from __future__ import absolute_import
import os
import glob
import re
import sys
import os.path as osp

"""Dataset classes"""

class Market1501(object):
    """
    Market1501

    Reference:
    Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015.
    ==========================
    Dataset statistics:
    # identities: 1501 (+1 for background)
    # images: 12936 (train) + 3368 (query) + 15913 (gallery) = 
    """
    root = './data/market1501'
    train_dir = osp.join(root, 'bounding_box_train')
    query_dir = osp.join(root, 'query')
    gallery_dir = osp.join(root, 'bounding_box_test')

    def __init__(self):
        self._check_dir(self.root)
        self._check_dir(self.train_dir)
        self._check_dir(self.query_dir)
        self._check_dir(self.gallery_dir)

        train, num_train_pids, num_train_imgs = self._process_dir(self.train_dir, relabel=True)
        query, num_query_pids, num_query_imgs = self._process_dir(self.query_dir, relabel=False)
        gallery, num_gallery_pids, num_gallery_imgs = self._process_dir(self.gallery_dir, relabel=False)
        num_total_pids = num_train_pids + num_query_pids
        num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs

        print("=> Market1501 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 _process_dir(self, dir_path, relabel=False):
        print("Processing directory '{}'".format(dir_path))
        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())
            if pid == -1: continue  # junk images are just ignored
            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())
            if pid == -1: continue  # junk images are just ignored
            assert 0 <= pid <= 1501  # pid == 0 means background
            assert 1 <= camid <= 6
            camid -= 1 # index starts from 0
            if relabel: pid = pid2label[pid]
            dataset.append((img_path, pid, camid))

        num_pids = len(pid_container)
        num_imgs = len(dataset)
        return dataset, num_pids, num_imgs

    def _check_dir(self, dir_path):
        if not osp.exists(dir_path):
            print("Error: '{}' is not available.".format(dir_path))
            sys.exit()

"""Create dataset"""

__factory = {
    'market1501': Market1501,
}

def get_names():
    return __factory.keys()

def init_dataset(name, *args, **kwargs):
    if name not in __factory.keys():
        raise KeyError("Unknown dataset: {}".format(name))
    return __factory[name](*args, **kwargs)

if __name__ == '__main__':
    dataset = Market1501()