pred_boxes_right = pred_boxes_right.squeeze()
pred_kpts = torch.cat(
(pred_kpts, kpts_type, max_prob, pred_left, pred_right), 2)
pred_kpts = pred_kpts.squeeze()
dim_orien = dim_orien.squeeze()
det_toc = time.time()
detect_time = det_toc - det_tic
calib = kitti_utils.read_obj_calibration('demo/calib.txt')
im2show_left = np.copy(cv2.imread(img_l_path))
im2show_right = np.copy(cv2.imread(img_r_path))
pointcloud = kitti_utils.get_point_cloud('demo/lidar.bin', calib)
im_box = vis_utils.vis_lidar_in_bev(pointcloud,
width=im2show_left.shape[0] * 2)
for j in xrange(1, len(kitti_classes)):
inds = torch.nonzero(scores[:, j] > eval_thresh).view(-1)
# if there is det
if inds.numel() > 0:
cls_scores = scores[:, j][inds]
_, order = torch.sort(cls_scores, 0, True)
cls_boxes_left = pred_boxes_left[inds][:, j * 4:(j + 1) * 4]
cls_boxes_right = pred_boxes_right[inds][:, j * 4:(j + 1) * 4]
cls_dim_orien = dim_orien[inds][:, j * 5:(j + 1) * 5]
cls_kpts = pred_kpts[inds]
detect_time = det_toc - det_tic
img_path = imdb.img_left_path_at(i)
split_path = img_path.split('/')
image_number = split_path[len(split_path) - 1].split('.')[0]
calib_path = img_path.replace("image_2", "calib")
calib_path = calib_path.replace("png", "txt")
calib = kitti_utils.read_obj_calibration(calib_path)
label_path = calib_path.replace("calib", "label_2")
lidar_path = calib_path.replace("calib", "velodyne")
lidar_path = lidar_path.replace("txt", "bin")
im2show_left = np.copy(cv2.imread(imdb.img_left_path_at(i)))
im2show_right = np.copy(cv2.imread(imdb.img_right_path_at(i)))
pointcloud = kitti_utils.get_point_cloud(lidar_path, calib)
im_box = vis_utils.vis_lidar_in_bev(pointcloud,
width=im2show_left.shape[0] *
2)
for j in xrange(1, imdb.num_classes):
inds = torch.nonzero(scores[:, j] > eval_thresh).view(-1)
# if there is det
if inds.numel() > 0:
cls_scores = scores[:, j][inds]
_, order = torch.sort(cls_scores, 0, True)
cls_boxes_left = pred_boxes_left[inds][:,
j * 4:(j + 1) * 4]
cls_boxes_right = pred_boxes_right[inds][:,
j * 4:(j + 1) * 4]