o ~ri@sXddlZddlZddlmZddlmZmZddlmZddl m Z m Z ddl Z ddl ZddlmZmZmZmZmZmZmZmZmZddlmZmZmZmZmZmZerddl Z!ddl"Z!e!j#j$Z%erdd l&m'Z'e%j(e'j)e%j*e'j*e%j+e'j+e%j,e'j,e%j-e'j-e%j.e'j.iZ/d d e/0DZ1niZ/iZ1erddl2Z2e3e4Z5e d ej6d e7d e7ej6e7d fZ8GdddeZ9GdddeZ:e;eddZ?GdddeZ@ddZAddZBde7fddZCddZDdd ZEd!d"ZFd#ej6d$eGfd%d&ZHdpd(e=d$e7e8fd)d*ZI 'dpde7e8e8Bd(e=d$e8fd+d,ZJ 'dpde7e8e8Bd(e=d$e7e8fd-d.ZKd$ej6fd/d0ZL dqd#ej6d1e=eMe=d2fBdBd$e9fd3d4ZNdqd#ej6d5e9ed?ZQd@ee d$e7e fdAdBZRe9jSfde7e d ej6fd5e@srzPIL.Image.Imagez torch.Tensorc@eZdZdZdZdS)ChannelDimensionchannels_first channels_lastN)__name__ __module__ __qualname__FIRSTLASTrrrrr Rr c@r)AnnotationFormatcoco_detection coco_panopticN)r#r$r%COCO_DETECTION COCO_PANOPTICrrrrr)Wr(r)cCsto t|tjjSN)r isinstancePILImageimgrrr is_pil_image_sr4c@seZdZdZdZdZdS) ImageTypepillowtorchnumpyN)r#r$r%r0TORCHNUMPYrrrrr5csr5cCs<t|rtjSt|rtjSt|rtjStdt|)NzUnrecognized image type ) r4r5r0r r9r r: ValueErrortypeimagerrrget_image_typeisr?cCst|p t|p t|Sr.)r4r r r2rrris_valid_imagesr@imagescCs|o tdd|DS)Ncs|]}t|VqdSr.)r@rr>rrr xz*is_valid_list_of_images..all)rBrrris_valid_list_of_imageswsrIcCs\t|dtrdd|DSt|dtjrtj|ddSt|dtjr,tj|ddSdS)NrcSg|] }|D]}|qqSrr)rsublistitemrrr }z$concatenate_list..axis)dim)r/listnpndarray concatenater7Tensorcat) input_listrrrconcatenate_list{srYcCs:t|ttfr|D] }t|sdSq dSt|sdSdS)NFT)r/rRtuple valid_imagesr@)imgsr3rrrr[sr[cCst|ttfr t|dSdS)NrF)r/rRrZr@r2rrr is_batcheds r]r>returncCs,|jtjkrdSt|dkot|dkS)zV Checks to see whether the pixel values have already been rescaled to [0, 1]. Frr)dtyperSuint8minmaxr=rrris_scaled_images rcexpected_ndimscCst|r|St|r |gSt|r9|j|dkrt|}|S|j|kr(|g}|Std|dd|d|jdtdt|d)a Ensure that the output is a list of images. If the input is a single image, it is converted to a list of length 1. If the input is a batch of images, it is converted to a list of images. Args: images (`ImageInput`): Image of images to turn into a list of images. expected_ndims (`int`, *optional*, defaults to 3): Expected number of dimensions for a single input image. If the input image has a different number of dimensions, an error is raised. rz%Invalid image shape. Expected either z or z dimensions, but got z dimensions.z]Invalid image type. Expected either PIL.Image.Image, numpy.ndarray, or torch.Tensor, but got .)r]r4r@ndimrRr;r<rBrerrrmake_list_of_imagess&  ricCst|ttfr tdd|Dr tdd|Dr dd|DSt|ttfrJt|rJt|ds8|dj|kr:|S|dj|dkrJdd|DSt|ret|sW|j|krZ|gS|j|dkret|Std |) a Ensure that the output is a flat list of images. If the input is a single image, it is converted to a list of length 1. If the input is a nested list of images, it is converted to a flat list of images. Args: images (`Union[list[ImageInput], ImageInput]`): The input image. expected_ndims (`int`, *optional*, defaults to 3): The expected number of dimensions for a single input image. Returns: list: A list of images or a 4d array of images. cs|] }t|ttfVqdSr.r/rRrZrimages_irrrrEz+make_flat_list_of_images..cs|] }t|p | VqdSr.rIrlrrrrErncSrJrrrimg_listr3rrrrMrNz,make_flat_list_of_images..rrcSrJrrrqrrrrMrNz*Could not make a flat list of images from r/rRrZrHrIr4rgr@r;rhrrrmake_flat_list_of_imagess$ rtcCst|ttfrtdd|Drtdd|Dr|St|ttfrFt|rFt|ds3|dj|kr6|gS|dj|dkrFdd|DSt|rct|sS|j|krW|ggS|j|dkrct|gStd) as Ensure that the output is a nested list of images. Args: images (`Union[list[ImageInput], ImageInput]`): The input image. expected_ndims (`int`, *optional*, defaults to 3): The expected number of dimensions for a single input image. Returns: list: A list of list of images or a list of 4d array of images. csrjr.rkrlrrrrErnz-make_nested_list_of_images..csror.rprlrrrrErnrrcSg|]}t|qSr)rRrDrrrrMz.make_nested_list_of_images..z]Invalid input type. Must be a single image, a list of images, or a list of batches of images.rsrhrrrmake_nested_list_of_imagess$  rwcCs@t|s tdt|trt|tjjrt|St |S)NzInvalid image type: ) r@r;r<rr/r0r1rSarrayrr2rrrto_numpy_arrays  ry num_channels.cCs|dur|nd}t|tr|fn|}|jdkrd\}}n|jdkr&d\}}n|jdkr0d\}}ntd|j|j||vrS|j||vrStd |jd tjS|j||vr]tjS|j||vrgtj Std ) a[ Infers the channel dimension format of `image`. Args: image (`np.ndarray`): The image to infer the channel dimension of. num_channels (`int` or `tuple[int, ...]`, *optional*, defaults to `(1, 3)`): The number of channels of the image. Returns: The channel dimension of the image. Nrrdrd)r)r|r}z(Unsupported number of image dimensions: z4The channel dimension is ambiguous. Got image shape z. Assuming channels are the first dimension. Use the [input_data_format](https://huggingface.co/docs/transformers/main/internal/image_processing_utils#transformers.image_transforms.rescale.input_data_format) parameter to assign the channel dimension.z(Unable to infer channel dimension format) r/intrgr;shapeloggerwarningr r&r')r>rz first_dimlast_dimrrrinfer_channel_dimension_formats&       rinput_data_formatcCsF|durt|}|tjkr|jdS|tjkr|jdStd|)a Returns the channel dimension axis of the image. Args: image (`np.ndarray`): The image to get the channel dimension axis of. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the image. If `None`, will infer the channel dimension from the image. Returns: The channel dimension axis of the image. NrdrUnsupported data format: )rr r&rgr'r;)r>rrrrget_channel_dimension_axisDs     r channel_dimcCsZ|durt|}|tjkr|jd|jdfS|tjkr&|jd|jdfStd|)a Returns the (height, width) dimensions of the image. Args: image (`np.ndarray`): The image to get the dimensions of. channel_dim (`ChannelDimension`, *optional*): Which dimension the channel dimension is in. If `None`, will infer the channel dimension from the image. Returns: A tuple of the image's height and width. Nr)rr r&rr'r;)r>rrrrget_image_sizeZs   r image_size max_height max_widthc CsB|\}}||}||}t||}t||}t||} || fS)a Computes the output image size given the input image and the maximum allowed height and width. Keep aspect ratio. Important, even if image_height < max_height and image_width < max_width, the image will be resized to at least one of the edges be equal to max_height or max_width. For example: - input_size: (100, 200), max_height: 50, max_width: 50 -> output_size: (25, 50) - input_size: (100, 200), max_height: 200, max_width: 500 -> output_size: (200, 400) Args: image_size (`tuple[int, int]`): The image to resize. max_height (`int`): The maximum allowed height. max_width (`int`): The maximum allowed width. )rar) rrrheightwidth height_scale width_scale min_scale new_height new_widthrrr#get_image_size_for_max_height_widthrs   rvaluescCsddt|DS)zO Return the maximum value across all indices of an iterable of values. cSrur)rb)rvalues_irrrrMrvz&max_across_indices..)zip)rrrrmax_across_indicessrcCsb|tjkrtdd|D\}}}||fS|tjkr*tdd|D\}}}||fStd|)zH Get the maximum height and width across all images in a batch. cSg|]}|jqSrrrr3rrrrMz(get_max_height_width..cSrrrrrrrrMrz"Invalid channel dimension format: )r r&rr'r;)rBr_rrrrrget_max_height_widths  r annotationcCsVt|tr)d|vr)d|vr)t|dttfr)t|ddks't|ddtr)dSdS)Nimage_id annotationsrTFr/dictrRrZlenrrrr"is_valid_annotation_coco_detections"rcCs^t|tr-d|vr-d|vr-d|vr-t|dttfr-t|ddks+t|ddtr-dSdS)Nr segments_info file_namerTFrrrrr!is_valid_annotation_coco_panoptics"rrcCtdd|DS)NcsrCr.)rrannrrrrErFz3valid_coco_detection_annotations..rGrrrr valid_coco_detection_annotationsrcCr)NcsrCr.)rrrrrrErFz2valid_coco_panoptic_annotations..rGrrrrvalid_coco_panoptic_annotationsrrtimeoutc Csttdgt|trf|ds|dr%tjtt j ||ddj }nLt j |r2tj|}n?|dr>|dd}zt|}tjt|}Wn!tye}z td |d |d }~wwt|tjjsqtd tj|}|d }|S)a3 Loads `image` to a PIL Image. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. timeout (`float`, *optional*): The timeout value in seconds for the URL request. Returns: `PIL.Image.Image`: A PIL Image. visionzhttp://zhttps://T)rfollow_redirectsz data:image/,rzIncorrect image source. Must be a valid URL starting with `http://` or `https://`, a valid path to an image file, or a base64 encoded string. Got z. Failed with NzuIncorrect format used for image. Should be an url linking to an image, a base64 string, a local path, or a PIL image.RGB)r load_imager/str startswithr0r1openrhttpxgetcontentospathisfilesplitbase64 decodebytesencode Exceptionr; TypeErrorImageOpsexif_transposeconvert)r>rb64errrrs0      rcsXt|ttfr&t|rt|dttfrfdd|DSfdd|DSt|dS)aLoads images, handling different levels of nesting. Args: images: A single image, a list of images, or a list of lists of images to load. timeout: Timeout for loading images. Returns: A single image, a list of images, a list of lists of images. rcsg|] }fdd|DqS)cg|]}t|dqSrrrDrrrrMz*load_images...r)r image_grouprrrrMszload_images..crrrrDrrrrMrr)r/rRrZrr)rBrrrr load_imagess   r do_rescalerescale_factor do_normalize image_mean image_stddo_padpad_sizedo_center_crop crop_size do_resizesizeresamplePILImageResamplingrc Cs||r |dur td|r|durtd|r"|dus|dur"td|r,|dur,td| r:| dur6| dusrrr_ensure_format_supported@s z4ImageFeatureExtractionMixin._ensure_format_supportedNcCs||t|r |}t|tjrE|dur t|jdtj}|jdkr3|j ddvr3| ddd}|r9|d}| tj }t j|S|S)a" Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if needed. Args: image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`): The image to convert to the PIL Image format. rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default to `True` if the image type is a floating type, `False` otherwise. Nrrdr{rr|)rr r8r/rSrTflatfloatingrgr transposeastyper`r0r1 fromarray)rr>rescalerrr to_pil_imageGs    z(ImageFeatureExtractionMixin.to_pil_imagecCs&||t|tjjs|S|dS)z Converts `PIL.Image.Image` to RGB format. Args: image (`PIL.Image.Image`): The image to convert. r)rr/r0r1rrrrr convert_rgbes  z'ImageFeatureExtractionMixin.convert_rgbr>scaler^cCs||||S)z7 Rescale a numpy image by scale amount )r)rr>rrrrrss z#ImageFeatureExtractionMixin.rescaleTcCs||t|tjjrt|}t|r|}|dur&t|jdtj n|}|r4| | tj d}|rB|j dkrB|ddd}|S)a Converts `image` to a numpy array. Optionally rescales it and puts the channel dimension as the first dimension. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to convert to a NumPy array. rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Will default to `True` if the image is a PIL Image or an array/tensor of integers, `False` otherwise. channel_first (`bool`, *optional*, defaults to `True`): Whether or not to permute the dimensions of the image to put the channel dimension first. Nrp?rdr|r)rr/r0r1rSrxr r8rintegerrrfloat32rgr)rr>r channel_firstrrrryzs  z*ImageFeatureExtractionMixin.to_numpy_arraycCsD||t|tjjr|St|r|d}|Stj|dd}|S)z Expands 2-dimensional `image` to 3 dimensions. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to expand. rrO)rr/r0r1r unsqueezerS expand_dimsrrrrrs  z'ImageFeatureExtractionMixin.expand_dimsFcCsh||t|tjjr|j|dd}n|r3t|tjr'||tj d}n t |r3|| d}t|tjrXt|tjsHt ||j }t|tjsWt ||j }n6t |rddl}t||jswt|tjrr||}n||}t||jst|tjr||}n||}|jdkr|jddvr||ddddf|ddddfS|||S)a  Normalizes `image` with `mean` and `std`. Note that this will trigger a conversion of `image` to a NumPy array if it's a PIL Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to normalize. mean (`list[float]` or `np.ndarray` or `torch.Tensor`): The mean (per channel) to use for normalization. std (`list[float]` or `np.ndarray` or `torch.Tensor`): The standard deviation (per channel) to use for normalization. rescale (`bool`, *optional*, defaults to `False`): Whether or not to rescale the image to be between 0 and 1. If a PIL image is provided, scaling will happen automatically. T)rrrNrdr{)rr/r0r1ryrSrTrrrr floatrxr_r7rV from_numpytensorrgr)rr>meanstdrr7rrr normalizes6             ( z%ImageFeatureExtractionMixin.normalizec CsJ|dur|ntj}||t|tjjs||}t|tr#t|}t|t s.t |dkr|rBt|t r9||fn|d|df}n\|j \}}||krO||fn||f\}} t|t r\|n|d} || krf|S| t | | |} } |dur|| krt d|d|| |krt || | |} } ||kr| | fn| | f}|j ||dS)a Resizes `image`. Enforces conversion of input to PIL.Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to resize. size (`int` or `tuple[int, int]`): The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be matched to this. If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If `size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size). resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): The filter to user for resampling. default_to_square (`bool`, *optional*, defaults to `True`): How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a square (`size`,`size`). If set to `False`, will replicate [`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize) with support for resizing only the smallest edge and providing an optional `max_size`. max_size (`int`, *optional*, defaults to `None`): The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater than `max_size` after being resized according to `size`, then the image is resized again so that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller edge may be shorter than `size`. Only used if `default_to_square` is `False`. Returns: image: A resized `PIL.Image.Image`. Nrrz max_size = zN must be strictly greater than the requested size for the smaller edge size = )r)rBILINEARrr/r0r1rrRrZrrrr;resize) rr>rrdefault_to_squaremax_sizerrshortlongrequested_new_short new_shortnew_longrrrrs4   $ z"ImageFeatureExtractionMixin.resizecCsx||t|ts||f}t|st|tjr8|jdkr"||}|jddvr0|jddn|jdd}n |j d|j df}|d|dd}||d}|d|dd}||d}t|t j j rr| ||||fS|jddv}|st|tjr| ddd}t|r|ddd}|dkr||dkr|dkr||dkr|d||||fS|jddt|d|dt|d|df} t|tjrtj|| d} n t|r|| } | d|dd} | |d} | d |dd} | |d}|| d| | | |f<|| 7}|| 7}|| 7}|| 7}| dtd|t| jd|td|t| jd |f} | S) a Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the size given, it will be padded (so the returned result has the size asked). Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape (n_channels, height, width) or (height, width, n_channels)): The image to resize. size (`int` or `tuple[int, int]`): The size to which crop the image. Returns: new_image: A center cropped `PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape: (n_channels, height, width). r|rr{rN.rrr)rr/rZr rSrTrgrrrr0r1croprpermuterb zeros_like new_zerosra)rr>r image_shapetopbottomleftrightr new_shape new_imagetop_pad bottom_padleft_pad right_padrrr center_crop%sP    ,   (2    4z'ImageFeatureExtractionMixin.center_cropcCs>||t|tjjr||}|dddddddfS)a Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of `image` to a NumPy array if it's a PIL Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should be first. Nr)rr/r0r1ryrrrrflip_channel_orderps  z.ImageFeatureExtractionMixin.flip_channel_orderrcCsL|dur|ntjj}||t|tjjs||}|j||||||dS)a Returns a rotated copy of `image`. This method returns a copy of `image`, rotated the given number of degrees counter clockwise around its centre. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to rotate. If `np.ndarray` or `torch.Tensor`, will be converted to `PIL.Image.Image` before rotating. Returns: image: A rotated `PIL.Image.Image`. N)rexpandcenter translate fillcolor)r0r1NEARESTrr/rrotate)rr>anglerrrrrrrrrs   z"ImageFeatureExtractionMixin.rotater.)NT)F)NTN)NrNNN)r#r$r%__doc__rrrrSrTrrrryrrrrrrrrrrr;s    4CKrannotation_formatsupported_annotation_formatscCsX||vrtdtd||tjurt|std|tjur(t|s*tddSdS)NzUnsupported annotation format: z must be one of zInvalid COCO detection annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id` and `annotations`, with the latter being a list of annotations in the COCO format.zInvalid COCO panoptic annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id`, `file_name` and `segments_info`, with the latter being a list of annotations in the COCO format.)r;formatr)r,rr-r)r r!rrrrvalidate_annotationss  r#valid_processor_keyscaptured_kwargscCs:t|t|}|rd|}td|ddSdS)Nz, zUnused or unrecognized kwargs: rf)set differencejoinrr)r$r% unused_keysunused_key_strrrrvalidate_kwargss  r+c@seZdZUdZdZedBed<dZedBed<dZedBed<dZ edBed<dZ edBed<dZ edBed<d d Z dd d Z d dZddZddZddZddZdddZdefddZdS)SizeDictz> Hashable dictionary to store image size information. Nrr longest_edge shortest_edgerrcCs$t||r t||Std|d)NKey z not found in SizeDict.)hasattrgetattrKeyErrorrkeyrrr __getitem__s  zSizeDict.__getitem__cCs&t||rt||durt||S|Sr.r0r1)rr4defaultrrrrs z SizeDict.getccs4t|D]}t||j}|dur|j|fVqdSr.)rr1name)rfvalrrr__iter__s   zSizeDict.__iter__cCs t|j|j|j|j|j|jfSr.)hashrrr-r.rrrrrr__hash__s zSizeDict.__hash__cCst||o t||duSr.r6r3rrr __contains__rAzSizeDict.__contains__cCs,t||s td|dt|||dS)Nr/z" is not a valid field of SizeDict.)r0r2object __setattr__)rr4valuerrr __setitem__s zSizeDict.__setitem__csXttr tkSttr*tfddtDtfddtDkStS)Nc3|] }t|jVqdSr.r1r8rr9r=rrrEsz"SizeDict.__eq__..c3rDr.rErF)otherrrrEs  )r/rr,rZrNotImplemented)rrGr)rGrr__eq__s   $ zSizeDict.__eq__r^cCs6t|ttBrt|}|t|tdi|StS)Nr)r/rr,updaterHrrGmergedrrr__or__s zSizeDict.__or__cCs(t|trt|}|t||StSr.)r/rrJrHrKrrr__ror__s zSizeDict.__ror__r.)r^r,)r#r$r%rrr__annotations__rr-r.rrr5rr;r>r?rCrIrMrrNrrrrr,s"   r,)rdr.) NNNNNNNNNNNN)arrcollections.abcr dataclassesrriortypingrrrr8rSutilsr r r r r rrrrutils.constantsrrrrrr PIL.Imager0 PIL.ImageOpsr1 Resamplingrtorchvision.transformsrr NEAREST_EXACTBOXrHAMMINGBICUBICLANCZOSpil_torch_interpolation_mappingitemstorch_pil_interpolation_mappingr7 get_loggerr#rrTrR ImageInputr r)rrrAnnotationTyper4r5r?r@rIrYr[r]boolrcrirtrwryrZrrrrrr&rrrrrrrrrrr#r+r,rrrrs6  ,         (  (  '  "'&    ""$+     .a