def generate_single_sample_dict(batch_index, box_dict):
pred_scores = box_dict['pred_scores'].cpu().numpy()
pred_boxes = box_dict['pred_boxes'].cpu().numpy()
pred_labels = box_dict['pred_labels'].cpu().numpy()
pred_dict = get_template_prediction(pred_scores.shape[0])
if pred_scores.shape[0] == 0:
return pred_dict
calib = batch_dict['calib'][batch_index]
image_shape = batch_dict['image_shape'][batch_index]
pred_boxes_camera = box_utils.boxes3d_lidar_to_kitti_camera(pred_boxes, calib)
pred_boxes_img = box_utils.boxes3d_kitti_camera_to_imageboxes(
pred_boxes_camera, calib, image_shape=image_shape
)
pred_dict['name'] = np.array(class_names)[pred_labels - 1] # 这里是 label - 1 ！！
pred_dict['alpha'] = -np.arctan2(-pred_boxes[:, 1], pred_boxes[:, 0]) + pred_boxes_camera[:, 6]
pred_dict['bbox'] = pred_boxes_img
pred_dict['dimensions'] = pred_boxes_camera[:, 3:6]
pred_dict['location'] = pred_boxes_camera[:, 0:3]
pred_dict['rotation_y'] = pred_boxes_camera[:, 6]
pred_dict['score'] = pred_scores
pred_dict['boxes_lidar'] = pred_boxes
return pred_dict
def run(self, points, calib, frame):
t_t = time.time()
num_features = 4 # X,Y,Z,intensity
self.points = points.reshape([-1, num_features])
frame = 0
timestamps = np.empty((len(self.points), 1))
timestamps[:] = frame
self.points = np.append(self.points, timestamps, axis=1)
self.points[:, 0] += move_lidar_center
input_dict = {
'points': self.points,
'frame_id': frame,
}
data_dict = self.demo_dataset.prepare_data(data_dict=input_dict)
data_dict = self.demo_dataset.collate_batch([data_dict])
load_data_to_gpu(data_dict)
torch.cuda.synchronize()
t = time.time()
pred_dicts, _ = self.net.forward(data_dict)
torch.cuda.synchronize()
inference_time = time.time() - t
inference_time_list.append(inference_time)
mean_inference_time = sum(inference_time_list) / len(
inference_time_list)
boxes_lidar = pred_dicts[0]["pred_boxes"].detach().cpu().numpy()
scores = pred_dicts[0]["pred_scores"].detach().cpu().numpy()
types = pred_dicts[0]["pred_labels"].detach().cpu().numpy()
pred_boxes = np.copy(boxes_lidar)
pred_dict = self.get_template_prediction(scores.shape[0])
if scores.shape[0] == 0:
return pred_dict
pred_boxes_camera = box_utils.boxes3d_lidar_to_kitti_camera(
pred_boxes, calib)
pred_boxes_img = box_utils.boxes3d_kitti_camera_to_imageboxes(
pred_boxes_camera, calib, image_shape=image_shape)
pred_dict['name'] = np.array(cfg.CLASS_NAMES)[types - 1]
pred_dict['alpha'] = -np.arctan2(
-pred_boxes[:, 1], pred_boxes[:, 0]) + pred_boxes_camera[:, 6]
pred_dict['bbox'] = pred_boxes_img
pred_dict['dimensions'] = pred_boxes_camera[:, 3:6]
pred_dict['location'] = pred_boxes_camera[:, 0:3]
pred_dict['rotation_y'] = pred_boxes_camera[:, 6]
pred_dict['score'] = scores
pred_dict['boxes_lidar'] = pred_boxes
return scores, boxes_lidar, types, pred_dict
def run(self, points, calib, image_shape):
t_t = time.time()
print(f"input points shape: {points.shape}")
num_features = 4
self.points = points.reshape([-1, num_features])
#print("points", self.points)
timestamps = np.zeros((len(self.points), 1))
self.points = np.append(self.points, timestamps, axis=1)
self.points[:, 0] += movelidarcenter
#print("points2", self.points)
input_dict = {
'points': self.points,
'frame_id': 0,
}
data_dict = self.demo_dataset.prepare_data(data_dict=input_dict)
data_dict = self.demo_dataset.collate_batch([data_dict])
load_data_to_gpu(data_dict)
torch.cuda.synchronize()
t = time.time()
pred_dicts, _ = self.net.forward(data_dict)
torch.cuda.synchronize()
print(f"inference time: {time.time() - t}")
boxes_lidar = pred_dicts[0]["pred_boxes"].detach().cpu().numpy()
scores = pred_dicts[0]["pred_scores"].detach().cpu().numpy()
types = pred_dicts[0]["pred_labels"].detach().cpu().numpy()
# print(f" pred boxes: { boxes_lidar }")
# print(f" pred_scores: { scores }")
# print(f" pred_labels: { types }")
pred_boxes = boxes_lidar
pred_dict = self.get_template_prediction(scores.shape[0])
if scores.shape[0] == 0:
return pred_dict
#calib = get_calib(sample_idx)
#P2 = np.concatenate([calib.P2, np.array([[0., 0., 0., 1.]])], axis=0)
#R0_4x4 = np.zeros([4, 4], dtype=calib.R0.dtype)
#R0_4x4[3, 3] = 1.
#R0_4x4[:3, :3] = calib.R0
#V2C_4x4 = np.concatenate([calib.V2C, np.array([[0., 0., 0., 1.]])], axis=0)
#calib = {'P2': P2, 'R0_rect': R0_4x4, 'Tr_velo_to_cam': V2C_4x4}
#calib = input_dict['calib'][batch_index]
#image_shape = input_dict['image_shape'][batch_index]
pred_boxes_camera = box_utils.boxes3d_lidar_to_kitti_camera(
pred_boxes, calib)
pred_boxes_img = box_utils.boxes3d_kitti_camera_to_imageboxes(
pred_boxes_camera, calib, image_shape=image_shape)
pred_dict['name'] = np.array(cfg.CLASS_NAMES)[types - 1]
pred_dict['alpha'] = -np.arctan2(
-pred_boxes[:, 1], pred_boxes[:, 0]) + pred_boxes_camera[:, 6]
pred_dict['bbox'] = pred_boxes_img
pred_dict['dimensions'] = pred_boxes_camera[:, 3:6]
pred_dict['location'] = pred_boxes_camera[:, 0:3]
pred_dict['rotation_y'] = pred_boxes_camera[:, 6]
pred_dict['score'] = scores
pred_dict['boxes_lidar'] = pred_boxes
return pred_dict