from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import torch.utils.data as data
import pycocotools.coco as coco
import numpy as np
import torch
import json
import cv2
import os
import math

import torch.utils.data as data


class DanceTrackKITTI(data.Dataset):
    num_classes = 3
#     default_resolution = [384, 1280]
    default_resolution = [544, 960]
    mean = np.array([0.485, 0.456, 0.406], np.float32).reshape(1, 1, 3)
    std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3)
    calib = np.array([[707.0493, 0, 960/2, 0],
                      [0, 707.0493, 544/2, 0],
                      [0, 0,        1.,    0]], dtype=np.float32)
    def __init__(self, opt, split):
        super(DanceTrackKITTI, self).__init__()
        self.data_dir = os.path.join(opt.data_dir, 'dancetrack_kitti')
        self.img_dir = os.path.join(self.data_dir)
        if split == 'train':
            self.annot_path = os.path.join(self.data_dir, 'annotations', 'train.json')
        else:
            assert 0, 'only train is supported!'
        self.max_objs = 50
        self.class_name = [
            '__background__', 'Pedestrian', 'Car', 'Cyclist']
        self.cat_ids = {1: 0, 2: 1, 3: 2, 4: -3, 5: -3, 6: -2, 7: -99, 8: -99, 9: -1}

        self._data_rng = np.random.RandomState(123)
        self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
                                 dtype=np.float32)
        self._eig_vec = np.array([
            [-0.58752847, -0.69563484, 0.41340352],
            [-0.5832747, 0.00994535, -0.81221408],
            [-0.56089297, 0.71832671, 0.41158938]
        ], dtype=np.float32)
        self.split = split
        self.opt = opt
        self.alpha_in_degree = False

        print('==> initializing dancetrack_kitti {} data.'.format(split))
        self.coco = coco.COCO(self.annot_path)
        self.images = self.coco.getImgIds()
        self.num_samples = len(self.images)

        print('Loaded {} {} samples'.format(split, self.num_samples))

    def __len__(self):
        return self.num_samples

    def _to_float(self, x):
        return float("{:.2f}".format(x))

    def convert_eval_format(self, all_bboxes):
        pass

    def save_results(self, results, save_dir):
        results_dir = os.path.join(save_dir, 'results')
        if not os.path.exists(results_dir):
            os.mkdir(results_dir)
        for img_id in results.keys():
            out_path = os.path.join(results_dir, '{:06d}.txt'.format(img_id))
            f = open(out_path, 'w')
            for cls_ind in results[img_id]:
                for j in range(len(results[img_id][cls_ind])):
                    class_name = self.class_name[cls_ind]
                    f.write('{} 0.0 0'.format(class_name))
                    for i in range(len(results[img_id][cls_ind][j])):
                        f.write(' {:.2f}'.format(results[img_id][cls_ind][j][i]))
                    f.write('\n')
            f.close()

    def run_eval(self, results, save_dir):
        self.save_results(results, save_dir)
        os.system('./tools/kitti_eval/evaluate_object_3d_offline ' + \
                  '../data/kitti/training/label_val ' + \
                  '{}/results/'.format(save_dir))

