a d<@s.dZddlZddlmZddlZddlmmZddl m Z ddl m Z ddl m Z d ejeeefeeejejfd d d Zejeeefejd d dZejeejdddZd!ejeeeejdddZejejejdddZejejejdddZe d"ejeeeefeejdddZdS)#zHIn this module several equalization methods are exposed: he, ahe, clahe.N)Tuple_torch_histc_cast)perform_keep_shape_image) histogramF)imgs grid_sizeeven_tile_sizereturnc Cs@|}|jdd\}}t||d}t||d}|rf||drLdnd7}||dr`dnd7}||d|}||d|} ||jdks| |jdkrtd|dks| dkrtj|d| d|gdd }|jd } |d| | d||d ||d} | jd |dkr t | jd |dkr8t | |fS)aCompute tiles on an image according to a grid size. Note that padding can be added to the image in order to crop properly the image. So, the grid_size (GH, GW) x tile_size (TH, TW) >= image_size (H, W) Args: imgs: batch of 2D images with shape (B, C, H, W) or (C, H, W). grid_size: number of tiles to be cropped in each direction (GH, GW) even_tile_size: Determine if the width and height of the tiles must be even. Returns: tensor with tiles (B, GH, GW, C, TH, TW). B = 1 in case of a single image is provided. tensor with the padded batch of 2D imageswith shape (B, C, H', W'). NrrzBCannot compute tiles on the image according to the given grid sizeZreflect)mode) shapemathceil ValueErrorFpadunfoldsqueeze contiguousAssertionError) rr r batchhwZ kernel_vertZ kernel_horzZpad_vertZpad_horzctilesr#d/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/enhance/equalization.py_compute_tiless6 r%) padded_imgs tile_sizer cCs|dkrtd|jd|ddkr2td|jd|ddkrPtd|dd}|dd}|jd }|d||d||d ||d}|jd |krt|jd|ddkrt|jd|ddkrt|S) aCompute interpolation tiles on a properly padded set of images. Note that images must be padded. So, the tile_size (TH, TW) * grid_size (GH, GW) = image_size (H, W) Args: padded_imgs: batch of 2D images with shape (B, C, H, W) already padded to extract tiles of size (TH, TW). tile_size: shape of the current tiles (TH, TW). Returns: tensor with the interpolation tiles (B, 2GH, 2GW, C, TH/2, TW/2). zImages Tensor must be 4D.r rz Images are not correctly padded.rrr rr)dimrrrrr)r&r'Zinterp_kernel_vertZinterp_kernel_horzr! interp_tilesr#r#r$_compute_interpolation_tilesHs0    r+)r"binsr cCst||dddS)Nrrr,minmaxr)r"r,r#r#r$ _my_histcvsr0D@) tiles_x_imnum_binsclipdiffr c s|dkrtd|j\}}}}}} || } |d| } |stjrbtfdd| D} qttt t | gt | } n2tj dd| j d} t| | td } | | 9} |d krTt|| d}| j|d | | d}t|}||}| |d dd7} tj| j d}|| jdd}| ||d ddk7} d| }t| d|}|dd}|s|}|||||f}|S) aCompute luts for a batched set of tiles. Same approach as in OpenCV (https://github.com/opencv/opencv/blob/master/modules/imgproc/src/clahe.cpp) Args: tiles_x_im: set of tiles per image to apply the lut. (B, GH, GW, C, TH, TW) num_bins: number of bins. default: 256 clip: threshold value for contrast limiting. If it is 0 then the clipping is disabled. diff: denote if the differentiable histagram will be used. Default: False Returns: Lut for each tile (B, GH, GW, C, 256). zTensor must be 6D.rcsg|]}t|dddqS)rrr-r).0Ztiler4r#r$ z!_compute_luts..rr)devicegMbP?g)r/N)r)rrviewtorchZjitZ is_scriptingstacklistmapr0lenZlinspacer<rtensorrr/Zclamp_sum remainderdiv transposearangerepeatZcumsumclampfloor)r3r4r5r6bghgwr!thtwZpixelsr"Zhistosr,Zmax_valZclippedZresidualZredistZv_rangeZ mat_rangeZ lut_scalelutsr#r9r$ _compute_lutszs:   "    rR)r*rQr c Cs:|dkrtd|dkr(td|j\}}}}}}tjddtjd}|dkrtd |d |dd jdd d }t gd gd g|dd}||7}tjddtjd} |dkrtd |d |dd jdd d } t gdgdg|dd} | | 7} tj |||d||jdfd|j |j d} |ddddt |dd d ddt |dd d f| dddd|d dd|d df<|dd|dddfddt |dd d f| ddd ddd|d df<|dd|dddfddt |dd d f| ddd ddd|d d f<|ddddt |dd d | dddff| dddd|d d ddf<|ddddt |dd d | ddd ff| dddd|d d dd f<|dd|t |dd d d d dd| t |dd d d f| ddd dd dddf<| S)a Assign the required luts to each tile. Args: interp_tiles: set of interpolation tiles. (B, 2GH, 2GW, C, TH/2, TW/2) luts: luts for each one of the original tiles. (B, GH, GW, C, 256) Returns: mapped luts (B, 2GH, 2GW, 4, C, 256) r7interp_tiles tensor must be 6D.luts tensor must be 5D.rr()dtyper rtrunc)Z rounding_mode)rrrr)rrrr)rrrr)rrrrrrVr<N)r)rrr>emptylongrHr=rFrCfullrVr<r/rIpermute) r*rQnum_imgsrMrNr!_Zj_idxsZj_floorZi_idxsZi_floorZluts_x_interp_tilesr#r#r$ _map_lutss6  &" &"^PPPP> r_c Cs|dkrtd|dkr(tdt||}|j\}}}}}}|ddd} | d|||d |||} t |d|  | |||d |||} t |} tj d |d dd|j|jd d |d dddd ||} | d||d ||} tj d |d dd|j|jd d |d |d |} | d||d ||} | |d d d || |d d ||d}| |d d d d d d }| ddd dd dfd\}}}}t||t||}t||t||}t||t||| ddd dd df<| dddd|d dd|d df| dddd|d dd|d f<| ddd ddfd \}}}}t||d t||| ddd ddf<| ddd d|d fd \}}}}t||d t||| ddd d|d f<| dddd dfd \}}}}t||t||| dddd df<| dd|d d dfd \}}}}t||t||| dd|d d df<| dS)a.Equalize the tiles. Args: interp_tiles: set of interpolation tiles, values must be in the range [0, 1]. (B, 2GH, 2GW, C, TH/2, TW/2) luts: luts for each one of the original tiles. (B, GH, GW, C, 256) Returns: equalized tiles (B, 2GH, 2GW, C, TH/2, TW/2) r7rSrTrUr rrr(r rrXg@Nrrgo@)r)rr_rrZflattenZ unsqueezeexpandr>ZgathertoZreshapeZ zeros_likerHrVr<rFrGrrIZunbindZaddcmulsubr)r*rQZ mapped_lutsr]rMrNr!rOrPZflatten_interp_tilesZpreinterp_tiles_equalizedZtiles_equalizedZihiwZtiwZtihtltrblbrtrLr^lrr#r#r$_compute_equalized_tilessb        .(,J$.(2$((,rmro)input clip_limitr slow_and_differentiabler cCsLt|tstdt|t|ts8tdt|t|dkrVtdt|t|dtsrt|dtrztd|ddks|ddkrtd||}t||d \}}|jd |jd f}t ||}t |||d } t || } | dd dddd |} |jd d\} } | dd| d| f} || krH| d} | S)aApply clahe equalization on the input tensor. .. image:: _static/img/equalize_clahe.png NOTE: Lut computation uses the same approach as in OpenCV, in next versions this can change. Args: input: images tensor to equalize with values in the range [0, 1] and shape :math:`(*, C, H, W)`. clip_limit: threshold value for contrast limiting. If 0 clipping is disabled. grid_size: number of tiles to be cropped in each direction (GH, GW). slow_and_differentiable: flag to select implementation Returns: Equalized image or images with shape as the input. Examples: >>> img = torch.rand(1, 10, 20) >>> res = equalize_clahe(img) >>> res.shape torch.Size([1, 10, 20]) >>> img = torch.rand(2, 3, 10, 20) >>> res = equalize_clahe(img) >>> res.shape torch.Size([2, 3, 10, 20]) z(Input clip_limit type is not float. Got z'Input grid_size type is not Tuple. Got r z4Input grid_size is not a Tuple with 2 elements. Got rrz=Input grid_size type is not valid, must be a Tuple[int, int].z/Input grid_size elements must be positive. Got Tr r)r5r6rr(rTN.) isinstancefloat TypeErrortypetuplerBrr%rr+rRrmr\Z reshape_asr)r)rprqr rrrZ hist_tilesZ img_paddedr'r*rQZequalized_tilesZeq_imgsrr r#r#r$equalize_clahe5s,!      rx)F)r1r2F)r2rnF)__doc__rtypingrr>Ztorch.nn.functionalnnZ functionalrZkornia.utils.helpersrZkornia.utils.imagerrZTensorintboolr%r+r0rtrRr_rmrxr#r#r#r$s@     9.  57O