def get_pose_init(self): # get noisy initializations obtained with the linear method cam_file = '{0}/cameras_linear_init.npz'.format(self.instance_dir) camera_dict = np.load(cam_file) scale_mats = [ camera_dict['scale_mat_%d' % idx].astype(np.float32) for idx in range(self.n_images) ] world_mats = [ camera_dict['world_mat_%d' % idx].astype(np.float32) for idx in range(self.n_images) ] init_pose = [] for scale_mat, world_mat in zip(scale_mats, world_mats): P = world_mat @ scale_mat P = P[:3, :4] _, pose = rend_util.load_K_Rt_from_P(None, P) init_pose.append(pose) init_pose = torch.cat( [torch.Tensor(pose).float().unsqueeze(0) for pose in init_pose], 0).cuda() init_quat = rend_util.rot_to_quat(init_pose[:, :3, :3]) init_quat = torch.cat([init_quat, init_pose[:, :3, 3]], 1) return init_quat
def __init__(self, train_cameras, data_dir, img_res, scan_id=0, cam_file=None): self.instance_dir = os.path.join('../data', data_dir, 'scan{0}'.format(scan_id)) self.total_pixels = img_res[0] * img_res[1] self.img_res = img_res assert os.path.exists(self.instance_dir), "Data directory is empty" self.sampling_idx = None self.train_cameras = train_cameras image_dir = '{0}/image'.format(self.instance_dir) image_paths = sorted(utils.glob_imgs(image_dir)) mask_dir = '{0}/mask'.format(self.instance_dir) mask_paths = sorted(utils.glob_imgs(mask_dir)) self.n_images = len(image_paths) self.cam_file = '{0}/cameras.npz'.format(self.instance_dir) if cam_file is not None: self.cam_file = '{0}/{1}'.format(self.instance_dir, cam_file) camera_dict = np.load(self.cam_file) scale_mats = [ camera_dict['scale_mat_%d' % idx].astype(np.float32) for idx in range(self.n_images) ] world_mats = [ camera_dict['world_mat_%d' % idx].astype(np.float32) for idx in range(self.n_images) ] self.intrinsics_all = [] self.pose_all = [] for scale_mat, world_mat in zip(scale_mats, world_mats): P = world_mat @ scale_mat P = P[:3, :4] intrinsics, pose = rend_util.load_K_Rt_from_P(None, P) self.intrinsics_all.append(torch.from_numpy(intrinsics).float()) self.pose_all.append(torch.from_numpy(pose).float()) self.rgb_images = [] for path in image_paths: rgb = rend_util.load_rgb(path) rgb = rgb.reshape(3, -1).transpose(1, 0) self.rgb_images.append(torch.from_numpy(rgb).float()) self.object_masks = [] for path in mask_paths: object_mask = rend_util.load_mask_white_bg(path) object_mask = object_mask.reshape(-1) self.object_masks.append(torch.from_numpy(object_mask).bool())
def get_gt_pose(self, scaled=False): # Load gt pose without normalization to unit sphere camera_dict = np.load(self.cam_file) world_mats = [camera_dict['world_mat_%d' % idx].astype(np.float32) for idx in range(self.n_images)] scale_mats = [camera_dict['scale_mat_%d' % idx].astype(np.float32) for idx in range(self.n_images)] pose_all = [] for scale_mat, world_mat in zip(scale_mats, world_mats): P = world_mat if scaled: P = world_mat @ scale_mat P = P[:3, :4] _, pose = rend_util.load_K_Rt_from_P(None, P) pose_all.append(torch.from_numpy(pose).float()) return torch.cat([p.float().unsqueeze(0) for p in pose_all], 0)