kitti_tracking.py 文件源码

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]))