def keep_gt_inside_range(self, train_gt_labels, train_gt_boxes3d, obstacles): train_gt_labels = np.array(train_gt_labels, dtype=np.int32) train_gt_boxes3d = np.array(train_gt_boxes3d, dtype=np.float32) if train_gt_labels.shape[0] == 0: return False, None, None, None assert train_gt_labels.shape[0] == train_gt_boxes3d.shape[0] # get limited train_gt_boxes3d and train_gt_labels. keep = np.zeros((len(train_gt_labels)), dtype=bool) for i in range(len(train_gt_labels)): if box.box3d_in_top_view(train_gt_boxes3d[i]): keep[i] = 1 # if all targets are out of range in selected top view, return True. if np.sum(keep) == 0: return False, None, None, None train_gt_labels = train_gt_labels[keep] train_gt_boxes3d = train_gt_boxes3d[keep] obstacles_keep = [] for i in range(keep.shape[0]): if (keep[i] == 1): obstacles_keep.append(obstacles[i]) return True, train_gt_labels, train_gt_boxes3d, obstacles_keep
def batch_data_is_invalid(self, train_gt_boxes3d): # todo : support batch size >1 for i in range(len(train_gt_boxes3d)): if box.box3d_in_top_view(train_gt_boxes3d[i]): continue else: return True return False
def batch_data_is_invalid(self,train_gt_boxes3d): # todo : support batch size >1 for i in range(len(train_gt_boxes3d)): if box.box3d_in_top_view(train_gt_boxes3d[i]): continue else: return True return False
def keep_gt_inside_range(self, train_gt_labels, train_gt_boxes3d): # todo : support batch size >1 if train_gt_labels.shape[0] == 0: return False, None, None assert train_gt_labels.shape[0] == train_gt_boxes3d.shape[0] # get limited train_gt_boxes3d and train_gt_labels. keep = np.zeros((len(train_gt_labels)), dtype=bool) for i in range(len(train_gt_labels)): if box.box3d_in_top_view(train_gt_boxes3d[i]): keep[i] = 1 # if all targets are out of range in selected top view, return True. if np.sum(keep) == 0: return False, None, None train_gt_labels = train_gt_labels[keep] train_gt_boxes3d = train_gt_boxes3d[keep] return True, train_gt_labels, train_gt_boxes3d
def keep_gt_inside_range(self, train_gt_labels, train_gt_boxes3d): import pdb pdb.set_trace() train_gt_labels = np.array(train_gt_labels, dtype=np.int32) train_gt_boxes3d = np.array(train_gt_boxes3d, dtype=np.float32) if train_gt_labels.shape[0] == 0: return False, None, None assert train_gt_labels.shape[0] == train_gt_boxes3d.shape[0] # get limited train_gt_boxes3d and train_gt_labels. keep = np.zeros((len(train_gt_labels)), dtype=bool) for i in range(len(train_gt_labels)): if box.box3d_in_top_view(train_gt_boxes3d[i]): keep[i] = 1 # if all targets are out of range in selected top view, return True. if np.sum(keep) == 0: return False, None, None train_gt_labels = train_gt_labels[keep] train_gt_boxes3d = train_gt_boxes3d[keep] return True, train_gt_labels, train_gt_boxes3d