# encoding: utf-8
"""
@author:  liaoxingyu
@contact: liaoxingyu2@jd.com
"""

import glob
import re
import urllib
import zipfile

import os.path as osp

from utils.iotools import mkdir_if_missing
from .bases import BaseImageDataset


class DukeMTMCreID(BaseImageDataset):
    """
    DukeMTMC-reID
    Reference:
    1. Ristani et al. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. ECCVW 2016.
    2. Zheng et al. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro. ICCV 2017.
    URL: https://github.com/layumi/DukeMTMC-reID_evaluation

    Dataset statistics:
    # identities: 1404 (train + query)
    # images:16522 (train) + 2228 (query) + 17661 (gallery)
    # cameras: 8
    """
    dataset_dir = 'dukemtmc-reid'

    def __init__(self, root='/home/haoluo/data', verbose=True, **kwargs):
        super(DukeMTMCreID, self).__init__()
        self.dataset_dir = osp.join(root, self.dataset_dir)
        self.dataset_url = 'http://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-reID.zip'
        self.train_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/bounding_box_train')
        self.query_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/query')
        self.gallery_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/bounding_box_test')

        self._download_data()
        self._check_before_run()

        train = self._process_dir(self.train_dir, relabel=True)
        query = self._process_dir(self.query_dir, relabel=False)
        gallery = self._process_dir(self.gallery_dir, relabel=False)

        if verbose:
            print("=> DukeMTMC-reID loaded")
            self.print_dataset_statistics(train, query, gallery)

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

        self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(self.train)
        self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(self.query)
        self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info(self.gallery)

    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, osp.basename(self.dataset_url))

        print("Downloading DukeMTMC-reID 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 _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.query_dir):
            raise RuntimeError("'{}' is not available".format(self.query_dir))
        if not osp.exists(self.gallery_dir):
            raise RuntimeError("'{}' is not available".format(self.gallery_dir))

    def _process_dir(self, dir_path, relabel=False):
        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
        pattern = re.compile(r'([-\d]+)_c(\d)')

        pid_container = set()
        for img_path in img_paths:
            pid, _ = map(int, pattern.search(img_path).groups())
            pid_container.add(pid)
        pid2label = {pid: label for label, pid in enumerate(pid_container)}

        dataset = []
        for img_path in img_paths:
            pid, camid = map(int, pattern.search(img_path).groups())
            assert 1 <= camid <= 8
            camid -= 1  # index starts from 0
            if relabel: pid = pid2label[pid]
            dataset.append((img_path, pid, camid))

        return dataset