o vi@ @s2ddlZddlZddlZddlZddlmZddlmZ ddl m Z ddl m Z d+dededBdefd d Zd,d ejd edejfddZdejdejfddZdejfddZddZd-ddZd-ddZdejdedejfdd Zd.dejd!edejfd"d#Z  $ d/dejd%ed&ed'ed(edejf d)d*ZdS)0N)Image) transforms) functional)tqdmdata_diris_trainreturnc Csg}tj|sJd|dtj|ddD]H\}}}|D]@}|dr^tj||}|durYtjtj|| tj }|rMd|vrM| |q|sXd|vrX| |q| |qq|S) a Recursively get a list of all HDF5 files in the specified directory and its subdirectories, including those reached via symbolic links. Args: data_dir (str): Path to the directory to search is_train (bool | None): If None, returns all HDF5 files. If True, returns only files in 'train' subdirectories. If False, returns only files in 'val' subdirectories. Returns: list: List of paths to HDF5 files zError: Directory 'z' does not exist.T) followlinks)z.h5z.hdf5z.he5Ntrainval) ospathexistswalklowerendswithjoinnormpathrelpathsplitsepappend) rr hdf5_filesrootdirsfilesfilefilepath path_partsrJ/data/cameron/vidgen/cosmos-policy/cosmos_policy/datasets/dataset_utils.pyget_hdf5_filess"     r!_image_npqualitycsfdtjdtjffdd |jdkr|S|jdkr+fdd|D}tj|d d Std |j) uApply JPEG compression/decompression to a NumPy image or batch of images. Accepts either a single image with shape (H, W, C) **or** a batch of images with shape (B, H, W, C). All inputs must be uint8 RGB. Args: image_np (np.ndarray): Input image(s) as uint8 array(s). quality (int): JPEG quality factor (1–95). Returns: np.ndarray: JPEG-compressed (and re-decoded) image(s) with the same shape as the input. imgrcs\|jtjksJd|jt|}t}|j|dd|dt |}t |S)z'JPEG-compress a single image (H, W, C).zExpected uint8 image but got JPEG)formatr$r) dtypenpuint8r fromarrayioBytesIOsaveseekopenarray)r%Zpil_imgbufferZcompressed_img)r$rr _compress_singleUs    z3apply_jpeg_compression_np.._compress_singlecsg|]}|qSrr).0r%)r3rr esz-apply_jpeg_compression_np..raxiszEExpected image_np with shape (H, W, C) or (B, H, W, C) but got shape )r)ndarrayndimstack ValueErrorshape)r#r$ compressedr)r3r$r apply_jpeg_compression_npFs  r@ jpeg_bytescCs&tt|}t|tjS)z5Decode a single JPEG frame from bytes to numpy array.) rr0r,r-tobytesr)r1astyper*)rAr%rrr decode_single_jpeg_frameksrDcCsHg}|D]}tt|}|t|qtj|dd tj S)uGDecode a variable-length JPEG byte dataset (T,) → (T, H, W, 3) uint8.rr8) rr0r,r-rBrr)r1r<rCr*)Zjpeg_dsframesZjpeg_arrr%rrr decode_jpeg_bytes_datasetqs rFc Csg}g}tdt|D]\}}|d}|d}||||qtj|dd}tj|dd}tdtj|dd} tj|dd} tj|dd} tj |dd} tj |dd} tj|dd}tj|dd}tj|dd}tj |dd}tj |dd}| | | | | |||||d }|S)z Calculate statistics over all actions and proprio in the dataset. Args: data (dict): Dataset dictionary Returns: dict: Dataset statistics dictionary z%Collecting all actions and proprio...actionspropriorr8zComputing dataset statistics...) actions_min actions_max actions_mean actions_stdactions_median proprio_min proprio_max proprio_mean proprio_stdproprio_median) printritemsrr) concatenateminmaxmeanstdmedian)dataZ all_actionsZ all_proprio episode_idx episode_datarGrHZall_actions_arrayZall_proprio_arrayrIrJrKrLrMrNrOrPrQrRstatsrrr calculate_dataset_statisticszsB    r_F?c Csi}|D]=\}}||}||d} ||d} |s+d|| | | d} n|| | | } || } |} | | |<| ||<q|S)a Rescale some dataset element to the range [-1,+1] or [0,+1]. If `non_negative_only` is True, then the target range will be [0,+1]. Else, it will be [-1,+1]. The `scale_multiplier` can be used to change the final range. For example, if `scale_multiplier==2.0`, then we use [-2,+2] instead of [-1,+1] (or [0,+2] instead of [0,+1] if `non_negative_only==True`). Args: data (dict): Dataset dictionary dataset_stats (dict): Dataset statistics (pre-normalization) data_key (str): Key to the item that should be normalized (e.g., "actions", "proprio") scale_multiplier (float): Multiplier to adjust scale from [-1,+1] to [-scale_multiplier,+scale_multiplier] Returns: dict: Rescaled dataset _min_max)rTcopy) r[ dataset_statsdata_keynon_negative_onlyscale_multiplierZ rescaled_datar\r]arrcurr_mincurr_max rescaled_arrZrescaled_episoderrr rescale_datas rnc Cs^||}||d}||d}|s!d||||d}n||||}||}|S)a Rescale a single episode's data to the range [-1,+1] or [0,+1]. Args: episode_data (dict): Single episode data dictionary dataset_stats (dict): Dataset statistics (pre-normalization) data_key (str): Key to the item that should be normalized (e.g., "actions", "proprio") non_negative_only (bool): If True, scale to [0,+1], else [-1,+1] scale_multiplier (float): Multiplier to adjust scale Returns: np.ndarray: Rescaled array rarbrcrdr) r]rfrgrhrirjrkrlrmrrr rescale_episode_datasroimages target_sizecCst|jdksJdt|j|jdd||dfkr!|S|jd}|jd}tj||||f|jd}t|D]}tt || ||f||<q;|S)aM Resizes multiple images to some target size. Assumes that the resulting images will be square. Args: images (np.ndarray): Input images with shape (T, H, W, C) target_size (int): Target image size (square) Returns: np.ndarray: Resized images with shape (T, target_size, target_size, C) r5z)Expected 4 dimensions in images but got: Nr4r)r() lenr>rer)emptyr(ranger1rr+resize)rprq num_imagesCZresized_imagesirrr resize_imagess"    $rzstrongerc Cs|j\}}}}||ksJd|jtjksJd|j|dddd}g}t|}d}||krs|}|d|kr[t||||dr[|d7}|d|kr[t||||dsE|d} | |} ||| | f|d7}||ks1tj j t ||ddd \}} } } |rt d d d }nd }|rt d d d}t d dd}t d dd}n!t d dd}t d dd}t d dd}t d dd}g}t|D]I\}\}} } ||}tj||| | | ||gdd}|rtj||dd}t||}t||}t||}t||}t| D]}||q*qt|}|dddd}|S)a Apply image augmentations to a batch of images represented as a torch.Tensor of shape (C, T, H, W). Args: images: A torch.Tensor of shape (C, T, H, W) and dtype torch.uint8 representing a set of images. stronger (bool): Whether to apply stronger augmentations Returns: A torch.Tensor of the same shape and dtype with augmentations applied. z$Image height and width must be equalz.Expected images dtype == torch.uint8 but got: rdrrcr4)?r|)r`r`)r%scaleratioggffffff?g?g333333?gffffff??g?g?g333333?gg?T)size antialiasF)expand)r>r(torchr*permutersequalrTRandomResizedCrop get_paramszeros FloatTensoruniform_item enumerateF resized_croprotateadjust_brightnessadjust_contrastadjust_saturation adjust_huerur<)rpr{_HWZ unique_groupsrwryZ group_startZ group_end group_sizejhwanglebrightness_factorcontrast_factorsaturation_factor hue_factorresults group_idxr%Zaugmented_imagesrrr apply_image_aug!sb $$       rTfinal_image_sizenormalize_images use_image_augstronger_image_augcCst|tjsJdt||jtjksJd|jt|jdkr+|jddks3Jd|jt||}t |d}t |}|rKt ||d}|ri| t j}|d }tjgd gd d d }||}|S| t j}|S) a Preprocesses images for training. Resizes to final_image_size, permutes from (T, H, W, C) to (C, T, H, W), converts to torch.Tensor, optionally applies image augmentations, and optionally normalizes (no need to normalize if, e.g., the dataloader logic will normalize later). Args: images (np.ndarray): Images to be preprocessed final_image_size (int): Target size for resized images (square) normalize_images (bool): Whether the images should be normalized in the end stronger_image_aug (bool): Whether to apply stronger image augmentations Returns: torch.Tensor: Preprocessed images z/Images are not of type `np.ndarray`! Got type: z0Images do not have dtype `np.uint8`! Got dtype: r5r4z8Unexpected images shape! Expected (T, H, W, 3) but got: )r4rrdrc)r{go@)rrrT)rXrYinplace) isinstancer)r:typer(r*rsr>rz transposer from_numpyrtofloat32r Normalize)rprrrr norm_funcrrr preprocess_image{s$       r)N)r")Fr`)F)FTF)r,r numpyr)rPILr torchvisionrrZtorchvision.transformsrrrstrboollistr!r:intr@rDrFr_rnrorzTensorrrrrrr sB    +% 9 ."]