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 convnext from typing import dict list optional union import numpy as np from imageprocessingutils import baseimageprocessor batchfeature getsizedict from imagetransforms import centercrop getresizeoutputimagesize resize tochanneldimensionformat from imageutils import imagenetstandardmean imagenetstandardstd channeldimension imageinput pilimageresampling inferchanneldimensionformat isscaledimage makelistofimages tonumpyarray validimages from utils import tensortype isvisionavailable logging if isvisionavailable import pil logger logging getloggername class convnextimageprocessorbaseimageprocessor r constructs a convnext image processor args doresize bool optional defaults to true controls whether to resize the image s height width dimensions to the specified size can be overriden by doresize in the preprocess method size dictstr int optional defaults to shortestedge 384 resolution of the output image after resize is applied if sizeshortestedge 384 the image is resized to sizeshortestedge sizeshortestedge otherwise the smaller edge of the image will be matched to intsizeshortestedgecroppct after which the image is cropped to sizeshortestedge sizeshortestedge only has an effect if doresize is set to true can be overriden by size in the preprocess method croppct float optional defaults to 224 256 percentage of the image to crop only has an effect if doresize is true and size 384 can be overriden by croppct in the preprocess method resample pilimageresampling optional defaults to resampling bilinear resampling filter to use if resizing the image can be overriden by resample in the preprocess method dorescale bool optional defaults to true whether to rescale the image by the specified scale rescalefactor can be overriden by dorescale in the preprocess method rescalefactor int or float optional defaults to 1255 scale factor to use if rescaling the image can be overriden by rescalefactor 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 default value set here for backwards compatibility where the value in config is none resize an image args image np ndarray image to resize size dictstr int dictionary of the form shortestedge int specifying the size of the output image if sizeshortestedge 384 image is resized to sizeshortestedge sizeshortestedge otherwise the smaller edge of the image will be matched to intsizeshortestedge croppct after which the image is cropped to sizeshortestedge sizeshortestedge croppct float percentage of the image to crop only has an effect if size 384 resample pilimageresampling optional defaults to pilimageresampling bicubic resampling filter to use when resizing 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 from the input image maintain same ratio resizing shortest edge to shortestedgecroppct then crop to shortestedge shortestedge warping no cropping when evaluated at 384 or larger 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 output image after resize has been applied if sizeshortestedge 384 the image is resized to sizeshortestedge sizeshortestedge otherwise the smaller edge of the image will be matched to intsizeshortestedge croppct after which the image is cropped to sizeshortestedge sizeshortestedge only has an effect if doresize is set to true croppct float optional defaults to self croppct percentage of the image to crop if size 384 resample int optional defaults to self resample resampling filter to use if resizing the image this can be one of pilimageresampling filters only has an effect if doresize is set to true dorescale bool optional defaults to self dorescale whether to rescale the image values between 0 1 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 imagestd float or listfloat optional defaults to self imagestd image standard deviation 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 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 convnext constructs a convnext image processor args do_resize bool optional defaults to true controls whether to resize the image s height width dimensions to the specified size can be overriden by do_resize in the preprocess method size dict str int optional defaults to shortest_edge 384 resolution of the output image after resize is applied if size shortest_edge 384 the image is resized to size shortest_edge size shortest_edge otherwise the smaller edge of the image will be matched to int size shortest_edge crop_pct after which the image is cropped to size shortest_edge size shortest_edge only has an effect if do_resize is set to true can be overriden by size in the preprocess method crop_pct float optional defaults to 224 256 percentage of the image to crop only has an effect if do_resize is true and size 384 can be overriden by crop_pct in the preprocess method resample pilimageresampling optional defaults to resampling bilinear resampling filter to use if resizing the image can be overriden by resample in the preprocess method do_rescale bool optional defaults to true whether to rescale the image by the specified scale rescale_factor can be overriden 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 overriden by rescale_factor 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 default value set here for backwards compatibility where the value in config is none resize an image args image np ndarray image to resize size dict str int dictionary of the form shortest_edge int specifying the size of the output image if size shortest_edge 384 image is resized to size shortest_edge size shortest_edge otherwise the smaller edge of the image will be matched to int size shortest_edge crop_pct after which the image is cropped to size shortest_edge size shortest_edge crop_pct float percentage of the image to crop only has an effect if size 384 resample pilimageresampling optional defaults to pilimageresampling bicubic resampling filter to use when resizing 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 from the input image maintain same ratio resizing shortest edge to shortest_edge crop_pct then crop to shortest_edge shortest_edge warping no cropping when evaluated at 384 or larger 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 output image after resize has been applied if size shortest_edge 384 the image is resized to size shortest_edge size shortest_edge otherwise the smaller edge of the image will be matched to int size shortest_edge crop_pct after which the image is cropped to size shortest_edge size shortest_edge only has an effect if do_resize is set to true crop_pct float optional defaults to self crop_pct percentage of the image to crop if size 384 resample int optional defaults to self resample resampling filter to use if resizing the image this can be one of pilimageresampling filters only has an effect if do_resize is set to true 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 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 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 (
    center_crop,
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


logger = logging.get_logger(__name__)


class ConvNextImageProcessor(BaseImageProcessor):
    r

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        crop_pct: float = None,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        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,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        size = size if size is not None else {"shortest_edge": 384}
        size = get_size_dict(size, default_to_square=False)

        self.do_resize = do_resize
        self.size = size
        
        self.crop_pct = crop_pct if crop_pct is not None else 224 / 256
        self.resample = resample
        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 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],
        crop_pct: float,
        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=False)
        if "shortest_edge" not in size:
            raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}")
        shortest_edge = size["shortest_edge"]

        if shortest_edge < 384:
            
            resize_shortest_edge = int(shortest_edge / crop_pct)
            resize_size = get_resize_output_image_size(
                image, size=resize_shortest_edge, default_to_square=False, input_data_format=input_data_format
            )
            image = resize(
                image=image,
                size=resize_size,
                resample=resample,
                data_format=data_format,
                input_data_format=input_data_format,
                **kwargs,
            )
            
            return center_crop(
                image=image,
                size=(shortest_edge, shortest_edge),
                data_format=data_format,
                input_data_format=input_data_format,
                **kwargs,
            )
        else:
            
            return resize(
                image,
                size=(shortest_edge, shortest_edge),
                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,
        crop_pct: float = None,
        resample: PILImageResampling = 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,
        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
        crop_pct = crop_pct if crop_pct is not None else self.crop_pct
        resample = resample if resample is not None else self.resample
        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)

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

        if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
            raise ValueError("crop_pct must be specified if size < 384.")

        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.")

        
        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, crop_pct=crop_pct, resample=resample, 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)