def geometry_pose_component_get(pose, component_name):
"""
Get an XYZ-RPY component of a pose.
Parameters:
* pose (Pose): The pose
* component_name (str): The component to get. May be `X`, `Y`, `Z`, `R_R`, `R_P`, or `R_Y`
Return (float): The pose value
"""
geom_util = GeometryUtil(node = PyriSandboxContext.node)
xyz,rpy = geom_util.pose_to_xyz_rpy(_convert_to_pose(pose))
rpy = np.rad2deg(rpy)
if component_name == "X":
return float(xyz[0])
if component_name == "Y":
return float(xyz[1])
if component_name == "Z":
return float(xyz[2])
if component_name == "R_R":
return float(rpy[0])
if component_name == "R_P":
return float(rpy[1])
if component_name == "R_Y":
return float(rpy[2])
assert False, "Invalid pose component"
def __init__(self,
device_manager,
device_info=None,
node: RR.RobotRaconteurNode = None):
if node is None:
self._node = RR.RobotRaconteurNode.s
else:
self._node = node
self.device_info = device_info
self.service_path = None
self.ctx = None
self._pose2d_dtype = self._node.GetNamedArrayDType(
"com.robotraconteur.geometry.Pose2D")
self._point2d_dtype = self._node.GetNamedArrayDType(
"com.robotraconteur.geometry.Point2D")
self._geom_util = GeometryUtil(node=self._node)
self._robot_util = RobotUtil(node=self._node)
self.device_manager = device_manager
self.device_manager.connect_device_type(
"com.robotraconteur.robotics.robot.Robot")
self.device_manager.connect_device_type(
"com.robotraconteur.robotics.tool.Tool")
self.device_manager.connect_device_type(
"tech.pyri.variable_storage.VariableStorage")
self.device_manager.device_added += self._device_added
self.device_manager.device_removed += self._device_removed
self.device_manager.refresh_devices(5)
def __init__(self,
device_manager,
device_info=None,
node: RR.RobotRaconteurNode = None):
if node is None:
self._node = RR.RobotRaconteurNode.s
else:
self._node = node
self.device_info = device_info
self.service_path = None
self.ctx = None
self._detected_marker = self._node.GetStructureType(
"tech.pyri.vision.aruco_detection.DetectedMarker")
self._aruco_detection_result = self._node.GetStructureType(
"tech.pyri.vision.aruco_detection.ArucoDetectionResult")
self._pose2d_dtype = self._node.GetNamedArrayDType(
"com.robotraconteur.geometry.Pose2D")
self._point2d_dtype = self._node.GetNamedArrayDType(
"com.robotraconteur.geometry.Point2D")
self._image_util = ImageUtil(node=self._node)
self._geom_util = GeometryUtil(node=self._node)
self.device_manager = device_manager
self.device_manager.connect_device_type(
"tech.pyri.variable_storage.VariableStorage")
self.device_manager.connect_device_type(
"com.robotraconteur.imaging.Camera")
self.device_manager.device_added += self._device_added
self.device_manager.device_removed += self._device_removed
self.device_manager.refresh_devices(5)
def _calibrate_camera_intrinsic(images, calibration_target):
ret, mtx, dist, rvecs, tvecs, mean_error, imgs = _calibrate_camera_intrinsic2(
images, calibration_target)
if not ret:
raise RR.OperationFailedException(
"Camera intrinsic calibration failed")
geom_util = GeometryUtil()
calib = RRN.NewStructure(
"com.robotraconteur.imaging.camerainfo.CameraCalibration")
calib.image_size = geom_util.wh_to_size2d(
[images[0].image_info.width, images[0].image_info.height],
dtype=np.int32)
calib.K = mtx
dist_rr = RRN.NewStructure(
"com.robotraconteur.imaging.camerainfo.PlumbBobDistortionInfo")
dist_rr.k1 = dist[0]
dist_rr.k2 = dist[1]
dist_rr.p1 = dist[2]
dist_rr.p2 = dist[3]
dist_rr.k3 = dist[4]
calib.distortion_info = RR.VarValue(
dist_rr,
"com.robotraconteur.imaging.camerainfo.PlumbBobDistortionInfo")
imgs2 = []
for img in imgs:
imgs2.append(_cv_img_to_rr_display_img(img))
return calib, imgs2, mean_error
def __init__(self, device_manager, device_info = None, node: RR.RobotRaconteurNode = None):
if node is None:
self._node = RR.RobotRaconteurNode.s
else:
self._node = node
self.device_info = device_info
self.service_path = None
self.ctx = None
self._matched_template_2d_type = self._node.GetStructureType("tech.pyri.vision.template_matching.MatchedTemplate2D")
self._template_matching_result_2d_type = self._node.GetStructureType("tech.pyri.vision.template_matching.TemplateMatchingResult2D")
self._template_matching_result_3d_type = self._node.GetStructureType("tech.pyri.vision.template_matching.TemplateMatchingResult3D")
self._matched_template_3d_type = self._node.GetStructureType("tech.pyri.vision.template_matching.MatchedTemplate3D")
self._named_pose_with_covariance_type = self._node.GetStructureType("com.robotraconteur.geometry.NamedPoseWithCovariance")
self._pose2d_dtype = self._node.GetNamedArrayDType("com.robotraconteur.geometry.Pose2D")
self._image_util = ImageUtil(node=self._node)
self._geom_util = GeometryUtil(node=self._node)
self.device_manager = device_manager
self.device_manager.connect_device_type("tech.pyri.variable_storage.VariableStorage")
self.device_manager.connect_device_type("com.robotraconteur.imaging.Camera")
self.device_manager.device_added += self._device_added
self.device_manager.device_removed += self._device_removed
self.device_manager.refresh_devices(5)
def handle_create(self, *args):
try:
robot_local_device_name = self.vue["$data"].robot_selected
intrinsic_calib = self.vue["$data"].camera_intrinsic_selected
extrinsic_calib = self.vue["$data"].camera_extrinsic_selected
image_sequence_global_name = self.vue[
"$data"].image_sequence_selected
aruco_dict = self.vue["$data"].aruco_dict_selected
aruco_tag_id = int(self.vue["$data"].aruco_tag_id)
aruco_tag_size = float(self.vue["$data"].aruco_tag_size)
xyz = np.zeros((3, ), dtype=np.float64)
rpy = np.zeros((3, ), dtype=np.float64)
xyz[0] = float(self.vue["$data"].marker_pose_x)
xyz[1] = float(self.vue["$data"].marker_pose_y)
xyz[2] = float(self.vue["$data"].marker_pose_z)
rpy[0] = float(self.vue["$data"].marker_pose_r_r)
rpy[1] = float(self.vue["$data"].marker_pose_r_p)
rpy[2] = float(self.vue["$data"].marker_pose_r_y)
rpy = np.deg2rad(rpy)
robot_calib = self.core.device_manager.get_device_subscription(
"vision_robot_calibration").GetDefaultClient()
geom_util = GeometryUtil(client_obj=robot_calib)
marker_pose = geom_util.xyz_rpy_to_pose(xyz, rpy)
self.core.create_task(do_calibration(robot_local_device_name,intrinsic_calib,extrinsic_calib,\
image_sequence_global_name,aruco_dict,aruco_tag_id, aruco_tag_size, marker_pose, self.new_name,self.core))
except:
traceback.print_exc()
def geometry_pose_component_set(pose, component_name, value):
"""
Set an XYZ-RPY component of a pose. This function does not modify in place.
It returns a new pose.
Parameters:
* pose (Pose): The pose
* component_name (str): The component to get. May be `X`, `Y`, `Z`, `R_R`, `R_P`, or `R_Y`
* value (float): The new pose component value in meters or degrees
Return (Pose): The new pose with updated value
"""
geom_util = GeometryUtil(node = PyriSandboxContext.node)
xyz,rpy = geom_util.pose_to_xyz_rpy(_convert_to_pose(pose))
rpy = np.rad2deg(rpy)
if component_name == "X":
xyz[0] = value
elif component_name == "Y":
xyz[1] = value
elif component_name == "Z":
xyz[2] = value
elif component_name == "R_R":
rpy[0] = value
elif component_name == "R_P":
rpy[1] = value
elif component_name == "R_Y":
rpy[2] = value
else:
assert False, "Invalid pose component"
rpy = np.deg2rad(rpy)
return geom_util.xyz_rpy_to_pose(xyz,rpy)
def _calibrate_camera_extrinsic(intrinsic_calib, image, board,
camera_local_device_name):
# TODO: verify calibration data
mtx = intrinsic_calib.K
dist_rr = intrinsic_calib.distortion_info.data
dist = np.array(
[dist_rr.k1, dist_rr.k2, dist_rr.p1, dist_rr.p2, dist_rr.k3],
dtype=np.float64)
image_util = ImageUtil()
frame = image_util.compressed_image_to_array(image)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if board == "chessboard":
width = 7
height = 6
square_size = 0.03
else:
raise RR.InvalidOperationException(
f"Invalid calibration board {board}")
ret, corners = cv2.findChessboardCorners(gray, (width, height), None)
assert ret, "Could not find calibration target"
objp = np.zeros((height * width, 3), np.float32)
objp[:, :2] = np.mgrid[0:width, 0:height].T.reshape(-1, 2)
objp = objp * square_size
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
ret, rvecs, tvecs = cv2.solvePnP(objp, corners2, mtx, dist)
R = cv2.Rodrigues(rvecs.flatten())[0]
R_landmark = np.array([[0, 1, 0], [1, 0, 0], [0, 0, -1]], dtype=np.float64)
R_cam1 = R.transpose()
p_cam1 = -R.transpose() @ tvecs
R_cam = R_landmark.transpose() @ R_cam1
p_cam = R_landmark.transpose() @ p_cam1
cv_image2 = cv2.aruco.drawAxis(frame, mtx, dist,
cv2.Rodrigues(R_cam.transpose())[0],
-R_cam.transpose() @ p_cam, 0.1)
T = rox.Transform(R_cam, p_cam, "world", camera_local_device_name)
geom_util = GeometryUtil()
cov = np.eye(6) * 1e-5
return geom_util.rox_transform_to_named_pose(
T), cov, image_util.array_to_compressed_image_jpg(cv_image2), 0.0
def do_show_new_camera_calibration_extrinsic_dialog2(new_name: str, camera_pose, display_image, core: "PyriWebUIBrowser"):
try:
camera_calib = core.device_manager.get_device_subscription("vision_camera_calibration").GetDefaultClient()
dialog2_html = importlib_resources.read_text(__package__,"new_calibrate_extrinsic_dialog2.html")
el = js.document.createElement('div')
el.id = "new_calibrate_extrinsic_dialog2_wrapper"
js.document.getElementById("wrapper").appendChild(el)
def handle_hidden(*args):
try:
el.parentElement.removeChild(el)
except:
traceback.print_exc()
geom_util = GeometryUtil(client_obj=camera_calib)
xyz, rpy1, _, _ = geom_util.named_pose_to_xyz_rpy(camera_pose.pose)
rpy = np.rad2deg(rpy1)
x = f"{xyz[0]:4e}"
y = f"{xyz[1]:4e}"
z = f"{xyz[2]:4e}"
r_r = f"{rpy[0]:4e}"
r_p = f"{rpy[1]:4e}"
r_y = f"{rpy[2]:4e}"
i=0
d_encoded = str(base64.b64encode(display_image.data))[2:-1]
disp_img_src = "data:image/jpeg;base64," + d_encoded
# TODO: check for png?
dialog = js.Vue.new(js.python_to_js({
"el": "#new_calibrate_extrinsic_dialog2_wrapper",
"template": dialog2_html,
"data":
{
"x": x,
"y": y,
"z": z,
"r_r": r_r,
"r_p": r_p,
"r_y": r_y,
"disp_img": disp_img_src
},
"methods":
{
"handle_hidden": handle_hidden
}
}))
dialog["$bvModal"].show("new_vision_camera_calibrate_extrinsic2")
except:
traceback.print_exc()
def geometry_pose_inv(pose):
"""
Invert a pose
Parameters:
pose (Pose): The pose to invert
Return (Pose): The inverted pose
"""
geom_util = GeometryUtil(node = PyriSandboxContext.node)
T_rox = geom_util.pose_to_rox_transform(_convert_to_pose(pose))
T_res = T_rox.inv()
return geom_util.rox_transform_to_pose(T_res)
def test_geometry_util_transform_types():
node = RR.RobotRaconteurNode()
node.SetLogLevelFromString("DEBUG")
node.Init()
try:
RRC.RegisterStdRobDefServiceTypes(node)
geom_util = GeometryUtil(node)
_do_transform_test(geom_util.rox_transform_to_transform,
geom_util.transform_to_rox_transform, "Transform",
node)
_do_transform_test(geom_util.rox_transform_to_pose,
geom_util.pose_to_rox_transform, "Pose", node)
_do_named_transform_test(geom_util.rox_transform_to_named_transform,
geom_util.named_transform_to_rox_transform,
"NamedTransform", node)
_do_named_transform_test(geom_util.rox_transform_to_named_pose,
geom_util.named_pose_to_rox_transform,
"NamedPose", node)
_do_transform_test_xyz_rpy(geom_util.xyz_rpy_to_transform,
geom_util.transform_to_xyz_rpy, "Transform",
node)
_do_transform_test_xyz_rpy(geom_util.xyz_rpy_to_pose,
geom_util.pose_to_xyz_rpy, "Pose", node)
_do_named_transform_test_xyz_rpy(geom_util.xyz_rpy_to_named_transform,
geom_util.named_transform_to_xyz_rpy,
"NamedTransform", node)
_do_named_transform_test_xyz_rpy(geom_util.xyz_rpy_to_named_pose,
geom_util.named_pose_to_xyz_rpy,
"NamedPose", node)
finally:
node.Shutdown()
def geometry_pose_multiply(pose_a, pose_b):
"""
Multiply one pose with another
Parameters:
* pose_a (Pose): The first pose
* pose_b (Pose): The second pose
Return (Pose): The result of the multiplication
"""
geom_util = GeometryUtil(node = PyriSandboxContext.node)
T_a = geom_util.pose_to_rox_transform(_convert_to_pose(pose_a))
T_b = geom_util.pose_to_rox_transform(_convert_to_pose(pose_b))
T_res = T_a * T_b
return geom_util.rox_transform_to_pose(T_res)
def __init__(self, device_manager, device_info = None, node : RR.RobotRaconteurNode = None):
if node is None:
self._node = RR.RobotRaconteurNode.s
else:
self._node = node
self.device_info = device_info
self.service_path = None
self.ctx = None
self.device_manager = device_manager
self.device_manager.connect_device_type("tech.pyri.variable_storage.VariableStorage")
self.device_manager.connect_device_type("com.robotraconteur.robotics.robot.Robot")
self.device_manager.device_added += self._device_added
self.device_manager.device_removed += self._device_removed
self.device_manager.refresh_devices(5)
self.robot_util = RobotUtil(self._node)
self.geom_util = GeometryUtil(self._node)
self.image_util = ImageUtil(self._node)
def geometry_pose_new(x,y,z,r_r,r_p,r_y):
"""
Create a new pose using XYZ-RPY format. Units are meters and degrees
Parameters:
* x (float): X position in meters
* y (float): Y position in meters
* z (float): Z position in meters
* r_r (float): Roll in degrees
* r_p (float): Pitch in degrees
* r_y (float): Yaw in degrees
Return (Pose): Pose named array
"""
xyz = np.array([x,y,z],dtype=np.float64)
rpy = np.deg2rad(np.array([r_r,r_p,r_y],dtype=np.float64))
geom_util = GeometryUtil(node = PyriSandboxContext.node)
return geom_util.xyz_rpy_to_pose(xyz,rpy)
def jog_joints_to_pose(self, pose, speed_perc):
print("Jog Joints to Pose is called")
# Similar to jog_joints_with_limits. But,
# Moves the robot to the specified joint angles with max speed
robot = self.robot
if robot is not None:
robot_state, _ = self.robot.robot_state.PeekInValue()
q_current = robot_state.joint_position
geom_util = GeometryUtil(client_obj=robot)
T_des = geom_util.pose_to_rox_transform(pose)
q_des, res = invkin.update_ik_info3(self.robot_rox, T_des,
q_current)
assert res, "Inverse kinematics failed"
self.jog_joints_with_limits(
q_des,
float(speed_perc) * 0.01 * self.joint_vel_limits, True)
else:
# Give an error message to show that the robot is not connected
print("Robot is not connected to RoboticsJog service yet!")
def robot_get_end_pose(frame):
"""
Returns the end pose of a robot. This end pose is reported by the
robot driver. It is typically defined as the flange of the robot,
but may be the tool if the driver is configured to report
the tool pose.
Parameters:
* frame (str): The frame to return the pose in. May be `robot` or `world`.
Return (Pose): The robot end pose in the requested frame
"""
robot_name = _get_active_robot_name()
device_manager = PyriSandboxContext.device_manager
robot = device_manager.get_device_client(robot_name,1)
robot_state, _ = robot.robot_state.PeekInValue()
robot_util = RobotUtil(client_obj = robot)
geom_util = GeometryUtil(client_obj = robot)
# TODO: cache robot_info
rox_robot = robot_util.robot_info_to_rox_robot(robot.robot_info,0)
T1 = rox.fwdkin(rox_robot,robot_state.joint_position)
if frame =="ROBOT":
return geom_util.rox_transform_to_pose(T1)
elif frame == "WORLD":
var_storage = device_manager.get_device_client("variable_storage")
# TODO: don't hard code robot origin
robot_origin_pose = var_storage.getf_variable_value("globals",_get_robot_origin_calibration()).data
T_rob = geom_util.named_pose_to_rox_transform(robot_origin_pose.pose)
T2 = T_rob * T1
return geom_util.rox_transform_to_pose(T2)
else:
assert False, "Invalid frame"
def test_geometry_util_array_types():
node = RR.RobotRaconteurNode()
node.SetLogLevelFromString("DEBUG")
node.Init()
try:
RRC.RegisterStdRobDefServiceTypes(node)
geom_util = GeometryUtil(node)
_do_array_test(geom_util.xy_to_vector2, geom_util.vector2_to_xy, (2, ),
"Vector2", node)
_do_array_test(geom_util.xyz_to_vector3, geom_util.vector3_to_xyz,
(3, ), "Vector3", node)
_do_array_test(geom_util.abgxyz_to_vector6,
geom_util.vector6_to_abgxyz, (6, ), "Vector6", node)
_do_array_test(geom_util.xy_to_point2d, geom_util.point2d_to_xy, (2, ),
"Point2D", node)
_do_array_test(geom_util.xyz_to_point, geom_util.point_to_xyz, (3, ),
"Point", node)
_do_array_test(geom_util.wh_to_size2d, geom_util.size2d_to_wh, (2, ),
"Size2D", node)
_do_array_test(geom_util.whd_to_size, geom_util.size_to_whd, (3, ),
"Size", node)
_do_array_test(geom_util.q_to_quaternion, geom_util.quaternion_to_q,
(3, ), "Quaternion", node,
lambda a: rox.R2q(rox.rpy2R(a)))
_do_array_test(geom_util.R_to_quaternion, geom_util.quaternion_to_R,
(3, ), "Quaternion", node, lambda a: rox.rpy2R(a))
_do_array_test(geom_util.rpy_to_quaternion,
geom_util.quaternion_to_rpy, (3, ), "Quaternion", node)
_do_array_test(geom_util.array_to_spatial_velocity,
geom_util.spatial_velocity_to_array, (6, ),
"SpatialVelocity", node)
_do_array_test(geom_util.array_to_spatial_acceleration,
geom_util.spatial_acceleration_to_array, (6, ),
"SpatialAcceleration", node)
_do_array_test(geom_util.array_to_wrench, geom_util.wrench_to_array,
(6, ), "Wrench", node)
finally:
node.Shutdown()
def movel(self,
robot_local_device_name,
pose_final,
frame,
robot_origin_calib_global_name,
speed_perc,
final_seed=None):
robot = self.device_manager.get_device_client(robot_local_device_name)
geom_util = GeometryUtil(client_obj=robot)
if frame.lower() == "world":
var_storage = self.device_manager.get_device_client(
"variable_storage")
robot_origin_pose = var_storage.getf_variable_value(
"globals", robot_origin_calib_global_name).data
T_rob = geom_util.named_pose_to_rox_transform(
robot_origin_pose.pose)
T_des1 = geom_util.pose_to_rox_transform(pose_final)
T_des = T_rob.inv() * T_des1
pose_final = geom_util.rox_transform_to_pose(T_des)
elif frame.lower() == "robot":
T_des = geom_util.pose_to_rox_transform(pose_final)
else:
assert False, "Unknown parent frame for movel"
robot_info = robot.robot_info
rox_robot = self._robot_util.robot_info_to_rox_robot(robot_info, 0)
robot_state = robot.robot_state.PeekInValue()[0]
q_initial = robot_state.joint_position
traj = self._generate_movel_trajectory(robot, rox_robot, q_initial,
T_des, speed_perc, final_seed)
if traj is None:
return EmptyGenerator()
return TrajectoryMoveGenerator(robot, rox_robot, traj, self._node)
d.connect_device("robot")
c = d.get_device_client("robotics_motion", 1)
#p_target = np.array([-np.random.uniform(0.4,0.8),np.random.uniform(-0.1,0.1),np.random.uniform(0.0,0.4)])
p_target = np.array([
-np.random.uniform(-0.1, 0.1),
np.random.uniform(-0.1, 0.1),
np.random.uniform(0.0, 0.4)
])
rpy_target = np.random.randn(3) * 0.5
rpy_target[0] += np.pi
R_target = rox.rpy2R(rpy_target)
# p_target = np.array([-0.6, 0.0, 0.1])
# R_target = np.array([[0,1,0],[1,0,0],[0,0,-1]])
T_target = rox.Transform(R_target, p_target)
r = d.get_device_client("robot", 1)
geom_util = GeometryUtil(client_obj=r)
p_target = geom_util.rox_transform_to_pose(T_target)
print("Begin movel")
gen = c.movel("robot", p_target, "world", "robot_origin_calibration", 50)
while True:
try:
gen.Next()
except RR.StopIterationException:
break
print("End movel")
class VisionTemplateMatching_impl(object):
def __init__(self, device_manager, device_info = None, node: RR.RobotRaconteurNode = None):
if node is None:
self._node = RR.RobotRaconteurNode.s
else:
self._node = node
self.device_info = device_info
self.service_path = None
self.ctx = None
self._matched_template_2d_type = self._node.GetStructureType("tech.pyri.vision.template_matching.MatchedTemplate2D")
self._template_matching_result_2d_type = self._node.GetStructureType("tech.pyri.vision.template_matching.TemplateMatchingResult2D")
self._template_matching_result_3d_type = self._node.GetStructureType("tech.pyri.vision.template_matching.TemplateMatchingResult3D")
self._matched_template_3d_type = self._node.GetStructureType("tech.pyri.vision.template_matching.MatchedTemplate3D")
self._named_pose_with_covariance_type = self._node.GetStructureType("com.robotraconteur.geometry.NamedPoseWithCovariance")
self._pose2d_dtype = self._node.GetNamedArrayDType("com.robotraconteur.geometry.Pose2D")
self._image_util = ImageUtil(node=self._node)
self._geom_util = GeometryUtil(node=self._node)
self.device_manager = device_manager
self.device_manager.connect_device_type("tech.pyri.variable_storage.VariableStorage")
self.device_manager.connect_device_type("com.robotraconteur.imaging.Camera")
self.device_manager.device_added += self._device_added
self.device_manager.device_removed += self._device_removed
self.device_manager.refresh_devices(5)
def RRServiceObjectInit(self, ctx, service_path):
self.service_path = service_path
self.ctx = ctx
def _device_added(self, local_device_name):
pass
def _device_removed(self, local_device_name):
pass
def match_template_stored_image(self, image_global_name, template_global_name, roi):
var_storage = self.device_manager.get_device_client("variable_storage",1)
image_var = var_storage.getf_variable_value("globals", image_global_name)
image = self._image_util.compressed_image_to_array(image_var.data)
template_var = var_storage.getf_variable_value("globals", template_global_name)
template = self._image_util.compressed_image_to_array(template_var.data)
return self._do_template_match(template, image, roi)
def match_template_camera_capture(self, camera_local_device_name, template_global_name, roi):
var_storage = self.device_manager.get_device_client("variable_storage",1)
template_var = var_storage.getf_variable_value("globals", template_global_name)
template = self._image_util.compressed_image_to_array(template_var.data)
camera_device = self.device_manager.get_device_client(camera_local_device_name)
image_compressed = camera_device.capture_frame_compressed()
image = self._image_util.compressed_image_to_array(image_compressed)
return self._do_template_match(template, image, roi)
def _do_template_match(self, template, image, roi):
matcher_roi = None
if roi is not None:
roi_x = roi.center.pose[0]["position"]["x"]
roi_y = roi.center.pose[0]["position"]["y"]
roi_theta = roi.center.pose[0]["orientation"]
roi_w = roi.size[0]["width"]
roi_h = roi.size[0]["height"]
matcher_roi = (roi_x, roi_y, roi_w, roi_h, -roi_theta)
# execute the image detection using opencv
matcher = TemplateMatchingMultiAngleWithROI(template,image,matcher_roi)
# return_result_image = True
match_center, template_wh, match_angle, detection_result_img = matcher.detect_object(True)
# return the pose of the object
print("the object is found..:")
print("center coordinates in img frame(x,y): " + str(match_center))
print("(w,h): " + str(template_wh))
print("angle: " + str(match_angle))
match_result = self._template_matching_result_2d_type()
matched_template_result = self._matched_template_2d_type()
centroid = np.zeros((1,),dtype=self._pose2d_dtype)
centroid[0]["position"]["x"] = match_center[0]
centroid[0]["position"]["y"] = match_center[1]
centroid[0]["orientation"] = match_angle
matched_template_result.match_centroid = centroid
matched_template_result.template_size = self._geom_util.wh_to_size2d(template_wh,dtype=np.int32)
matched_template_result.confidence = 0
match_result.template_matches =[matched_template_result]
match_result.display_image = self._image_util.array_to_compressed_image_jpg(detection_result_img, 70)
return match_result
def _do_match_with_pose(self,image, template, intrinsic_calib, extrinsic_calib, object_z, roi):
matcher_roi = None
if roi is not None:
roi_x = roi.center.pose[0]["position"]["x"]
roi_y = roi.center.pose[0]["position"]["y"]
roi_theta = roi.center.pose[0]["orientation"]
roi_w = roi.size[0]["width"]
roi_h = roi.size[0]["height"]
matcher_roi = (roi_x, roi_y, roi_w, roi_h, -roi_theta)
# execute the image detection using opencv
matcher = TemplateMatchingMultiAngleWithROI(template,image,matcher_roi)
# return_result_image = True
match_center, template_wh, match_angle, detection_result_img = matcher.detect_object(True)
# detection_result.width
# detection_result.height
x = match_center[0]
y = match_center[1]
theta = match_angle
src = np.asarray([x,y], dtype=np.float32)
src = np.reshape(src,(-1,1,2)) # Rehsape as opencv requires (N,1,2)
# print(src)
# Now the detection results in image frame is found,
# Hence, find the detected pose in camera frame with given z distance to camera
# To do that first we need to get the camera parameters
# Load the camera matrix and distortion coefficients from the calibration result.
mtx = intrinsic_calib.K
d = intrinsic_calib.distortion_info.data
dist = np.array([d.k1,d.k2,d.p1,d.p2,d.k3])
T_cam = self._geom_util.named_pose_to_rox_transform(extrinsic_calib.pose)
#TODO: Figure out a better value for this
object_z_cam_dist = abs(T_cam.p[2]) - object_z
# Find the corresponding world pose of the detected pose in camera frame
dst = cv2.undistortPoints(src,mtx,dist) # dst is Xc/Zc and Yc/Zc in the same shape of src
dst = dst * float(object_z_cam_dist) * 1000.0 # Multiply by given Zc distance to find all cordinates, multiply by 1000 is because of Zc is given in meters but others are in millimeters
dst = np.squeeze(dst) * 0.001 # Xc and Yc as vector
# Finally the translation between the detected object center and the camera frame represented in camera frame is T = [Xc,Yc,Zc]
Xc = dst[0]
Yc = dst[1]
Zc = float(object_z_cam_dist)
T = np.asarray([Xc,Yc,Zc])
# Now lets find the orientation of the detected object with respect to camera
# We are assuming +z axis is looking towards the camera and xy axes of the both object and camera are parallel planes
# So the rotation matrix would be
theta = np.deg2rad(theta) #convert theta from degrees to radian
R_co = np.asarray([[math.cos(theta),-math.sin(theta),0],[-math.sin(theta),-math.cos(theta),0],[0,0,-1]])
T_obj_cam_frame = rox.Transform(R_co, T, "camera", "object")
T_obj = T_cam * T_obj_cam_frame
# TODO: Better adjustment of Z height?
T_obj.p[2] = object_z
ret1 = self._matched_template_3d_type()
ret1.pose = self._named_pose_with_covariance_type()
ret1.pose.pose = self._geom_util.rox_transform_to_named_pose(T_obj)
ret1.pose.covariance= np.zeros((6,6))
ret1.confidence = 0
ret = self._template_matching_result_3d_type()
ret.template_matches = [ret1]
ret.display_image = self._image_util.array_to_compressed_image_jpg(detection_result_img,70)
return ret
def _do_match_template_world_pose(self, image, template_global_name,
camera_calibration_intrinsic, camera_calibration_extrinsic, object_z_height, roi, var_storage):
template_var = var_storage.getf_variable_value("globals", template_global_name)
template = self._image_util.compressed_image_to_array(template_var.data)
intrinsic_calib = var_storage.getf_variable_value("globals", camera_calibration_intrinsic).data
extrinsic_calib = var_storage.getf_variable_value("globals", camera_calibration_extrinsic).data
return self._do_match_with_pose(image,template,intrinsic_calib,extrinsic_calib,object_z_height,roi)
def match_template_world_pose_stored_image(self, image_global_name, template_global_name,
camera_calibration_intrinsic, camera_calibration_extrinsic, object_z_height, roi):
var_storage = self.device_manager.get_device_client("variable_storage",1)
image_var = var_storage.getf_variable_value("globals", image_global_name)
image = self._image_util.compressed_image_to_array(image_var.data)
return self._do_match_template_world_pose(image, template_global_name, camera_calibration_intrinsic,
camera_calibration_extrinsic, object_z_height, roi, var_storage)
def match_template_world_pose_camera_capture(self, camera_local_device_name, template_global_name,
camera_calibration_intrinsic, camera_calibration_extrinsic, object_z_height, roi):
var_storage = self.device_manager.get_device_client("variable_storage",1)
camera_device = self.device_manager.get_device_client(camera_local_device_name,1)
img_rr = camera_device.capture_frame_compressed()
image = self._image_util.compressed_image_to_array(img_rr)
return self._do_match_template_world_pose(image, template_global_name, camera_calibration_intrinsic,
camera_calibration_extrinsic, object_z_height, roi, var_storage)
class CameraRobotCalibrationService_impl(object):
def __init__(self, device_manager, device_info = None, node : RR.RobotRaconteurNode = None):
if node is None:
self._node = RR.RobotRaconteurNode.s
else:
self._node = node
self.device_info = device_info
self.service_path = None
self.ctx = None
self.device_manager = device_manager
self.device_manager.connect_device_type("tech.pyri.variable_storage.VariableStorage")
self.device_manager.connect_device_type("com.robotraconteur.robotics.robot.Robot")
self.device_manager.device_added += self._device_added
self.device_manager.device_removed += self._device_removed
self.device_manager.refresh_devices(5)
self.robot_util = RobotUtil(self._node)
self.geom_util = GeometryUtil(self._node)
self.image_util = ImageUtil(self._node)
def RRServiceObjectInit(self, ctx, service_path):
self.service_path = service_path
self.ctx = ctx
def _device_added(self, local_device_name):
pass
def _device_removed(self, local_device_name):
pass
def calibrate_robot_origin(self, robot_local_device_name, camera_intrinsic_calibration_global_name, camera_extrinsic_calibration_global_name, \
image_sequence_global_name, aruco_dict, aruco_id, aruco_markersize, flange_to_marker, output_global_name):
var_storage = self.device_manager.get_device_client("variable_storage",1)
var_consts = var_storage.RRGetNode().GetConstants('tech.pyri.variable_storage', var_storage)
variable_persistence = var_consts["VariablePersistence"]
variable_protection_level = var_consts["VariableProtectionLevel"]
if len(output_global_name) > 0:
if len(var_storage.filter_variables("globals",output_global_name,[])) > 0:
raise RR.InvalidOperationException(f"Global {output_global_name} already exists")
image_sequence = []
joint_pos_sequence = []
image_sequence_vars = var_storage.getf_variable_value("globals",image_sequence_global_name)
for image_var in image_sequence_vars.data.splitlines():
var2 = var_storage.getf_variable_value("globals",image_var)
image_sequence.append(self.image_util.compressed_image_to_array(var2.data))
var2_tags = var_storage.getf_variable_attributes("globals", image_var)
var2_state_var_name = var2_tags["system_state"]
var2_state = var_storage.getf_variable_value("globals", var2_state_var_name)
joint_pos = None
for s in var2_state.data.devices_states[robot_local_device_name].state:
if s.type == "com.robotraconteur.robotics.robot.RobotState":
joint_pos = s.state_data.data.joint_position
assert joint_pos is not None, "Could not find joint position in state sequence"
joint_pos_sequence.append(joint_pos)
cam_intrinsic_calib = var_storage.getf_variable_value("globals",camera_intrinsic_calibration_global_name).data
cam_extrinsic_calib = var_storage.getf_variable_value("globals",camera_extrinsic_calibration_global_name).data
mtx = cam_intrinsic_calib.K
dist_rr = cam_intrinsic_calib.distortion_info.data
dist = np.array([dist_rr.k1, dist_rr.k2, dist_rr.p1, dist_rr.p2, dist_rr.k3],dtype=np.float64)
cam_pose = self.geom_util.named_pose_to_rox_transform(cam_extrinsic_calib.pose)
robot = self.device_manager.get_device_client(robot_local_device_name,1)
robot_info = robot.robot_info
rox_robot = self.robot_util.robot_info_to_rox_robot(robot_info,0)
# Calibrate
robot_pose1, robot_pose_cov, img, calibration_error = calibrator.calibrate(image_sequence, joint_pos_sequence, aruco_dict, aruco_id, aruco_markersize, flange_to_marker, mtx, dist, cam_pose, rox_robot, robot_local_device_name)
robot_pose = self._node.NewStructure("com.robotraconteur.geometry.NamedPoseWithCovariance")
robot_pose.pose = robot_pose1
robot_pose.covariance = robot_pose_cov
if len(output_global_name) > 0:
var_storage.add_variable2("globals",output_global_name,"com.robotraconteur.geometry.NamedPoseWithCovariance", \
RR.VarValue(robot_pose,"com.robotraconteur.geometry.NamedPoseWithCovariance"), ["robot_origin_pose_calibration"],
{"device": robot_local_device_name}, variable_persistence["const"], None, variable_protection_level["read_write"], \
[], f"Robot \"{robot_local_device_name}\" origin pose calibration", False)
ret = RRN.NewStructure("tech.pyri.vision.robot_calibration.CameraRobotBaseCalibrateResult")
ret.robot_pose = robot_pose
ret.display_images = img
ret.calibration_error = calibration_error
return ret
import time
from RobotRaconteurCompanion.Util.ImageUtil import ImageUtil
from RobotRaconteurCompanion.Util.GeometryUtil import GeometryUtil
import numpy as np
import cv2
d = DeviceManagerClient('rr+tcp://localhost:59902?service=device_manager',
autoconnect=False)
d.refresh_devices(1)
d.connect_device("vision_robot_calibration")
calibration_service = d.get_device_client("vision_robot_calibration", 1)
geom_util = GeometryUtil(client_obj=calibration_service)
marker_pose = geom_util.xyz_rpy_to_pose(
np.array([0.0393, -0.0091, 0.055]), np.array(np.deg2rad([90.0, 0.0,
180.0])))
ret = calibration_service.calibrate_robot_origin(
"robot", "camera_intrinsic_calibration", "camera_extrinsic_calibration",
"robot_calib0", "DICT_6X6_250", 150, 0.0316, marker_pose,
"") # "robot_origin_calibration0"
image_util = ImageUtil(client_obj=calibration_service)
geom_util = GeometryUtil(client_obj=calibration_service)
T = geom_util.named_pose_to_rox_transform(ret.robot_pose.pose)
print(T)
print(ret)
import time
from RobotRaconteurCompanion.Util.ImageUtil import ImageUtil
from RobotRaconteurCompanion.Util.GeometryUtil import GeometryUtil
import cv2
import numpy as np
d = DeviceManagerClient('rr+tcp://localhost:59902?service=device_manager',
autoconnect=False)
d.refresh_devices(1)
d.connect_device("robotics_motion")
c = d.get_device_client("robotics_motion", 1)
geom_util = GeometryUtil(client_obj=c)
def _run_grab(gen):
while True:
try:
res = gen.Next()
print(res)
except RR.StopIterationException:
break
for i in range(5):
pose2d_dtype = RRN.GetNamedArrayDType("com.robotraconteur.geometry.Pose2D",
c)
obj_pose = np.zeros((1, ), dtype=pose2d_dtype)
print(jog_service.device_info.device.name)
jog = jog_service.get_jog("robot")
jog.setf_jog_mode()
#for x in range(100):
#jog.jog_joints3(1,1)
#jog.setf_halt_mode()
robot = d.get_device_client("robot", 1)
robot_state, _ = robot.robot_state.PeekInValue()
q_current = robot_state.joint_position
robot_util = RobotUtil(client_obj=robot)
rox_robot = robot_util.robot_info_to_rox_robot(robot.robot_info, 0)
geom_util = GeometryUtil(client_obj=jog_service)
T = rox.fwdkin(rox_robot, q_current)
print(f"Current xyz = {T.p}, rpy = {np.rad2deg(rox.R2rpy(T.R))}")
T2 = copy.deepcopy(T)
T2.p[1] += 0.1
T3 = copy.deepcopy(T)
T3.p[1] -= 0.1
pose_des = geom_util.rox_transform_to_pose(T2)
pose_des2 = geom_util.rox_transform_to_pose(T3)
for i in range(10):
jog.jog_joints_to_pose(pose_des, 50)
jog.jog_joints_to_pose(pose_des2, 50)
import time
from RobotRaconteurCompanion.Util.ImageUtil import ImageUtil
from RobotRaconteurCompanion.Util.GeometryUtil import GeometryUtil
import cv2
d = DeviceManagerClient('rr+tcp://localhost:59902?service=device_manager',
autoconnect=False)
d.refresh_devices(1)
d.connect_device("vision_camera_calibration")
calibration_service = d.get_device_client("vision_camera_calibration", 1)
ret = calibration_service.calibrate_camera_extrinsic(
"camera", "camera_intrinsic_calibration", "extrinsic_image0", "chessboard",
"camera_extrinsic_calibration0") # "camera_extrinsic_calibration1")
image_util = ImageUtil(client_obj=calibration_service)
geom_util = GeometryUtil(client_obj=calibration_service)
T = geom_util.named_pose_to_rox_transform(ret.camera_pose.pose)
print(T)
print(ret)
img = image_util.compressed_image_to_array(ret.display_image)
cv2.imshow(f"img", img)
cv2.waitKey()
cv2.destroyAllWindows()
def do_show_new_robot_origin_calibration_dialog2(new_name: str, robot_pose,
display_images,
core: "PyriWebUIBrowser"):
try:
dialog2_html = importlib_resources.read_text(
__package__, "new_calibrate_robot_origin_dialog2.html")
robot_calib = core.device_manager.get_device_subscription(
"vision_robot_calibration").GetDefaultClient()
geom_util = GeometryUtil(client_obj=robot_calib)
marker_xyz, marker_rpy, _, _ = geom_util.named_pose_to_xyz_rpy(
robot_pose.pose)
el = js.document.createElement('div')
el.id = "new_calibrate_robot_origin_dialog2_wrapper"
js.document.getElementById("wrapper").appendChild(el)
def handle_hidden(*args):
try:
el.parentElement.removeChild(el)
except:
traceback.print_exc()
x = f"{marker_xyz[0]:4e}"
y = f"{marker_xyz[1]:4e}"
z = f"{marker_xyz[2]:4e}"
r_r = f"{marker_rpy[0]:4e}"
r_p = f"{marker_rpy[1]:4e}"
r_y = f"{marker_rpy[2]:4e}"
imgs = []
i = 0
for d in display_images:
d_encoded = str(base64.b64encode(d.data))[2:-1]
d2 = {
"id": i,
"caption": f"Calibration result {i+1}",
"img": "data:image/jpeg;base64," + d_encoded
}
del d_encoded
imgs.append(d2)
i += 1
#TODO: check for png?
dialog = js.Vue.new(
js.python_to_js({
"el": "#new_calibrate_robot_origin_dialog2_wrapper",
"template": dialog2_html,
"data": {
"x": x,
"y": y,
"z": z,
"r_r": r_r,
"r_p": r_p,
"r_y": r_y,
"display_images": imgs
},
"methods": {
"handle_hidden": handle_hidden
}
}))
dialog["$bvModal"].show("new_vision_camera_calibrate_robot_origin2")
except:
traceback.print_exc()
c = d.get_device_client("vision_template_matching", 1)
bounding_box2d_type = RRN.GetStructureType(
'com.robotraconteur.geometry.BoundingBox2D', c)
named_pose2d_type = RRN.GetStructureType(
'com.robotraconteur.geometry.NamedPose2D', c)
pose2d_dtype = RRN.GetNamedArrayDType('com.robotraconteur.geometry.Pose2D', c)
var_storage = d.get_device_client("variable_storage", 1)
roi = var_storage.getf_variable_value("globals", roi_name).data
#res = c.match_template_stored_image("extrinsic_image0", "test10", None)
res = c.match_template_world_pose_camera_capture(
"camera", template_name, "camera_calibration_intrinsic",
"camera_calibration_extrinsic", 0, None)
img_util = ImageUtil(client_obj=c)
res_img = img_util.compressed_image_to_array(res.display_image)
geom_util = GeometryUtil(client_obj=c)
xyz, rpy, _, _ = geom_util.named_pose_to_xyz_rpy(
res.template_matches[0].pose.pose)
cv2.imshow("", res_img)
cv2.waitKey()
cv2.destroyAllWindows()
print(res)
class VisionArucoDetection_impl(object):
def __init__(self,
device_manager,
device_info=None,
node: RR.RobotRaconteurNode = None):
if node is None:
self._node = RR.RobotRaconteurNode.s
else:
self._node = node
self.device_info = device_info
self.service_path = None
self.ctx = None
self._detected_marker = self._node.GetStructureType(
"tech.pyri.vision.aruco_detection.DetectedMarker")
self._aruco_detection_result = self._node.GetStructureType(
"tech.pyri.vision.aruco_detection.ArucoDetectionResult")
self._pose2d_dtype = self._node.GetNamedArrayDType(
"com.robotraconteur.geometry.Pose2D")
self._point2d_dtype = self._node.GetNamedArrayDType(
"com.robotraconteur.geometry.Point2D")
self._image_util = ImageUtil(node=self._node)
self._geom_util = GeometryUtil(node=self._node)
self.device_manager = device_manager
self.device_manager.connect_device_type(
"tech.pyri.variable_storage.VariableStorage")
self.device_manager.connect_device_type(
"com.robotraconteur.imaging.Camera")
self.device_manager.device_added += self._device_added
self.device_manager.device_removed += self._device_removed
self.device_manager.refresh_devices(5)
def RRServiceObjectInit(self, ctx, service_path):
self.service_path = service_path
self.ctx = ctx
def _device_added(self, local_device_name):
pass
def _device_removed(self, local_device_name):
pass
def _do_aruco_detection(self, img, intrinsic_global_name,
extrinsic_global_name, aruco_dict_str, aruco_id,
aruco_markersize, roi):
intrinsic_calib = None
extrinsic_calib = None
if intrinsic_global_name is not None and extrinsic_global_name is not None:
var_storage = self.device_manager.get_device_client(
"variable_storage", 0.1)
intrinsic_calib = var_storage.getf_variable_value(
"globals", intrinsic_global_name).data
extrinsic_calib = var_storage.getf_variable_value(
"globals", extrinsic_global_name).data
display_img = img.copy()
assert aruco_dict_str.startswith(
"DICT_"), "Invalid aruco dictionary name"
aruco_dict_i = getattr(aruco,
aruco_dict_str) # convert string to python
aruco_dict = cv2.aruco.Dictionary_get(aruco_dict_i)
aruco_params = cv2.aruco.DetectorParameters_create()
aruco_params.cornerRefinementMethod = cv2.aruco.CORNER_REFINE_SUBPIX
corners1, ids1, rejected = cv2.aruco.detectMarkers(
img, aruco_dict, parameters=aruco_params)
if corners1 is None:
corners1 = []
if ids1 is None:
ids1 = []
if aruco_id < 0:
corners2 = corners1
ids2 = ids1
else:
corners2 = []
ids2 = []
for id2, corner2 in zip(ids1, corners1):
if id2 == aruco_id:
corners2.append(corner2)
ids2.append(id2)
ids2 = np.array(ids2)
if roi is None:
corners = corners2
ids = ids2
else:
roi_x = roi.center.pose[0]["position"]["x"]
roi_y = roi.center.pose[0]["position"]["y"]
roi_theta = roi.center.pose[0]["orientation"]
roi_w = roi.size[0]["width"]
roi_h = roi.size[0]["height"]
geom_roi1 = shapely.geometry.box(-roi_w / 2.,
-roi_h / 2.,
roi_w / 2.,
roi_h / 2.,
ccw=True)
geom_roi2 = shapely.affinity.translate(geom_roi1,
xoff=roi_x,
yoff=roi_y)
geom_roi = shapely.affinity.rotate(geom_roi2,
roi_theta,
origin='centroid',
use_radians=True)
corners = []
ids = []
for id3, corner3 in zip(ids2, corners2):
centroid = shapely.geometry.Polygon([
shapely.geometry.Point(corner3[0, i, 0], corner3[0, i, 1])
for i in range(4)
])
if geom_roi.contains(centroid):
corners.append(corner3)
ids.append(id3)
ids = np.array(ids)
roi_outline = np.array([geom_roi.exterior.coords], dtype=np.int32)
display_img = cv2.polylines(display_img,
roi_outline,
True,
color=(255, 255, 0))
if len(ids) > 0:
display_img = aruco.drawDetectedMarkers(display_img, corners, ids)
poses = None
if intrinsic_calib is not None and extrinsic_calib is not None:
poses = []
mtx = intrinsic_calib.K
d = intrinsic_calib.distortion_info.data
dist = np.array([d.k1, d.k2, d.p1, d.p2, d.k3])
T_cam = self._geom_util.named_pose_to_rox_transform(
extrinsic_calib.pose)
for id4, corner4 in zip(ids, corners):
rvec, tvec, markerPoints = cv2.aruco.estimatePoseSingleMarkers(
corner4, aruco_markersize, mtx, dist)
display_img = cv2.aruco.drawAxis(display_img, mtx, dist, rvec,
tvec, 0.05)
# R_marker1 = cv2.Rodrigues(rvec.flatten())[0]
# TODO: 3D pose estimation from rvec is very innaccurate. Use a simple trigonometry
# to estimate the Z rotation of the tag
# compute vectors from opposite corners
v1 = corner4[0, 2, :] - corner4[0, 0, :]
v2 = corner4[0, 3, :] - corner4[0, 1, :]
# Use atan2 on each vector and average
theta1 = (np.arctan2(v1[1], v1[0]) - np.deg2rad(45)) % (2. *
np.pi)
theta2 = (np.arctan2(v2[1], v2[0]) - np.deg2rad(135)) % (2. *
np.pi)
theta = (theta1 + theta2) / 2.
R_marker1 = rox.rot([1., 0., 0.], np.pi) @ rox.rot(
[0., 0., -1.], theta)
p_marker1 = tvec.flatten()
T_marker1 = rox.Transform(R_marker1, p_marker1, "camera",
f"aruco{int(id4)}")
T_marker = T_cam * T_marker1
rr_marker_pose = self._geom_util.rox_transform_to_named_pose(
T_marker)
poses.append(rr_marker_pose)
ret_markers = []
if poses is None:
poses = [None] * len(ids)
for id_, corner, pose in zip(ids, corners, poses):
m = self._detected_marker()
m.marker_id = int(id_)
m.marker_corners = np.zeros((4, ), dtype=self._point2d_dtype)
for i in range(4):
m.marker_corners[i] = self._geom_util.xy_to_point2d(
corner[0, i, :])
m.marker_pose = pose
ret_markers.append(m)
ret = self._aruco_detection_result()
ret.detected_markers = ret_markers
ret.display_image = self._image_util.array_to_compressed_image_jpg(
display_img, 70)
return ret
def detect_aruco_stored_image(self, image_global_name,
camera_calibration_intrinsic,
camera_calibration_extrinsic, aruco_dict,
aruco_id, aruco_markersize, roi):
if len(camera_calibration_intrinsic) == 0:
camera_calibration_intrinsic = None
if len(camera_calibration_extrinsic) == 0:
camera_calibration_extrinsic = None
var_storage = self.device_manager.get_device_client(
"variable_storage", 0.1)
img_rr = var_storage.getf_variable_value("globals", image_global_name)
img = self._image_util.compressed_image_to_array(img_rr.data)
return self._do_aruco_detection(img, camera_calibration_intrinsic,
camera_calibration_extrinsic,
aruco_dict, aruco_id, aruco_markersize,
roi)
def detect_aruco_camera_capture(self, camera_local_device_name,
camera_calibration_intrinsic,
camera_calibration_extrinsic, aruco_dict,
aruco_id, aruco_markersize, roi):
if len(camera_calibration_intrinsic) == 0:
camera_calibration_intrinsic = None
if len(camera_calibration_extrinsic) == 0:
camera_calibration_extrinsic = None
camera_device = self.device_manager.get_device_client(
camera_local_device_name, 1)
img_rr = camera_device.capture_frame_compressed()
img = self._image_util.compressed_image_to_array(img_rr)
return self._do_aruco_detection(img, camera_calibration_intrinsic,
camera_calibration_extrinsic,
aruco_dict, aruco_id, aruco_markersize,
roi)
def calibrate(images, joint_poses, aruco_dict, aruco_id, aruco_markersize,
flange_to_marker, mtx, dist, cam_pose, rox_robot,
robot_local_device_name):
""" Apply extrinsic camera calibration operation for images in the given directory path
using opencv aruco marker detection, the extrinsic marker poses given in a json file,
and the given intrinsic camera parameters."""
assert aruco_dict.startswith("DICT_"), "Invalid aruco dictionary name"
aruco_dict = getattr(aruco, aruco_dict) # convert string to python
aruco_dict = cv2.aruco.Dictionary_get(aruco_dict)
aruco_params = cv2.aruco.DetectorParameters_create()
i = 0
imgs_out = []
geom_util = GeometryUtil()
image_util = ImageUtil()
object_points = []
image_points = []
for img, joints in zip(images, joint_poses):
# Find the aruco tag corners
# corners, ids, rejected = cv2.aruco.detectMarkers(img, aruco_dict, parameters=aruco_params,cameraMatrix=mtx, distCoeff=dist)
corners, ids, rejected = cv2.aruco.detectMarkers(
img, aruco_dict, parameters=aruco_params)
# #debug
# print(str(type(corners))) # <class 'list'>
# print(str(corners)) # list of numpy arrays of corners
# print(str(type(ids))) # <class 'numpy.ndarray'>
# print(str(ids))
if len(corners) > 0:
# Only find the id that we desire
indexes = np.flatnonzero(ids.flatten() == aruco_id).tolist()
corners = [corners[index] for index in indexes]
ids = np.asarray([ids[index] for index in indexes])
# #debug
# print(str(type(corners))) # <class 'list'>
# print(str(corners)) # list of numpy arrays of corners
# print(str(type(ids))) # <class 'numpy.ndarray'>
# print(str(ids))
if len(ids) > 0: # if there exist at least one id that we desire
# Select the first detected one, discard the others
corners = corners[0] # now corners is 1 by 4
# # extract the marker corners (which are always returned
# # in top-left, top-right, bottom-right, and bottom-left
# # order)
# corners = corners.reshape((4, 2))
# (topLeft, topRight, bottomRight, bottomLeft) = corners
# Estimate the pose of the detected marker in camera frame
rvec, tvec, markerPoints = cv2.aruco.estimatePoseSingleMarkers(
corners, aruco_markersize, mtx, dist)
# # Debug: Show the detected tag and axis in the image
# # # cv2.aruco.drawDetectedMarkers(img, corners) # Draw A square around the markers (Does not work)
img1 = img.copy()
img_out = cv2.aruco.drawAxis(img1, mtx, dist, rvec, tvec,
aruco_markersize *
0.75) # Draw Axis
imgs_out.append(img_out)
transform_base_2_flange = rox.fwdkin(rox_robot, joints)
transform_flange_2_marker = geom_util.pose_to_rox_transform(
flange_to_marker)
transform_base_2_marker = transform_base_2_flange * transform_flange_2_marker
transform_base_2_marker_corners = _marker_corner_poses(
transform_base_2_marker, aruco_markersize)
# Structure of this disctionary is "filename":[[R_base2marker],[T_base2marker],[R_cam2marker],[T_cam2marker]]
for j in range(4):
object_points.append(transform_base_2_marker_corners[j].p)
image_points.append(corners[0, j])
#pose_pairs_dict[i] = (transform_base_2_marker_corners, corners)
i += 1
object_points_np = np.array(object_points, dtype=np.float64)
image_points_np = np.array(image_points, dtype=np.float32)
# Finally execute the calibration
retval, rvec, tvec = cv2.solvePnP(object_points_np, image_points_np, mtx,
dist)
R_cam2base = cv2.Rodrigues(rvec)[0]
T_cam2base = tvec
# Add another display image of marker at robot base
img_out = cv2.aruco.drawAxis(img, mtx, dist,
cv2.Rodrigues(R_cam2base)[0], T_cam2base,
aruco_markersize * 0.75) # Draw Axis
imgs_out.append(img_out)
rox_transform_cam2base = rox.Transform(R_cam2base, T_cam2base,
cam_pose.parent_frame_id,
robot_local_device_name)
rox_transform_world2base = cam_pose * rox_transform_cam2base
#R_base2cam = R_cam2base.T
#T_base2cam = - R_base2cam @ T_cam2base
R_base2cam = rox_transform_world2base.inv().R
T_base2cam = rox_transform_world2base.inv().p
#debug
print("FINAL RESULTS: ")
print("str(R_cam2base)")
# print(str(type(R_cam2base)))
print(str(R_cam2base))
print("str(T_cam2base)")
# print(str(type(T_cam2base.flatten())))
print(str(T_cam2base))
print("str(R_base2cam)")
# print(str(type(R_base2cam)))
print(str(R_base2cam))
print("str(T_base2cam)")
# print(str(type(T_base2cam.flatten())))
print(str(T_base2cam))
pose_res = geom_util.rox_transform_to_named_pose(rox_transform_world2base)
cov = np.eye(6) * 1e-5
imgs_out2 = [
image_util.array_to_compressed_image_jpg(i, 70) for i in imgs_out
]
return pose_res, cov, imgs_out2, 0.0
from RobotRaconteur.Client import *
import time
from RobotRaconteurCompanion.Util.ImageUtil import ImageUtil
from RobotRaconteurCompanion.Util.GeometryUtil import GeometryUtil
import cv2
import numpy as np
d = DeviceManagerClient('rr+tcp://localhost:59902?service=device_manager', autoconnect=False)
d.refresh_devices(1)
d.connect_device("vision_aruco_detection")
c = d.get_device_client("vision_aruco_detection",1)
geom_util = GeometryUtil(client_obj = c)
b = RRN.NewStructure("com.robotraconteur.geometry.BoundingBox2D", c)
center = RRN.NewStructure("com.robotraconteur.geometry.NamedPose2D", c)
pose2d_dtype = RRN.GetNamedArrayDType("com.robotraconteur.geometry.Pose2D", c)
size2d_dtype = RRN.GetNamedArrayDType("com.robotraconteur.geometry.Size2D", c)
center.pose = np.zeros((1,),dtype=pose2d_dtype)
center.pose[0]["position"]["x"] = 990
center.pose[0]["position"]["y"] = 64
center.pose[0]["orientation"] = np.deg2rad(10)
b.center = center
size = np.zeros((1,),dtype=size2d_dtype)
size[0]["width"] = 100
size[0]["height"] = 100
b.size = size
