_processed.dtype}")
    print(f"- state: {states.shape} {states.dtype}")
    print(f"- side_image: {side_images.shape} {side_images.dtype}")
    print(f"- wrist_image: {wrist_images.shape} {wrist_images.dtype}")
    print(f"- episode_ends: {len(episode_ends)} points")

if __name__ == '__main__':
    convert_pkl_to_zarr(
        pkl_path='/opt/ts/cfmp/data/lk_data/apple/is_random_False_pick_apple_50_demos_2025-08-14_09-55-57.pkl',
        zarr_path='data/lk_data-1/apple/apple_50_demos_2025-08-14_09-55-57.zarr'
    )