o eCO @sdZddlmZddlZddlmZddlmZmZddl Z ddl Z ddl Z ddl mZddlmZmZddlmZdd l mZdd lmZmZdd lmZmZdd lmZdd lmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%ddl&m'Z'ede(eGddde!e ee$e#e%eee' ZdS)zgr.Image() component.) annotationsN)Path)AnyLiteral)utils)documentset_documentation_group)ImgSerializable)Image)processing_utilsr) IOComponent _Keywords)warn_style_method_deprecation) Changeable ClearableEditableEventListenerMethod Selectable Streamable Uploadable)TokenInterpretable componentc@seZdZdZ daddddddddddddddddddddddd d dd dbd5d6Zd7d8Zeejddddddddddddddfdcd:d;Z ddd@dAZ dedEdFZ dfdIdJZ dgdhdMdNZ dOdPZdQdRZdSdTZ dadidVdWZdddXdjdYdZZd[d\Zdkd_d`ZdS)lr ab Creates an image component that can be used to upload/draw images (as an input) or display images (as an output). Preprocessing: passes the uploaded image as a {numpy.array}, {PIL.Image} or {str} filepath depending on `type` -- unless `tool` is `sketch` AND source is one of `upload` or `webcam`. In these cases, a {dict} with keys `image` and `mask` is passed, and the format of the corresponding values depends on `type`. Postprocessing: expects a {numpy.array}, {PIL.Image} or {str} or {pathlib.Path} filepath to an image and displays the image. Examples-format: a {str} local filepath or URL to an image. Demos: image_mod, image_mod_default_image Guides: image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers, building-a-pictionary_app, create-your-own-friends-with-a-gan NRGBFuploadnumpyTz#000000gffffff?)shapeheightwidth image_mode invert_colorssourcetooltypelabelevery show_labelshow_download_button containerscale min_width interactivevisible streamingelem_id elem_classes mirror_webcam brush_radius brush_color mask_opacityshow_share_buttonvalue&str | _Image.Image | np.ndarray | Nonertuple[int, int] | Noner int | NonerrNLiteral['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F']r boolr!%Literal['upload', 'webcam', 'canvas']r"   zImage.__init__cCsL|j|j|j|j|j|j|j|j|j|j |j |j |j |j |jdt|S)N)rrrrr!r"r5r-r0r1r2r3 selectabler4r')rrrrr!r"r5r-r0r1r2r3rQr4r'r get_configrMrOrOrPrRs$zImage.get_config(Any | Literal[_Keywords.NO_VALUE] | NonecCsdid|d|d|d|d|d|d|d|d | d | d |d | d | d| d|ddS)Nrrr$r&r'r(r)r*r+r,r5r1r2r3r4Z__type__updaterO)r5rrr$r&r'r(r)r*r+r,r1r2r3r4rOrOrPrUsB     z Image.updateim_Image.Image | Nonereturn&np.ndarray | _Image.Image | str | NonecCsz|dur|S|j}|jdkr|S|jdkrt|S|jdkr2|j||j|p'dd}|j||Stdt |jd) z3Helper method to format an image based on self.typeNrErrFZpng)dirformatzUnknown type: z1. Please choose from: 'numpy', 'pil', 'filepath'.) r[r#nparrayZpil_to_temp_fileZDEFAULT_TEMP_DIRZ temp_filesaddrKrC)rMrVfmtpathrOrOrP _format_images(      zImage._format_imagexstr | dict[str, str]-np.ndarray | _Image.Image | str | dict | NonecCsX|dur|Sd}|jdkr"|jdvr"t|tsJ|d|d}}t|ts)Jt|}tt d| |j }Wdn1sHwY|j durYt ||j }|jrbtj|}|jdkrw|jd urw|jd krwtj|}|jdkr|jdvrt|}|jd kr|d  d }td|||g}||||dS||S)z Parameters: x: base64 url data, or (if tool == "sketch") a dict of image and mask base64 url data Returns: image in requested format, or (if tool == "sketch") a dict of image and mask in requested format NrI)rrGimagemaskignorerGTz color-sketchZRGBAALr)rfrg)r"r! isinstancedictrCr decode_base64_to_imagewarningscatch_warnings simplefilterconvertrrresize_and_cropr PILZImageOpsinvertr0ZmirrormodeZ getchannel_Imagemergera)rMrbrgrVZmask_imZ alpha_datarOrOrP preprocesss:             zImage.preprocessy-np.ndarray | _Image.Image | str | Path | NonecCsX|durdSt|tjrt|St|tjrt|St|tt fr(t |St d)z Parameters: y: image as a numpy array, PIL Image, string/Path filepath, or string URL Returns: base64 url data Nz%Cannot process this value as an Image) rkr\Zndarrayr encode_array_to_base64rvr Zencode_pil_to_base64rCr client_utilsZencode_url_or_file_to_base64rK)rMryrOrOrP postprocess.s      zImage.postprocesssegmentscCs ||_|S)ai Calculates interpretation score of image subsections by splitting the image into subsections, then using a "leave one out" method to calculate the score of each subsection by whiting out the subsection and measuring the delta of the output value. Parameters: segments: Number of interpretation segments to split image into. )interpretation_segments)rMrrOrOrPset_interpret_parametersBszImage.set_interpret_parametersc Cst|}|jdurt||j}t|}zddlm}Wntt fy1}zt d|d}~wwz|||j dddd}W||fSt yV|||j ddd}Y||fSw) z Helper method that segments an image into superpixels using slic. Parameters: x: base64 representation of an image Nr)slicz]Error: running this interpretation for images requires scikit-image, please install it first. ) compactnesssigmaZ start_label)rr) r rmrrrr\r]Zskimage.segmentationr ImportErrorModuleNotFoundErrorrKr TypeError)rMrbresized_and_cropped_imagererr segments_slicrOrOrP_segment_by_slicKs@    zImage._segment_by_slicc Cs||\}}ggg}}}tj|dd}t|D].}||k} t|} || ||k<|t| t|} d| ||k<|| || q|||fS)a Segments image into tokens, masks, and leave-one-out-tokens Parameters: x: base64 representation of an image Returns: tokens: list of tokens, used by the get_masked_input() method leave_one_out_tokens: list of left-out tokens, used by the get_interpretation_neighbors() method masks: list of masks, used by the get_interpretation_neighbors() method )rr)Zaxisr)rr\meanuniquecopyappendr r{) rMrbrrtokensmasksZleave_one_out_tokensZ replace_colorZ segment_valuergZ image_screentokenrOrOrPtokenizels        zImage.tokenizecCsXg}|D]%}tj|dtd}t||D] \}}||t|}q|t|q|S)Nr)Zdtype)r\Z zeros_likerAziprr r{)rMrZbinary_mask_matrixZ masked_inputsZbinary_mask_vectorZ masked_inputrbrOrOrPget_masked_inputsszImage.get_masked_inputslist[list[float]]c Kst|}|jdurt||j}t|}t|jd|jdf}t||D] \}} ||| 7}q(t|t |} } | dkrJ|| | | }| S)zs Returns: A 2D array representing the interpretation score of each pixel of the image. Nrr) r rmrrrr\r]Zzerosrmaxmintolist) rMrbZ neighborsZscoresrrrNZ output_scoresZscorergZmax_valZmin_valrOrOrPget_interpretation_scoress   zImage.get_interpretation_scores)rrcKs&t|dur ||_|dur||_|S)zc This method is deprecated. Please set these arguments in the constructor instead. N)rrr)rMrrrNrOrOrPstyles z Image.stylecCs|jdkr tddS)NrGrJ)r!rKrSrOrOrPcheck_streamables zImage.check_streamable input_datastr | Path | NonecCs6|durdSt|}|jst|r|Stt|S)Nre)rCZroot_urlr|Zis_http_url_likerabspath)rMrrOrOrP as_examples zImage.as_example)N)4r5r6rr7rr8rr8rr9r r:r!r;r"r<r#r=r$r>r%r?r&r@r'r:r(r:r)r8r*rAr+r@r,r:r-r:r.r>r/rBr0r:r1r?r2rCr3rDr4r@)r5rTrr8rr8r$r>r&r@r'r@r(r@r)r8r*r8r+r@r,r@r1r?r2r>r3r?r4r@)rVrWrXrY)rbrcrXrd)ryrzrXr>)r~)rrA)rXr)rr8rr8)rrrXrC)__name__ __module__ __qualname____doc__rLrR staticmethodr ZNO_VALUErUrarxr}rrrrrrrrrOrOrOrPr #sx  w $  .  !  r ))r __future__rrnpathlibrtypingrrrr\rsZ PIL.ImageOpsZ gradio_clientrr|Zgradio_client.documentationrrZgradio_client.serializingr r rvZgradior Zgradio.components.baser r Zgradio.deprecationrZ gradio.eventsrrrrrrrZgradio.interpretationrinitrOrOrOrPs>      $