nissan.py 文件源码

def __init__(self, image_set, nissan_path=None):
        datasets.imdb.__init__(self, 'nissan_' + image_set)
        self._image_set = image_set
        self._nissan_path = self._get_default_path() if nissan_path is None \
                            else nissan_path
        self._data_path = os.path.join(self._nissan_path, 'Images')
        self._classes = ('__background__', 'Car', 'Pedestrian', 'Cyclist')
        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
        self._image_ext = '.png'
        self._image_index = self._load_image_set_index()
        # Default to roidb handler
        if cfg.IS_RPN:
            self._roidb_handler = self.gt_roidb
        else:
            self._roidb_handler = self.region_proposal_roidb

        # num of subclasses
        self._num_subclasses = 227 + 36 + 36 + 1

        # load the mapping for subcalss to class
        filename = os.path.join(self._nissan_path, 'mapping.txt')
        assert os.path.exists(filename), 'Path does not exist: {}'.format(filename)

        mapping = np.zeros(self._num_subclasses, dtype=np.int)
        with open(filename) as f:
            for line in f:
                words = line.split()
                subcls = int(words[0])
                mapping[subcls] = self._class_to_ind[words[1]]
        self._subclass_mapping = mapping

        self.config = {'top_k': 100000}

        # statistics for computing recall
        self._num_boxes_all = np.zeros(self.num_classes, dtype=np.int)
        self._num_boxes_covered = np.zeros(self.num_classes, dtype=np.int)
        self._num_boxes_proposal = 0

        assert os.path.exists(self._nissan_path), \
                'Nissan path does not exist: {}'.format(self._nissan_path)
        assert os.path.exists(self._data_path), \
                'Path does not exist: {}'.format(self._data_path)