a  d3@szddlZddlZddlZddlZddlZddlmZdddZdej dfddZ dd d Z dd dZ dddZ ddZdS)N) make_gridTcs8ddt|tr(fdd|DS|SdS)aONumpy array to tensor. Args: imgs (list[ndarray] | ndarray): Input images. bgr2rgb (bool): Whether to change bgr to rgb. float32 (bool): Whether to change to float32. Returns: list[tensor] | tensor: Tensor images. If returned results only have one element, just return tensor. cSsX|jddkr4|r4|jdkr&|d}t|tj}t|ddd}|rT| }|S)Nfloat64float32r) shapeZdtypeastypecv2cvtColorZ COLOR_BGR2RGBtorchZ from_numpy transposefloat)imgbgr2rgbrr_/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/basicsr/utils/img_util.py _totensors  zimg2tensor.._totensorcsg|]}|qSrr).0rrrrrr !zimg2tensor..N isinstancelist)imgsrrrrr img2tensor s  rrrc Cst|s8t|tr&tdd|Ds8tdt|t|rH|g}g}|D]4}|d j |}||d|d|d}| }|dkrt |tt|ddd}|dd d}|rt|tj}nx|d kr8|}|dd d}|jd dkr"tj|d d }n|rZt|tj}n"|d krL|}ntd ||tjkrr|d }||}||qPt|dkr|d}|S)aTConvert torch Tensors into image numpy arrays. After clamping to [min, max], values will be normalized to [0, 1]. Args: tensor (Tensor or list[Tensor]): Accept shapes: 1) 4D mini-batch Tensor of shape (B x 3/1 x H x W); 2) 3D Tensor of shape (3/1 x H x W); 3) 2D Tensor of shape (H x W). Tensor channel should be in RGB order. rgb2bgr (bool): Whether to change rgb to bgr. out_type (numpy type): output types. If ``np.uint8``, transform outputs to uint8 type with range [0, 255]; otherwise, float type with range [0, 1]. Default: ``np.uint8``. min_max (tuple[int]): min and max values for clamp. Returns: (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of shape (H x W). The channel order is BGR. css|]}t|VqdS)N)r is_tensor)rtrrr ;rztensor2img..z(tensor or list of tensors expected, got rrF)Znrow normalizerr)Zaxisz?Only support 4D, 3D or 2D tensor. But received with dimension: o@)r rrrall TypeErrortypesqueezerdetachcpuclamp_Zdimrintmathsqrtsizenumpyr r r COLOR_RGB2BGRrnpuint8roundr appendlen)tensorrgb2bgrZout_typemin_maxresultZ_tensorZn_dimimg_nprrr tensor2img&s>&  "       r;cCsh|dj|ddd}||d|d|dd}|tj}|rdt |t j }|S)aQThis implementation is slightly faster than tensor2img. It now only supports torch tensor with shape (1, c, h, w). Args: tensor (Tensor): Now only support torch tensor with (1, c, h, w). rgb2bgr (bool): Whether to change rgb to bgr. Default: True. min_max (tuple[int]): min and max values for clamp. rrr) r'r(r*Zpermuter&r r2r)r/r r r0)r6r7r8outputrrrtensor2img_fastas  r>colorFcCsHt|tj}tjtjtjd}t|||}|rD|tj d}|S)aRead an image from bytes. Args: content (bytes): Image bytes got from files or other streams. flag (str): Flags specifying the color type of a loaded image, candidates are `color`, `grayscale` and `unchanged`. float32 (bool): Whether to change to float32., If True, will also norm to [0, 1]. Default: False. Returns: ndarray: Loaded image array. )r?Z grayscaleZ unchangedr#) r1Z frombufferr2r Z IMREAD_COLORZIMREAD_GRAYSCALEZIMREAD_UNCHANGEDZimdecoder r)contentflagrr:Z imread_flagsrrrr imfrombytesrs rBcCsD|r&tjtj|}tj|ddt|||}|s@tddS)ayWrite image to file. Args: img (ndarray): Image array to be written. file_path (str): Image file path. params (None or list): Same as opencv's :func:`imwrite` interface. auto_mkdir (bool): If the parent folder of `file_path` does not exist, whether to create it automatically. Returns: bool: Successful or not. T)exist_okzFailed in writing images.N)ospathabspathdirnamemakedirsr imwriteIOError)r file_pathparamsZ auto_mkdirdir_nameokrrrrIs rIcsFdkr |St|tr(fdd|DS|  dfSdS)zCrop borders of images. Args: imgs (list[ndarray] | ndarray): Images with shape (h, w, c). crop_border (int): Crop border for each end of height and weight. Returns: list[ndarray]: Cropped images. rcs&g|]}|  dfqS).r)rv crop_borderrrrrzcrop_border...Nr)rrQrrPrrQs  rQ)TT)Tr)r?F)NT)r r,r/r1rDr Ztorchvision.utilsrrr2r;r>rBrIrQrrrrs  ;