# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# 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,
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"""Image processor class for SuperPoint."""

from typing import TYPE_CHECKING, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    ImageType,
    PILImageResampling,
    get_image_type,
    infer_channel_dimension_format,
    is_pil_image,
    is_scaled_image,
    is_torch_available,
    is_valid_image,
    is_vision_available,
    to_numpy_array,
    valid_images,
    validate_preprocess_arguments,
)
from ...processing_utils import ImagesKwargs
from ...utils import TensorType, logging, requires_backends
from ...utils.import_utils import requires


if is_torch_available():
    import torch

if TYPE_CHECKING:
    from .modeling_superglue import KeypointMatchingOutput

if is_vision_available():
    import PIL
    from PIL import Image, ImageDraw

logger = logging.get_logger(__name__)


# Copied from transformers.models.superpoint.image_processing_superpoint.is_grayscale
def is_grayscale(
    image: np.ndarray,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
    if input_data_format == ChannelDimension.FIRST:
        if image.shape[0] == 1:
            return True
        return np.all(image[0, ...] == image[1, ...]) and np.all(image[1, ...] == image[2, ...])
    elif input_data_format == ChannelDimension.LAST:
        if image.shape[-1] == 1:
            return True
        return np.all(image[..., 0] == image[..., 1]) and np.all(image[..., 1] == image[..., 2])


# Copied from transformers.models.superpoint.image_processing_superpoint.convert_to_grayscale
def convert_to_grayscale(
    image: ImageInput,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> ImageInput:
    """
    Converts an image to grayscale format using the NTSC formula. Only support numpy and PIL Image.

    This function is supposed to return a 1-channel image, but it returns a 3-channel image with the same value in each
    channel, because of an issue that is discussed in :
    https://github.com/huggingface/transformers/pull/25786#issuecomment-1730176446

    Args:
        image (Image):
            The image to convert.
        input_data_format (`ChannelDimension` or `str`, *optional*):
            The channel dimension format for the input image.
    """
    requires_backends(convert_to_grayscale, ["vision"])

    if isinstance(image, np.ndarray):
        if is_grayscale(image, input_data_format=input_data_format):
            return image
        if input_data_format == ChannelDimension.FIRST:
            gray_image = image[0, ...] * 0.2989 + image[1, ...] * 0.5870 + image[2, ...] * 0.1140
            gray_image = np.stack([gray_image] * 3, axis=0)
        elif input_data_format == ChannelDimension.LAST:
            gray_image = image[..., 0] * 0.2989 + image[..., 1] * 0.5870 + image[..., 2] * 0.1140
            gray_image = np.stack([gray_image] * 3, axis=-1)
        return gray_image

    if not isinstance(image, PIL.Image.Image):
        return image

    image = image.convert("L")
    return image


def validate_and_format_image_pairs(images: ImageInput):
    error_message = (
        "Input images must be a one of the following :",
        " - A pair of PIL images.",
        " - A pair of 3D arrays.",
        " - A list of pairs of PIL images.",
        " - A list of pairs of 3D arrays.",
    )

    def _is_valid_image(image):
        """images is a PIL Image or a 3D array."""
        return is_pil_image(image) or (
            is_valid_image(image) and get_image_type(image) != ImageType.PIL and len(image.shape) == 3
        )

    if isinstance(images, list):
        if len(images) == 2 and all((_is_valid_image(image)) for image in images):
            return images
        if all(
            isinstance(image_pair, list)
            and len(image_pair) == 2
            and all(_is_valid_image(image) for image in image_pair)
            for image_pair in images
        ):
            return [image for image_pair in images for image in image_pair]
    raise ValueError(error_message)


class SuperGlueImageProcessorKwargs(ImagesKwargs, total=False):
    r"""
    do_grayscale (`bool`, *optional*, defaults to `True`):
        Whether to convert the image to grayscale. Can be overridden by `do_grayscale` in the `preprocess` method.
    """

    do_grayscale: bool


@requires(backends=("torch",))
class SuperGlueImageProcessor(BaseImageProcessor):
    r"""
    Constructs a SuperGlue 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 overridden
            by `do_resize` in the `preprocess` method.
        size (`dict[str, int]` *optional*, defaults to `{"height": 480, "width": 640}`):
            Resolution of the output image after `resize` is applied. Only has an effect if `do_resize` is set to
            `True`. Can be overridden 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 `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 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_grayscale (`bool`, *optional*, defaults to `True`):
            Whether to convert the image to grayscale. Can be overridden by `do_grayscale` in the `preprocess` method.
    """

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        do_resize: bool = True,
        size: Optional[dict[str, int]] = None,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        do_rescale: bool = True,
        rescale_factor: float = 1 / 255,
        do_grayscale: bool = True,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        size = size if size is not None else {"height": 480, "width": 640}
        size = get_size_dict(size, default_to_square=False)

        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_grayscale = do_grayscale

    # Copied from transformers.models.superpoint.image_processing_superpoint.SuperPointImageProcessor.resize
    def resize(
        self,
        image: np.ndarray,
        size: dict[str, int],
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ):
        """
        Resize an image.

        Args:
            image (`np.ndarray`):
                Image to resize.
            size (`dict[str, int]`):
                Dictionary of the form `{"height": int, "width": int}`, specifying the size of the output image.
            data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format of the output image. If not provided, it will be 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.
            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.
        """
        size = get_size_dict(size, default_to_square=False)

        return resize(
            image,
            size=(size["height"], size["width"]),
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )

    def preprocess(
        self,
        images,
        do_resize: Optional[bool] = None,
        size: Optional[dict[str, int]] = None,
        resample: Optional[PILImageResampling] = None,
        do_rescale: Optional[bool] = None,
        rescale_factor: Optional[float] = None,
        do_grayscale: Optional[bool] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: ChannelDimension = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> BatchFeature:
        """
        Preprocess an image or batch of images.

        Args:
            images (`ImageInput`):
                Image pairs to preprocess. Expects either a list of 2 images or a list of list of 2 images list 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`.
            resample (`PILImageResampling`, *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_grayscale (`bool`, *optional*, defaults to `self.do_grayscale`):
                Whether to convert the image to grayscale.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                    - Unset: Return a list of `np.ndarray`.
                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.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.
        """

        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_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_grayscale = do_grayscale if do_grayscale is not None else self.do_grayscale

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

        # Validate and convert the input images into a flattened list of images for all subsequent processing steps.
        images = validate_and_format_image_pairs(images)

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

        validate_preprocess_arguments(
            do_resize=do_resize,
            size=size,
            resample=resample,
            do_rescale=do_rescale,
            rescale_factor=rescale_factor,
        )

        # All transformations expect numpy arrays.
        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:
            # We assume that all images have the same channel dimension format.
            input_data_format = infer_channel_dimension_format(images[0])

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

            image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
            all_images.append(image)

        # Convert back the flattened list of images into a list of pairs of images.
        image_pairs = [all_images[i : i + 2] for i in range(0, len(all_images), 2)]

        data = {"pixel_values": image_pairs}

        return BatchFeature(data=data, tensor_type=return_tensors)

    def post_process_keypoint_matching(
        self,
        outputs: "KeypointMatchingOutput",
        target_sizes: Union[TensorType, list[tuple]],
        threshold: float = 0.0,
    ) -> list[dict[str, torch.Tensor]]:
        """
        Converts the raw output of [`KeypointMatchingOutput`] into lists of keypoints, scores and descriptors
        with coordinates absolute to the original image sizes.
        Args:
            outputs ([`KeypointMatchingOutput`]):
                Raw outputs of the model.
            target_sizes (`torch.Tensor` or `list[tuple[tuple[int, int]]]`, *optional*):
                Tensor of shape `(batch_size, 2, 2)` or list of tuples of tuples (`tuple[int, int]`) containing the
                target size `(height, width)` of each image in the batch. This must be the original image size (before
                any processing).
            threshold (`float`, *optional*, defaults to 0.0):
                Threshold to filter out the matches with low scores.
        Returns:
            `list[Dict]`: A list of dictionaries, each dictionary containing the keypoints in the first and second image
            of the pair, the matching scores and the matching indices.
        """
        if outputs.mask.shape[0] != len(target_sizes):
            raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the mask")
        if not all(len(target_size) == 2 for target_size in target_sizes):
            raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")

        if isinstance(target_sizes, list):
            image_pair_sizes = torch.tensor(target_sizes, device=outputs.mask.device)
        else:
            if target_sizes.shape[1] != 2 or target_sizes.shape[2] != 2:
                raise ValueError(
                    "Each element of target_sizes must contain the size (h, w) of each image of the batch"
                )
            image_pair_sizes = target_sizes

        keypoints = outputs.keypoints.clone()
        keypoints = keypoints * image_pair_sizes.flip(-1).reshape(-1, 2, 1, 2)
        keypoints = keypoints.to(torch.int32)

        results = []
        for mask_pair, keypoints_pair, matches, scores in zip(
            outputs.mask, keypoints, outputs.matches[:, 0], outputs.matching_scores[:, 0]
        ):
            mask0 = mask_pair[0] > 0
            mask1 = mask_pair[1] > 0
            keypoints0 = keypoints_pair[0][mask0]
            keypoints1 = keypoints_pair[1][mask1]
            matches0 = matches[mask0]
            scores0 = scores[mask0]

            # Filter out matches with low scores
            valid_matches = torch.logical_and(scores0 > threshold, matches0 > -1)

            matched_keypoints0 = keypoints0[valid_matches]
            matched_keypoints1 = keypoints1[matches0[valid_matches]]
            matching_scores = scores0[valid_matches]

            results.append(
                {
                    "keypoints0": matched_keypoints0,
                    "keypoints1": matched_keypoints1,
                    "matching_scores": matching_scores,
                }
            )

        return results

    # Copied from transformers.models.efficientloftr.image_processing_efficientloftr.EfficientLoFTRImageProcessor.visualize_keypoint_matching with EfficientLoFTR->SuperGlue
    def visualize_keypoint_matching(
        self,
        images: ImageInput,
        keypoint_matching_output: list[dict[str, torch.Tensor]],
    ) -> list["Image.Image"]:
        """
        Plots the image pairs side by side with the detected keypoints as well as the matching between them.

        Args:
            images (`ImageInput`):
                Image pairs to plot. Same as `SuperGlueImageProcessor.preprocess`. Expects either a list of 2
                images or a list of list of 2 images list with pixel values ranging from 0 to 255.
            keypoint_matching_output (List[Dict[str, torch.Tensor]]]):
                A post processed keypoint matching output

        Returns:
            `List[PIL.Image.Image]`: A list of PIL images, each containing the image pairs side by side with the detected
            keypoints as well as the matching between them.
        """
        images = validate_and_format_image_pairs(images)
        images = [to_numpy_array(image) for image in images]
        image_pairs = [images[i : i + 2] for i in range(0, len(images), 2)]

        results = []
        for image_pair, pair_output in zip(image_pairs, keypoint_matching_output):
            height0, width0 = image_pair[0].shape[:2]
            height1, width1 = image_pair[1].shape[:2]
            plot_image = np.zeros((max(height0, height1), width0 + width1, 3), dtype=np.uint8)
            plot_image[:height0, :width0] = image_pair[0]
            plot_image[:height1, width0:] = image_pair[1]

            plot_image_pil = Image.fromarray(plot_image)
            draw = ImageDraw.Draw(plot_image_pil)

            keypoints0_x, keypoints0_y = pair_output["keypoints0"].unbind(1)
            keypoints1_x, keypoints1_y = pair_output["keypoints1"].unbind(1)
            for keypoint0_x, keypoint0_y, keypoint1_x, keypoint1_y, matching_score in zip(
                keypoints0_x, keypoints0_y, keypoints1_x, keypoints1_y, pair_output["matching_scores"]
            ):
                color = self._get_color(matching_score)
                draw.line(
                    (keypoint0_x, keypoint0_y, keypoint1_x + width0, keypoint1_y),
                    fill=color,
                    width=3,
                )
                draw.ellipse((keypoint0_x - 2, keypoint0_y - 2, keypoint0_x + 2, keypoint0_y + 2), fill="black")
                draw.ellipse(
                    (keypoint1_x + width0 - 2, keypoint1_y - 2, keypoint1_x + width0 + 2, keypoint1_y + 2),
                    fill="black",
                )

            results.append(plot_image_pil)
        return results

    # Copied from transformers.models.efficientloftr.image_processing_efficientloftr.EfficientLoFTRImageProcessor._get_color
    def _get_color(self, score):
        """Maps a score to a color."""
        r = int(255 * (1 - score))
        g = int(255 * score)
        b = 0
        return (r, g, b)


__all__ = ["SuperGlueImageProcessor"]
