def nyudv2_to_lmdb(path_mat,
                   dst_prefix,
                   dir_dst,
                   val_list=[]):
    
    if not os.path.isfile(path_mat):
        raise IOError("Path is not a regular file (%s)" % path_mat)
    
    _, ext = os.path.splitext(path_mat)
    
    if ext != '.mat' and ext != '.h5'  and ext != '.hdf5' :
        raise IOError("Invalid file type, expecting mat/h5/hdf5 file (%s)" % path_mat)
    
    try:
        data = io.loadmat(path_mat)
    except (ValueError, NotImplementedError):
        data = h5py.File(path_mat) # support version >= 7.3 matfile HDF5 format
        pass
    
    lmdb_info = []
    train_idx = None
        
    for typ in [NYUDV2DataType.IMAGES,
                NYUDV2DataType.LABELS,
                NYUDV2DataType.DEPTHS]:
        
        if typ == NYUDV2DataType.IMAGES:
            
            dat = [mu.cwh_to_chw(x).astype(np.float) for x in data[typ]]
            
        elif typ == NYUDV2DataType.LABELS:
            
            dat = np.expand_dims(data[typ], axis=1).astype(int)
            dat = big_arr_to_arrs(dat)
            
        elif typ == NYUDV2DataType.DEPTHS:
            
            dat = np.expand_dims(data[typ], axis=1).astype(np.float)
            dat = big_arr_to_arrs(dat)
            
        else:
            raise ValueError("unknown NYUDV2DataType")
        
        
        if train_idx is None:
            train_idx, val_idx = get_train_val_split_from_idx(len(dat), val_list)
            shuffle(train_idx)
            print(train_idx)
        
    #     # len(ndarray) same as ndarray.shape[0]
    #     if  len(labels) != len(imgs):
    #         raise ValueError("No. of images != no. of labels. (%d) != (%d)",
    #                          len(imgs), len(labels))
    #         
    #     if  len(labels) != len(depths):
    #         raise ValueError("No. of depths != no. of labels. (%d) != (%d)",
    #                          len(depths), len(labels))
        
        print typ, len(dat), dat[0].shape
        
        fpath_lmdb = os.path.join(dir_dst, '%s%s_train_lmdb' % (dst_prefix, typ))
        to_lmdb.arrays_to_lmdb([dat[i] for i in train_idx], fpath_lmdb)
        
        lmdb_info.append((len(train_idx), fpath_lmdb))
        
        fpath_lmdb = os.path.join(dir_dst, '%s%s_val_lmdb' % (dst_prefix, typ))
        to_lmdb.arrays_to_lmdb([dat[i] for i in val_idx], fpath_lmdb)
        
        lmdb_info.append((len(val_idx), fpath_lmdb))
    
    return lmdb_info
dataset/ETHPy150Open kashefy/nideep/nideep/datasets/nyudv2_to_lmdb.py/nyudv2_to_lmdb/original