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

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

    import sys
    import os
    import os.path as osp
    import glob
    import numpy as np

    from torchreid.data.datasets import ImageDataset
    from torchreid.utils import read_json, write_json

    class VIPeR(ImageDataset):
        """VIPeR.

        Reference:
            Gray et al. Evaluating appearance models for recognition, reacquisition, and tracking. PETS 2007.

        URL: `<https://vision.soe.ucsc.edu/node/178>`_

        Dataset statistics:
            - identities: 632.
            - images: 632 x 2 = 1264.
            - cameras: 2.
        """
        dataset_dir = 'viper'
        dataset_url = 'http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip'

        def __init__(self, root='', split_id=0, **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)

            self.cam_a_dir = osp.join(self.dataset_dir, 'VIPeR', 'cam_a')
            self.cam_b_dir = osp.join(self.dataset_dir, 'VIPeR', 'cam_b')
            self.split_path = osp.join(self.dataset_dir, 'splits.json')

            required_files = [
                self.dataset_dir,
                self.cam_a_dir,
                self.cam_b_dir
            ]
            self.check_before_run(required_files)

            self.prepare_split()
            splits = read_json(self.split_path)
            if split_id >= len(splits):
                raise ValueError('split_id exceeds range, received {}, '
                                 'but expected between 0 and {}'.format(split_id, len(splits)-1))
            split = splits[split_id]

            train = split['train']
            query = split['query'] # query and gallery share the same images
            gallery = split['gallery']

            train = [tuple(item) for item in train]
            query = [tuple(item) for item in query]
            gallery = [tuple(item) for item in gallery]

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

        def prepare_split(self):
            if not osp.exists(self.split_path):
                print('Creating 10 random splits of train ids and test ids')

                cam_a_imgs = sorted(glob.glob(osp.join(self.cam_a_dir, '*.bmp')))
                cam_b_imgs = sorted(glob.glob(osp.join(self.cam_b_dir, '*.bmp')))
                assert len(cam_a_imgs) == len(cam_b_imgs)
                num_pids = len(cam_a_imgs)
                print('Number of identities: {}'.format(num_pids))
                num_train_pids = num_pids // 2

                """
                In total, there will be 20 splits because each random split creates two
                sub-splits, one using cameraA as query and cameraB as gallery
                while the other using cameraB as query and cameraA as gallery.
                Therefore, results should be averaged over 20 splits (split_id=0~19).

                In practice, a model trained on split_id=0 can be applied to split_id=0&1
                as split_id=0&1 share the same training data (so on and so forth).
                """
                splits = []
                for _ in range(10):
                    order = np.arange(num_pids)
                    np.random.shuffle(order)
                    train_idxs = order[:num_train_pids]
                    test_idxs = order[num_train_pids:]
                    assert not bool(set(train_idxs) & set(test_idxs)), 'Error: train and test overlap'

                    train = []
                    for pid, idx in enumerate(train_idxs):
                        cam_a_img = cam_a_imgs[idx]
                        cam_b_img = cam_b_imgs[idx]
                        train.append((cam_a_img, pid, 0))
                        train.append((cam_b_img, pid, 1))

                    test_a = []
                    test_b = []
                    for pid, idx in enumerate(test_idxs):
                        cam_a_img = cam_a_imgs[idx]
                        cam_b_img = cam_b_imgs[idx]
                        test_a.append((cam_a_img, pid, 0))
                        test_b.append((cam_b_img, pid, 1))

                    # use cameraA as query and cameraB as gallery
                    split = {
                        'train': train,
                        'query': test_a,
                        'gallery': test_b,
                        'num_train_pids': num_train_pids,
                        'num_query_pids': num_pids - num_train_pids,
                        'num_gallery_pids': num_pids - num_train_pids
                    }
                    splits.append(split)

                    # use cameraB as query and cameraA as gallery
                    split = {
                        'train': train,
                        'query': test_b,
                        'gallery': test_a,
                        'num_train_pids': num_train_pids,
                        'num_query_pids': num_pids - num_train_pids,
                        'num_gallery_pids': num_pids - num_train_pids
                    }
                    splits.append(split)

                print('Totally {} splits are created'.format(len(splits)))
                write_json(splits, self.split_path)
                print('Split file saved to {}'.format(self.split_path))