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