import os
from collections import OrderedDict

import json_tricks as json
import numpy as np

from mmpose.core.evaluation.top_down_eval import (keypoint_auc, keypoint_epe,
                                                  keypoint_pck_accuracy)
from ...registry import DATASETS
from .animal_base_dataset import AnimalBaseDataset


@DATASETS.register_module()
class AnimalZebraDataset(AnimalBaseDataset):
    """AnimalZebraDataset for animal pose estimation.

    `DeepPoseKit, a software toolkit for fast and robust animal
     pose estimation using deep learning'
    Elife'2019. More details can be found in the `paper.

    The dataset loads raw features and apply specified transforms
    to return a dict containing the image tensors and other information.

    Desert Locust keypoint indexes::

        0: "snout",
        1: "head",
        2: "neck",
        3: "forelegL1",
        4: "forelegR1",
        5: "hindlegL1",
        6: "hindlegR1",
        7: "tailbase",
        8: "tailtip"

    Args:
        ann_file (str): Path to the annotation file.
        img_prefix (str): Path to a directory where images are held.
            Default: None.
        data_cfg (dict): config
        pipeline (list[dict | callable]): A sequence of data transforms.
        test_mode (bool): Store True when building test or
            validation dataset. Default: False.
    """

    def __init__(self,
                 ann_file,
                 img_prefix,
                 data_cfg,
                 pipeline,
                 test_mode=False):

        super().__init__(
            ann_file, img_prefix, data_cfg, pipeline, test_mode=test_mode)

        self.ann_info['use_different_joint_weights'] = False
        assert self.ann_info['num_joints'] == 9
        self.ann_info['joint_weights'] = \
            np.ones((self.ann_info['num_joints'], 1), dtype=np.float32)

        self.ann_info['flip_pairs'] = [[3, 4], [5, 6]]

        self.dataset_name = 'zebra'
        self.db = self._get_db()

        print(f'=> num_images: {self.num_images}')
        print(f'=> load {len(self.db)} samples')

    def _get_db(self):
        """Load dataset."""
        gt_db = []
        bbox_id = 0
        num_joints = self.ann_info['num_joints']
        for img_id in self.img_ids:

            ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=False)
            objs = self.coco.loadAnns(ann_ids)

            for obj in objs:
                if max(obj['keypoints']) == 0:
                    continue
                joints_3d = np.zeros((num_joints, 3), dtype=np.float32)
                joints_3d_visible = np.zeros((num_joints, 3), dtype=np.float32)

                keypoints = np.array(obj['keypoints']).reshape(-1, 3)
                joints_3d[:, :2] = keypoints[:, :2]
                joints_3d_visible[:, :2] = np.minimum(1, keypoints[:, 2:3])

                # the ori image is 160x160
                center, scale = self._xywh2cs(0, 0, 160, 160, 0.8)

                image_file = os.path.join(self.img_prefix,
                                          self.id2name[img_id])

                gt_db.append({
                    'image_file': image_file,
                    'center': center,
                    'scale': scale,
                    'rotation': 0,
                    'joints_3d': joints_3d,
                    'joints_3d_visible': joints_3d_visible,
                    'dataset': self.dataset_name,
                    'bbox': obj['bbox'],
                    'bbox_score': 1,
                    'bbox_id': bbox_id
                })
                bbox_id = bbox_id + 1
        gt_db = sorted(gt_db, key=lambda x: x['bbox_id'])

        return gt_db

    def _report_metric(self, res_file, metrics, pck_thr=0.2, auc_nor=30):
        """Keypoint evaluation.

        Args:
            res_file (str): Json file stored prediction results.
            metrics (str | list[str]): Metric to be performed.
                Options: 'PCK', 'PCKh', 'AUC', 'EPE'.
            pck_thr (float): PCK threshold, default as 0.2.
            pckh_thr (float): PCKh threshold, default as 0.7.
            auc_nor (float): AUC normalization factor, default as 30 pixel.

        Returns:
            List: Evaluation results for evaluation metric.
        """
        info_str = []

        with open(res_file, 'r') as fin:
            preds = json.load(fin)
        assert len(preds) == len(self.db)

        outputs = []
        gts = []
        masks = []
        threshold_bbox = []

        for pred, item in zip(preds, self.db):
            outputs.append(np.array(pred['keypoints'])[:, :-1])
            gts.append(np.array(item['joints_3d'])[:, :-1])
            masks.append((np.array(item['joints_3d_visible'])[:, 0]) > 0)
            if 'PCK' in metrics:
                bbox = np.array(item['bbox'])
                bbox_thr = np.max(bbox[2:])
                threshold_bbox.append(np.array([bbox_thr, bbox_thr]))

        outputs = np.array(outputs)
        gts = np.array(gts)
        masks = np.array(masks)
        threshold_bbox = np.array(threshold_bbox)

        if 'PCK' in metrics:
            _, pck, _ = keypoint_pck_accuracy(outputs, gts, masks, pck_thr,
                                              threshold_bbox)
            info_str.append(('PCK', pck))

        if 'AUC' in metrics:
            info_str.append(('AUC', keypoint_auc(outputs, gts, masks,
                                                 auc_nor)))

        if 'EPE' in metrics:
            info_str.append(('EPE', keypoint_epe(outputs, gts, masks)))

        return info_str

    def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
        """Evaluate Fly keypoint results. The pose prediction results will be
        saved in `${res_folder}/result_keypoints.json`.

        Note:
            batch_size: N
            num_keypoints: K
            heatmap height: H
            heatmap width: W

        Args:
            outputs (list(preds, boxes, image_path, output_heatmap))
                :preds (np.ndarray[N,K,3]): The first two dimensions are
                    coordinates, score is the third dimension of the array.
                :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
                    , scale[1],area, score]
                :image_paths (list[str]): For example, ['Test/source/0.jpg']
                :output_heatmap (np.ndarray[N, K, H, W]): model outpus.

            res_folder (str): Path of directory to save the results.
            metric (str | list[str]): Metric to be performed.
                Options: 'PCK', 'AUC', 'EPE'.

        Returns:
            dict: Evaluation results for evaluation metric.
        """
        metrics = metric if isinstance(metric, list) else [metric]
        allowed_metrics = ['PCK', 'AUC', 'EPE']
        for metric in metrics:
            if metric not in allowed_metrics:
                raise KeyError(f'metric {metric} is not supported')

        res_file = os.path.join(res_folder, 'result_keypoints.json')

        kpts = []
        for output in outputs:
            preds = output['preds']
            boxes = output['boxes']
            image_paths = output['image_paths']
            bbox_ids = output['bbox_ids']

            batch_size = len(image_paths)
            for i in range(batch_size):
                image_id = self.name2id[image_paths[i][len(self.img_prefix):]]

                kpts.append({
                    'keypoints': preds[i].tolist(),
                    'center': boxes[i][0:2].tolist(),
                    'scale': boxes[i][2:4].tolist(),
                    'area': float(boxes[i][4]),
                    'score': float(boxes[i][5]),
                    'image_id': image_id,
                    'bbox_id': bbox_ids[i]
                })
        kpts = self._sort_and_unique_bboxes(kpts)

        self._write_keypoint_results(kpts, res_file)
        info_str = self._report_metric(res_file, metrics)
        name_value = OrderedDict(info_str)

        return name_value
