a  d.@sddlZddlZddlZddlmZddlmZ ddl m Z ddl m Z mZddd Zdd d Zd dZddZddZddZddZd ddZd!ddZdS)"N)path) functionalmod_crop) img2tensorscandirFcst|tr|}nttt|dd}dd|D}|rHfdd|D}t|ddd}tj|dd}|r~d d|D}||fS|Sd S) aRead a sequence of images from a given folder path. Args: path (list[str] | str): List of image paths or image folder path. require_mod_crop (bool): Require mod crop for each image. Default: False. scale (int): Scale factor for mod_crop. Default: 1. return_imgname(bool): Whether return image names. Default False. Returns: Tensor: size (t, c, h, w), RGB, [0, 1]. list[str]: Returned image name list. T) full_pathcSs"g|]}t|tjdqS)go@)cv2ZimreadZastypenpfloat32).0vr_/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/basicsr/data/data_util.py z read_img_seq..csg|]}t|qSrr)r imgscalerrr r)Zbgr2rgbr r)ZdimcSs g|]}tt|dqS)r)ospsplitextbasenamer rrrrr%rN) isinstancelistsortedrrtorchstack)rZrequire_mod_croprZreturn_imgnameZ img_pathsZimgsZimgnamesrrr read_img_seq s r reflectioncCs|ddksJd|dvs,Jd|d|d}|d}g}t||||dD]}|dkr|dkrpd}q|d kr| }q|d kr|||}q||}nT||kr|dkr|}q|d kr|d|}q|d kr||||}q||}n|}||qV|S) aGenerate an index list for reading `num_frames` frames from a sequence of images. Args: crt_idx (int): Current center index. max_frame_num (int): Max number of the sequence of images (from 1). num_frames (int): Reading num_frames frames. padding (str): Padding mode, one of 'replicate' | 'reflection' | 'reflection_circle' | 'circle' Examples: current_idx = 0, num_frames = 5 The generated frame indices under different padding mode: replicate: [0, 0, 0, 1, 2] reflection: [2, 1, 0, 1, 2] reflection_circle: [4, 3, 0, 1, 2] circle: [3, 4, 0, 1, 2] Returns: list[int]: A list of indices. rz#num_frames should be an odd number.) replicater reflection_circleZcirclezWrong padding mode: .rr"r r#)rangeappend)Zcrt_idxZ max_frame_numZ num_framespaddingZnum_padindicesiZpad_idxrrrgenerate_frame_indices+s2   r*c Csrt|dksJdt|t|dksg} t|D],} | t |d| f|d| fgq<| Sd S)uGenerate paired paths from lmdb files. Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is: lq.lmdb ├── data.mdb ├── lock.mdb ├── meta_info.txt The data.mdb and lock.mdb are standard lmdb files and you can refer to https://lmdb.readthedocs.io/en/release/ for more details. The meta_info.txt is a specified txt file to record the meta information of our datasets. It will be automatically created when preparing datasets by our provided dataset tools. Each line in the txt file records 1)image name (with extension), 2)image shape, 3)compression level, separated by a white space. Example: `baboon.png (120,125,3) 1` We use the image name without extension as the lmdb key. Note that we use the same key for the corresponding lq and gt images. Args: folders (list[str]): A list of folder path. The order of list should be [input_folder, gt_folder]. keys (list[str]): A list of keys identifying folders. The order should be in consistent with folders, e.g., ['lq', 'gt']. Note that this key is different from lmdb keys. Returns: list[str]: Returned path list. r!GThe len of folders should be 2 with [input_folder, gt_folder]. But got >The len of keys should be 2 with [input_key, gt_key]. But got .lmdbz folder and z2 folder should both in lmdb formats. But received z: z; meta_info.txtcSsg|]}|ddqSr$rsplitr linerrrrrz*paired_paths_from_lmdb..NcSsg|]}|ddqSr/r0r2rrrrrzKeys in z _folder and z_folder are different._path) lenendswith ValueErroropenrjoinsetrr&dict) folderskeys input_folder gt_folder input_keygt_keyfinZinput_lmdb_keysZ gt_lmdb_keyspathsZlmdb_keyrrrpaired_paths_from_lmdb_s4#,, *rDcCst|dksJdt|t|dks.Nr4) r5r8rrrformatr9r&r;)r<r=Zmeta_info_file filename_tmplr>r?r@rArBZgt_namesrCZgt_namerext input_name input_pathgt_pathrrr paired_paths_from_meta_info_files  ,  (rNc Cs.t|dksJdt|t|dksr?r@rAZ input_pathsZgt_pathsrCrMrrJrKrLrrrpaired_paths_from_folders,     (rOcs"tt}fdd|D}|S)zGenerate paths from folder. Args: folder (str): Folder path. Returns: list[str]: Returned path list. csg|]}t|qSr)rr9rfolderrrrrz%paths_from_folder..)rr)rQrCrrPrpaths_from_folders rRcCs\|dstd|dtt|d}dd|D}Wdn1sN0Y|S)zGenerate paths from lmdb. Args: folder (str): Folder path. Returns: list[str]: Returned path list. r-zFolder zfolder should in lmdb format.r.cSsg|]}|ddqSr/r0r2rrrrrz#paths_from_lmdb..N)r6r7r8rr9)rQrBrCrrrpaths_from_lmdbs ,rS 皙?cCs:ddlm}t||f}d||d|df<|||S)zGenerate Gaussian kernel used in `duf_downsample`. Args: kernel_size (int): Kernel size. Default: 13. sigma (float): Sigma of the Gaussian kernel. Default: 1.6. Returns: np.array: The Gaussian kernel. r)filtersrr!)Z scipy.ndimagerVr zerosgaussian_filter) kernel_sizesigmarVkernelrrrgenerate_gaussian_kernel s r\c Cs|dvsJd|dd}|jdkr4d}|d}|\}}}}}|dd ||}|d |d |d |d } } t|| | | | fd }t|d |} t|  |dd} tj || |d }|ddddd dd df}|||||d |d}|r| d}|S)abDownsamping with Gaussian kernel used in the DUF official code. Args: x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w). kernel_size (int): Kernel size. Default: 13. scale (int): Downsampling factor. Supported scale: (2, 3, 4). Default: 4. Returns: Tensor: DUF downsampled frames. )r!r]z&Only support scale (2, 3, 4), but got r$Fr]Trrr!Zreflectg?)ZstrideNr^) ndimZ unsqueezesizeviewFpadr\rZ from_numpyZtype_asZconv2dZsqueeze) xrYrZ squeeze_flagbtchwZpad_wZpad_hrXrrrduf_downsamples"   "  rl)FrF)r )rTrU)rTr])r numpyr rosrrZtorch.nnrrdZbasicsr.data.transformsrZ basicsr.utilsrrrr*rDrNrOrRrSr\rlrrrrs    4;,$