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track_v3.py
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track_v3.py
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"""
Author: Konstantinos Angelopoulos
Date: 04/02/2020
All rights reserved.
Feel free to use and modify and if you like it give it a star.
MAIN FILE
"""
import os
import sys
import threading
from functools import wraps
from pykinect2.PyKinectV2 import *
from pykinect2 import PyKinectV2
from pykinect2 import PyKinectRuntime
import numpy as np
import json, codecs
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import ctypes
import cv2
import time
import camera_calibrate
import QuaternionOperations
import transformations
import KalmanFilter
import mapper
import fabric_dimensions
import decision
import orientation
import robot_handle
import robotSim
from robotSim import CloudSkeleton
from hand_gesture import HandGestureClassifier
from Gripper.ros_handler import Client, graspModel, touch
from ATI_FT.ati_ft_sensor import AtiFtClient
# Flag for pygame to hide greeting message
os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = '1'
import pygame
elapsed_time = time.time()
# Global Variables
ABSOLUTE_FILE_PATH = os.path.dirname(os.path.realpath(__file__))
"""====================== Camera Settings =================="""
# This values are calculated from the calibration of the camera
# Open file with calibrated configurations
with open(os.path.join(ABSOLUTE_FILE_PATH, 'calibrate/IR/config.json')) as config_file:
data = json.load(config_file)
DEPTH_OPTIMAL_CAMERA_MATRIX = data['Optimal Camera Matrix']
DEPTH_OLD_CAMERA_MATRIX = data['Old Camera Matrix']
# Open file with calibrated configurations
with open(os.path.join(ABSOLUTE_FILE_PATH, 'calibrate/Color/config.json')) as config_file:
data = json.load(config_file)
COLOR_OPTIMAL_CAMERA_MATRIX = data['Optimal Camera Matrix']
COLOR_OLD_CAMERA_MATRIX = data['Old Camera Matrix']
# Open file with transformation coefficients
with open(os.path.join(ABSOLUTE_FILE_PATH, 'mapper/matrix.json')) as config_file:
data = json.load(config_file)
RGB2D_TRANSFORMATION_MATRIX = np.array(data['Transformation Matrix'])
DEPTH_FOV_HORIZONTAL = 70.6 # in degrees http://www.smeenk.com/webgl/kinectfovexplorer.html
DEPTH_FOV_VERTICAL = 60 # in degrees https://weekly-geekly.github.io/articles/272629/index.html
DEPTH_FIELD_OF_VIEW = np.arctan(0.5 * DEPTH_FOV_HORIZONTAL / DEPTH_FOV_VERTICAL) * 180 / np.pi
COLOR_FOV_HORIZONTAL = 84.1 # in degrees http://www.smeenk.com/webgl/kinectfovexplorer.html
COLOR_FOV_VERTICAL = 53.8 # in degrees https://weekly-geekly.github.io/articles/272629/index.html
COLOR_FIELD_OF_VIEW = np.arctan(0.5 * COLOR_FOV_HORIZONTAL / COLOR_FOV_VERTICAL) * 180 / np.pi
DEPTH_FOCAL_LENGTH = (512 * 0.5) / np.tan(DEPTH_FOV_HORIZONTAL * 0.5 * np.pi / 180) # https://answers.opencv.org/question/17076/conversion-focal-distance-from-mm-to-pixels/
COLOR_FOCAL_LENGTH = (1920 * 0.5) / np.tan(COLOR_FOV_HORIZONTAL * 0.5 * np.pi / 180) # https://answers.opencv.org/question/17076/conversion-focal-distance-from-mm-to-pixels/
DEPTH_SCALE = 0.001 # Default kinect depth scale where 1 unit = 0.001 m = 1 mm
DEPTH_FOCAL_LENGTH_X = DEPTH_OLD_CAMERA_MATRIX[0][0] # mm
DEPTH_FOCAL_LENGTH_Y = DEPTH_OLD_CAMERA_MATRIX[1][1] # mm
DEPTH_PRINCIPAL_POINT_OFFSET_XO = DEPTH_OLD_CAMERA_MATRIX[0][2] # pixels
DEPTH_PRINCIPAL_POINT_OFFSET_YO = DEPTH_OLD_CAMERA_MATRIX[1][2] # pixels
COLOR_FOCAL_LENGTH_X = COLOR_OLD_CAMERA_MATRIX[0][0] # mm
COLOR_FOCAL_LENGTH_Y = COLOR_OLD_CAMERA_MATRIX[1][1] # mm
COLOR_PRINCIPAL_POINT_OFFSET_XO = COLOR_OLD_CAMERA_MATRIX[0][2] # pixels
COLOR_PRINCIPAL_POINT_OFFSET_YO = COLOR_OLD_CAMERA_MATRIX[1][2] # pixels
DEPH_PIXEL_WIDTH = DEPTH_FOCAL_LENGTH * 2 * np.tan(DEPTH_FOV_HORIZONTAL * 0.5 * np.pi / 180) / 512 # pixels
DEPTH_PIXEL_HEIGHT = DEPTH_FOCAL_LENGTH * 2 * np.tan(DEPTH_FOV_VERTICAL * 0.5 * np.pi / 180) / 424 # pixels
COLOR_PIXEL_WIDTH = COLOR_FOCAL_LENGTH * 2 * np.tan(COLOR_FOV_HORIZONTAL * 0.5 * np.pi / 180) / 1920 # pixels
COLOR_PIXEL_HEIGHT = COLOR_FOCAL_LENGTH * 2 * np.tan(COLOR_FOV_VERTICAL * 0.5 * np.pi / 180) / 1080 # pixels
"""======================== ROBOT CONFIGS ==========================="""
""" The robot is a KUKA LWR IV+ 7-DOF robot """
# Connection Parameters to Connect to KRC2 of KUKA LWR IV+ using KUKAVARPROXY server
ROBOT_IP = '169.254.98.120' # KRC2 LAN IP
ROBOT_PORT = 7000 # KRC2 LAN port
# States for cooperation ( see decision for more details )
robot_state = 'IDLE'
human_state = 'IDLE'
gripper_state = 'OPEN'
robot_corner = 'BR'
# Flag for Simulation with RoboDK
robot_end_effector_path = [] # store robot's movements
movements_rdk = [] # movements calculated from operator's hand
""" ========== Always have these flags at Default values even if you do not want the simulation to run ========== """
startSim = True # Flag to move robot to corner (Always at True)
startGripper = False # Flag to move the gripper after the robot and human is ready in position (always at False)
waitGripper = False # Flag to wait for the gripper to catch the cloth (Always at False)
startSensor = False # Flag to start reading from the ForceTorque Sensor when robot has grasped the cloth (always at False)
graspBias = True # Flag to bias the Force Torque Sensor when the robot catches the fabric (Always at True)
graspUnbias = False # Flag to unbias the Force Torque Sensor (Always at False)
kalman_ft_first_time = True # Flag to initialize first values for force torque kalman filtering (Always at True)
robotInit = True # Flag for Robot Initialisation of Space (always at True)
monitorStop = False # Flag to monitor the distance between the hand and the stopping corner (Always at False)
gripperRelease = False # Flag to release the fabric after folding it (Always at False)
gripper_thread = False # Flag to start using the gripper in thread in order to catch the fabric (Always at False)
daq_thread = False # Flag to stsart using the daq in thread (Always at False)
start_thread = True # Flag to start thread one time (Always at True)
fabric_folded = False # Flag that fabric is folded and close loop (Always at False)
# Init Variables and Flags for __main__ ( Main Program Loop in def run inside class KinectMain)
""" =========== Change the below flags if you do not want some parts of the code to execute =========== """
dim = True # Flag for finding the fabric's dimensions
cal = False # Flag for calibrating the camera
Sim = True # Flag for starting RoboDK
RealMovement = True # Flag to move the real robot with RoboDK
gestureInit = True # Flag for custom Gesture classifier
gripperInit = True # Flag to connect to gripper
sensorInit = True # Flag to connect to the ATI FT Sensor
kalmanInit = True # Flag for drawing kalman on screen
skeletonInit = True # Flag for drawing kinect's skeleton tracking on screen
cloudInit = False # Flag for Cloud Skeleton Visualize in RoboDK
cloudPointInit = False # Flag to import the workspace as a pointCloud in RoboDK
full_screen = False # flag to open pygame in fullscreen
"""============================= Gripper Configs ==========================="""
"""
Gripper Model is ReFlex 1 and it is only compatible with ROS Jade (ros_handler.py) or Indigo (gripper.py) on Ubuntu 14.04.6 (Trusty) LTS.
So a VM with Ubuntu is running ROS and has a static IP in the same network with the Laptop and the Robot Controller.
The VM is running an instance of ROS with the gripper software and a python Server to communicate with the Windows.
A client on windows is controlling the gripper state through the python server.
"""
VM_IP = '192.168.56.2' # Vm with Ubuntu Host only Static IP
VM_PORT = 20000 # Port to communicate with Ubuntu running ROS
# Flag to communicate with server (True = using encryption (SHA-256), False = Plain Text communication)
# Both the Server and the Client must have the same flag, either both True or either both False
VM_SERVER_ENCRYPTION = True
"""========================== ATI FT Sensor Configs ====================="""
"""
ATI FT Sensor configurations.
ATI FT Sensor communication is established using the ATI FT controller and an adapter
to convert serial to usb. The python code has a sampling rate at approximately 150 Hz.
To match the frequency of the kinect we restrict the sampling rate to 30 Hz.
"""
ATI_FT_IP = 'localhost'
ATI_FT_PORT = 10000
ATI_FT_SERVER_ENCRYPTION = True
ATI_FT_COM_PORT_WINDOWS = 'COM1' # Port that the DAQ ATI is connected to the windows computer
ATI_FT_COM_PORT_LINUX = '/dev/ttyUSB0' # Port that the ATI Controller FT is connected to the linux computer
"""=========================== Kalman Settings ========================="""
SHOW_KALMAN_KINECT_GRAPHS = True # Flag to show the comparison of kalman and kinect values at the end of program
SHOW_FINAL_PLOTS = True # Flag to plot the robot's and operator's path
ex, dt, frame_rate = KalmanFilter.initialise() # Initialize Kalman Filters
ex_6 = KalmanFilter.initialise(force_torque=True) # Initialize Kalman Filters for Forces and Torques
# Variables for storing kinect and kalman values
kinect_world_hand_right = []
kinect_color_hand_right = []
kalman_world_hand_right = []
kalman_color_hand_right = []
kinect_world_wrist_right = []
kinect_color_wrist_right = []
kalman_world_wrist_right = []
kalman_color_wrist_right = []
kinect_world_hand_tip_right = []
kinect_color_hand_tip_right = []
kalman_world_hand_tip_right = []
kalman_color_hand_tip_right = []
sensor_forces = []
kalman_forces = []
forces_control = []
sum_time = [] # store time for plots
history = [] # store absolute time for plots
# colors for drawing different bodies
SKELETON_COLORS = [pygame.color.THECOLORS["red"], pygame.color.THECOLORS["blue"], pygame.color.THECOLORS["green"],
pygame.color.THECOLORS["orange"], pygame.color.THECOLORS["purple"], pygame.color.THECOLORS["yellow"],
pygame.color.THECOLORS["violet"], pygame.color.THECOLORS['black'], pygame.color.THECOLORS['deepskyblue'],
pygame.color.THECOLORS['cyan2'], pygame.color.THECOLORS['aquamarine2'], pygame.color.THECOLORS['darkgoldenrod']]
def plot(sum_time, kinect_world_hand_right, kalman_world_hand_right, kinect_color_hand_right, kalman_color_hand_right):
"""
Plots kinect and kalman movements in 2D or 3D
:param sum_time: list with time of each loop
:param kinect_world_hand_right: list with kinect's world tracking points
:param kalman_world_hand_right: list with kalman's world tracking points
:param kinect_color_hand_right: list with kinect's color tracking points
:param kalman_color_hand_right: list with kalman's color tracking points
:return: None
"""
global SHOW_KALMAN_KINECT_GRAPHS
if SHOW_KALMAN_KINECT_GRAPHS:
# Plots 1D
fig, axs = plt.subplots(3, sharex='all', gridspec_kw={'hspace': 0.4})
l1 = axs[0].plot(sum_time, extract_x(kinect_world_hand_right), 'r-', sum_time, extract_x(kalman_world_hand_right), 'b-', linewidth=2)
axs[0].set_title('X AXIS', fontweight='bold', fontsize=16)
l2 = axs[1].plot(sum_time, extract_y(kinect_world_hand_right), 'r-', sum_time, extract_y(kalman_world_hand_right), 'b-', linewidth=2)
axs[1].set_title('Y AXIS', fontweight='bold', fontsize=16)
l3 = axs[2].plot(sum_time, extract_z(kinect_world_hand_right), 'r-', sum_time, extract_z(kalman_world_hand_right), 'b-', linewidth=2)
axs[2].set_title('Z AXIS', fontweight='bold', fontsize=16)
fig.legend([l1, l2, l3], labels=["Kinect Values", "Kalman Values"], loc="lower right", borderaxespad=0.1, prop={'weight': 'bold'})
fig.text(0.5, 0.04, 'Time (sec)', ha='center', fontweight='bold', fontsize=16) # X Axis Label
fig.text(0.04, 0.5, 'Position of Right Hand (mm)', va='center', rotation='vertical', fontweight='bold', fontsize=16) # Y Axis Label
# Hide x labels and tick labels for all but bottom plot.
for ax in axs:
ax.tick_params(axis='both', which='major', labelsize=14)
ax.tick_params(axis='both', which='minor', labelsize=14)
ax.label_outer()
# Plots 3D
fig3 = plt.figure()
ax = Axes3D(fig3)
ax.plot(extract_x(kinect_color_hand_right), extract_y(kinect_color_hand_right), zs=extract_z(kinect_color_hand_right), color='red')
ax.plot(extract_x(kalman_color_hand_right), extract_y(kalman_color_hand_right), zs=extract_z(kalman_color_hand_right), color='blue')
ax.set_title('Kinect and Kalman Values in 3D space', fontsize=14)
ax.set_axis_on()
ax.set_xlabel('X Axis (mm)', fontsize=14)
ax.set_ylabel('Y Axis (mm)', fontsize=14)
ax.set_zlabel('Z Axis (mm)', fontsize=14)
# ax.clabel(cset, fontsize=9, inline=1)
plt.show() # Show plots
def final_plots(time, kinect, kalman, robot, movements, history):
"""
Plots with kinect, kalman and robot movement
:param time: list with time on each loop
:param kinect: list with kinect's world tracking points
:param kalman: list with kalman's world tracking points
:param robot: list with robot's movement points
:param movements: list with distance moved in each axis
:param history: list with time of every loop
:return: None
"""
global SHOW_FINAL_PLOTS
if SHOW_FINAL_PLOTS:
time, kinect, kalman, movements, history = time[-len(robot):], kinect[-len(robot):], kalman[-len(robot):], movements[-len(robot):], history[-len(robot):]
# Plots 1D
fig, axs = plt.subplots(3, sharex='all', gridspec_kw={'hspace': 0.4})
l1 = axs[0].plot(time, extract_x(kinect), 'r-', time, extract_x(kalman), 'b-', time, extract_x(robot), 'g--', linewidth=2)
axs[0].set_title('X AXIS', fontweight='bold', fontsize=16)
l2 = axs[1].plot(time, extract_z(kinect), 'r-', time, extract_z(kalman), 'b-', time, extract_y(robot), 'g--', linewidth=2)
axs[1].set_title('Y AXIS', fontweight='bold', fontsize=16)
l3 = axs[2].plot(time, extract_y(kinect), 'r-', time, extract_y(kalman), 'b-', time, extract_z(robot), 'g--', linewidth=2)
axs[2].set_title('Z AXIS', fontweight='bold', fontsize=16)
fig.legend([l1, l2, l3], labels=["Kinect Values", "Kalman Values", "Robot"], loc="lower right", borderaxespad=0.1, prop={'weight': 'bold'})
fig.text(0.5, 0.04, 'Time (sec)', ha='center', fontweight='bold', fontsize=16) # X Axis Label
fig.text(0.04, 0.5, 'Position of Right Hand (mm)', va='center', rotation='vertical', fontweight='bold', fontsize=16) # Y Axis Label
# Hide x labels and tick labels for all but bottom plot.
for ax in axs:
ax.tick_params(axis='both', which='major', labelsize=14)
ax.tick_params(axis='both', which='minor', labelsize=14)
ax.label_outer()
plt.show()
def reader():
"""
Open saved data and return the values in lists
:return: list with time, list with kinect values, list with kalman values, list with robot movements, list with distance moved, list with time in each loop
"""
# Delete old files
if not os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/time.txt')):
return False
if not os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/kinect.txt')):
return False
if not os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/kalman.txt')):
return False
if not os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/robot.txt')):
return False
if not os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/movements.txt')):
return False
if not os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/history.txt')):
return False
# write new files
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/time.txt'), 'rb') as file:
t = json.loads(file.read())
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/kinect.txt'), 'rb') as file:
kin = json.loads(file.read())
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/kalman.txt'), 'rb') as file:
kal = json.loads(file.read())
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/robot.txt'), 'rb') as file:
rob = json.loads(file.read())
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/movements.txt'), 'rb') as file:
move = json.loads(file.read())
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/history.txt'), 'rb') as file:
hist = json.loads(file.read())
return t, kin, kal, rob, move, hist
def _write(sum_time, kinect_world_hand_right, kalman_world_hand_right, robot_end_effector_path, movements, history, sensor_forces, kalman_forces, force_control):
"""
Write lists in files for plotting
:param sum_time: list with time in each loop
:param kinect_world_hand_right: list with kinect tracked coordinates
:param kalman_world_hand_right: list with kalman tracked coordinates
:param robot_end_effector_path: list with robot end effector movements
:param movements: list with distance moved in each axis
:param history: list with time on each loop
:param sensor_forces: list with sensor raw forces
:param kalman_forces: list with kalman predictions of sensor forces
:param force_control: list with force control feedback
:return: boolean
"""
# Delete old files
if os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/time.txt')):
os.remove(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/time.txt'))
if os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/kinect.txt')):
os.remove(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/kinect.txt'))
if os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/kalman.txt')):
os.remove(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/kalman.txt'))
if os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/robot.txt')):
os.remove(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/robot.txt'))
if os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/movements.txt')):
os.remove(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/movements.txt'))
if os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/history.txt')):
os.remove(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/history.txt'))
if os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/ati_ft.txt')):
os.remove(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/ati_ft.txt'))
if os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/kalman_ati_ft.txt')):
os.remove(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/kalman_ati_ft.txt'))
if os.path.exists(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/control_ati_ft.txt')):
os.remove(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/control_ati_ft.txt'))
# write new files
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/time.txt'), 'wb') as file:
json.dump(sum_time, codecs.getwriter('utf-8')(file), ensure_ascii=False)
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/kinect.txt'), 'wb') as file:
json.dump(kinect_world_hand_right, codecs.getwriter('utf-8')(file), ensure_ascii=False)
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/kalman.txt'), 'wb') as file:
json.dump(kalman_world_hand_right, codecs.getwriter('utf-8')(file), ensure_ascii=False)
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/robot.txt'), 'wb') as file:
json.dump(robot_end_effector_path, codecs.getwriter('utf-8')(file), ensure_ascii=False)
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/movements.txt'), 'wb') as file:
json.dump(movements, codecs.getwriter('utf-8')(file), ensure_ascii=False)
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/history.txt'), 'wb') as file:
json.dump(history, codecs.getwriter('utf-8')(file), ensure_ascii=False)
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/ati_ft.txt'), 'wb') as file:
json.dump(sensor_forces, codecs.getwriter('utf-8')(file), ensure_ascii=False)
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/kalman_ati_ft.txt'), 'wb') as file:
json.dump(kalman_forces, codecs.getwriter('utf-8')(file), ensure_ascii=False)
with open(os.path.join(ABSOLUTE_FILE_PATH, 'CasePlots/control_ati_ft.txt'), 'wb') as file:
json.dump(force_control, codecs.getwriter('utf-8')(file), ensure_ascii=False)
return True
def extract_x(lst):
"""
Extract x coordinate from list with x, y, z coordinates
:param lst: list with [[x, y, z], ..., [x, y, z]]
:return: list with x coordinates [x, ..., x]
"""
return [item[0] for item in lst] # Extract x values for plots
def extract_y(lst):
"""
Extract y coordinate from list with x, y, z coordinates
:param lst: list with [[x, y, z], ..., [x, y, z]]
:return: list with y coordinates [x, ..., x]
"""
return [item[1] for item in lst] # Extract y values for plots
def extract_z(lst):
"""
Extract z coordinate from list with x, y, z coordinates
:param lst: list with [[x, y, z], ..., [x, y, z]]
:return: list with z coordinates [x, ..., x]
"""
return [item[2] for item in lst] # Extract z values for plots
def time_func(func):
"""
Time any function
:param func: any python function
:return: None
"""
@wraps(func)
def Timer(*args, **kwargs):
"""
execute function and time it
:param args: any argument
:param kwargs: any keyword argument
:return: None
"""
started_time = time.time()
try:
return func(*args, **kwargs)
finally:
end = time.time() - started_time
print("[TIMER] Finished {} seconds".format(end if end > 0 else 0))
return Timer
def Gesture_Classifier():
"""
Initialize hand gesture classifier
:return: instance of class HandGestureClassifier
"""
global gestureInit
# Initialize classifier only one time
if gestureInit:
return HandGestureClassifier()
class Clients:
def __init__(self):
self.rdk = None # link to robodk application instance
self.daq = None # link to ati ft controller
self.gripper_client = None # link to the gripper API
def __exit__(self):
# Open gripper
self.gripper_client.send('hello')
self.gripper_client.receive()
self.gripper_client.send('pub')
self.gripper_client.send('open')
self.gripper_client.receive()
self.gripper_client.close()
self.daq.close()
def ati_client_connect(self):
"""
Connect to the ati ft controller
:return: boolean sensor initialized, sensor started pulling data
"""
global sensorInit
"""========= Initialize connection to the DAQ Sensor =========="""
if sensorInit is True:
try:
self.daq = AtiFtClient(ATI_FT_IP, ATI_FT_PORT, encrypt_flag=ATI_FT_SERVER_ENCRYPTION)
self.daq.send('hello')
self.daq.receive()
self.daq.send('status')
status = self.daq.receive(ret=True)
if status == 'INACTIVE':
self.daq.send('hello')
self.daq.receive()
self.daq.send('start')
self.daq.receive()
sensorInit = False
startSensor = True
return sensorInit, startSensor
except Exception as e:
print("[ATI FT CLIENT]: {}".format(e))
def gripper_client_connect(self):
"""
Connect to the gripper ROS API
:return: boolean gripepr initialized, boolean gripper connected, string gripper status
"""
global gripperInit
"""========== Initialize connection to the robot's gripper"""
if gripperInit:
try:
print('+-------------------+')
print('Connecting to Gripper Server')
# Connect to Gripper Server running on Ubuntu with ROS
self.gripper_client = Client(VM_IP, VM_PORT, encrypt_flag=VM_SERVER_ENCRYPTION)
# Always start with Hello ( see Gripper/gripper.py for more info or send 'Hello' and then 'help' to see available commands )
self.gripper_client.send('hello')
# Always receive after commands ( except some exceptions!!! )
self.gripper_client.receive()
self.gripper_client.send('status')
ros_status = self.gripper_client.receive(ret=True)
# Check to see if ROS has started
if ros_status == 'INACTIVE':
self.gripper_client.send('Hello')
self.gripper_client.receive()
self.gripper_client.send('start')
self.gripper_client.receive()
# Open gripper
self.gripper_client.send('hello')
self.gripper_client.receive()
self.gripper_client.send('pub')
self.gripper_client.send('open')
self.gripper_client.receive()
# Update gripper status
gripper_status = 'OPEN'
gripperInit = False
startGripper = True
return gripperInit, startGripper, gripper_status
# Handle exceptions
except Exception as e:
print("[GRIPPER CLIENT]: {}".format(e))
def robodk_client_connect(self):
"""
Connect to RoboDK and KUKA robot
:return: connection to the RoboDK instance
"""
global Sim, RealMovement
if Sim:
self.rdk = robotSim.reset(ROBOT_IP, ROBOT_PORT, simulation=False if RealMovement else True)
def close_clients(self):
self.__exit__()
class ReflexOne(Clients):
def __init__(self, clients):
super().__init__()
self._done = False # flag to stop gripper
self.rdk = clients.rdk # link to robodk
self.daq = clients.daq # link to ati controller
self.gripper_client = clients.gripper_client # link to ROS API
self.gripper_state = "OPEN" # gripper status
self.startGripper = startGripper
self.waitGripper = waitGripper
self.monitorStop = monitorStop
self.graspBias = graspBias
self.daq_thread = daq_thread
def grasp(self):
"""
Initialize grasping model to grasp fabric from the table
:return: boolean gripper started, boolean wait for gripper to grab, boolean start monitoring hand position, boolean to bias ft sensor, boolean gripper thread started
"""
global waitGripper, startGripper, daq_thread, gripper_state, monitorStop, graspBias
""" ================== Gripper Reflex Logic ================= """
try:
"""
# gripper_client.start_grasping(self.rdk)
if graspModel(self.rdk, self.daq, self.gripper_client):
pass
"""
if touch(self.rdk, self.daq, self.gripper_client):
pass
self.startGripper = False
self.waitGripper = False
self.gripper_state = 'CLOSED'
self.monitorStop = True
self.graspBias = True
self.daq_thread = False
self._done = True
return startGripper, waitGripper, gripper_state, monitorStop, graspBias, daq_thread
except Exception as e:
print("[GRIPPER CLIENT]: {}".format(e))
def close(self):
self.__exit__()
class RoboDK_CloudSkeleton(Clients):
def __init__(self, clients):
super().__init__()
self.skeleton = None # skeleton class
self.rdk = clients.rdk # link to RoboDK
self.daq = clients.daq # link to ati controller
self.gripper_client = clients.gripper_client # link to gripper ROS API
def reset(self):
"""
Reset Cloud Skeleton in RoboDK
:return: None
"""
# Initialize CloudSkeleton in RoboDK
self.skeleton = CloudSkeleton(self.rdk)
if cloudInit:
self.skeleton.Visible()
self.skeleton.Circle()
else:
self.skeleton.Invisible()
def RoboDK_CloudSkeleton_Update(self, joints):
"""
Update cloud skeleton joint position
:param joints: list with body joints data
:return: None
"""
self.skeleton.Update(joints)
self.skeleton.UpdateView()
# Main Class for Kinect Object
class KinectMain(Clients):
def __init__(self, clients, operator):
super().__init__()
self.cloud_skeleton = operator # Cloud Skeleton class in RoboDK
self.rdk = clients.rdk # connection to RoboDK
self.daq = clients.daq # connection to ati ft controller
self.gripper_client = clients.gripper_client # connection to ROS API
pygame.init() # initialize pygame
self._clock = pygame.time.Clock() # Used to manage how fast the screen updates
pygame.display.set_caption("Kalman Kinect Tracking") # Pygame Window Name
self._done = False # Flag for when the user clicks the close button.
# Kinect runtime object
# self._kinect = PyKinectRuntime.PyKinectRuntime(PyKinectV2.FrameSourceTypes_Color | PyKinectV2.FrameSourceTypes_Body | PyKinectV2.FrameSourceTypes_Depth | PyKinectV2.FrameSourceTypes_BodyIndex | PyKinectV2.FrameSourceTypes_Infrared | PyKinectV2.FrameSourceTypes_Audio)
self._kinect = PyKinectRuntime.PyKinectRuntime(PyKinectV2.FrameSourceTypes_Color | PyKinectV2.FrameSourceTypes_Body | PyKinectV2.FrameSourceTypes_Depth)
# Set the width and height of the screen [width, height]
if full_screen:
# self._infoObject = pygame.display.Info()
# self._screen = pygame.display.set_mode((self._infoObject.current_w >> 1, self._infoObject.current_h >> 1), pygame.FULLSCREEN | pygame.HWSURFACE | pygame.DOUBLEBUF | pygame.RESIZABLE, 32)
self._screen = pygame.display.set_mode((0, 0), pygame.FULLSCREEN | pygame.HWSURFACE | pygame.DOUBLEBUF | pygame.RESIZABLE, 32)
else:
self._screen = pygame.display.set_mode((int(self._kinect.color_frame_desc.Width / 3.5), int(self._kinect.color_frame_desc.Height / 3.5)), pygame.HWSURFACE | pygame.DOUBLEBUF | pygame.RESIZABLE, 32)
# back buffer surface for getting Kinect color frames, 32bit color, width and height equal to the Kinect color frame size
self._frame_surface = pygame.Surface((self._kinect.color_frame_desc.Width, self._kinect.color_frame_desc.Height), 0, 32)
self._bodies = None # Skeleton Data
self._bodies_index = None # Body Index Data
self._body_index_image = None # Body Index Image
self._depth = None # Depth Frame Data
self._depth_image = None # Depth Image
self._color = None # Color Frame Data
self._color_image = None # Color Image
self._ir = None # IR Frame Data
self._ir_image = None # IR Image
self._color_fabric_points = None # Color Fabric Points
self.rgb_focal_length = COLOR_FOCAL_LENGTH # Kinect's color_focal_length
self.rgb_field_of_view = COLOR_FIELD_OF_VIEW # Kinect's color field of view
self._points = np.ndarray(shape=(4, 3), dtype=float) # Fabric world points
self._counter = 0 # Counter for storing loops
self.distance = [] # distance of hand on every loop
self.velocity_x = [] # velocity of hand on x axis
self.velocity_y = [] # velocity of hand on y axis
self.velocity_z = [] # velocity of hand on z axis
# self.Limit = 1800 * frame_rate # Limit to break loop
self.Limit = 300 * frame_rate # Limit to test kalman figure plots
self.body_id = -1 # store operators body id to track only him
self.mass_x = 0.0 # Mass Center Coordinates for the human body
self.mass_y = 0.0 # Mass Center Coordinates for the human body
self.mass_z = 0.0 # Mass Center Coordinates for the human body
# Store points to calculate the transformation matrix for depth to color space
self.mapper_color_points = []
self.mapper_depth_points = []
# estimate of initial position variance (covariance matrix) for all values
self.world_hand_right_p = ex
self.color_hand_right_p = ex
self.world_wrist_right_p = ex
self.color_wrist_right_p = ex
self.world_hand_tip_right_p = ex
self.color_hand_tip_right_p = ex
self.forces_p = ex_6
self.mass_init = True # To only wait once for the body to enter robot Space
self.hand_right_state = 'UNKNOWN' # Right Hand State
self.hand_right_confidence = 'LOW' # Right Hand confidence
self.joints = None # store body joints of the tracked person
def draw_body_bone(self, joint_points, color, joint0, joint1):
"""
Draw skeleton on pygame screen
:param joint_points: joint data
:param color: string pygame color
:param joint0: string link joint 1
:param joint1: string link joint 2
:return: None
"""
# both joints are not tracked
if (self.joints[joint0].TrackingState == PyKinectV2.TrackingState_NotTracked) or (self.joints[joint1].TrackingState == PyKinectV2.TrackingState_NotTracked):
return
# both joints are not *really* tracked
if (self.joints[joint0].TrackingState == PyKinectV2.TrackingState_Inferred) and (self.joints[joint1].TrackingState == PyKinectV2.TrackingState_Inferred):
return
# ok, at least one is good
start = (joint_points[joint0].x, joint_points[joint0].y)
end = (joint_points[joint1].x, joint_points[joint1].y)
try:
pygame.draw.line(self._frame_surface, color, start, end, 20)
except Exception as e: # need to catch it due to possible invalid positions (with inf)
print("[MAIN - DRAW SKELETON]: {}".format(e))
def draw_body(self, joint_points, color):
"""
Draw Body joints
:param joint_points: body joint data
:param color: pygame color
:return: None
"""
# Right Arm
self.draw_body_bone(joint_points, color, PyKinectV2.JointType_ElbowRight, PyKinectV2.JointType_WristRight)
self.draw_body_bone(joint_points, color, PyKinectV2.JointType_WristRight, PyKinectV2.JointType_HandRight)
self.draw_body_bone(joint_points, color, PyKinectV2.JointType_HandRight, PyKinectV2.JointType_HandTipRight)
def draw_color_frame(self, frame, target_surface):
"""
Map color frame on screen
:param frame: frame array
:param target_surface: pygame surface
:return: None
"""
target_surface.lock()
address = self._kinect.surface_as_array(target_surface.get_buffer())
ctypes.memmove(address, frame.ctypes.data, frame.size)
del address
target_surface.unlock()
def draw_kinect(self, points, color):
"""
Draw Kalman prediction on Screen
:param points: list with color pixel coordinates
:param color: pygame color
:return: None
"""
try:
pygame.draw.circle(self._frame_surface, color, (points[0], points[1]), 40, 15)
except Exception as e: # need to catch it due to possible invalid positions (with inf)
print("[MAIN - DRAW KALMAN PREDICTION]: {}".format(e))
def draw_infrared_frame(self, frame, target_surface):
"""
Draw infrared frame on surface
:param frame: infrared frame
:param target_surface: pygame surface
:return: None
"""
if frame is None: # some usb hub do not provide the infrared image. it works with Kinect studio though
return
target_surface.lock()
f8 = np.uint8(frame.clip(1, 4000) / 16.)
frame8bit = np.dstack((f8, f8, f8))
address = self._kinect.surface_as_array(target_surface.get_buffer())
ctypes.memmove(address, frame8bit.ctypes.data, frame8bit.size)
del address
target_surface.unlock()
def draw_fabric(self, f_points, p_color):
"""
Draw fabric edges on pygame screen
:param f_points: list with fabric color pixel coordinates
:param p_color: pygame color
:return: None
"""
try:
for point in f_points:
pygame.draw.circle(self._frame_surface, p_color, (point[0], point[1]), 40, 15)
except Exception as e:
print("MAIN - DRAW FABRIC]: {}".format(e))
def find_fabric(self, dim):
"""
locate fabric with image process
:param dim: boolean flag to locate image
:return: boolean, list with fabric world points, list with fabric center coordinates, float fabric width, float fabric height, float quasi state robot speed
"""
# Color Space Coordinates
# fabric_points, fabric_width, fabric_height, fabric_angle, fabric_ret = fabric_dimensions.find_dimensions(self._color_image, self.rgb_focal_length)
fabric_points, fabric_width, fabric_height, fabric_angle, fabric_ret = fabric_dimensions.box_hull(self._color_image, self.rgb_focal_length)
self._color_fabric_points = fabric_points
# Flag to ensure that a valid fabric is found
if fabric_ret:
# Run mapper here to avoid severe segmentation errors
# Map Color Points to Depth Space, using kinect mapper, for each fabric point
world_points = mapper.color_2_world(self._kinect, self._kinect._depth_frame_data, _CameraSpacePoint)
world_points = ctypes.cast(world_points, ctypes.POINTER(ctypes.c_float))
world_points = np.ctypeslib.as_array(world_points, shape=(self._kinect.color_frame_desc.Height * self._kinect.color_frame_desc.Width, 3))
world_points *= 1000 # transform to mm
for i, point in enumerate(fabric_points):
fabric_depth_x, fabric_depth_y = mapper.color_point_2_depth_point(self._kinect, _DepthSpacePoint, self._kinect._depth_frame_data, point) # pixel
cv2.circle(self._depth_image, (fabric_depth_x, fabric_depth_y), 7, (255, 0, 0))
# cv2.imwrite('images/fab7.png', self._depth_image)
self._points[i] = world_points[point[1] * 1920 + point[0]]
cv2.imshow('Depth', cv2.flip(self._depth_image, 1))
cv2.waitKey(1500)
cv2.destroyAllWindows()
# World Coordinates
fabric_points, fabric_width, fabric_height, fabric_angle = fabric_dimensions.world_dimensions(self._points)
fabric_points = fabric_dimensions.arrange(fabric_points) # always arrange to correct way
# Calculate fabric orientations
fabric_orientation, fabric_center = orientation.fabric_orientation(fabric_points)
""" ================================ TEST POINTS =============================== """
# fabric_points = np.asarray([[-785, 250, 760], [-485, 250, 760], [-485, -250, 760], [-785, -250, 760]])
# fabric_center = np.asarray([-635, 0, 760])
# fabric_width, fabric_height = np.asarray([785 - 485]), np.asarray([250 + 250])
""" ============================================================================ """
""" ========================= Robot ISO Parameters ============================= """
# max_speed for Quasi-static contact and Transient contact ( see robot_handle.iso for more details )
q_relev_max_speed, t_relev_max_speed, relev_speed = robot_handle.iso(fabric_width, fabric_height)
# Find edges closer to robot
print('+-------------------+')
print(' Fabric Detected ')
print(' Width (mm): {}\n Height (mm): {}\n World Center Point (XYZ in mm): {}'.format(fabric_width, fabric_height, fabric_center))
print(' Color2World Fabric Points (mm): ')
print(str(fabric_points[0]))
print(str(fabric_points[1]))
print(str(fabric_points[2]))
print(str(fabric_points[3]))
print(' ISO Speed Calculated')
print('+-------------------+')
print('+-------------------+')
print(' Starting Tracking ')
print('+-------------------+')
dim = False
else:
print('+-------------------+')
print(" Fabric not detected properly")
print('+-------------------+')
return dim, fabric_points, fabric_center, fabric_width, fabric_height, q_relev_max_speed
def update_cloud_skeleton(self):
"""
Update cloud skeleton position in RoboDK
:return:
"""
self.cloud_skeleton.RoboDK_CloudSkeleton_Update(self.joints)
def run(self):
"""
Main loop for human robot collaboration
:return: None
"""
"""================= Global Variables ================"""
global human_state, robot_state, gripper_state, robot_corner, monitorStop, gripperRelease, cloudPointInit, fabric_folded
global graspUnbias, frame_rate, dt, kalman_ft_first_time, waitGripper, graspBias, Sim, dim, cloudInit, start_thread
global gestureInit, kalmanInit, sensorInit, gripperInit, startGripper, startSensor, robotInit, cal, startSim, MOVING_DISTANCE
global gesture_classifier, operator, gripper, robot_end_effector_path, gripper_thread, daq_thread, skeletonInit
"""================== Main Program Loop ================="""
while not self._done:
# time frames
time_it = time.time()
"""===== Listen for user actions ====="""
for event in pygame.event.get(): # User did something
if event.type == pygame.QUIT: # If user clicked close
self._done = True # Flag that we are done so we exit this loop
# Check for screen resizing and update the surface
elif event.type == pygame.VIDEORESIZE: # window resized
self._screen = pygame.display.set_mode(event.dict['size'], pygame.HWSURFACE | pygame.DOUBLEBUF | pygame.RESIZABLE, 32)
"""======== Color Frame - Update the surface ====="""
if self._kinect.has_new_color_frame():
self._color = self._kinect.get_last_color_frame()
self._color_image = self._color.reshape((self._kinect.color_frame_desc.Height, self._kinect.color_frame_desc.Width, 4)).astype(np.uint8)
self.draw_color_frame(self._color, self._frame_surface)
"""=============== Infrared frames ========"""
"""
if self._kinect.has_new_infrared_frame():
self._ir = self._kinect.get_last_infrared_frame()
self._ir_image = self._ir.reshape((self._kinect.infrared_frame_desc.Height, self._kinect.infrared_frame_desc.Width)).astype(np.uint16)
ir_img = cv2.flip(ir_img, 1)
cv2.imshow('Kinect IR Stream', self._ir_image)
"""
"""=============== Depth Frame ============="""
if self._kinect.has_new_depth_frame():
self._depth = self._kinect.get_last_depth_frame()
self._depth_image = self._depth.reshape((self._kinect.depth_frame_desc.Height, self._kinect.depth_frame_desc.Width)).astype(np.uint8)
"""
self._depth_image = cv2.flip(depth_image, 1)
cv2.imshow('Kinect Depth Stream', self._depth_image)
"""
"""=============== Body Frame =============="""
if self._kinect.has_new_body_frame():
self._bodies = self._kinect.get_last_body_frame()
"""============= Body Index Frame ========"""
"""
if self._kinect.has_new_body_index_frame():
self._bodies_index = self._kinect.get_last_body_index_frame()
self._body_index_image = self._bodies_index.reshape((self._kinect.depth_frame_desc.Height, self._kinect.depth_frame_desc.Width)).astype(np.uint8)
cv2.imshow('Body Index Stream', self._body_index_image)
"""
"""======== Import Cloud Point in RoboDK ============="""
if cloudPointInit and self._depth is not None:
world_points = mapper.depth_2_world(self._kinect, self._kinect._depth_frame_data, _CameraSpacePoint)
world_points = ctypes.cast(world_points, ctypes.POINTER(ctypes.c_float))
world_points = np.ctypeslib.as_array(world_points, shape=(self._kinect.depth_frame_desc.Height * self._kinect.depth_frame_desc.Width, 3))
# store points
point_cloud = np.ndarray(shape=(len(world_points), 3), dtype=np.float32)
point_cloud[:, 0] = world_points[:, 0] * 1000
point_cloud[:, 1] = world_points[:, 2] * 1000
point_cloud[:, 2] = world_points[:, 1] * 1000
point_cloud = point_cloud[np.all(point_cloud != float('-inf'), axis=1)]
"""============== Calibrate Camera ================="""
if cal and self._kinect.has_new_color_frame():
print('Starting Calibration')
ret_cal = camera_calibrate.calibrate('calibrate/Color/*.png')
if ret_cal:
print("Calibration was successful")
print("Continuing with Tracking...")
print("Tracking...")
cal = False
else:
print('Something went wrong...Calibration unsuccessful')
# You can also get the Depth Camera Intrinsics from the mapper
# Make sure that you have at least one full depth frame, if the
# kinect does not have a full depth frame all values will be 0
if self._depth is not None:
intrinsics_matrix = mapper.intrinsics(self._kinect, write=True)
"""============ Track only one person and within a certain distance from kinect origin"""
if self._bodies is not None and self.body_id == -1 and not dim and not cal and not sensorInit and not gripperInit:
for i in range(0, self._kinect.max_body_count):
body = self._bodies.bodies[i]
if not body.is_tracked:
continue
# Only wait for the human to enter robot space once, then track him all the time
if self.mass_init:
# Compute mass center for the tracked human
self.joints = body.joints
for j in range(0, PyKinectV2.JointType_Count):
self.mass_x += self.joints[j].Position.x
self.mass_y += self.joints[j].Position.y
self.mass_z += self.joints[j].Position.z
self.mass_x = self.mass_x / PyKinectV2.JointType_Count
self.mass_y = self.mass_y / PyKinectV2.JointType_Count
self.mass_z = self.mass_z / PyKinectV2.JointType_Count
# Map mass center to color for drawing
color_mass = mapper.world_point_2_color(self._kinect, _CameraSpacePoint, np.asarray([self.mass_x, self.mass_y, self.mass_z]))
# Draw mass center
self.draw_kinect([color_mass[0], color_mass[1]], SKELETON_COLORS[8])
# print(np.abs(self.mass_x*1000), np.abs(self.mass_y*1000))
# If mass center is less than 250 mm from x and y axis track him
if np.abs(self.mass_x * 1000) <= 400 and np.abs(self.mass_y * 1000) <= 700:
self.body_id = i
self.mass_init = False
print(' Found Body ')
print('+-------------------+')
else:
self.body_id = i
"""============ Main Loop =========="""
if self._bodies is not None and self.body_id != -1 and not dim and not cal and not sensorInit and not gripperInit:
# Initialize body
body = self._bodies.bodies[self.body_id]
"""====== Escape Loop if no body is tracked"""
if not body.is_tracked:
self.body_id = -1
print(' Lost Body ')
print('+-------------------+')
continue
floor_vector = self._bodies.floor_clip_plane
self.joints = body.joints
# Kinect's Distance from Floor
kinect_distance_from_plane = floor_vector.w * 1000 # mm
kinect_tilt = np.arctan(floor_vector.z / floor_vector.y) * (180.0 / np.pi) # degrees
# Real world coordinates in meters
world_hand_right_x = self.joints[PyKinectV2.JointType_HandRight].Position.x * 1000 # mm
world_hand_right_y = self.joints[PyKinectV2.JointType_HandRight].Position.y * 1000 # mm
world_hand_right_z = self.joints[PyKinectV2.JointType_HandRight].Position.z * 1000 # mm
world_wrist_right_x = self.joints[PyKinectV2.JointType_WristRight].Position.x * 1000 # mm
world_wrist_right_y = self.joints[PyKinectV2.JointType_WristRight].Position.y * 1000 # mm
world_wrist_right_z = self.joints[PyKinectV2.JointType_WristRight].Position.z * 1000 # mm
world_hand_tip_right_x = self.joints[PyKinectV2.JointType_HandTipRight].Position.x * 1000 # mm
world_hand_tip_right_y = self.joints[PyKinectV2.JointType_HandTipRight].Position.y * 1000 # mm
world_hand_tip_right_z = self.joints[PyKinectV2.JointType_HandTipRight].Position.z * 1000 # mm
# convert joint coordinates to color space
color_joint_points = self._kinect.body_joints_to_color_space(self.joints)
joint_points_depth = self._kinect.body_joints_to_depth_space(self.joints)
color_hand_right_x = color_joint_points[PyKinectV2.JointType_HandRight].x # pixels
color_hand_right_y = color_joint_points[PyKinectV2.JointType_HandRight].y # pixels
depth_hand_right_x = joint_points_depth[PyKinectV2.JointType_HandRight].x # pixels
depth_hand_right_y = joint_points_depth[PyKinectV2.JointType_HandRight].y # pixels
"""---------------------------------------------------------------------------------------
There is no guaranty that a depth value will have a corresponding depth pixel, because the
skeleton joints frame is obtained after bodyIndex frame going through a complex algorithm.
---------------------------------------------------------------------------------------"""
if (int(depth_hand_right_y) * 512 + int(depth_hand_right_x)) < 512 * 424:
color_hand_right_z = float(self._depth[int(depth_hand_right_y) * 512 + int(depth_hand_right_x)]) # mm
else:
# If it exceeds return the last value to catch overflow
color_hand_right_z = float(self._depth[int((512 * 424) - 1)]) # mm
color_wrist_right_x = color_joint_points[PyKinectV2.JointType_WristRight].x # pixels
color_wrist_right_y = color_joint_points[PyKinectV2.JointType_WristRight].y # pixels
depth_wrist_right_x = joint_points_depth[PyKinectV2.JointType_WristRight].x # pixels
depth_wrist_right_y = joint_points_depth[PyKinectV2.JointType_WristRight].y # pixels
if (int(depth_wrist_right_y) * 512 + int(depth_wrist_right_x)) < 512 * 424:
color_wrist_right_z = float(self._depth[int(depth_wrist_right_y) * 512 + int(depth_wrist_right_x)]) # mm
else:
# If it exceeds return the last value to catch overflow
color_wrist_right_z = float(self._depth[int((512 * 424) - 1)]) # mm