bases.py

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# encoding: utf-8
"""
@author:  sherlock
@contact: sherlockliao01@gmail.com
"""
 
import copy
import logging
import os
from tabulate import tabulate
from termcolor import colored
 
logger = logging.getLogger(__name__)
 
 
class Dataset(object):
    """An abstract class representing a Dataset.
    This is the base class for ``ImageDataset`` and ``VideoDataset``.
    Args:
        train (list): contains tuples of (img_path(s), pid, camid).
        query (list): contains tuples of (img_path(s), pid, camid).
        gallery (list): contains tuples of (img_path(s), pid, camid).
        transform: transform function.
        mode (str): 'train', 'query' or 'gallery'.
        combineall (bool): combines train, query and gallery in a
            dataset for training.
        verbose (bool): show information.
    """
    _junk_pids = []  # contains useless person IDs, e.g. background, false detections
 
    def __init__(self, train, query, gallery, transform=None, mode='train',
                 combineall=False, verbose=True, **kwargs):
        self.train = train
        self.query = query
        self.gallery = gallery
        self.transform = transform
        self.mode = mode
        self.combineall = combineall
        self.verbose = verbose
 
        self.num_train_pids = self.get_num_pids(self.train)
        self.num_train_cams = self.get_num_cams(self.train)
 
        if self.combineall:
            self.combine_all()
 
        if self.mode == 'train':
            self.data = self.train
        elif self.mode == 'query':
            self.data = self.query
        elif self.mode == 'gallery':
            self.data = self.gallery
        else:
            raise ValueError('Invalid mode. Got {}, but expected to be '
                             'one of [train | query | gallery]'.format(self.mode))
 
    def __getitem__(self, index):
        raise NotImplementedError
 
    def __len__(self):
        return len(self.data)
 
    def __radd__(self, other):
        """Supports sum([dataset1, dataset2, dataset3])."""
        if other == 0:
            return self
        else:
            return self.__add__(other)
 
    def parse_data(self, data):
        """Parses data list and returns the number of person IDs
        and the number of camera views.
        Args:
            data (list): contains tuples of (img_path(s), pid, camid)
        """
        pids = set()
        cams = set()
        for _, pid, camid in data:
            pids.add(pid)
            cams.add(camid)
        return len(pids), len(cams)
 
    def get_num_pids(self, data):
        """Returns the number of training person identities."""
        return self.parse_data(data)[0]
 
    def get_num_cams(self, data):
        """Returns the number of training cameras."""
        return self.parse_data(data)[1]
 
    def show_summary(self):
        """Shows dataset statistics."""
        pass
 
    def combine_all(self):
        """Combines train, query and gallery in a dataset for training."""
        combined = copy.deepcopy(self.train)
 
        def _combine_data(data):
            for img_path, pid, camid in data:
                if pid in self._junk_pids:
                    continue
                pid = self.dataset_name + "_" + str(pid)
                camid = self.dataset_name + "_" + str(camid)
                combined.append((img_path, pid, camid))
 
        _combine_data(self.query)
        _combine_data(self.gallery)
 
        self.train = combined
        self.num_train_pids = self.get_num_pids(self.train)
 
    def check_before_run(self, required_files):
        """Checks if required files exist before going deeper.
        Args:
            required_files (str or list): string file name(s).
        """
        if isinstance(required_files, str):
            required_files = [required_files]
 
        for fpath in required_files:
            if not os.path.exists(fpath):
                raise RuntimeError('"{}" is not found'.format(fpath))
 
 
class ImageDataset(Dataset):
    """A base class representing ImageDataset.
    All other image datasets should subclass it.
    ``__getitem__`` returns an image given index.
    It will return ``img``, ``pid``, ``camid`` and ``img_path``
    where ``img`` has shape (channel, height, width). As a result,
    data in each batch has shape (batch_size, channel, height, width).
    """
 
    def __init__(self, train, query, gallery, **kwargs):
        super(ImageDataset, self).__init__(train, query, gallery, **kwargs)
 
    def show_train(self):
        num_train_pids, num_train_cams = self.parse_data(self.train)
 
        headers = ['subset', '# ids', '# images', '# cameras']
        csv_results = [['train', num_train_pids, len(self.train), num_train_cams]]
 
        # tabulate it
        table = tabulate(
            csv_results,
            tablefmt="pipe",
            headers=headers,
            numalign="left",
        )
        logger.info(f"=> Loaded {self.__class__.__name__} in csv format: \n" + colored(table, "cyan"))
 
    def show_test(self):
        num_query_pids, num_query_cams = self.parse_data(self.queryquery)
        num_gallery_pids, num_gallery_cams = self.parse_data(self.gallerygallery)
 
        headers = ['subset', '# ids', '# images', '# cameras']
        csv_results = [
            ['query', num_query_pids, len(self.queryquery), num_query_cams],
            ['gallery', num_gallery_pids, len(self.gallerygallery), num_gallery_cams],
        ]
 
        # tabulate it
        table = tabulate(
            csv_results,
            tablefmt="pipe",
            headers=headers,
            numalign="left",
        )
        logger.info(f"=> Loaded {self.__class__.__name__} in csv format: \n" + colored(table, "cyan"))