import os
import warnings
from collections import OrderedDict, defaultdict

import json_tricks as json
import numpy as np
from xtcocotools.coco import COCO
from xtcocotools.cocoeval import COCOeval

from ....core.post_processing import oks_nms, soft_oks_nms
from ...registry import DATASETS
from .topdown_base_dataset import TopDownBaseDataset


@DATASETS.register_module()
class TopDownCocoDataset(TopDownBaseDataset):
    """CocoDataset dataset for top-down pose estimation.

    `Microsoft COCO: Common Objects in Context' ECCV'2014
    More details can be found in the `paper
    <https://arxiv.org/abs/1405.0312>`__ .

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

    COCO keypoint indexes::

        0: 'nose',
        1: 'left_eye',
        2: 'right_eye',
        3: 'left_ear',
        4: 'right_ear',
        5: 'left_shoulder',
        6: 'right_shoulder',
        7: 'left_elbow',
        8: 'right_elbow',
        9: 'left_wrist',
        10: 'right_wrist',
        11: 'left_hip',
        12: 'right_hip',
        13: 'left_knee',
        14: 'right_knee',
        15: 'left_ankle',
        16: 'right_ankle'

    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.use_gt_bbox = data_cfg['use_gt_bbox']
        self.bbox_file = data_cfg['bbox_file']
        self.det_bbox_thr = data_cfg.get('det_bbox_thr', 0.0)
        if 'image_thr' in data_cfg:
            warnings.warn(
                'image_thr is deprecated, '
                'please use det_bbox_thr instead', DeprecationWarning)
            self.det_bbox_thr = data_cfg['image_thr']
        self.use_nms = data_cfg.get('use_nms', True)
        self.soft_nms = data_cfg['soft_nms']
        self.nms_thr = data_cfg['nms_thr']
        self.oks_thr = data_cfg['oks_thr']
        self.vis_thr = data_cfg['vis_thr']

        self.ann_info['flip_pairs'] = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10],
                                       [11, 12], [13, 14], [15, 16]]

        self.ann_info['upper_body_ids'] = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
        self.ann_info['lower_body_ids'] = (11, 12, 13, 14, 15, 16)

        self.ann_info['use_different_joint_weights'] = False
        self.ann_info['joint_weights'] = np.array(
            [
                1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2,
                1.2, 1.5, 1.5
            ],
            dtype=np.float32).reshape((self.ann_info['num_joints'], 1))

        # 'https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/'
        # 'pycocotools/cocoeval.py#L523'
        self.sigmas = np.array([
            .26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07,
            .87, .87, .89, .89
        ]) / 10.0

        self.coco = COCO(ann_file)

        cats = [
            cat['name'] for cat in self.coco.loadCats(self.coco.getCatIds())
        ]
        self.classes = ['__background__'] + cats
        self.num_classes = len(self.classes)
        self._class_to_ind = dict(zip(self.classes, range(self.num_classes)))
        self._class_to_coco_ind = dict(zip(cats, self.coco.getCatIds()))
        self._coco_ind_to_class_ind = dict(
            (self._class_to_coco_ind[cls], self._class_to_ind[cls])
            for cls in self.classes[1:])
        self.img_ids = self.coco.getImgIds()
        self.num_images = len(self.img_ids)
        self.id2name, self.name2id = self._get_mapping_id_name(self.coco.imgs)
        self.dataset_name = 'coco'

        self.db = self._get_db()

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

    @staticmethod
    def _get_mapping_id_name(imgs):
        """
        Args:
            imgs (dict): dict of image info.

        Returns:
            tuple: Image name & id mapping dicts.

            - id2name (dict): Mapping image id to name.
            - name2id (dict): Mapping image name to id.
        """
        id2name = {}
        name2id = {}
        for image_id, image in imgs.items():
            file_name = image['file_name']
            id2name[image_id] = file_name
            name2id[file_name] = image_id

        return id2name, name2id

    def _get_db(self):
        """Load dataset."""
        if (not self.test_mode) or self.use_gt_bbox:
            # use ground truth bbox
            gt_db = self._load_coco_keypoint_annotations()
        else:
            # use bbox from detection
            gt_db = self._load_coco_person_detection_results()
        return gt_db

    def _load_coco_keypoint_annotations(self):
        """Ground truth bbox and keypoints."""
        gt_db = []
        for img_id in self.img_ids:
            gt_db.extend(self._load_coco_keypoint_annotation_kernel(img_id))
        return gt_db

    def _load_coco_keypoint_annotation_kernel(self, img_id):
        """load annotation from COCOAPI.

        Note:
            bbox:[x1, y1, w, h]
        Args:
            img_id: coco image id
        Returns:
            dict: db entry
        """
        img_ann = self.coco.loadImgs(img_id)[0]
        width = img_ann['width']
        height = img_ann['height']
        num_joints = self.ann_info['num_joints']

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

        # sanitize bboxes
        valid_objs = []
        for obj in objs:
            if 'bbox' not in obj:
                continue
            x, y, w, h = obj['bbox']
            x1 = max(0, x)
            y1 = max(0, y)
            x2 = min(width - 1, x1 + max(0, w - 1))
            y2 = min(height - 1, y1 + max(0, h - 1))
            if ('area' not in obj or obj['area'] > 0) and x2 > x1 and y2 > y1:
                obj['clean_bbox'] = [x1, y1, x2 - x1, y2 - y1]
                valid_objs.append(obj)
        objs = valid_objs

        bbox_id = 0
        rec = []
        for obj in objs:
            if 'keypoints' not in obj:
                continue
            if max(obj['keypoints']) == 0:
                continue
            if 'num_keypoints' in obj and obj['num_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])

            center, scale = self._xywh2cs(*obj['clean_bbox'][:4])

            image_file = os.path.join(self.img_prefix, self.id2name[img_id])
            rec.append({
                'image_file': image_file,
                'center': center,
                'scale': scale,
                'bbox': obj['clean_bbox'][:4],
                'rotation': 0,
                'joints_3d': joints_3d,
                'joints_3d_visible': joints_3d_visible,
                'dataset': self.dataset_name,
                'bbox_score': 1,
                'bbox_id': bbox_id
            })
            bbox_id = bbox_id + 1

        return rec

    def _xywh2cs(self, x, y, w, h):
        """This encodes bbox(x,y,w,w) into (center, scale)

        Args:
            x, y, w, h

        Returns:
            tuple: A tuple containing center and scale.

            - center (np.ndarray[float32](2,)): center of the bbox (x, y).
            - scale (np.ndarray[float32](2,)): scale of the bbox w & h.
        """
        aspect_ratio = self.ann_info['image_size'][0] / self.ann_info[
            'image_size'][1]
        center = np.array([x + w * 0.5, y + h * 0.5], dtype=np.float32)

        if (not self.test_mode) and np.random.rand() < 0.3:
            center += 0.4 * (np.random.rand(2) - 0.5) * [w, h]

        if w > aspect_ratio * h:
            h = w * 1.0 / aspect_ratio
        elif w < aspect_ratio * h:
            w = h * aspect_ratio

        # pixel std is 200.0
        scale = np.array([w / 200.0, h / 200.0], dtype=np.float32)
        # padding to include proper amount of context
        scale = scale * 1.25

        return center, scale

    def _load_coco_person_detection_results(self):
        """Load coco person detection results."""
        num_joints = self.ann_info['num_joints']
        all_boxes = None
        with open(self.bbox_file, 'r') as f:
            all_boxes = json.load(f)

        if not all_boxes:
            raise ValueError('=> Load %s fail!' % self.bbox_file)

        print(f'=> Total boxes: {len(all_boxes)}')

        kpt_db = []
        bbox_id = 0
        for det_res in all_boxes:
            if det_res['category_id'] != 1:
                continue

            image_file = os.path.join(self.img_prefix,
                                      self.id2name[det_res['image_id']])
            box = det_res['bbox']
            score = det_res['score']

            if score < self.det_bbox_thr:
                continue

            center, scale = self._xywh2cs(*box[:4])
            joints_3d = np.zeros((num_joints, 3), dtype=np.float32)
            joints_3d_visible = np.ones((num_joints, 3), dtype=np.float32)
            kpt_db.append({
                'image_file': image_file,
                'center': center,
                'scale': scale,
                'rotation': 0,
                'bbox': box[:4],
                'bbox_score': score,
                'dataset': self.dataset_name,
                'joints_3d': joints_3d,
                'joints_3d_visible': joints_3d_visible,
                'bbox_id': bbox_id
            })
            bbox_id = bbox_id + 1
        print(f'=> Total boxes after filter '
              f'low score@{self.det_bbox_thr}: {bbox_id}')
        return kpt_db

    def evaluate(self, outputs, res_folder, metric='mAP', **kwargs):
        """Evaluate coco 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(dict))
                :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, ['data/coco/val2017
                    /000000393226.jpg']
                :heatmap (np.ndarray[N, K, H, W]): model output heatmap
                :bbox_id (list(int)).
            res_folder (str): Path of directory to save the results.
            metric (str | list[str]): Metric to be performed. Defaults: 'mAP'.

        Returns:
            dict: Evaluation results for evaluation metric.
        """
        metrics = metric if isinstance(metric, list) else [metric]
        allowed_metrics = ['mAP']
        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 = defaultdict(list)

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

        # rescoring and oks nms
        num_joints = self.ann_info['num_joints']
        vis_thr = self.vis_thr
        oks_thr = self.oks_thr
        valid_kpts = []
        for image_id in kpts.keys():
            img_kpts = kpts[image_id]
            for n_p in img_kpts:
                box_score = n_p['score']
                kpt_score = 0
                valid_num = 0
                for n_jt in range(0, num_joints):
                    t_s = n_p['keypoints'][n_jt][2]
                    if t_s > vis_thr:
                        kpt_score = kpt_score + t_s
                        valid_num = valid_num + 1
                if valid_num != 0:
                    kpt_score = kpt_score / valid_num
                # rescoring
                n_p['score'] = kpt_score * box_score

            if self.use_nms:
                nms = soft_oks_nms if self.soft_nms else oks_nms
                keep = nms(img_kpts, oks_thr, sigmas=self.sigmas)
                valid_kpts.append([img_kpts[_keep] for _keep in keep])
            else:
                valid_kpts.append(img_kpts)

        self._write_coco_keypoint_results(valid_kpts, res_file)

        info_str = self._do_python_keypoint_eval(res_file)
        name_value = OrderedDict(info_str)

        return name_value

    def _write_coco_keypoint_results(self, keypoints, res_file):
        """Write results into a json file."""
        data_pack = [{
            'cat_id': self._class_to_coco_ind[cls],
            'cls_ind': cls_ind,
            'cls': cls,
            'ann_type': 'keypoints',
            'keypoints': keypoints
        } for cls_ind, cls in enumerate(self.classes)
                     if not cls == '__background__']

        results = self._coco_keypoint_results_one_category_kernel(data_pack[0])

        with open(res_file, 'w') as f:
            json.dump(results, f, sort_keys=True, indent=4)

    def _coco_keypoint_results_one_category_kernel(self, data_pack):
        """Get coco keypoint results."""
        cat_id = data_pack['cat_id']
        keypoints = data_pack['keypoints']
        cat_results = []

        for img_kpts in keypoints:
            if len(img_kpts) == 0:
                continue

            _key_points = np.array(
                [img_kpt['keypoints'] for img_kpt in img_kpts])
            key_points = _key_points.reshape(-1,
                                             self.ann_info['num_joints'] * 3)

            result = [{
                'image_id': img_kpt['image_id'],
                'category_id': cat_id,
                'keypoints': key_point.tolist(),
                'score': float(img_kpt['score']),
                'center': img_kpt['center'].tolist(),
                'scale': img_kpt['scale'].tolist()
            } for img_kpt, key_point in zip(img_kpts, key_points)]

            cat_results.extend(result)

        return cat_results

    def _do_python_keypoint_eval(self, res_file):
        """Keypoint evaluation using COCOAPI."""
        coco_det = self.coco.loadRes(res_file)
        coco_eval = COCOeval(self.coco, coco_det, 'keypoints', self.sigmas)
        coco_eval.params.useSegm = None
        coco_eval.evaluate()
        coco_eval.accumulate()
        coco_eval.summarize()

        stats_names = [
            'AP', 'AP .5', 'AP .75', 'AP (M)', 'AP (L)', 'AR', 'AR .5',
            'AR .75', 'AR (M)', 'AR (L)'
        ]

        info_str = list(zip(stats_names, coco_eval.stats))

        return info_str

    def _sort_and_unique_bboxes(self, kpts, key='bbox_id'):
        """sort kpts and remove the repeated ones."""
        for img_id, persons in kpts.items():
            num = len(persons)
            kpts[img_id] = sorted(kpts[img_id], key=lambda x: x[key])
            for i in range(num - 1, 0, -1):
                if kpts[img_id][i][key] == kpts[img_id][i - 1][key]:
                    del kpts[img_id][i]

        return kpts
