import contextlib
import copy
import io
import logging
import numpy as np
import os
import os.path as osp
import torch

from cvpods.structures import BoxMode
from cvpods.utils import PathManager, Timer

from ..registry import DATASETS
from ..base_dataset import BaseDataset
from ..detection_utils import (
    annotations_to_instances, check_image_size, create_keypoint_hflip_indices,
    filter_empty_instances, read_image)
from .paths_route import _PREDEFINED_SPLITS_WIDERFACE

"""
This file contains functions to parse COCO-format annotations into dicts in "cvpods format".
"""

logger = logging.getLogger(__name__)


@DATASETS.register()
class WiderFaceDataset(BaseDataset):
    def __init__(self, cfg, dataset_name, transforms=[], is_train=True):
        super(WiderFaceDataset, self).__init__(cfg, dataset_name, transforms, is_train)
        self.dataset_key = "_".join(self.name.split('_')[:-1])
        image_root, json_file = _PREDEFINED_SPLITS_WIDERFACE[self.dataset_key][self.name]
        self.json_file = osp.join(self.data_root, json_file) \
            if "://" not in image_root else osp.join(image_root, json_file)
        self.image_root = osp.join(self.data_root, image_root) \
            if "://" not in image_root else image_root

        self.meta = self._get_metadata()

        self.dataset_dicts = self._load_annotations(
            self.json_file,
            self.image_root,
            self.name,
            extra_annotation_keys=None)

        if is_train:
            self.dataset_dicts = self._filter_annotations()
            self._set_group_flag()

        self.eval_with_gt = cfg.TEST.get("WITH_GT", False)

        # fmt: off
        self.data_format = cfg.INPUT.FORMAT
        self.mask_on = cfg.MODEL.MASK_ON
        self.mask_format = cfg.INPUT.MASK_FORMAT
        self.keypoint_on = cfg.MODEL.KEYPOINT_ON
        self.load_proposals = cfg.MODEL.LOAD_PROPOSALS
        # fmt: on

        if self.keypoint_on:
            # Flip only makes sense in training
            self.keypoint_hflip_indices = create_keypoint_hflip_indices(
                cfg.DATASETS.TRAIN)
        else:
            self.keypoint_hflip_indices = None

    def __getitem__(self, index):
        """Load data, apply transforms, converto to Instances.
        """
        dataset_dict = copy.deepcopy(self.dataset_dicts[index])

        # read image
        image = read_image(dataset_dict["file_name"], format=self.data_format)
        check_image_size(dataset_dict, image)

        if "annotations" in dataset_dict:
            annotations = dataset_dict.pop("annotations")
            annotations = [
                ann for ann in annotations if ann.get("iscrowd", 0) == 0]
        else:
            annotations = None

        # apply transfrom
        image, annotations = self._apply_transforms(
            image, annotations)

        if annotations is not None:
            image_shape = image.shape[:2]  # h, w

            instances = annotations_to_instances(
                annotations, image_shape, mask_format=self.mask_format
            )

            # # Create a tight bounding box from masks, useful when image is cropped
            # if self.crop_gen and instances.has("gt_masks"):
            #     instances.gt_boxes = instances.gt_masks.get_bounding_boxes()

            dataset_dict["instances"] = filter_empty_instances(instances)

        # convert to Instance type
        # 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.
        # h, w, c -> c, h, w
        dataset_dict["image"] = torch.as_tensor(
            np.ascontiguousarray(image.transpose(2, 0, 1)))

        return dataset_dict

    def __reset__(self):
        raise NotImplementedError

    def __len__(self):
        return len(self.dataset_dicts)

    def _load_annotations(
        self, json_file, image_root,
        dataset_name=None, extra_annotation_keys=None
    ):
        """
        Load a json file with WiderFace's instances annotation format.
        Currently supports instance detection, instance segmentation,
        and person keypoints annotations.

        Args:
            json_file (str): full path to the json file in WiderFace instances annotation format.
            image_root (str): the directory where the images in this json file exists.
            dataset_name (str): the name of the datasets (e.g., widerface_2019_train).
                If provided, this function will also put "thing_classes" into
                the metadata associated with this datasets.
            extra_annotation_keys (list[str]): list of per-annotation keys that should also be
                loaded into the datasets dict (besides "iscrowd", "bbox", "keypoints",
                "category_id", "segmentation"). The values for these keys will be returned as-is.
                For example, the densepose annotations are loaded in this way.

        Returns:
            list[dict]: a list of dicts in cvpods standard format. (See
            `Using Custom Datasets </tutorials/datasets.html>`_ )

        Notes:
            1. This function does not read the image files.
               The results do not have the "image" field.
        """
        from pycocotools.coco import COCO

        timer = Timer()
        json_file = PathManager.get_local_path(json_file)
        with contextlib.redirect_stdout(io.StringIO()):
            coco_api = COCO(json_file)
        if timer.seconds() > 1:
            logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))

        id_map = None
        if dataset_name is not None:
            meta = self.meta
            cat_ids = sorted(coco_api.getCatIds())

            id_map = {v: i for i, v in enumerate(cat_ids)}
            meta["thing_dataset_id_to_contiguous_id"] = id_map

        # sort indices for reproducible results
        img_ids = sorted(list(coco_api.imgs.keys()))
        # imgs is a list of dicts, each looks something like:
        # {'license': 4,
        #  'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
        #  'file_name': 'COCO_val2014_000000001268.jpg',
        #  'height': 427,
        #  'width': 640,
        #  'date_captured': '2013-11-17 05:57:24',
        #  'id': 1268}
        imgs = coco_api.loadImgs(img_ids)
        # anns is a list[list[dict]], where each dict is an annotation
        # record for an object. The inner list enumerates the objects in an image
        # and the outer list enumerates over images. Example of anns[0]:
        # [{'segmentation': [[192.81,
        #     247.09,
        #     ...
        #     219.03,
        #     249.06]],
        #   'area': 1035.749,
        #   'iscrowd': 0,
        #   'image_id': 1268,
        #   'bbox': [192.81, 224.8, 74.73, 33.43],
        #   'category_id': 16,
        #   'id': 42986},
        #  ...]
        anns = [coco_api.imgToAnns[img_id] for img_id in img_ids]

        if "minival" not in json_file:
            # The popular valminusminival & minival annotations for COCO2014 contain this bug.
            # However the ratio of buggy annotations there is tiny and does not affect accuracy.
            # Therefore we explicitly white-list them.
            ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
            assert len(set(ann_ids)) == len(ann_ids), \
                "Annotation ids in '{}' are not unique!".format(json_file)

        imgs_anns = list(zip(imgs, anns))

        logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file))

        dataset_dicts = []

        ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + (extra_annotation_keys or [])

        for (img_dict, anno_dict_list) in imgs_anns:
            record = {}
            record["file_name"] = os.path.join(image_root, img_dict["file_name"])
            record["height"] = img_dict["height"]
            record["width"] = img_dict["width"]
            image_id = record["image_id"] = img_dict["id"]

            objs = []
            for anno in anno_dict_list:
                # Check that the image_id in this annotation is the same as
                # the image_id we're looking at.
                # This fails only when the data parsing logic or the annotation file is buggy.

                # The original COCO valminusminival2014 & minival2014 annotation files
                # actually contains bugs that, together with certain ways of using COCO API,
                # can trigger this assertion.
                assert anno["image_id"] == image_id

                # ensure the width and height of bbox are greater than 0
                if anno["bbox"][2] <= 0 or anno["bbox"][3] <= 0:
                    continue

                if anno.get("ignore", 0) != 0:
                    continue

                obj = {key: anno[key] for key in ann_keys if key in anno}

                obj["bbox_mode"] = BoxMode.XYWH_ABS
                if id_map:
                    obj["category_id"] = id_map[obj["category_id"]]
                objs.append(obj)
            record["annotations"] = objs
            dataset_dicts.append(record)
        return dataset_dicts

    def _get_metadata(self):
        meta = {}
        meta["image_root"] = self.image_root
        meta["json_file"] = self.json_file
        meta["evaluator_type"] = _PREDEFINED_SPLITS_WIDERFACE["evaluator_type"][self.dataset_key]
        meta["thing_classes"] = ['face']

        return meta

    def evaluate(self, predictions):
        """Dataset must provide a evaluation function to evaluate model."""
        raise NotImplementedError

    @property
    def ground_truth_annotations(self):
        return self.dataset_dicts
