codingutf8 2023 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 vivit from typing import dict list optional union import numpy as np from transformers utils import isvisionavailable from transformers utils generic import tensortype from imageprocessingutils import baseimageprocessor batchfeature getsizedict from imagetransforms import getresizeoutputimagesize rescale resize tochanneldimensionformat from imageutils import imagenetstandardmean imagenetstandardstd channeldimension imageinput pilimageresampling inferchanneldimensionformat isscaledimage isvalidimage tonumpyarray validimages from utils import logging if isvisionavailable import pil logger logging getloggername def makebatchedvideos listlistimageinput if isinstancevideos list tuple and isinstancevideos0 list tuple and isvalidimagevideos00 return videos elif isinstancevideos list tuple and isvalidimagevideos0 return videos elif isvalidimagevideos return videos raise valueerrorfcould not make batched video from videos class vivitimageprocessorbaseimageprocessor r constructs a vivit 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 the doresize parameter in the preprocess method size dictstr int optional defaults to shortestedge 256 size of the output image after resizing the shortest edge of the image will be resized to sizeshortestedge while maintaining the aspect ratio of the original image can be overriden by size in the preprocess method resample pilimageresampling optional defaults to resampling bilinear resampling filter to use if resizing the image can be overridden by the resample parameter in the preprocess method docentercrop bool optional defaults to true whether to center crop the image to the specified cropsize can be overridden by the docentercrop parameter in the preprocess method cropsize dictstr int optional defaults to height 224 width 224 size of the image after applying the center crop can be overridden by the cropsize parameter in the preprocess method dorescale bool optional defaults to true whether to rescale the image by the specified scale rescalefactor can be overridden by the dorescale parameter in the preprocess method rescalefactor int or float optional defaults to 1127 5 defines the scale factor to use if rescaling the image can be overridden by the rescalefactor parameter in the preprocess method offset bool optional defaults to true whether to scale the image in both negative and positive directions can be overriden by the offset in the preprocess method donormalize bool optional defaults to true whether to normalize the image can be overridden by the donormalize parameter in the preprocess method imagemean float or listfloat optional defaults to imagenetstandardmean 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 imagenetstandardstd 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 resize an image args image np ndarray image to resize size dictstr int size of the output image if size is of the form height h width w the output image will have the size h w if size is of the form shortestedge s the output image will have its shortest edge of length s while keeping the aspect ratio of the original image resample pilimageresampling optional defaults to pilimageresampling bilinear 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 str or channeldimension optional the channel dimension format of the input image if not provided it will be inferred copied from transformers models efficientnet imageprocessingefficientnet efficientnetimageprocessor rescale rescale an image by a scale factor if offset is true the image has its values rescaled by scale and then offset by 1 if scale is 1127 5 the image is rescaled between 1 1 image image scale 1 if offset is false and scale is 1255 the image is rescaled between 0 1 image image scale args image np ndarray image to rescale scale int or float scale to apply to the image offset bool optional whether to scale the image in both negative and positive directions 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 preprocesses a single image if doresize and size is none or resample is none raise valueerrorsize and resample must be specified if doresize is true if docentercrop and cropsize is none raise valueerrorcrop size must be specified if docentercrop is true if dorescale and rescalefactor is none raise valueerrorrescale factor must be specified if dorescale is true if donormalize and imagemean is none or imagestd is none raise valueerrorimage mean and std must be specified if donormalize is true if offset and not dorescale raise valueerrorfor offset dorescale must also be set to true all transformations expect numpy arrays image tonumpyarrayimage if isscaledimageimage and dorescale logger warningonce it looks like you are trying to rescale already rescaled images if the input images have pixel values between 0 and 1 set dorescalefalse to avoid rescaling them again if inputdataformat is none inputdataformat inferchanneldimensionformatimage if doresize image self resizeimageimage sizesize resampleresample inputdataformatinputdataformat if docentercrop image self centercropimage sizecropsize inputdataformatinputdataformat if dorescale image self rescaleimageimage scalerescalefactor offsetoffset inputdataformatinputdataformat if donormalize image self normalizeimageimage meanimagemean stdimagestd inputdataformatinputdataformat image tochanneldimensionformatimage dataformat inputchanneldiminputdataformat return image def preprocess self videos imageinput doresize bool none size dictstr int none resample pilimageresampling none docentercrop bool none cropsize dictstr int none dorescale bool none rescalefactor float none offset bool none donormalize bool none imagemean optionalunionfloat listfloat none imagestd optionalunionfloat listfloat none returntensors optionalunionstr tensortype none dataformat channeldimension channeldimension first inputdataformat optionalunionstr channeldimension none kwargs pil image image doresize doresize if doresize is not none else self doresize resample resample if resample is not none else self resample docentercrop docentercrop if docentercrop is not none else self docentercrop dorescale dorescale if dorescale is not none else self dorescale rescalefactor rescalefactor if rescalefactor is not none else self rescalefactor offset offset if offset is not none else self offset donormalize donormalize if donormalize is not none else self donormalize imagemean imagemean if imagemean is not none else self imagemean imagestd imagestd if imagestd is not none else self imagestd size size if size is not none else self size size getsizedictsize defaulttosquarefalse cropsize cropsize if cropsize is not none else self cropsize cropsize getsizedictcropsize paramnamecropsize if not validimagesvideos raise valueerror invalid image type must be of type pil image image numpy ndarray torch tensor tf tensor or jax ndarray videos makebatchedvideos videos self preprocessimage imageimg doresizedoresize sizesize resampleresample docentercropdocentercrop cropsizecropsize dorescaledorescale rescalefactorrescalefactor offsetoffset donormalizedonormalize imagemeanimagemean imagestdimagestd dataformatdataformat inputdataformatinputdataformat for img in video for video in videos data pixelvalues videos return batchfeaturedatadata tensortypereturntensors coding utf 8 2023 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 vivit constructs a vivit 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 the do_resize parameter in the preprocess method size dict str int optional defaults to shortest_edge 256 size of the output image after resizing the shortest edge of the image will be resized to size shortest_edge while maintaining the aspect ratio of the original image can be overriden by size in the preprocess method resample pilimageresampling optional defaults to resampling bilinear resampling filter to use if resizing the image can be overridden by the resample parameter 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 the do_center_crop parameter in the preprocess method crop_size dict str int optional defaults to height 224 width 224 size of the image after applying the center crop can be overridden by the crop_size parameter 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 the do_rescale parameter in the preprocess method rescale_factor int or float optional defaults to 1 127 5 defines the scale factor to use if rescaling the image can be overridden by the rescale_factor parameter in the preprocess method offset bool optional defaults to true whether to scale the image in both negative and positive directions can be overriden by the offset in the preprocess method do_normalize bool optional defaults to true whether to normalize the image can be overridden by the do_normalize parameter in the preprocess method image_mean float or list float optional defaults to imagenet_standard_mean 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 imagenet_standard_std 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 resize an image args image np ndarray image to resize size dict str int size of the output image if size is of the form height h width w the output image will have the size h w if size is of the form shortest_edge s the output image will have its shortest edge of length s while keeping the aspect ratio of the original image resample pilimageresampling optional defaults to pilimageresampling bilinear 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 str or channeldimension optional the channel dimension format of the input image if not provided it will be inferred copied from transformers models efficientnet image_processing_efficientnet efficientnetimageprocessor rescale rescale an image by a scale factor if offset is true the image has its values rescaled by scale and then offset by 1 if scale is 1 127 5 the image is rescaled between 1 1 image image scale 1 if offset is false and scale is 1 255 the image is rescaled between 0 1 image image scale args image np ndarray image to rescale scale int or float scale to apply to the image offset bool optional whether to scale the image in both negative and positive directions 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 preprocesses a single image all transformations expect numpy arrays preprocess an image or batch of images args videos imageinput video frames to preprocess expects a single or batch of video frames with pixel values ranging from 0 to 255 if passing in frames 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 applying resize resample pilimageresampling 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_centre_crop whether to centre crop the image crop_size dict str int optional defaults to self crop_size size of the image after applying the centre crop do_rescale bool optional defaults to self do_rescale whether to rescale the image values between 1 1 if offset is true 0 1 otherwise rescale_factor float optional defaults to self rescale_factor rescale factor to rescale the image by if do_rescale is set to true offset bool optional defaults to self offset whether to scale the image in both negative and positive directions 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 image_std float or list float optional defaults to self image_std image standard deviation 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 channeldimension first image in num_channels height width format channeldimension last image in height width num_channels format unset use the inferred 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
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    infer_channel_dimension_format,
    is_scaled_image,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
    import PIL

logger = logging.get_logger(__name__)


def make_batched(videos) -> List[List[ImageInput]]:
    if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]):
        return videos

    elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
        return [videos]

    elif is_valid_image(videos):
        return [[videos]]

    raise ValueError(f"Could not make batched video from {videos}")


class VivitImageProcessor(BaseImageProcessor):
    r

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        do_center_crop: bool = True,
        crop_size: Dict[str, int] = None,
        do_rescale: bool = True,
        rescale_factor: Union[int, float] = 1 / 127.5,
        offset: bool = True,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        size = size if size is not None else {"shortest_edge": 256}
        size = get_size_dict(size, default_to_square=False)
        crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
        crop_size = get_size_dict(crop_size, param_name="crop_size")

        self.do_resize = do_resize
        self.size = size
        self.do_center_crop = do_center_crop
        self.crop_size = crop_size
        self.resample = resample
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.offset = offset
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD

    def resize(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        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=False)
        if "shortest_edge" in size:
            output_size = get_resize_output_image_size(
                image, size["shortest_edge"], default_to_square=False, input_data_format=input_data_format
            )
        elif "height" in size and "width" in size:
            output_size = (size["height"], size["width"])
        else:
            raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
        return resize(
            image,
            size=output_size,
            resample=resample,
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )

    
    def rescale(
        self,
        image: np.ndarray,
        scale: Union[int, float],
        offset: bool = True,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ):
        
        rescaled_image = rescale(
            image, scale=scale, data_format=data_format, input_data_format=input_data_format, **kwargs
        )

        if offset:
            rescaled_image = rescaled_image - 1

        return rescaled_image

    def _preprocess_image(
        self,
        image: ImageInput,
        do_resize: bool = None,
        size: Dict[str, int] = None,
        resample: PILImageResampling = None,
        do_center_crop: bool = None,
        crop_size: Dict[str, int] = None,
        do_rescale: bool = None,
        rescale_factor: float = None,
        offset: bool = None,
        do_normalize: bool = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ) -> np.ndarray:
        
        if do_resize and size is None or resample is None:
            raise ValueError("Size and resample 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 offset and not do_rescale:
            raise ValueError("For offset, do_rescale must also be set to True.")

        
        image = to_numpy_array(image)

        if is_scaled_image(image) 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(image)

        if do_resize:
            image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)

        if do_center_crop:
            image = self.center_crop(image, size=crop_size, input_data_format=input_data_format)

        if do_rescale:
            image = self.rescale(image=image, scale=rescale_factor, offset=offset, input_data_format=input_data_format)

        if do_normalize:
            image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)

        image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
        return image

    def preprocess(
        self,
        videos: ImageInput,
        do_resize: bool = None,
        size: Dict[str, int] = None,
        resample: PILImageResampling = None,
        do_center_crop: bool = None,
        crop_size: Dict[str, int] = None,
        do_rescale: bool = None,
        rescale_factor: float = None,
        offset: bool = None,
        do_normalize: bool = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: 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
        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
        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
        offset = offset if offset is not None else self.offset
        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

        size = size if size is not None else self.size
        size = get_size_dict(size, default_to_square=False)
        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")

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

        videos = make_batched(videos)

        videos = [
            [
                self._preprocess_image(
                    image=img,
                    do_resize=do_resize,
                    size=size,
                    resample=resample,
                    do_center_crop=do_center_crop,
                    crop_size=crop_size,
                    do_rescale=do_rescale,
                    rescale_factor=rescale_factor,
                    offset=offset,
                    do_normalize=do_normalize,
                    image_mean=image_mean,
                    image_std=image_std,
                    data_format=data_format,
                    input_data_format=input_data_format,
                )
                for img in video
            ]
            for video in videos
        ]

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