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 tvlt from typing import dict list optional union import numpy as np from imageprocessingutils import baseimageprocessor batchfeature getsizedict from imagetransforms import getresizeoutputimagesize resize tochanneldimensionformat from imageutils import imagenetstandardmean imagenetstandardstd channeldimension imageinput pilimageresampling inferchanneldimensionformat isscaledimage isvalidimage tonumpyarray validimages from utils import tensortype logging logger logging getloggername def makebatchedvideos listlistimageinput if isinstancevideos list tuple and isinstancevideos0 list tuple return videos elif isinstancevideos list tuple and isvalidimagevideos0 videosdim np arrayvideos0 ndim if videosdim 3 return videos elif videosdim 4 return videos elif isvalidimagevideos videosdim np arrayvideos ndim if videosdim 3 return videos elif videosdim 4 return videos elif videosdim 5 return videos raise valueerrorfcould not make batched video from videos class tvltimageprocessorbaseimageprocessor r constructs a tvlt image processor this processor can be used to prepare either videos or images for the model by converting images to 1frame videos 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 224 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 patchsize listint optional defaults to 16 16 the patch size of image patch embedding numframes int optional defaults to 8 the maximum number of video frames resample pilimageresampling optional defaults to pilimageresampling 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 1255 defines the scale factor to use if rescaling the image can be overridden by the rescalefactor parameter 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 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 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 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 patchsize listint none numframes int none resample pilimageresampling none docentercrop bool none cropsize dictstr int none dorescale bool none rescalefactor float none donormalize bool none imagemean optionalunionfloat listfloat none imagestd optionalunionfloat listfloat none ismixed bool false returntensors optionalunionstr tensortype none dataformat channeldimension channeldimension first inputdataformat optionalunionstr channeldimension none kwargs batchfeature 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 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 patchsize patchsize if patchsize is not none else self patchsize numframes numframes if patchsize is not none else self numframes if not validimagesvideos raise valueerror invalid image or video type must be of type pil image image numpy ndarray torch tensor tf tensor or jax ndarray videos makebatchedvideos check number of frames is fewer than maximum frames for video in videos if lenvideo self numframes raise valueerror fnumber of frames must not be greater than the maximum frames of the model self numframes maxnumframes maxlenvideo for video in videos numpatchesperimage sizeshortestedge patchsize0 2 videomasks np array lenvideo numpatchesperimage 1 maxnumframes lenvideo numpatchesperimage 0 for video in videos videos self preprocessimage imageimg doresizedoresize sizesize resampleresample docentercropdocentercrop cropsizecropsize dorescaledorescale rescalefactorrescalefactor donormalizedonormalize imagemeanimagemean imagestdimagestd dataformatdataformat inputdataformatinputdataformat for img in video for video in videos if videos contain both positivenegative use mixed key for videoaudio matching task if ismixed data pixelvaluesmixed videos pixelmaskmixed videomasks else data pixelvalues videos pixelmask videomasks 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 tvlt constructs a tvlt image processor this processor can be used to prepare either videos or images for the model by converting images to 1 frame videos 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 224 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 patch_size list int optional defaults to 16 16 the patch size of image patch embedding num_frames int optional defaults to 8 the maximum number of video frames resample pilimageresampling optional defaults to pilimageresampling 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 1 255 defines the scale factor to use if rescaling the image can be overridden by the rescale_factor parameter 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 preprocesses a single image all transformations expect numpy arrays preprocess an videos or image or batch of videos or images args videos imageinput images or videos to preprocess expects a single or batch of 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 patch_size list int optional defaults to self patch_size the patch size of image patch embedding num_frames int optional defaults to self num_frames the maximum number of video frames 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 0 1 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 image_std float or list float optional defaults to self image_std image standard deviation is_mixed bool optional if the input video has negative samples 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 returns batchfeature a batchfeature with the following fields pixel_values pixel values to be fed to a model of shape batch_size num_channels height width pixel_mask pixel masks to be fed to a model of shape batch_size num_pixel_patches pixel_values_mixed pixel values with both postive or negative to be fed to a model of shape batch_size num_channels height width pixel_mask_mixed pixel masks with both postive or negative to be fed to a model of shape batch_size num_pixel_patches check number of frames is fewer than maximum frames if videos contain both positive negative use mixed key for video audio matching task
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 (
    get_resize_output_image_size,
    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 TensorType, logging


logger = logging.get_logger(__name__)


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

    elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
        videos_dim = np.array(videos[0]).ndim
        if videos_dim == 3:
            return [videos]
        elif videos_dim == 4:
            return videos

    elif is_valid_image(videos):
        videos_dim = np.array(videos).ndim
        if videos_dim == 3:
            return [[videos]]
        elif videos_dim == 4:
            return [videos]
        elif videos_dim == 5:
            return videos

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


class TvltImageProcessor(BaseImageProcessor):
    r

    model_input_names = [
        "pixel_values",
        "pixel_mask",
        "pixel_values_mixed",
        "pixel_mask_mixed",
    ]

    def __init__(
        self,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        patch_size: List[int] = [16, 16],
        num_frames: int = 8,
        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 / 255,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = IMAGENET_STANDARD_MEAN,
        image_std: Optional[Union[float, List[float]]] = IMAGENET_STANDARD_STD,
        init_mask_generator=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=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.patch_size = patch_size
        self.num_frames = num_frames
        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.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_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 _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,
        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.")

        
        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, 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,
        patch_size: List[int] = None,
        num_frames: int = None,
        resample: PILImageResampling = None,
        do_center_crop: bool = None,
        crop_size: Dict[str, 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,
        is_mixed: bool = False,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: ChannelDimension = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> BatchFeature:
        
        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
        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")
        patch_size = patch_size if patch_size is not None else self.patch_size
        num_frames = num_frames if patch_size is not None else self.num_frames

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

        videos = make_batched(videos)

        
        for video in videos:
            if len(video) > self.num_frames:
                raise ValueError(
                    f"number of frames must not be greater than the maximum frames of the model {self.num_frames}."
                )

        max_num_frames = max([len(video) for video in videos])
        num_patches_per_image = (size["shortest_edge"] // patch_size[0]) ** 2
        video_masks = np.array(
            [
                len(video) * num_patches_per_image * [1] + (max_num_frames - len(video)) * num_patches_per_image * [0]
                for video in 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,
                    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
        ]

        
        if is_mixed:
            data = {"pixel_values_mixed": videos, "pixel_mask_mixed": video_masks}
        else:
            data = {"pixel_values": videos, "pixel_mask": video_masks}

        return BatchFeature(data=data, tensor_type=return_tensors)