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


class PRID450S(object):
    """
    PRID450S

    Reference:
    Roth et al. Mahalanobis Distance Learning for Person Re-Identification. PR 2014.

    URL: https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/prid450s/
    
    Dataset statistics:
    # identities: 450
    # images: 900
    # cameras: 2
    """
    dataset_dir = 'prid450s'

    def __init__(self, root='data', split_id=0, min_seq_len=0, **kwargs):
        self.dataset_dir = osp.join(root, self.dataset_dir)
        self.dataset_url = 'https://files.icg.tugraz.at/f/8c709245bb/?raw=1'
        self.split_path = osp.join(self.dataset_dir, 'splits.json')
        self.cam_a_path = osp.join(self.dataset_dir, 'cam_a')
        self.cam_b_path = osp.join(self.dataset_dir, 'cam_b')

        self._download_data()
        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

        print("=> PRID450S 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

    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.cam_a_path):
            raise RuntimeError("'{}' is not available".format(self.cam_a_path))
        if not osp.exists(self.cam_b_path):
            raise RuntimeError("'{}' is not available".format(self.cam_b_path))

    def _download_data(self):
        if osp.exists(self.dataset_dir):
            print("This dataset has been downloaded.")
            return

        print("Creating directory {}".format(self.dataset_dir))
        mkdir_if_missing(self.dataset_dir)
        fpath = osp.join(self.dataset_dir, 'prid_450s.zip')

        print("Downloading PRID450S dataset")
        urllib.urlretrieve(self.dataset_url, fpath)

        print("Extracting files")
        zip_ref = zipfile.ZipFile(fpath, 'r')
        zip_ref.extractall(self.dataset_dir)
        zip_ref.close()

    def _prepare_split(self):
        if not osp.exists(self.split_path):
            cam_a_imgs = sorted(glob.glob(osp.join(self.cam_a_path, 'img_*.png')))
            cam_b_imgs = sorted(glob.glob(osp.join(self.cam_b_path, 'img_*.png')))
            assert len(cam_a_imgs) == len(cam_b_imgs)

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

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

                train, test = [], []

                # processing camera a
                for img_path in cam_a_imgs:
                    img_name = osp.basename(img_path)
                    img_idx = int(img_name.split('_')[1].split('.')[0])
                    if img_idx in train_idxs:
                        train.append((img_path, idx2label[img_idx], 0))
                    else:
                        test.append((img_path, img_idx, 0))

                # processing camera b
                for img_path in cam_b_imgs:
                    img_name = osp.basename(img_path)
                    img_idx = int(img_name.split('_')[1].split('.')[0])
                    if img_idx in train_idxs:
                        train.append((img_path, idx2label[img_idx], 1))
                    else:
                        test.append((img_path, img_idx, 1))

                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")