h*SrSSKJr SSKrSSKJr SSKJrJrJ r SSK r SSK J r SSKJs Js Jr SSKJrJr SSKJrJr SS KJrJr S /r"S S \5rg) zTransforms for text rendering and augmentation on images. This module provides transforms for adding and manipulating text on images, including text augmentation techniques like word insertion, deletion, and swapping. ) annotationsN)Path) AnnotatedAnyLiteral)AfterValidator) check_bboxesdenormalize_bboxes)check_range_bounds nondecreasing)BaseTransformInitSchemaImageOnlyTransform TextImagec^\rSrSrSr"SS\5rS SU4Sjjjr\SSj5r SSjr SSjr SS jr SS jr SU4S jjrS rU=r$)raApply text rendering transformations on images. This class supports rendering text directly onto images using a variety of configurations, such as custom fonts, font sizes, colors, and augmentation methods. The text can be placed inside specified bounding boxes. Args: font_path (str | Path): Path to the font file to use for rendering text. stopwords (list[str] | None): List of stopwords for text augmentation. augmentations (tuple[str | None, ...]): List of text augmentations to apply. None: text is printed as is "insertion": insert random stop words into the text. "swap": swap random words in the text. "deletion": delete random words from the text. fraction_range (tuple[float, float]): Range for selecting a fraction of bounding boxes to modify. font_size_fraction_range (tuple[float, float]): Range for selecting the font size as a fraction of bounding box height. font_color (tuple[float, ...]): Font color as RGB values (e.g., (0, 0, 0) for black). clear_bg (bool): Whether to clear the background before rendering text. metadata_key (str): Key to access metadata in the parameters. p (float): Probability of applying the transform. Targets: image, volume Image types: uint8, float32 References: doc-augmentation: https://github.com/danaaubakirova/doc-augmentation Examples: >>> import albumentations as A >>> transform = A.Compose([ A.TextImage( font_path=Path("/path/to/font.ttf"), stopwords=("the", "is", "in"), augmentations=("insertion", "deletion"), fraction_range=(0.5, 1.0), font_size_fraction_range=(0.5, 0.9), font_color=(255, 0, 0), # red in RGB metadata_key="text_metadata", p=0.5 ) ]) >>> transformed = transform(image=my_image, text_metadata=my_metadata) >>> image = transformed['image'] # This will render text on `my_image` based on the metadata provided in `my_metadata`. cf\rSrSr%S\S'S\S'S\S'S\S 'S\S 'S \S 'S \S'S\S'Srg)TextImage.InitSchemaL str | Path font_pathtuple[str, ...] stopwordsztuple[str | None, ...] augmentationszgAnnotated[tuple[float, float], AfterValidator(nondecreasing), AfterValidator(check_range_bounds(0, 1))]fraction_rangefont_size_fraction_rangetuple[float, ...] font_colorboolclear_bgstr metadata_keyN)__name__ __module__ __qualname____firstlineno____annotations____static_attributes__r"f/var/www/fran/franai/venv/lib/python3.13/site-packages/albumentations/augmentations/text/transforms.py InitSchemarLs;""--  #  &%r)r+c >[T U]U S9 XlXlX@lX l[ U5UlXPlX`l Xpl g)N)p) super__init__r!rrrlistrrrr) selfrrrrrrrr!r- __class__s r*r/TextImage.__init__^sH 1(","!-0(@%$ r)cUR/$)zGet list of targets that should be passed as parameters to transforms. Returns: list[str]: List containing the metadata key name )r!)r1s r*targets_as_paramsTextImage.targets_as_paramsts!!""r)c6UR5R5Vs/sH oD(dM UPM nn[U5n[S[ X&-55nUS:Xa-[ R "XWURUR5nO[US:Xa"[ R"XWUR5nO3US:Xa"[ R"XWUR5nO [S5e[R"SSU5R5nX:waU$S$s snf) anApply a random text augmentation to the input text. Args: text (str): Original text to augment fraction (float): Fraction of words to modify choice (Literal["insertion", "swap", "deletion"]): Type of augmentation to apply Returns: str: Augmented text or empty string if no change was made Raises: ValueError: If an invalid choice is provided  insertionswapdeletionz?Invalid choice. Choose from 'insertion', 'swap', or 'deletion'.z + )stripsplitlenmaxintftextinsert_random_stopwordsr py_randomswap_random_wordsdelete_random_words ValueErrorresub) r1textfractionchoicewordwords num_wordsnum_words_to_modifyresult_sentences r* random_augTextImage.random_aug~s(#'**,"4"4"6?"6$$"6?J !!S)=%>? [ #;;EX\XfXfhlhvhvwO v #55eRVR`R`aO z !#77TXTbTbcO^_ _&&sO<BBD"1"9ArA@s DDcSSKJn [[R "U/55 [ [R "U/5URSS5SnSUSS5uppX- n URR"UR6n UR[UR5[X-55nUR(a URcUnO;URR!UR5nUcUOUR#USUS 9nUR$nXX4UUUUUS .$![an[S5UeSnAff=f) aPreprocess text metadata for a single bounding box. Args: image (np.ndarray): Input image bbox (tuple[float, float, float, float]): Normalized bounding box coordinates text (str): Text to render in the bounding box bbox_index (int): Index of the bounding box in the original metadata Returns: dict[str, Any]: Processed metadata including font, position, and text information Raises: ImportError: If PIL.ImageFont is not installed r) ImageFontz`ImageFont from PIL is required to use TextImage transform. Install it with `pip install Pillow`.Nc38# UHn[U5v M g7fN)rB).0xs r* 0TextImage.preprocess_metadata..s%L6Kc!ff6Ks?)rM) bbox_coords bbox_index original_textrKfontr)PILrV ImportErrorr nparrayr shaperEuniformrtruetyper rrBrrMrSr)r1imagebboxrKrarVerrdenormalized_bboxx_miny_minx_maxy_max bbox_heightfont_size_fractionrcaugmented_text augmentationrs r*preprocess_metadataTextImage.preprocess_metadatasE,  % RXXtf%&.rxx/?RaQRST%L6G6K%L"em !^^33T5R5RS!!#dnn"5s;M;[7\]!!T%7%7%?!N>>001C1CDL%1%9TttUXam?nN__ "%7$!"$   / r  sD22 E < EE c SU;aUSOUSSnX RnU/:XaS/0$[U[5(aU/nURR"UR 6n[ [U5U-5nURR[[U55U5nUVs/sH!nURX4USXHSU5PM# n nSU 0$s snf)aGenerate parameters based on input data. Args: params (dict[str, Any]): Dictionary of existing parameters data (dict[str, Any]): Dictionary containing input data with image and metadata Returns: dict[str, Any]: Dictionary containing the overlay data for text rendering rkimagesr overlay_datarlrK) r! isinstancedictrErirrBr@samplerangerw) r1paramsdatarkmetadatarLnum_bboxes_to_modifybbox_indices_to_updaterar{s r*get_params_dependent_on_data&TextImage.get_params_dependent_on_datas")DW d8nQ6G))* r>  h % % zH>>))4+>+>?"3x=8#;<!%!6!6uS]7KMa!b5 4   $ $UZ,@,H(J^_eJfhr s4  L   s0(Cc @[R"XURS9$)aApply text rendering to the input image. Args: img (np.ndarray): Input image overlay_data (list[dict[str, Any]]): List of dictionaries containing text rendering information **params (Any): Additional parameters Returns: np.ndarray: Image with rendered text )r)rC render_textr)r1imgr{rs r*applyTextImage.applys"  T]]KKr)c >[TU]"U/UQ70UD6nUSVs/sHnUSUSUSUSUSS.PM snUS'U$s snf)aLApply the transform and include overlay data in the result. Args: params (dict[str, Any]): Parameters for the transform *args (Any): Additional positional arguments **kwargs (Any): Additional keyword arguments Returns: dict[str, Any]: Dictionary containing the transformed data and simplified overlay information r{r`rKrbrar)r`rKrbrar)r.apply_with_params)r1rargskwargsresoverlayr2s r*rTextImage.apply_with_paramssg'@@@".1  2 '}5!(!9%l3%l3  2  N   s!A)rrrrrrr!r))theisinatofrY)?r)g?g?)rrrFtextimage_metadatar_)rrrrrz;tuple[Literal['insertion', 'swap', 'deletion'] | None, ...]rtuple[float, float]rrrrrrr!r r-floatreturnNone)rz list[str])rKr rLrrMz(Literal['insertion', 'swap', 'deletion']rr ) rk np.ndarrayrlz!tuple[float, float, float, float]rKr rarBrdict[str, Any])rrrrrr)rrr{zlist[dict[str, Any]]rrrr)rrrrrrrr)r#r$r%r&__doc__r r+r/propertyr5rSrwrrrr( __classcell__)r2s@r*rrsY1f,*&EU\.88B(10!!#!S ! , ! #6 !&!!! ! !!,##"B"B"B9 "B "BH6 6 06  6  6  6 p$ LL L+L L  L&r))r __future__rrIpathlibrtypingrrrnumpyrfpydanticr,albumentations.augmentations.text.functionalrrK functionalrCalbumentations.core.bbox_utilsr r albumentations.core.pydanticr r (albumentations.core.transforms_interfacer r__all__rr"r)r*rsF # **#<<KJ` -S"Sr)