import copy
import logging

import numpy as np
import torch

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 BoxMode
from PIL import Image

__all__ = ["SWINTSDatasetMapper"]


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 = []
    tfm_gens.append(T.RandomBrightness(0.5,2))
    tfm_gens.append(T.RandomContrast(0.5,2))
    tfm_gens.append(T.RandomSaturation(0.5,2))
    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

@torch.no_grad()
class SWINTSDatasetMapper:
    """
    A callable which takes a dataset dict in Detectron2 Dataset format,
    and map it into a format used by SparseRCNN.

    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
    """

    def __init__(self, cfg, is_train=True):
        if cfg.INPUT.CROP.ENABLED and is_train:
            self.crop_gen = [
                #T.ResizeShortestEdge([400, 500, 600], sample_style="choice"),
                #T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE),
                T.RandomCropWithInstance(
                    cfg.INPUT.CROP.TYPE,
                    cfg.INPUT.CROP.SIZE,
                    cfg.INPUT.CROP.CROP_INSTANCE
                    )
            ]
            self.rotate_gen = [
                    T.RandomRotation(angle=[-90,90],sample_style="range")
                    ]
        else:
            self.crop_gen = None
        self.tfm_gens = build_transform_gen(cfg, is_train)
        logging.getLogger(__name__).info(
            "Full TransformGens used in training: {}, crop: {}".format(str(self.tfm_gens), str(self.crop_gen))
        )

        self.img_format = cfg.INPUT.FORMAT
        self.is_train = is_train

    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)

        boxes = np.asarray(
            [
                BoxMode.convert(
                    instance["bbox"], instance["bbox_mode"], BoxMode.XYXY_ABS
                )
                for instance in dataset_dict["annotations"]
            ]
        )
        augmentation = []
        if self.crop_gen is None:
            image, transforms = T.apply_transform_gens(self.tfm_gens, image)
        else:
            if np.random.rand() > 0.5:
                augmentation = self.tfm_gens[:-1] + self.crop_gen + self.tfm_gens[-1:]
            else:
                augmentation = self.tfm_gens
            if np.random.rand() > 0.5:
                augmentation = augmentation[:-1] + self.rotate_gen + augmentation[-1:]
            aug_input = T.StandardAugInput(image, boxes=boxes)
            transforms = aug_input.apply_augmentations(augmentation)
            image = aug_input.image
        
        image_shape = image.shape[:2]  # h, w
        # print(image_shape)

        # 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 "annotations" in dataset_dict:
            # USER: Modify this if you want to keep them for some reason.
            for anno in dataset_dict["annotations"]:
                # anno.pop("segmentation", None)
                anno.pop("keypoints", None)

            # USER: Implement additional transformations if you have other types of data
            annos = [
                utils.transform_instance_annotations(obj, transforms, image_shape)
                for obj in dataset_dict.pop("annotations")
                if obj.get("iscrowd", 0) == 0
            ]
            instances = utils.annotations_to_instances(annos, image_shape)
            dataset_dict["instances"] = utils.filter_empty_instances(instances)
        return dataset_dict