from __future__ import print_function, absolute_import

import os
import glob
import re
import sys
import urllib
import tarfile
import zipfile
import os.path as osp
from scipy.io import loadmat
import numpy as np
import h5py
from scipy.misc import imsave

from .base import BaseImgDataset


class MSMT17(BaseImgDataset):
    """
    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'

    def __init__(self, root='data', verbose=True, use_lmdb=False, **kwargs):
        super(MSMT17, self).__init__()
        self.dataset_dir = osp.join(root, self.dataset_dir)
        self.train_dir = osp.join(self.dataset_dir, 'MSMT17_V1/train')
        self.test_dir = osp.join(self.dataset_dir, 'MSMT17_V1/test')
        self.list_train_path = osp.join(self.dataset_dir, 'MSMT17_V1/list_train.txt')
        self.list_val_path = osp.join(self.dataset_dir, 'MSMT17_V1/list_val.txt')
        self.list_query_path = osp.join(self.dataset_dir, 'MSMT17_V1/list_query.txt')
        self.list_gallery_path = osp.join(self.dataset_dir, 'MSMT17_V1/list_gallery.txt')

        self._check_before_run()
        train, num_train_pids, num_train_imgs = self._process_dir(self.train_dir, self.list_train_path)
        #val, num_val_pids, num_val_imgs = self._process_dir(self.train_dir, self.list_val_path)
        query, num_query_pids, num_query_imgs = self._process_dir(self.test_dir, self.list_query_path)
        gallery, num_gallery_pids, num_gallery_imgs = self._process_dir(self.test_dir, self.list_gallery_path)

        #train += val
        #num_train_imgs += num_val_imgs

        num_total_pids = num_train_pids + num_query_pids
        num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs

        if verbose:
            print("=> MSMT17 loaded")
            print("Dataset statistics:")
            print("  ------------------------------")
            print("  subset   | # ids | # images")
            print("  ------------------------------")
            print("  train    | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
            print("  query    | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
            print("  gallery  | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
            print("  ------------------------------")
            print("  total    | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
            print("  ------------------------------")

        self.train = train
        self.query = query
        self.gallery = gallery

        self.num_train_pids = num_train_pids
        self.num_query_pids = num_query_pids
        self.num_gallery_pids = num_gallery_pids

        if use_lmdb:
            self.generate_lmdb()

    def _check_before_run(self):
        """Check if all files are available before going deeper"""
        if not osp.exists(self.dataset_dir):
            raise RuntimeError("'{}' is not available".format(self.dataset_dir))
        if not osp.exists(self.train_dir):
            raise RuntimeError("'{}' is not available".format(self.train_dir))
        if not osp.exists(self.test_dir):
            raise RuntimeError("'{}' is not available".format(self.test_dir))

    def _process_dir(self, dir_path, list_path):
        with open(list_path, 'r') as txt:
            lines = txt.readlines()
        dataset = []
        pid_container = set()
        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])
            img_path = osp.join(dir_path, img_path)
            dataset.append((img_path, pid, camid))
            pid_container.add(pid)
        num_imgs = len(dataset)
        num_pids = len(pid_container)
        # check if pid starts from 0 and increments with 1
        for idx, pid in enumerate(pid_container):
            assert idx == pid, "See code comment for explanation"
        return dataset, num_pids, num_imgs