o پiN@sddlZddlZddlZddlZddlmZmZmZmZddl Z ddl m Z ddl m m Zz ddl mZdZWn eyDdZYnwddlmZddlZgdZGdd d ZGd d d ZGd d d e jZGdddZeedrejjdejjdejjdejj dejj!dejj"diZ#nejdejdejdej dej!dej"diZ#dde#$DZ%erejdejdejdej dej!dej"diZ&dde&$DZ'niZ(iZ&ddZ)ddZ*ddZ+e*de*dfZ,d=d!d"Z-Gd#d$d$Z. %d>d&e j/d'ee0ee0fd(ee0ee0e0e0ffd)e1d*e j/f d+d,Z2Gd-d.d.e j3j4Z5 %d>d&e j/d/e0d0e0d1e0d2e0d(ee0ee0e0e0ffd)e1d*e j/fd3d4Z6Gd5d6d6e j3j4Z7Gd7d8d8Z8Gd9d:d:Z9Gd;d<dr(strrE __classcell__r&r&r9r'r,s  rcs8eZdZdZd fdd ZddZdefdd ZZS) rzSConvert a PIL Image to a tensor of the same type - this does not scale values. r4Ncr5r,r6r8r9r&r'r-Gr;zMaybePILToTensor.__init__cCr<)z Note: A deep copy of the underlying array is performed. Args: pic (PIL Image): Image to be converted to tensor. Returns: Tensor: Converted image. )r=r2r>r.r/r@r&r&r'r(Js  zMaybePILToTensor.__call__cCrBrCrDr8r&r&r'rEXr;zMaybePILToTensor.__repr__rF) r)r*r+r1r-r(rGrErHr&r&r9r'rCs r ResamplingnearestbilinearbicubicboxhamminglanczoscCi|]\}}||qSr&r&.0abr&r&r' rrUcCrPr&r&rQr&r&r'rU~rVcCst|Sr,)_str_to_pil_interpolationmode_strr&r&r'r sr cCtrt|St|Sr,)has_interpolation_mode_str_to_torch_interpolationrWrXr&r&r'r r cCrZr,)r[_torch_interpolation_to_str_pil_interpolation_to_str)moder&r&r'r r]r 3Please provide only two dimensions (h, w) for size.cCsZt|tjrt|t|fSt|tr!t|dkr!|d|dfSt|dkr+t||S)Nrr)r=numbersNumberintrlen ValueError)size error_msgr&r&r' _setup_sizes  rjc@s<eZdZdZ   dddZeddZd d Zd d Zd S)r aYCrop the given PIL Image to random size and aspect ratio with random interpolation. A crop of random size (default: of 0.08 to 1.0) of the original size and a random aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop is finally resized to given size. This is popularly used to train the Inception networks. Args: size: expected output size of each edge scale: range of size of the origin size cropped ratio: range of aspect ratio of the origin aspect ratio cropped interpolation: Default: PIL.Image.BILINEAR g{Gz??g?gUUUUUU?rKcCsxt|ttfr t||_n||f|_|d|dks"|d|dkr'td|dkr/t|_nt||_||_ ||_ dS)Nrrbz"range should be of kind (min, max)random) r=listtuplerhwarningswarn_RANDOM_INTERPOLATION interpolationr scaleratio)r#rhrurvrtr&r&r'r-s      z*RandomResizedCropAndInterpolation.__init__cCsJt|\}}||}tdD]U}tj||}t|dt|df}ttj|} tt t || } tt t || } | |krd| |krdt d|| } t d|| } | | | | fSq||}|t |kr||} tt | t |} n|t |kr|} tt | t |} n|} |} || d} || d} | | | | fS)aGet parameters for ``crop`` for a random sized crop. Args: img (PIL Image): Image to be cropped. scale (tuple): range of size of the origin size cropped ratio (tuple): range of aspect ratio of the origin aspect ratio cropped Returns: tuple: params (i, j, h, w) to be passed to ``crop`` for a random sized crop. rrbr)r.get_image_sizerangernuniformmathlogexpreroundsqrtrandintminmax)imgrurvimg_wimg_hareaattempt target_area log_ratio aspect_ratiotarget_wtarget_hijin_ratior&r&r' get_paramss2       z,RandomResizedCropAndInterpolation.get_paramsc CsV|||j|j\}}}}t|jttfrt|j}n|j}t ||||||j |S)z Args: img (PIL Image): Image to be cropped and resized. Returns: PIL Image: Randomly cropped and resized image. ) rrurvr=rtrprornchoicer. resized_croprh)r#rrrhwrtr&r&r'r(s z*RandomResizedCropAndInterpolation.__call__cCst|jttfrddd|jD}nt|j}|jjd|j }|dtdd|j D7}|dtd d|j D7}|d |7}|S) N cSg|]}t|qSr&r rRxr&r&r' z>RandomResizedCropAndInterpolation.__repr__.. (size={0}z , scale={0}cs|]}t|dVqdSNr~)rRsr&r&r' z=RandomResizedCropAndInterpolation.__repr__..z , ratio={0}csrrr)rRrr&r&r'r rz, interpolation={0})) r=rtrprojoinr r:r)formatrhrurvr#interpolate_str format_stringr&r&r'rEs z*RandomResizedCropAndInterpolation.__repr__N)rkrmrK r)r*r+r1r- staticmethodrr(rEr&r&r&r'r s  * r constantr output_sizefill padding_moder4c Cst|}|\}}t|\}}}||ks||kre||kr"||dnd||kr-||dnd||kr:||ddnd||krG||ddndg} tj|| ||d}t|\}}}||kre||kre|Stt||d} tt||d} t|| | ||S)akCenter crops and/or pads the given image. If the image is torch Tensor, it is expected to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. Args: img (PIL Image or Tensor): Image to be cropped. output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int, it is used for both directions. fill (int, Tuple[int]): Padding color Returns: PIL Image or Tensor: Cropped image. rrrbrrg@)rjr.get_dimensionspadrer~crop) rrrr crop_height crop_width_ image_height image_width padding_ltrbcrop_top crop_leftr&r&r'rs rc sheZdZdZ  ddeeeefdeeeeeeffdeffdd Z d d Z d efd d Z Z S)ra/Crops the given image at the center. If the image is torch Tensor, it is expected to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. Args: size (sequence or int): Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). rrrhrrc$tt||_||_||_dSr,r7r-rjrhrrr#rhrrr9r&r'r-F   zCenterCropOrPad.__init__cCst||j|j|jdS) Args: img (PIL Image or Tensor): Image to be cropped. Returns: PIL Image or Tensor: Cropped image. r)rrhrr)r#rr&r&r'forwardQszCenterCropOrPad.forwardr4cC|jjd|jdSNz(size=)r:r)rhr8r&r&r'rE[zCenterCropOrPad.__repr__rr) r)r*r+r1rrerrrGr-rrErHr&r&r9r'r:s  rtopleftheightwidthc Cst|\}}} ||} ||} |dks |dks | | ks | |krQt| td| dt| td| dt| t| |dt| t||dg} tj|| ||d}t|d}t|d}t|||||S)z[ Crops and/or pads image to meet target size, with control over fill and padding_mode. rr)r.rrrrr) rrrrrrrrrrrightbottomrr&r&r'r_s    rc steZdZdZ  ddeeeefdeeeeeeffdeffdd Z e d d Z d d Z d efddZ ZS)rzP Crop and/or pad image with random placement within the crop or pad margin. rrrhrrcrr,rrr9r&r'r-rzRandomCropOrPad.__init__c Csht|\}}}||d}||d}tttdt||}tttdt||}||fS)Nrrb)r.rrer{copysignrnrabs) rrhrrr delta_height delta_widthrrr&r&r'rs   zRandomCropOrPad.get_paramsc Cs8|||j\}}t||||jd|jd|j|jdS)rrrb)rrrrrr)rrhrrr)r#rrrr&r&r'rszRandomCropOrPad.forwardr4cCrrrr8r&r&r'rErzRandomCropOrPad.__repr__r)r)r*r+r1rrerrrGr-rrrrErHr&r&r9r'r{s rc@s*eZdZd ddZeddZddZdS) rrcCs||_||_dSr,) input_sizer)r#rrr&r&r'r-s zRandomPad.__init__c Csft|\}}t|d|d}t|d|d}td|}td|}||}||} |||| fS)Nrbr)r.rxrrnr) rrrrrrpad_leftpad_top pad_right pad_bottomr&r&r'rs   zRandomPad.get_paramscCs"|||j}t|||j}|Sr,)rrr.rr)r#rpaddingr&r&r'r(szRandomPad.__call__N)r)r)r*r+r-rrr(r&r&r&r'rs    rc@sPeZdZdZ       dddZe    dd d Zd d ZddZdS)rz" Resize and Keep Aspect Ratio rKg333333?g?Fg?g(\?c Csjt|ttfr t||_n||f|_|dkrt|_nt||_t||_||_ ||_ ||_ ||_ ||_ dS)z Args: size: longest: interpolation: random_scale_prob: random_scale_range: random_scale_area: random_aspect_prob: random_aspect_range: rnN)r=rorprhrsrtr floatlongestrandom_scale_probrandom_scale_rangerandom_scale_arearandom_aspect_probrandom_aspect_range) r#rhrrtrrrrrr&r&r'r-s     zResizeKeepRatio.__init__rlgHzG?cst|dd\}} } |\} } || } | | }t| ||t| |d||dkrKt|krKt|d|d}|rFdt|}||f}nd}|dkr~t|kr~t|dt|df}t tj|}t|}|d||d|f}fddt | |D}|S)zGet parameters rbNrlr)rlrlcs g|] \}}t||qSr&r)rRrfrvr&r'rs z.ResizeKeepRatio.get_params..) r.rrrrnrzr{rr|r}zip)r target_sizerrrrrrrrimg_sizerrratio_hratio_w ratio_factor log_aspect aspect_factorrhr&rr'rs$    zResizeKeepRatio.get_paramsc Cs\|||j|j|j|j|j|j|j}t|j t t fr"t |j }n|j }t|||}|S)z Args: img (PIL Image): Image to be cropped and resized. Returns: PIL Image: Resized, padded to at least target size, possibly cropped to exactly target size )rrhrrrrrrr=rtrprornrr.resize)r#rrhrtr&r&r'r(s  zResizeKeepRatio.__call__cCst|jttfrddd|jD}nt|j}|jjd|j }|d|7}|d|j d7}|d|j d7}|d |j d dd |j d dd 7}|d|jd7}|d|j d dd |j d dd7}|S)NrcSrr&rrr&r&r'r(rz,ResizeKeepRatio.__repr__..rz, interpolation=z , longest=z.3fz, random_scale_prob=z, random_scale_range=(rz, rbrz, random_aspect_prob=z, random_aspect_range=(z)))r=rtrprorr r:r)rrhrrrrrrr&r&r'rE&s&       zResizeKeepRatio.__repr__N)rrKrrFrr)rrFrrrr&r&r&r'rs& & ' rcs*eZdZdeffdd ZddZZS)r border_sizecst||_dSr,)r7r-r)r#rr9r&r'r-9s  zTrimBorder.__init__cCsft|\}}|j}}t||}t||}td|d|j}td|d|j}t|||||S)Nrr)r.rxrrrr)r#rrrrrrrr&r&r'r@s   zTrimBorder.forward)r)r*r+rer-rrHr&r&r9r'r7s r)rar);r{rcrnrqtypingrrrrr2torchvision.transforms transforms!torchvision.transforms.functional functionalr.rr[ ImportErrorPILrnumpyr__all__rr rrhasattrrINEARESTBILINEARBICUBICBOXHAMMINGLANCZOSr_itemsrWr^r\_pil_interpolation_to_torchr r r rsrjr r>rerGrnnModulerrrrrrr&r&r&r's           m *+ /w