from torch.utils.data import Dataset
import random


class BaseDataset(Dataset):
    '''
    This is the Base Datasets.
    Itself does nothing and is not runnable.
    Check self.get_item function to see what it should return.
    '''

    @staticmethod
    def modify_commandline_options(parser, is_train):
        return parser

    def __init__(self, opt, phase='train'):
        self.opt = opt
        self.is_train = self.phase == 'train'
        self.projection_mode = 'orthogonal'  # Declare projection mode here

    def __len__(self):
        return 0

    def get_item(self, index):
        # In case of a missing file or IO error, switch to a random sample instead
        try:
            res = {
                'name': None,  # name of this subject
                'b_min': None,  # Bounding box (x_min, y_min, z_min) of target space
                'b_max': None,  # Bounding box (x_max, y_max, z_max) of target space

                'samples': None,  # [3, N] samples
                'labels': None,  # [1, N] labels

                'img': None,  # [num_views, C, H, W] input images
                'calib': None,  # [num_views, 4, 4] calibration matrix
                'extrinsic': None,  # [num_views, 4, 4] extrinsic matrix
                'mask': None,  # [num_views, 1, H, W] segmentation masks
            }
            return res
        except:
            print("Requested index %s has missing files. Using a random sample instead." % index)
            return self.get_item(index=random.randint(0, self.__len__() - 1))

    def __getitem__(self, index):
        return self.get_item(index)