def get_labels_depth_and_color(root_folder, filenamebase, label_suffix='_joint_pos.txt'): label_filename = root_folder + filenamebase + label_suffix labels_jointspace = _read_label(label_filename) labels_colorspace = np.zeros((labels_jointspace.shape[0], 2)) labels_joint_depth_z = np.zeros((labels_jointspace.shape[0], 1)) for i in range(labels_jointspace.shape[0]): labels_colorspace[i, 0], labels_colorspace[i, 1], labels_joint_depth_z[i] \ = camera.joint_depth2color(labels_jointspace[i], DEPTH_INTR_MTX) return labels_jointspace, labels_colorspace, labels_joint_depth_z
def get_colorspace_joint_of_example_ix(self, example_ix, joint_ix, halnet_res=(320, 240), orig_res=(640, 480)): prop_res_u = halnet_res[0] / orig_res[0] prop_res_v = halnet_res[1] / orig_res[1] lafile_ixs_randomizedbel = _read_label(self.filenamebases[example_ix]) u, v = camera.joint_depth2color(label[joint_ix], DEPTH_INTR_MTX) u = int(u * prop_res_u) v = int(v * prop_res_v) return u, v
def joints_globaldepth_to_colorspace(jornet_joints_global, dataset_handler, img_res=(320, 240), orig_res=(640, 480)): joints_colorspace = np.zeros((21, 3)) for i in range(21): u, v, z = camera.joint_depth2color(jornet_joints_global[i, :], dataset_handler.DEPTH_INTR_MTX) joints_colorspace[i, 0] = u joints_colorspace[i, 1] = v joints_colorspace[i, 2] = z joints_colorspace[:, 0] *= img_res[0] / orig_res[0] joints_colorspace[:, 1] *= img_res[1] / orig_res[1] return joints_colorspace
def _add_squares_for_joints(image, joints, depth_intr_matrix): ''' :param image: image to which add joint squares :param joints: joints in depth camera space :param depth_intr_mtx: depth camera intrinsic params :return: image with added square for each joint ''' joints_color_space = np.zeros((joints.shape[0], 2)) for joint_ix in range(joints.shape[0]): joint = joints[joint_ix, :] u, v = camera.joint_depth2color(joint, depth_intr_matrix) image = _add_small_square(image, u, v) joints_color_space[joint_ix, 0] = u joints_color_space[joint_ix, 1] = v return image, joints_color_space