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 utils.iotools import mkdir_if_missing, write_json, read_json
from .base import BaseImgDataset


class CUHK01(BaseImgDataset):
    """
    CUHK01

    Reference:
    Li et al. Human Reidentification with Transferred Metric Learning. ACCV 2012.

    URL: http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html
    
    Dataset statistics:
    # identities: 971
    # images: 3884
    # cameras: 4
    """
    dataset_dir = 'cuhk01'

    def __init__(self, root='data', split_id=0, verbose=True, use_lmdb=False, **kwargs):
        super(CUHK01, self).__init__()
        self.dataset_dir = osp.join(root, self.dataset_dir)
        self.zip_path = osp.join(self.dataset_dir, 'CUHK01.zip')
        self.campus_dir = osp.join(self.dataset_dir, 'campus')
        self.split_path = osp.join(self.dataset_dir, 'splits.json')

        self._extract_file()
        self._check_before_run()

        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']
        gallery = split['gallery']

        train = [tuple(item) for item in train]
        query = [tuple(item) for item in query]
        gallery = [tuple(item) for item in gallery]
        
        num_train_pids = split['num_train_pids']
        num_query_pids = split['num_query_pids']
        num_gallery_pids = split['num_gallery_pids']
        
        num_train_imgs = len(train)
        num_query_imgs = len(query)
        num_gallery_imgs = len(gallery)

        num_total_pids = num_train_pids + num_query_pids
        num_total_imgs = num_train_imgs + num_query_imgs

        if verbose:
            print("=> CUHK01 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 _extract_file(self):
        if not osp.exists(self.campus_dir):
            print("Extracting files")
            zip_ref = zipfile.ZipFile(self.zip_path, 'r')
            zip_ref.extractall(self.dataset_dir)
            zip_ref.close()
        print("Files extracted")

    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.campus_dir):
            raise RuntimeError("'{}' is not available".format(self.campus_dir))

    def _prepare_split(self):
        """
        Image name format: 0001001.png, where first four digits represent identity
        and last four digits represent cameras. Camera 1&2 are considered the same
        view and camera 3&4 are considered the same view.
        """
        if not osp.exists(self.split_path):
            print("Creating 10 random splits")
            img_paths = sorted(glob.glob(osp.join(self.campus_dir, '*.png')))
            img_list = []
            pid_container = set()
            for img_path in img_paths:
                img_name = osp.basename(img_path)
                pid = int(img_name[:4]) - 1
                camid = (int(img_name[4:7]) - 1) // 2
                img_list.append((img_path, pid, camid))
                pid_container.add(pid)

            num_pids = len(pid_container)
            num_train_pids = num_pids // 2

            splits = []
            for _ in range(10):
                order = np.arange(num_pids)
                np.random.shuffle(order)
                train_idxs = order[:num_train_pids]
                train_idxs = np.sort(train_idxs)
                idx2label = {idx: label for label, idx in enumerate(train_idxs)}

                train, test = [], []
                
                for img_path, pid, camid in img_list:
                    if pid in train_idxs:
                        train.append((img_path, idx2label[pid], camid))
                    else:
                        test.append((img_path, pid, camid))

                split = {'train': train, 'query': test, 'gallery': test,
                         '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))

        print("Splits created")