# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/d2/detr/dataset_mapper.py
import copy
import logging

import numpy as np
import torch

from detectron2.config import configurable
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as T
from detectron2.data.transforms import TransformGen
from detectron2.structures import BitMasks, Instances

__all__ = ["DETRPanopticDatasetMapper"]


def build_transform_gen(cfg, is_train):
    """
    Create a list of :class:`TransformGen` from config.
    Returns:
        list[TransformGen]
    """
    if is_train:
        min_size = cfg.INPUT.MIN_SIZE_TRAIN
        max_size = cfg.INPUT.MAX_SIZE_TRAIN
        sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING
    else:
        min_size = cfg.INPUT.MIN_SIZE_TEST
        max_size = cfg.INPUT.MAX_SIZE_TEST
        sample_style = "choice"
    if sample_style == "range":
        assert len(min_size) == 2, "more than 2 ({}) min_size(s) are provided for ranges".format(
            len(min_size)
        )

    logger = logging.getLogger(__name__)
    tfm_gens = []
    if is_train:
        tfm_gens.append(T.RandomFlip())
    tfm_gens.append(T.ResizeShortestEdge(min_size, max_size, sample_style))
    if is_train:
        logger.info("TransformGens used in training: " + str(tfm_gens))
    return tfm_gens


# This is specifically designed for the COCO dataset.
class DETRPanopticDatasetMapper:
    """
    A callable which takes a dataset dict in Detectron2 Dataset format,
    and map it into a format used by MaskFormer.

    This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.

    The callable currently does the following:

    1. Read the image from "file_name"
    2. Applies geometric transforms to the image and annotation
    3. Find and applies suitable cropping to the image and annotation
    4. Prepare image and annotation to Tensors
    """

    @configurable
    def __init__(
        self,
        is_train=True,
        *,
        crop_gen,
        tfm_gens,
        image_format,
    ):
        """
        NOTE: this interface is experimental.
        Args:
            is_train: for training or inference
            augmentations: a list of augmentations or deterministic transforms to apply
            crop_gen: crop augmentation
            tfm_gens: data augmentation
            image_format: an image format supported by :func:`detection_utils.read_image`.
        """
        self.crop_gen = crop_gen
        self.tfm_gens = tfm_gens
        logging.getLogger(__name__).info(
            "[DETRPanopticDatasetMapper] Full TransformGens used in training: {}, crop: {}".format(
                str(self.tfm_gens), str(self.crop_gen)
            )
        )

        self.img_format = image_format
        self.is_train = is_train

    @classmethod
    def from_config(cls, cfg, is_train=True):
        # Build augmentation
        if cfg.INPUT.CROP.ENABLED and is_train:
            crop_gen = [
                T.ResizeShortestEdge([400, 500, 600], sample_style="choice"),
                T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE),
            ]
        else:
            crop_gen = None

        tfm_gens = build_transform_gen(cfg, is_train)

        ret = {
            "is_train": is_train,
            "crop_gen": crop_gen,
            "tfm_gens": tfm_gens,
            "image_format": cfg.INPUT.FORMAT,
        }
        return ret

    def __call__(self, dataset_dict):
        """
        Args:
            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.

        Returns:
            dict: a format that builtin models in detectron2 accept
        """
        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
        utils.check_image_size(dataset_dict, image)

        if self.crop_gen is None:
            image, transforms = T.apply_transform_gens(self.tfm_gens, image)
        else:
            if np.random.rand() > 0.5:
                image, transforms = T.apply_transform_gens(self.tfm_gens, image)
            else:
                image, transforms = T.apply_transform_gens(
                    self.tfm_gens[:-1] + self.crop_gen + self.tfm_gens[-1:], image
                )

        image_shape = image.shape[:2]  # h, w

        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
        # Therefore it's important to use torch.Tensor.
        dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))

        if not self.is_train:
            # USER: Modify this if you want to keep them for some reason.
            dataset_dict.pop("annotations", None)
            return dataset_dict

        if "pan_seg_file_name" in dataset_dict:
            pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB")
            segments_info = dataset_dict["segments_info"]

            # apply the same transformation to panoptic segmentation
            pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)

            from panopticapi.utils import rgb2id

            pan_seg_gt = rgb2id(pan_seg_gt)

            instances = Instances(image_shape)
            classes = []
            masks = []
            for segment_info in segments_info:
                class_id = segment_info["category_id"]
                if not segment_info["iscrowd"]:
                    classes.append(class_id)
                    masks.append(pan_seg_gt == segment_info["id"])

            classes = np.array(classes)
            instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
            if len(masks) == 0:
                # Some image does not have annotation (all ignored)
                instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
            else:
                masks = BitMasks(
                    torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
                )
                instances.gt_masks = masks.tensor

            dataset_dict["instances"] = instances

        return dataset_dict