我们从Python开源项目中，提取了以下30个代码示例，用于说明如何使用datasets.kitti_tracking()。
def evaluate_proposals(self, all_boxes, output_dir): # for each image for im_ind, index in enumerate(self.image_index): filename = os.path.join(output_dir, index[5:] + '.txt') print 'Writing kitti_tracking results to file ' + filename with open(filename, 'wt') as f: # for each class for cls_ind, cls in enumerate(self.classes): if cls == '__background__': continue dets = all_boxes[cls_ind][im_ind] if dets == []: continue for k in xrange(dets.shape[0]): f.write('{:f} {:f} {:f} {:f} {:.32f}\n'.format(\ dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4]))
def _get_default_path(self): """ Return the default path where kitti_tracking is expected to be installed. """ return os.path.join(datasets.ROOT_DIR, 'data', 'KITTI_Tracking')
def evaluate_detections(self, all_boxes, output_dir): # load the mapping for subcalss the alpha (viewpoint) if self._image_set == 'training' and self._seq_name != 'trainval': filename = os.path.join(self._kitti_tracking_path, 'voxel_exemplars', 'train', 'mapping.txt') else: filename = os.path.join(self._kitti_tracking_path, 'voxel_exemplars', 'trainval', 'mapping.txt') assert os.path.exists(filename), 'Path does not exist: {}'.format(filename) mapping = np.zeros(self._num_subclasses, dtype=np.float) with open(filename) as f: for line in f: words = line.split() subcls = int(words[0]) mapping[subcls] = float(words[3]) # for each image for im_ind, index in enumerate(self.image_index): filename = os.path.join(output_dir, index[5:] + '.txt') print 'Writing kitti_tracking results to file ' + filename with open(filename, 'wt') as f: # for each class for cls_ind, cls in enumerate(self.classes): if cls == '__background__': continue dets = all_boxes[cls_ind][im_ind] if dets == []: continue for k in xrange(dets.shape[0]): subcls = int(dets[k, 5]) cls_name = self.classes[self.subclass_mapping[subcls]] assert (cls_name == cls), 'subclass not in class' alpha = mapping[subcls] f.write('{:s} -1 -1 {:f} {:f} {:f} {:f} {:f} -1 -1 -1 -1 -1 -1 -1 {:.32f}\n'.format(\ cls, alpha, dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4])) # write detection results into one file
def evaluate_detections_one_file(self, all_boxes, output_dir): # load the mapping for subcalss the alpha (viewpoint) if self._image_set == 'training' and self._seq_name != 'trainval': filename = os.path.join(self._kitti_tracking_path, 'voxel_exemplars', 'train', 'mapping.txt') else: filename = os.path.join(self._kitti_tracking_path, 'voxel_exemplars', 'trainval', 'mapping.txt') assert os.path.exists(filename), 'Path does not exist: {}'.format(filename) mapping = np.zeros(self._num_subclasses, dtype=np.float) with open(filename) as f: for line in f: words = line.split() subcls = int(words[0]) mapping[subcls] = float(words[3]) # open results file filename = os.path.join(output_dir, self._seq_name+'.txt') print 'Writing all kitti_tracking results to file ' + filename with open(filename, 'wt') as f: # for each image for im_ind, index in enumerate(self.image_index): # for each class for cls_ind, cls in enumerate(self.classes): if cls == '__background__': continue dets = all_boxes[cls_ind][im_ind] if dets == []: continue for k in xrange(dets.shape[0]): subcls = int(dets[k, 5]) cls_name = self.classes[self.subclass_mapping[subcls]] assert (cls_name == cls), 'subclass not in class' alpha = mapping[subcls] f.write('{:d} -1 {:s} -1 -1 {:f} {:f} {:f} {:f} {:f} -1 -1 -1 -1000 -1000 -1000 -10 {:f}\n'.format(\ im_ind, cls, alpha, dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4]))
def __init__(self, image_set, seq_name, kitti_tracking_path=None): datasets.imdb.__init__(self, 'kitti_tracking_' + image_set + '_' + seq_name) self._image_set = image_set self._seq_name = seq_name self._kitti_tracking_path = self._get_default_path() if kitti_tracking_path is None \ else kitti_tracking_path self._data_path = os.path.join(self._kitti_tracking_path, image_set, 'image_02') 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 if image_set == 'training' and seq_name != 'trainval': self._num_subclasses = 220 + 1 else: self._num_subclasses = 472 + 1 # load the mapping for subcalss to class if image_set == 'training' and seq_name != 'trainval': filename = os.path.join(self._kitti_tracking_path, 'voxel_exemplars', 'train', 'mapping.txt') else: filename = os.path.join(self._kitti_tracking_path, 'voxel_exemplars', 'trainval', '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._kitti_tracking_path), \ 'kitti_tracking path does not exist: {}'.format(self._kitti_tracking_path) assert os.path.exists(self._data_path), \ 'Path does not exist: {}'.format(self._data_path)
