indoor3d_util.py 文件源码

def collect_point_label(anno_path, out_filename, file_format='txt'):
    """ Convert original dataset files to data_label file (each line is XYZRGBL).
        We aggregated all the points from each instance in the room.

    Args:
        anno_path: path to annotations. e.g. Area_1/office_2/Annotations/
        out_filename: path to save collected points and labels (each line is XYZRGBL)
        file_format: txt or numpy, determines what file format to save.
    Returns:
        None
    Note:
        the points are shifted before save, the most negative point is now at origin.
    """
    points_list = []

    for f in glob.glob(os.path.join(anno_path, '*.txt')):
        cls = os.path.basename(f).split('_')[0]
        if cls not in g_classes: # note: in some room there is 'staris' class..
            cls = 'clutter'
        points = np.loadtxt(f)
        labels = np.ones((points.shape[0],1)) * g_class2label[cls]
        points_list.append(np.concatenate([points, labels], 1)) # Nx7

    data_label = np.concatenate(points_list, 0)
    xyz_min = np.amin(data_label, axis=0)[0:3]
    data_label[:, 0:3] -= xyz_min

    if file_format=='txt':
        fout = open(out_filename, 'w')
        for i in range(data_label.shape[0]):
            fout.write('%f %f %f %d %d %d %d\n' % \
                          (data_label[i,0], data_label[i,1], data_label[i,2],
                           data_label[i,3], data_label[i,4], data_label[i,5],
                           data_label[i,6]))
        fout.close()
    elif file_format=='numpy':
        np.save(out_filename, data_label)
    else:
        print('ERROR!! Unknown file format: %s, please use txt or numpy.' % \
            (file_format))
        exit()