import os
import os.path as osp
import numpy as np
import torch
import cv2
import json
import copy
from pycocotools.coco import COCO
from config import cfg
from utils.human_models import smpl_x
from utils.preprocessing import load_img, process_bbox, augmentation, process_db_coord, process_human_model_output, \
    get_fitting_error_3D
from utils.transforms import world2cam, cam2pixel, rigid_align
from humandata import HumanDataset


class PoseTrack(HumanDataset):
    def __init__(self, transform, data_split):
        super(PoseTrack, self).__init__(transform, data_split)

        self.datalist = []

        pre_prc_file = 'eft_posetrack.npz'
        if self.data_split == 'train':
            filename = getattr(cfg, 'filename', pre_prc_file)
        else:
            raise ValueError('PoseTrack test set is not support')

        self.img_dir = osp.join(cfg.data_dir, 'PoseTrack/data/images')
        self.annot_path = osp.join(cfg.data_dir, 'preprocessed_datasets', filename)
        self.annot_path_cache = osp.join(cfg.data_dir, 'cache', filename)
        self.use_cache = getattr(cfg, 'use_cache', False)
        self.img_shape = None
        self.cam_param = {}
        print("Various image shape in PoseTrack dataset.")

        # load data or cache
        if self.use_cache and osp.isfile(self.annot_path_cache):
            print('loading cache from {}'.format(self.annot_path_cache))
            self.datalist = self.load_cache(self.annot_path_cache)
        else:
            if self.use_cache:
                print(f'[{self.__class__.__name__}] Cache not found, generating cache...')
            self.datalist = self.load_data(
                train_sample_interval=getattr(cfg, f'{self.__class__.__name__}_train_sample_interval', 1))
            if self.use_cache:
                self.save_cache(self.annot_path_cache, self.datalist)