codingutf8 2022 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license image processor class for clip from typing import dict list optional union import numpy as np from imageprocessingutils import baseimageprocessor batchfeature getsizedict from imagetransforms import converttorgb getresizeoutputimagesize resize tochanneldimensionformat from imageutils import openaiclipmean openaiclipstd channeldimension imageinput pilimageresampling inferchanneldimensionformat isscaledimage makelistofimages tonumpyarray validimages from utils import tensortype isvisionavailable logging logger logging getloggername if isvisionavailable import pil class clipimageprocessorbaseimageprocessor r constructs a clip image processor args doresize bool optional defaults to true whether to resize the image s height width dimensions to the specified size can be overridden by doresize in the preprocess method size dictstr int optional defaults to shortestedge 224 size of the image after resizing the shortest edge of the image is resized to sizeshortestedge with the longest edge resized to keep the input aspect ratio can be overridden by size in the preprocess method resample pilimageresampling optional defaults to resampling bicubic resampling filter to use if resizing the image can be overridden by resample in the preprocess method docentercrop bool optional defaults to true whether to center crop the image to the specified cropsize can be overridden by docentercrop in the preprocess method cropsize dictstr int optional defaults to 224 size of the output image after applying centercrop can be overridden by cropsize in the preprocess method dorescale bool optional defaults to true whether to rescale the image by the specified scale rescalefactor can be overridden by dorescale in the preprocess method rescalefactor int or float optional defaults to 1255 scale factor to use if rescaling the image can be overridden by rescalefactor in the preprocess method donormalize bool optional defaults to true whether to normalize the image can be overridden by donormalize in the preprocess method imagemean float or listfloat optional defaults to 0 48145466 0 4578275 0 40821073 mean to use if normalizing the image this is a float or list of floats the length of the number of channels in the image can be overridden by the imagemean parameter in the preprocess method imagestd float or listfloat optional defaults to 0 26862954 0 26130258 0 27577711 standard deviation to use if normalizing the image this is a float or list of floats the length of the number of channels in the image can be overridden by the imagestd parameter in the preprocess method can be overridden by the imagestd parameter in the preprocess method doconvertrgb bool optional defaults to true whether to convert the image to rgb usesquaresize bool optional defaults to false the value to be passed to getsizedict as defaulttosquare when computing the image size if the size argument in getsizedict is an int it determines whether to default to a square image or not note that this attribute is not used in computing cropsize via calling getsizedict resize an image the shortest edge of the image is resized to sizeshortestedge with the longest edge resized to keep the input aspect ratio args image np ndarray image to resize size dictstr int size of the output image resample pilimageresampling optional defaults to pilimageresampling bicubic resampling filter to use when resiizing the image dataformat str or channeldimension optional the channel dimension format of the image if not provided it will be the same as the input image inputdataformat channeldimension or str optional the channel dimension format of the input image if not provided it will be inferred preprocess an image or batch of images args images imageinput image to preprocess expects a single or batch of images with pixel values ranging from 0 to 255 if passing in images with pixel values between 0 and 1 set dorescalefalse doresize bool optional defaults to self doresize whether to resize the image size dictstr int optional defaults to self size size of the image after resizing shortest edge of the image is resized to sizeshortestedge with the longest edge resized to keep the input aspect ratio resample int optional defaults to self resample resampling filter to use if resizing the image this can be one of the enum pilimageresampling only has an effect if doresize is set to true docentercrop bool optional defaults to self docentercrop whether to center crop the image cropsize dictstr int optional defaults to self cropsize size of the center crop only has an effect if docentercrop is set to true dorescale bool optional defaults to self dorescale whether to rescale the image rescalefactor float optional defaults to self rescalefactor rescale factor to rescale the image by if dorescale is set to true donormalize bool optional defaults to self donormalize whether to normalize the image imagemean float or listfloat optional defaults to self imagemean image mean to use for normalization only has an effect if donormalize is set to true imagestd float or listfloat optional defaults to self imagestd image standard deviation to use for normalization only has an effect if donormalize is set to true doconvertrgb bool optional defaults to self doconvertrgb whether to convert the image to rgb returntensors str or tensortype optional the type of tensors to return can be one of unset return a list of np ndarray tensortype tensorflow or tf return a batch of type tf tensor tensortype pytorch or pt return a batch of type torch tensor tensortype numpy or np return a batch of type np ndarray tensortype jax or jax return a batch of type jax numpy ndarray dataformat channeldimension or str optional defaults to channeldimension first the channel dimension format for the output image can be one of channelsfirst or channeldimension first image in numchannels height width format channelslast or channeldimension last image in height width numchannels format unset use the channel dimension format of the input image inputdataformat channeldimension or str optional the channel dimension format for the input image if unset the channel dimension format is inferred from the input image can be one of channelsfirst or channeldimension first image in numchannels height width format channelslast or channeldimension last image in height width numchannels format none or channeldimension none image in height width format pil rgba images are converted to rgb all transformations expect numpy arrays we assume that all images have the same channel dimension format coding utf 8 2022 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license image processor class for clip constructs a clip image processor args do_resize bool optional defaults to true whether to resize the image s height width dimensions to the specified size can be overridden by do_resize in the preprocess method size dict str int optional defaults to shortest_edge 224 size of the image after resizing the shortest edge of the image is resized to size shortest_edge with the longest edge resized to keep the input aspect ratio can be overridden by size in the preprocess method resample pilimageresampling optional defaults to resampling bicubic resampling filter to use if resizing the image can be overridden by resample in the preprocess method do_center_crop bool optional defaults to true whether to center crop the image to the specified crop_size can be overridden by do_center_crop in the preprocess method crop_size dict str int optional defaults to 224 size of the output image after applying center_crop can be overridden by crop_size in the preprocess method do_rescale bool optional defaults to true whether to rescale the image by the specified scale rescale_factor can be overridden by do_rescale in the preprocess method rescale_factor int or float optional defaults to 1 255 scale factor to use if rescaling the image can be overridden by rescale_factor in the preprocess method do_normalize bool optional defaults to true whether to normalize the image can be overridden by do_normalize in the preprocess method image_mean float or list float optional defaults to 0 48145466 0 4578275 0 40821073 mean to use if normalizing the image this is a float or list of floats the length of the number of channels in the image can be overridden by the image_mean parameter in the preprocess method image_std float or list float optional defaults to 0 26862954 0 26130258 0 27577711 standard deviation to use if normalizing the image this is a float or list of floats the length of the number of channels in the image can be overridden by the image_std parameter in the preprocess method can be overridden by the image_std parameter in the preprocess method do_convert_rgb bool optional defaults to true whether to convert the image to rgb use_square_size bool optional defaults to false the value to be passed to get_size_dict as default_to_square when computing the image size if the size argument in get_size_dict is an int it determines whether to default to a square image or not note that this attribute is not used in computing crop_size via calling get_size_dict resize an image the shortest edge of the image is resized to size shortest_edge with the longest edge resized to keep the input aspect ratio args image np ndarray image to resize size dict str int size of the output image resample pilimageresampling optional defaults to pilimageresampling bicubic resampling filter to use when resiizing the image data_format str or channeldimension optional the channel dimension format of the image if not provided it will be the same as the input image input_data_format channeldimension or str optional the channel dimension format of the input image if not provided it will be inferred preprocess an image or batch of images args images imageinput image to preprocess expects a single or batch of images with pixel values ranging from 0 to 255 if passing in images with pixel values between 0 and 1 set do_rescale false do_resize bool optional defaults to self do_resize whether to resize the image size dict str int optional defaults to self size size of the image after resizing shortest edge of the image is resized to size shortest_edge with the longest edge resized to keep the input aspect ratio resample int optional defaults to self resample resampling filter to use if resizing the image this can be one of the enum pilimageresampling only has an effect if do_resize is set to true do_center_crop bool optional defaults to self do_center_crop whether to center crop the image crop_size dict str int optional defaults to self crop_size size of the center crop only has an effect if do_center_crop is set to true do_rescale bool optional defaults to self do_rescale whether to rescale the image rescale_factor float optional defaults to self rescale_factor rescale factor to rescale the image by if do_rescale is set to true do_normalize bool optional defaults to self do_normalize whether to normalize the image image_mean float or list float optional defaults to self image_mean image mean to use for normalization only has an effect if do_normalize is set to true image_std float or list float optional defaults to self image_std image standard deviation to use for normalization only has an effect if do_normalize is set to true do_convert_rgb bool optional defaults to self do_convert_rgb whether to convert the image to rgb return_tensors str or tensortype optional the type of tensors to return can be one of unset return a list of np ndarray tensortype tensorflow or tf return a batch of type tf tensor tensortype pytorch or pt return a batch of type torch tensor tensortype numpy or np return a batch of type np ndarray tensortype jax or jax return a batch of type jax numpy ndarray data_format channeldimension or str optional defaults to channeldimension first the channel dimension format for the output image can be one of channels_first or channeldimension first image in num_channels height width format channels_last or channeldimension last image in height width num_channels format unset use the channel dimension format of the input image input_data_format channeldimension or str optional the channel dimension format for the input image if unset the channel dimension format is inferred from the input image can be one of channels_first or channeldimension first image in num_channels height width format channels_last or channeldimension last image in height width num_channels format none or channeldimension none image in height width format pil rgba images are converted to rgb all transformations expect numpy arrays we assume that all images have the same channel dimension format
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    convert_to_rgb,
    get_resize_output_image_size,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    infer_channel_dimension_format,
    is_scaled_image,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


logger = logging.get_logger(__name__)


if is_vision_available():
    import PIL


class CLIPImageProcessor(BaseImageProcessor):
    r

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        resample: PILImageResampling = PILImageResampling.BICUBIC,
        do_center_crop: bool = True,
        crop_size: Dict[str, int] = None,
        do_rescale: bool = True,
        rescale_factor: Union[int, float] = 1 / 255,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_convert_rgb: bool = True,
        use_square_size: bool = False,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        size = size if size is not None else {"shortest_edge": 224}
        size = get_size_dict(size, default_to_square=use_square_size)
        crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
        crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size")

        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.do_center_crop = do_center_crop
        self.crop_size = crop_size
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
        self.do_convert_rgb = do_convert_rgb
        self.use_square_size = use_square_size

    def resize(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        resample: PILImageResampling = PILImageResampling.BICUBIC,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        
        size = get_size_dict(size, default_to_square=self.use_square_size)
        if "shortest_edge" not in size:
            raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
        output_size = get_resize_output_image_size(
            image,
            size=size["shortest_edge"],
            default_to_square=self.use_square_size,
            input_data_format=input_data_format,
        )
        return resize(
            image,
            size=output_size,
            resample=resample,
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )

    def preprocess(
        self,
        images: ImageInput,
        do_resize: bool = None,
        size: Dict[str, int] = None,
        resample: PILImageResampling = None,
        do_center_crop: bool = None,
        crop_size: int = None,
        do_rescale: bool = None,
        rescale_factor: float = None,
        do_normalize: bool = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_convert_rgb: bool = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> PIL.Image.Image:
        
        do_resize = do_resize if do_resize is not None else self.do_resize
        size = size if size is not None else self.size
        size = get_size_dict(size, param_name="size", default_to_square=self.use_square_size)
        resample = resample if resample is not None else self.resample
        do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
        crop_size = crop_size if crop_size is not None else self.crop_size
        crop_size = get_size_dict(crop_size, param_name="crop_size", default_to_square=True)
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
        image_mean = image_mean if image_mean is not None else self.image_mean
        image_std = image_std if image_std is not None else self.image_std
        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        images = make_list_of_images(images)

        if not valid_images(images):
            raise ValueError(
                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "torch.Tensor, tf.Tensor or jax.ndarray."
            )

        if do_resize and size is None:
            raise ValueError("Size must be specified if do_resize is True.")

        if do_center_crop and crop_size is None:
            raise ValueError("Crop size must be specified if do_center_crop is True.")

        if do_rescale and rescale_factor is None:
            raise ValueError("Rescale factor must be specified if do_rescale is True.")

        if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("Image mean and std must be specified if do_normalize is True.")

        
        if do_convert_rgb:
            images = [convert_to_rgb(image) for image in images]

        
        images = [to_numpy_array(image) for image in images]

        if is_scaled_image(images[0]) and do_rescale:
            logger.warning_once(
                "It looks like you are trying to rescale already rescaled images. If the input"
                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
            )

        if input_data_format is None:
            
            input_data_format = infer_channel_dimension_format(images[0])

        if do_resize:
            images = [
                self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
                for image in images
            ]

        if do_center_crop:
            images = [
                self.center_crop(image=image, size=crop_size, input_data_format=input_data_format) for image in images
            ]

        if do_rescale:
            images = [
                self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
                for image in images
            ]

        if do_normalize:
            images = [
                self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
                for image in images
            ]

        images = [
            to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
        ]

        data = {"pixel_values": images}
        return BatchFeature(data=data, tensor_type=return_tensors)