# Source code for torchreid.data.datasets.image.msmt17

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

    import sys
    import os
    import os.path as osp

    from torchreid.data.datasets import ImageDataset

    # To adapt to different versions
    # Log:
    # 22.01.2019: v1 and v2 only differ in dir names
    TRAIN_DIR_KEY = 'train_dir'
    TEST_DIR_KEY = 'test_dir'
    VERSION_DICT = {
        'MSMT17_V1': {
            TRAIN_DIR_KEY: 'train',
            TEST_DIR_KEY: 'test',
        },
        'MSMT17_V2': {
            TRAIN_DIR_KEY: 'mask_train_v2',
            TEST_DIR_KEY: 'mask_test_v2',
        }
    }

    class MSMT17(ImageDataset):
        """MSMT17.

        Reference:
            Wei et al. Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. CVPR 2018.

        URL: `<http://www.pkuvmc.com/publications/msmt17.html>`_

        Dataset statistics:
            - identities: 4101.
            - images: 32621 (train) + 11659 (query) + 82161 (gallery).
            - cameras: 15.
        """
        dataset_dir = 'msmt17'
        dataset_url = None

        def __init__(self, root='', **kwargs):
            self.root = osp.abspath(osp.expanduser(root))
            self.dataset_dir = osp.join(self.root, self.dataset_dir)
            self.download_dataset(self.dataset_dir, self.dataset_url)

            has_main_dir = False
            for main_dir in VERSION_DICT:
                if osp.exists(osp.join(self.dataset_dir, main_dir)):
                    train_dir = VERSION_DICT[main_dir][TRAIN_DIR_KEY]
                    test_dir = VERSION_DICT[main_dir][TEST_DIR_KEY]
                    has_main_dir = True
                    break
            assert has_main_dir, 'Dataset folder not found'

            self.train_dir = osp.join(self.dataset_dir, main_dir, train_dir)
            self.test_dir = osp.join(self.dataset_dir, main_dir, test_dir)
            self.list_train_path = osp.join(self.dataset_dir, main_dir, 'list_train.txt')
            self.list_val_path = osp.join(self.dataset_dir, main_dir, 'list_val.txt')
            self.list_query_path = osp.join(self.dataset_dir, main_dir, 'list_query.txt')
            self.list_gallery_path = osp.join(self.dataset_dir, main_dir, 'list_gallery.txt')

            required_files = [
                self.dataset_dir,
                self.train_dir,
                self.test_dir
            ]
            self.check_before_run(required_files)

            train = self.process_dir(self.train_dir, self.list_train_path)
            val = self.process_dir(self.train_dir, self.list_val_path)
            query = self.process_dir(self.test_dir, self.list_query_path)
            gallery = self.process_dir(self.test_dir, self.list_gallery_path)

            # Note: to fairly compare with published methods on the conventional ReID setting,
            #       do not add val images to the training set.
            if 'combineall' in kwargs and kwargs['combineall']:
                train += val

            super(MSMT17, self).__init__(train, query, gallery, **kwargs)

        def process_dir(self, dir_path, list_path):
            with open(list_path, 'r') as txt:
                lines = txt.readlines()

            data = []

            for img_idx, img_info in enumerate(lines):
                img_path, pid = img_info.split(' ')
                pid = int(pid) # no need to relabel
                camid = int(img_path.split('_')[2]) - 1 # index starts from 0
                img_path = osp.join(dir_path, img_path)
                data.append((img_path, pid, camid))

            return data