import os
from .base_dataset import BaseDataset
from ltr.data.image_loader import default_image_loader
import torch
import random
from pycocotools.coco import COCO
from collections import OrderedDict
from ltr.admin.environment import env_settings


class MSCOCOSeq(BaseDataset):
    """ The COCO dataset. COCO is an image dataset. Thus, we treat each image as a sequence of length 1.

    Publication:
        Microsoft COCO: Common Objects in Context.
        Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona,
        Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick
        ECCV, 2014
        https://arxiv.org/pdf/1405.0312.pdf

    Download the images along with annotations from http://cocodataset.org/#download. The root folder should be
    organized as follows.
        - coco_root
            - annotations
                - instances_train2014.json
            - images
                - train2014

    Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi.
    """

    def __init__(self, root=None, image_loader=default_image_loader, data_fraction=None):
        """
        args:
            root - path to the coco dataset.
            image_loader (default_image_loader) -  The function to read the images. If installed,
                                                   jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else,
                                                   opencv's imread is used.
            data_fraction (None) - Fraction of images to be used. The images are selected randomly. If None, all the
                                  images  will be used
        """
        root = env_settings().coco_dir if root is None else root
        super().__init__(root, image_loader)

        self.img_pth = os.path.join(root, 'train2017')
        self.anno_path = os.path.join(root, 'annotations/instances_train2017.json')

        # Load the COCO set.
        self.coco_set = COCO(self.anno_path)

        self.cats = self.coco_set.cats
        self.sequence_list = self._get_sequence_list()

        if data_fraction is not None:
            self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))

    def _get_sequence_list(self):
        ann_list = list(self.coco_set.anns.keys())
        seq_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0]

        return seq_list

    def is_video_sequence(self):
        return False

    def get_name(self):
        return 'coco'

    def get_num_sequences(self):
        return len(self.sequence_list)

    def get_sequence_info(self, seq_id):
        anno = self._get_anno(seq_id)

        bbox = torch.Tensor(anno['bbox']).view(1, 4)
        valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)
        visible = valid.clone().byte()

        return {'bbox': bbox, 'valid': valid, 'visible': visible}

    def _get_anno(self, seq_id):
        anno = self.coco_set.anns[self.sequence_list[seq_id]]
        return anno

    def _get_frames(self, seq_id):
        path = self.coco_set.loadImgs([self.coco_set.anns[self.sequence_list[seq_id]]['image_id']])[0]['file_name']
        #print("coco image path: {}".format(path))
        img = self.image_loader(os.path.join(self.img_pth, path))
        return img

    def get_meta_info(self, seq_id):
        try:
            cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']]
            object_meta = OrderedDict({'object_class': cat_dict_current['name'],
                                       'motion_class': None,
                                       'major_class': cat_dict_current['supercategory'],
                                       'root_class': None,
                                       'motion_adverb': None})
        except:
            object_meta = OrderedDict({'object_class': None,
                                       'motion_class': None,
                                       'major_class': None,
                                       'root_class': None,
                                       'motion_adverb': None})
        return object_meta

    def get_frames(self, seq_id=None, frame_ids=None, anno=None):
        # COCO is an image dataset. Thus we replicate the image denoted by seq_id len(frame_ids) times, and return a
        # list containing these replicated images.
        frame = self._get_frames(seq_id)

        frame_list = [frame.copy() for _ in frame_ids]

        if anno is None:
            anno = self.get_sequence_info(seq_id)

        # Create anno dict
        anno_frames = {}
        for key, value in anno.items():
            anno_frames[key] = [value[0, ...] for _ in frame_ids]

        object_meta = self.get_meta_info(seq_id)

        return frame_list, anno_frames, object_meta
