pap_psm_ompl_experiments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jupyros as jr\n",
    "import rospy\n",
    "import numpy as np\n",
    "from sensor_msgs import msg\n",
    "import cv2\n",
    "import cv_bridge\n",
    "from copy import deepcopy\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import clear_output, Image, display\n",
    "import PIL.Image\n",
    "from cStringIO import StringIO\n",
    "import matplotlib.pyplot as plt\n",
    "import dvrk\n",
    "import PyKDL\n",
    "import tf\n",
    "import time\n",
    "from tf_conversions import posemath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['/dvrk/setup_joint/robot_state', 'std_msgs/String'],\n",
       " ['/dvrk/PSM1/current_state', 'std_msgs/String'],\n",
       " ['/dvrk/ECM/wrench_body_current', 'geometry_msgs/WrenchStamped'],\n",
       " ['/stereo/right/image_flipped', 'sensor_msgs/Image'],\n",
       " ['/dvrk/PSM3/goal_reached', 'std_msgs/Bool'],\n",
       " ['/dvrk/PSM2/position_cartesian_local_current', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/SUJ/ECM/position_cartesian_local_current',\n",
       "  'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/SUJ/PSM2/position_cartesian_current', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/PSM3/state_joint_current', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/console/teleop/teleop_psm_selected', 'diagnostic_msgs/KeyValue'],\n",
       " ['/dvrk/PSM2/joint_velocity_ratio', 'std_msgs/Float64'],\n",
       " ['/dvrk/PSM3/current_state', 'std_msgs/String'],\n",
       " ['/dvrk/PSM3/manip_clutch', 'std_msgs/Bool'],\n",
       " ['/dvrk/PSM3/position_cartesian_current', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/ECM/state_joint_desired', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/console/teleop/teleop_psm_unselected', 'diagnostic_msgs/KeyValue'],\n",
       " ['/dvrk/PSM2/position_cartesian_desired', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/ECM/position_cartesian_local_current', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/PSM1/status', 'std_msgs/String'],\n",
       " ['/dvrk/ECM/position_cartesian_desired', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/tf_broadcast/period_statistics', 'cisst_msgs/mtsIntervalStatistics'],\n",
       " ['/dvrk/PSM1/joint_velocity_ratio', 'std_msgs/Float64'],\n",
       " ['/dvrk/PSM1/desired_state', 'std_msgs/String'],\n",
       " ['/dvrk/PSM3/state_jaw_desired', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/SUJ/PSM3/position_cartesian_local_current',\n",
       "  'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/ECM/position_cartesian_current', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/SUJ/PSM1/position_cartesian_local_current',\n",
       "  'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/PSM1/position_cartesian_local_desired', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/PSM2/position_cartesian_local_desired', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/PSM1/wrench_body_current', 'geometry_msgs/WrenchStamped'],\n",
       " ['/dvrk/PSM2/robot_state', 'std_msgs/String'],\n",
       " ['/dvrk/SUJ/ECM/position_cartesian_current', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/ECM/status', 'std_msgs/String'],\n",
       " ['/dvrk/ECM/current_state', 'std_msgs/String'],\n",
       " ['/dvrk/PSM1/jacobian_body', 'std_msgs/Float64MultiArray'],\n",
       " ['/dvrk/ECM/joint_velocity_ratio', 'std_msgs/Float64'],\n",
       " ['/dvrk/PSM2/current_state', 'std_msgs/String'],\n",
       " ['/dvrk/PSM1/error', 'std_msgs/String'],\n",
       " ['/dvrk/PSM1/twist_body_current', 'geometry_msgs/TwistStamped'],\n",
       " ['/stereo/left/image_raw', 'sensor_msgs/Image'],\n",
       " ['/dvrk/PSM3/jacobian_spatial', 'std_msgs/Float64MultiArray'],\n",
       " ['/dvrk/ECM/manip_clutch', 'sensor_msgs/Joy'],\n",
       " ['/dvrk/PSM2/desired_state', 'std_msgs/String'],\n",
       " ['/dvrk/PSM3/position_cartesian_local_desired', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/SUJ/PSM2/position_cartesian_local_current',\n",
       "  'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/PSM3/jacobian_body', 'std_msgs/Float64MultiArray'],\n",
       " ['/dvrk/ECM/joint_states', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM1/state_joint_desired', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/ECM/twist_body_current', 'geometry_msgs/TwistStamped'],\n",
       " ['/dvrk/SUJ/PSM1/state_joint_current', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM2/manip_clutch', 'std_msgs/Bool'],\n",
       " ['/dvrk/PSM2/error', 'std_msgs/String'],\n",
       " ['/tf', 'tf2_msgs/TFMessage'],\n",
       " ['/stereo/right/camera_info', 'sensor_msgs/CameraInfo'],\n",
       " ['/dvrk/PSM2/wrench_body_current', 'geometry_msgs/WrenchStamped'],\n",
       " ['/dvrk/PSM3/state_jaw_current', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM3/joint_acceleration_ratio', 'std_msgs/Float64'],\n",
       " ['/dvrk/PSM2/state_jaw_current', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM3/desired_state', 'std_msgs/String'],\n",
       " ['/dvrk/PSM1/manip_clutch', 'std_msgs/Bool'],\n",
       " ['/stereo/left/camera_info', 'sensor_msgs/CameraInfo'],\n",
       " ['/dvrk/PSM1/position_cartesian_desired', 'geometry_msgs/PoseStamped'],\n",
       " ['/tf_static', 'tf2_msgs/TFMessage'],\n",
       " ['/dvrk/PSM2/jacobian_spatial', 'std_msgs/Float64MultiArray'],\n",
       " ['/dvrk/spin/period_statistics', 'cisst_msgs/mtsIntervalStatistics'],\n",
       " ['/dvrk/ECM/state_joint_current', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM3/twist_body_current', 'geometry_msgs/TwistStamped'],\n",
       " ['/rosout', 'rosgraph_msgs/Log'],\n",
       " ['/dvrk/PSM3/robot_state', 'std_msgs/String'],\n",
       " ['/dvrk/PSM2/warning', 'std_msgs/String'],\n",
       " ['/dvrk/PSM3/warning', 'std_msgs/String'],\n",
       " ['/dvrk/PSM2/goal_reached', 'std_msgs/Bool'],\n",
       " ['/dvrk/PSM2/joint_acceleration_ratio', 'std_msgs/Float64'],\n",
       " ['/dvrk/PSM2/jacobian_body', 'std_msgs/Float64MultiArray'],\n",
       " ['/dvrk/PSM2/status', 'std_msgs/String'],\n",
       " ['/dvrk/PSM3/status', 'std_msgs/String'],\n",
       " ['/dvrk/ECM/joint_acceleration_ratio', 'std_msgs/Float64'],\n",
       " ['/dvrk/PSM2/state_joint_current', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM1/goal_reached', 'std_msgs/Bool'],\n",
       " ['/dvrk/PSM2/joint_states', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM1/state_joint_current', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/SUJ/PSM3/state_joint_current', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM1/jacobian_spatial', 'std_msgs/Float64MultiArray'],\n",
       " ['/rosout_agg', 'rosgraph_msgs/Log'],\n",
       " ['/dvrk/PSM2/state_joint_desired', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/SUJ/PSM2/state_joint_current', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM3/state_joint_desired', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM1/joint_acceleration_ratio', 'std_msgs/Float64'],\n",
       " ['/dvrk/SUJ/PSM1/position_cartesian_current', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/ECM/desired_state', 'std_msgs/String'],\n",
       " ['/dvrk/SUJ/ECM/state_joint_current', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM2/state_jaw_desired', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM1/position_cartesian_local_current', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/ECM/warning', 'std_msgs/String'],\n",
       " ['/dvrk/ECM/jacobian_spatial', 'std_msgs/Float64MultiArray'],\n",
       " ['/stereo/left/image_flipped', 'sensor_msgs/Image'],\n",
       " ['/dvrk/PSM3/joint_states', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/ECM/goal_reached', 'std_msgs/Bool'],\n",
       " ['/dvrk/PSM3/wrench_body_current', 'geometry_msgs/WrenchStamped'],\n",
       " ['/dvrk/PSM1/state_jaw_current', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/setup_joint/state_joint_current', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM3/joint_velocity_ratio', 'std_msgs/Float64'],\n",
       " ['/dvrk/publishers/period_statistics', 'cisst_msgs/mtsIntervalStatistics'],\n",
       " ['/dvrk/PSM1/warning', 'std_msgs/String'],\n",
       " ['/dvrk/ECM/error', 'std_msgs/String'],\n",
       " ['/dvrk/console/teleop/scale', 'std_msgs/Float32'],\n",
       " ['/dvrk/PSM1/joint_states', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM1/position_cartesian_current', 'geometry_msgs/PoseStamped'],\n",
       " ['/stereo/right/image_raw', 'sensor_msgs/Image'],\n",
       " ['/dvrk/PSM3/position_cartesian_desired', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/PSM3/position_cartesian_local_current', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/SUJ/PSM3/position_cartesian_current', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/PSM3/error', 'std_msgs/String'],\n",
       " ['/dvrk/ECM/position_cartesian_local_desired', 'geometry_msgs/PoseStamped'],\n",
       " ['/dvrk/ECM/jacobian_body', 'std_msgs/Float64MultiArray'],\n",
       " ['/dvrk/PSM1/state_jaw_desired', 'sensor_msgs/JointState'],\n",
       " ['/dvrk/PSM2/twist_body_current', 'geometry_msgs/TwistStamped'],\n",
       " ['/dvrk/PSM2/position_cartesian_current', 'geometry_msgs/PoseStamped']]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rospy.init_node('notebook')\n",
    "rospy.get_published_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "left or right image receieved\n"
     ]
    }
   ],
   "source": [
    "bridge = cv_bridge.CvBridge()\n",
    "left_image = None\n",
    "left_image_msg = None\n",
    "left_camera_info = None\n",
    "\n",
    "right_image = None\n",
    "right_image_msg = None\n",
    "right_camera_info = None\n",
    "\n",
    "def left_image_callback(im_msg):\n",
    "    global left_image, left_image_msg\n",
    "    left_image = bridge.imgmsg_to_cv2(im_msg, desired_encoding='rgb8')\n",
    "    left_image_msg = im_msg\n",
    "    \n",
    "def right_image_callback(im_msg):\n",
    "    global right_image, right_image_msg\n",
    "    right_image = bridge.imgmsg_to_cv2(im_msg, desired_encoding='rgb8')\n",
    "    right_image_msg = im_msg\n",
    "    \n",
    "def left_camera_info_callback(camera_info_msg):\n",
    "    global left_camera_info\n",
    "    left_camera_info = camera_info_msg\n",
    "    \n",
    "def right_camera_info_callback(camera_info_msg):\n",
    "    global right_camera_info\n",
    "    right_camera_info = camera_info_msg\n",
    "    \n",
    "jr.subscribe('/stereo/left/image_flipped', msg.Image, left_image_callback)\n",
    "jr.subscribe('/stereo/left/camera_info', msg.CameraInfo, left_camera_info_callback)\n",
    "jr.subscribe('/stereo/right/image_flipped', msg.Image, right_image_callback)\n",
    "jr.subscribe('/stereo/right/camera_info', msg.CameraInfo, right_camera_info_callback)\n",
    "\n",
    "while left_image is None or right_image is None:\n",
    "    #print(\"left or right image not receieved\")\n",
    "    time.sleep(0.5)\n",
    "    \n",
    "print(\"left or right image receieved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe3c9633b50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.hstack((left_image, right_image)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LEFT CAM\n",
      "header: \n",
      "  seq: 402\n",
      "  stamp: \n",
      "    secs: 1627767395\n",
      "    nsecs: 804973364\n",
      "  frame_id: \"left_camera\"\n",
      "height: 288\n",
      "width: 360\n",
      "distortion_model: \"plumb_bob\"\n",
      "D: [0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "K: [311.9603517065951, 0.0, 180.0, 0.0, 311.9603517065951, 144.0, 0.0, 0.0, 1.0]\n",
      "R: [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]\n",
      "P: [311.9603517065951, 0.0, 180.0, 0.0, 0.0, 311.9603517065951, 144.0, 0.0, 0.0, 0.0, 1.0, 0.0]\n",
      "binning_x: 1\n",
      "binning_y: 1\n",
      "roi: \n",
      "  x_offset: 0\n",
      "  y_offset: 0\n",
      "  height: 0\n",
      "  width: 0\n",
      "  do_rectify: True\n",
      "RIGHT_CAM\n",
      "header: \n",
      "  seq: 402\n",
      "  stamp: \n",
      "    secs: 1627767395\n",
      "    nsecs: 804973364\n",
      "  frame_id: \"right_camera\"\n",
      "height: 288\n",
      "width: 360\n",
      "distortion_model: \"plumb_bob\"\n",
      "D: [0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "K: [311.9603517065951, 0.0, 180.0, 0.0, 311.9603517065951, 144.0, 0.0, 0.0, 1.0]\n",
      "R: [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]\n",
      "P: [311.9603517065951, 0.0, 180.0, -1.5598017585329753, 0.0, 311.9603517065951, 144.0, 0.0, 0.0, 0.0, 1.0, 0.0]\n",
      "binning_x: 1\n",
      "binning_y: 1\n",
      "roi: \n",
      "  x_offset: 0\n",
      "  y_offset: 0\n",
      "  height: 0\n",
      "  width: 0\n",
      "  do_rectify: True\n"
     ]
    }
   ],
   "source": [
    "print(\"LEFT CAM\")\n",
    "print(left_camera_info)\n",
    "print(\"RIGHT_CAM\")\n",
    "print(right_camera_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/iandre/dvrk_ws/src/geometry/tf/src/tf/listener.py:97: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
      "  data = yaml.load(self._buffer.all_frames_as_yaml()) or {}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['PSM2_tool_wrist_shaft_link',\n",
       " 'PSM3_outer_pitch_bottom_link',\n",
       " 'PSM1_RCM',\n",
       " 'PSM3_outer_pitch_front_link',\n",
       " 'PSM2_tool_main_link',\n",
       " 'PSM2_outer_insertion_link',\n",
       " 'PSM1_tool_wrist_sca_ee_link_0',\n",
       " 'PSM1_tool_wrist_sca_ee_link_1',\n",
       " 'PSM1_tool_wrist_sca_ee_link_2',\n",
       " 'PSM2_outer_pitch_bottom_link',\n",
       " 'PSM2_tool_wrist_caudier_link_shaft',\n",
       " 'PSM3_tool_wrist_link',\n",
       " 'ECM',\n",
       " 'PSM1_outer_pitch_link',\n",
       " 'PSM2_base',\n",
       " 'camera',\n",
       " 'PSM1_outer_pitch_front_link',\n",
       " 'ecm_pitch_link',\n",
       " 'PSM2_RCM',\n",
       " 'PSM2_tool_wrist_caudier_link',\n",
       " 'PSM3_tool_wrist_caudier_link_2_right',\n",
       " 'ecm_pitch_link_1',\n",
       " 'ecm_pitch_link_2',\n",
       " 'PSM3_tool_wrist_caudier_link',\n",
       " 'PSM1_tool_wrist_sca_link',\n",
       " 'PSM2_outer_pitch_top_link',\n",
       " 'PSM3_tool_wrist_caudier_link_2_left',\n",
       " 'PSM3_outer_pitch_back_link',\n",
       " 'PSM1_outer_pitch_top_link',\n",
       " 'Vision_sensor_left',\n",
       " 'simworld',\n",
       " 'PSM2_tool_wrist_caudier_link_2_right',\n",
       " 'ecm_base_link',\n",
       " 'PSM1_tool_wrist_link',\n",
       " 'PSM1_tool_main_link',\n",
       " 'PSM3_tool_wrist_caudier_ee_link_0',\n",
       " 'PSM2_tool_wrist_link',\n",
       " 'ecm_roll_link',\n",
       " 'ecm_setup_base_link',\n",
       " 'PSM3_tool_wrist_caudier_ee_link',\n",
       " 'PSM3_tool_wrist_shaft_link',\n",
       " 'PSM2_psm_base_link',\n",
       " 'PSM3_outer_pitch_link',\n",
       " 'PSM3_outer_insertion_link',\n",
       " 'PSM2_outer_pitch_front_link',\n",
       " 'Jp21_ECM',\n",
       " 'PSM2_tool_wrist_caudier_link_2_left',\n",
       " 'PSM1',\n",
       " 'PSM2',\n",
       " 'PSM3',\n",
       " 'PSM2_tool_wrist_caudier_ee_link',\n",
       " 'ecm_setup_link',\n",
       " 'ECM_RCM',\n",
       " 'ecm_yaw_link',\n",
       " 'PSM2_outer_pitch_link',\n",
       " 'ecm_pitch_link_3',\n",
       " 'PSM1_outer_yaw_link',\n",
       " 'PSM1_tool_wrist_shaft_link',\n",
       " 'PSM2_outer_pitch_back_link',\n",
       " 'PSM3_base',\n",
       " 'PSM2_tool_wrist_caudier_ee_link_0',\n",
       " 'PSM1_outer_insertion_link',\n",
       " 'PSM3_tool_main_link',\n",
       " 'PSM1_tool_tip_link',\n",
       " 'ECM_base',\n",
       " 'ecm_insertion_link',\n",
       " 'PSM1_outer_pitch_bottom_link',\n",
       " 'PSM3_outer_pitch_top_link',\n",
       " 'PSM1_tool_wrist_sca_shaft_link',\n",
       " 'PSM1_psm_base_link',\n",
       " 'PSM1_outer_pitch_back_link',\n",
       " 'PSM2_outer_yaw_link',\n",
       " 'PSM3_tool_wrist_caudier_link_shaft',\n",
       " 'PSM1_base',\n",
       " 'PSM3_RCM',\n",
       " 'PSM3_psm_base_link',\n",
       " 'J1_PSM1',\n",
       " 'PSM3_outer_yaw_link',\n",
       " 'J1_PSM3',\n",
       " 'J1_PSM2']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_listener = tf.TransformListener()\n",
    "time.sleep(1)\n",
    "tf_listener.getFrameStrings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "psm1 = None \n",
    "ecm = None\n",
    "suj = None\n",
    "debug_output = widgets.Output(layout={'border': '1px solid black'})\n",
    "\n",
    "with debug_output:\n",
    "    global psm1, psm2, psm3, ecm\n",
    "    psm1 = dvrk.psm('PSM1')\n",
    "    ecm = dvrk.ecm('ECM')\n",
    "    psm2 = dvrk.psm('PSM2')\n",
    "    psm3 = dvrk.psm('PSM3')\n",
    "    \n",
    "    psm1_suj = dvrk.suj('PSM1')\n",
    "    psm2_suj = dvrk.suj('PSM2')\n",
    "    psm3_suj = dvrk.suj('PSM3')\n",
    "    ecm_suj = dvrk.suj('ECM')\n",
    "    \n",
    "while ecm.get_current_position() == PyKDL.Frame() or ecm.get_desired_position() == PyKDL.Frame():\n",
    "    time.sleep(0.5)\n",
    "while psm1.get_current_position() == PyKDL.Frame() or psm1.get_desired_position() == PyKDL.Frame():\n",
    "    time.sleep(0.5)\n",
    "while psm2.get_current_position() == PyKDL.Frame() or psm2.get_desired_position() == PyKDL.Frame():\n",
    "    time.sleep(0.5)\n",
    "while psm3.get_current_position() == PyKDL.Frame() or psm3.get_desired_position() == PyKDL.Frame():\n",
    "    time.sleep(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.          1.39999998 -1.79999995 -0.38       -0.69999999]\n",
      "[ 0.51999998  0.94999999 -1.79999995  0.80000001]\n",
      "----------------------------------\n",
      "[[           1,           0,           0;\n",
      "            0,           1,           0;\n",
      "            0,           0,           1]\n",
      "[-1.50197e-06,      2.0009,      0.9163]]\n",
      "[[          -1, 6.27081e-06, 2.59709e-06;\n",
      " -6.27081e-06,   -0.707176,   -0.707037;\n",
      " -2.59709e-06,   -0.707037,    0.707176]\n",
      "[  3.6871e-06,    0.495318,    0.329477]]\n"
     ]
    }
   ],
   "source": [
    "from math import radians as r\n",
    "\n",
    "# 3 arm setup\n",
    "#height, shoulder, elbow, wrist, rotate\n",
    "# sm1 = [-0.0, -1.3, 1.9, -0.1, -0.8]  # right\n",
    "# sm2 = [-0.0, 1.3, -1.8, -0.2, -0.8]  #  left\n",
    "sm3 = [-0.0, 1.4, -1.8, -0.38, -0.7]  # middle\n",
    "cm = [0.52, 0.95, -1.8, 0.8, 0.0]\n",
    "time.sleep(1)\n",
    "\n",
    "# psm1_suj.move_joint(np.asarray(sm1))\n",
    "# psm2_suj.move_joint(np.asarray(sm2))\n",
    "psm3_suj.move_joint(np.asarray(sm3))\n",
    "ecm_suj.move_joint(np.asarray(cm))\n",
    "\n",
    "# print(psm1_suj.get_current_joint_position())\n",
    "# print(psm2_suj.get_current_joint_position())\n",
    "print(psm3_suj.get_current_joint_position())\n",
    "print(ecm_suj.get_current_joint_position())\n",
    "print('----------------------------------')\n",
    "# print(psm1_suj.get_current_position())\n",
    "# print(psm2_suj.get_current_position())\n",
    "print(psm3_suj.get_current_position())\n",
    "print(ecm_suj.get_current_position())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3 arm setup\n",
    "# ecm: rotate, up/down, zoom, rotate view\n",
    "HARDCODED_ECM_POS = np.array([-0.00, -0.13, 0.15, 0.0])\n",
    "PSM_HOME_POS = np.asarray([0.0, 0.2, 0.12, 0.0, 0.0, 0.0])\n",
    "\n",
    "# psm1.move_joint(deepcopy(PSM_HOME_POS))\n",
    "# psm1.close_jaw()\n",
    "# time.sleep(1)\n",
    "\n",
    "# psm2.move_joint(deepcopy(PSM_HOME_POS))\n",
    "# psm2.close_jaw()\n",
    "# time.sleep(1)\n",
    "\n",
    "psm3.move_joint(deepcopy(PSM_HOME_POS))\n",
    "psm3.close_jaw()\n",
    "time.sleep(1)\n",
    "\n",
    "ecm.move_joint(HARDCODED_ECM_POS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "([-1.0433012247085571, -0.02839038148522377, 1.2260448932647705], [0.661155104637146, 0.7228261828422546, 0.14688998460769653, 0.13718444108963013])\n",
      "[[  -0.0881087,    0.915498,    0.392555;\n",
      "     0.996102,   0.0825945,   0.0309515;\n",
      "  -0.00408689,    0.393752,   -0.919208]\n",
      "[     -1.0433,  -0.0283904,     1.22604]]\n",
      "----------------------------------\n",
      "[[ -0.00533695,    0.944897,    0.327324;\n",
      "    -0.999983, -0.00576148, 0.000327337;\n",
      "   0.00219517,   -0.327317,    0.944912]\n",
      "[    -0.29047,    -1.04558,    -1.21076]]\n"
     ]
    }
   ],
   "source": [
    "pick_and_place_utils = None\n",
    "from pick_and_place_utils import get_objects_and_img, tf_to_pykdl_frame, PSM_J1_TO_BASE_LINK_TF, World\n",
    "import image_geometry\n",
    "\n",
    "time.sleep(1)\n",
    "\n",
    "print(tf_listener.lookupTransform('simworld', 'Vision_sensor_left', rospy.Time()))\n",
    "\n",
    "rot = PyKDL.Rotation.Quaternion(0.661155104637146, 0.7228261828422546, 0.14688998460769653, 0.13718444108963013)\n",
    "pos2 = PyKDL.Vector(-1.0433012247085571, -0.02839038148522377, 1.2260448932647705)\n",
    "tf_cam_to_world = PyKDL.Frame(rot, pos2)\n",
    "\n",
    "# tf_cam_to_world = tf_to_pykdl_frame(tf_listener.lookupTransform('simworld', 'Vision_sensor_left', rospy.Time()))\n",
    "\n",
    "# tf_world_to_psm1 = tf_to_pykdl_frame(tf_listener.lookupTransform('PSM1_base', 'simworld', rospy.Time()))\n",
    "# tf_world_to_psm2 = tf_to_pykdl_frame(tf_listener.lookupTransform('PSM2_base', 'simworld', rospy.Time()))\n",
    "tf_world_to_psm3 = tf_to_pykdl_frame(tf_listener.lookupTransform('PSM3_base', 'simworld', rospy.Time()))\n",
    "\n",
    "# tf_world_to_psm1_j1 = tf_to_pykdl_frame(tf_listener.lookupTransform('J1_PSM1', 'simworld', rospy.Time()))\n",
    "# tf_world_to_psm1_base = PSM_J1_TO_BASE_LINK_TF * tf_world_to_psm1_j1\n",
    "\n",
    "# tf_world_to_psm2_j1 = tf_to_pykdl_frame(tf_listener.lookupTransform('J1_PSM2', 'simworld', rospy.Time()))\n",
    "# tf_world_to_psm2_base = PSM_J1_TO_BASE_LINK_TF * tf_world_to_psm2_j1\n",
    "\n",
    "tf_world_to_psm3_j1 = tf_to_pykdl_frame(tf_listener.lookupTransform('J1_PSM3', 'simworld', rospy.Time()))\n",
    "tf_world_to_psm3_base = PSM_J1_TO_BASE_LINK_TF * tf_world_to_psm3_j1\n",
    "\n",
    "print(tf_cam_to_world)\n",
    "print('----------------------------------')\n",
    "# print(tf_world_to_psm1_base)\n",
    "# print(tf_world_to_psm2_base)\n",
    "print(tf_world_to_psm3_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<World objects: [<Object3d pos: [   -0.979246,   0.0405265,     1.04361] type: FeatureType.BALL color: 2>, <Object3d pos: [    -0.97162,   -0.104373,     1.04199] type: FeatureType.BALL color: 0>, <Object3d pos: [   -0.994956, -0.00356303,     1.03541] type: FeatureType.BALL color: 1>, <Object3d pos: [    -1.02563,  -0.0448616,     1.04449] type: FeatureType.BALL color: 1>, <Object3d pos: [    -1.03305,   0.0158278,     1.04337] type: FeatureType.BALL color: 2>, <Object3d pos: [    -1.02647,  -0.0990452,     1.04231] type: FeatureType.BALL color: 0>]\n",
      "bowl: <Object3d pos: [    -0.91511,  -0.0390317,     1.06832] type: FeatureType.BOWL color: 0>>\n"
     ]
    }
   ],
   "source": [
    "stereo_cam = image_geometry.StereoCameraModel()\n",
    "stereo_cam.fromCameraInfo(left_camera_info, right_camera_info)\n",
    "\n",
    "objects, frame = get_objects_and_img(left_image_msg, right_image_msg, stereo_cam, \n",
    "                                          cam_to_world_tf=tf_cam_to_world)\n",
    "world = World(objects)\n",
    "\n",
    "print(world)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(objects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[    -0.91511,  -0.0390317,     1.06832]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe268097b50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bowl object position\n",
    "bowl_pos_cam = objects[0].pos\n",
    "print(bowl_pos_cam)\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.imshow(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------\n",
      "The remoteApi library could not be loaded. Make sure\n",
      "it is located in the same folder as \"sim.py\", or\n",
      "appropriately adjust the file \"sim.py\"\n",
      "----------------------------------------------------\n",
      "\n",
      "--------------------------------------------------------------\n",
      "\"sim.py\" could not be imported. This likely means that\n",
      "either \"sim.py\" or the remoteApi library could not be found.\n",
      "Make sure both are in the same folder as this file,\n",
      "or appropriately adjust the file \"sim.py\"\n",
      "--------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import IPython\n",
    "from timeit import default_timer as timer\n",
    "from pick_and_place_utils import get_objects_for_psms\n",
    "from enum import Enum\n",
    "import math\n",
    "\n",
    "try:\n",
    "    import sim as vrep\n",
    "except:\n",
    "    print ('--------------------------------------------------------------')\n",
    "    print ('\"sim.py\" could not be imported. This likely means that')\n",
    "    print ('either \"sim.py\" or the remoteApi library could not be found.')\n",
    "    print ('Make sure both are in the same folder as this file,')\n",
    "    print ('or appropriately adjust the file \"sim.py\"')\n",
    "    print ('--------------------------------------------------------------')\n",
    "    print ('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Program started\n",
      "VREP attempting to connect... Connected to remote API server\n",
      "VREP returned ID\n",
      "('ClientID is', 0)\n"
     ]
    }
   ],
   "source": [
    "# connect to remote API\n",
    "print ('Program started')\n",
    "vrep.simxFinish(-1)  # just in case, close all opened connections\n",
    "\n",
    "print('VREP attempting to connect...'),\n",
    "clientID = vrep.simxStart('127.0.0.1', 19997, True, True, 5000, 5) # start a connection\n",
    "\n",
    "if clientID!=-1:\n",
    "    print ('Connected to remote API server')\n",
    "    print('VREP returned ID')\n",
    "    print('ClientID is', clientID)\n",
    "else:\n",
    "    print ('Failed connecting to remote API server')  \n",
    "    sys.exit(\"Could not connect, exiting process\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emptyBuff = bytearray()\n",
    "    \n",
    "# Start the simulation:\n",
    "vrep.simxStartSimulation(clientID,vrep.simx_opmode_oneshot_wait)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "the_image = IPython.display.Image(frame)\n",
    "\n",
    "objects_to_pick = deepcopy(world.objects)\n",
    "\n",
    "# this vector is empirically determined\n",
    "approach_vec = PyKDL.Vector(0.000, -0.000, 0.002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# VREP IK Joint calculation test\n",
    "VECTOR_EPS = 0.005\n",
    "\n",
    "DOWN_JAW_ORIENTATION = PyKDL.Rotation.RPY(math.pi, 0, - math.pi / 2.0)\n",
    "\n",
    "PSM_HOME_POS = PyKDL.Vector(0., 0., -0.1)\n",
    "\n",
    "obj_dest = world.bowl.pos + PyKDL.Vector(0, 0, 0.05)\n",
    "\n",
    "# OPEN JAW\n",
    "psm3.open_jaw()\n",
    "\n",
    "# APPROACH OBJECT\n",
    "# send dest to child scripts\n",
    "#res, retInts, retFloats, retStrings, retBuffer = \\\n",
    "#vrep.simxCallScriptFunction(clientID, 'remoteApiCommandServer', vrep.sim_scripttype_childscript, 'visualizePath', \\\n",
    "#                            [robotHandle, 255, 0, 255], path, [], emptyBuff, vrep.simx_opmode_oneshot_wait)\n",
    "\n",
    "# while loop\n",
    "# send dest to child script\n",
    "# get path from child script\n",
    "dest = tf_world_to_psm3_base * objects[1].pos\n",
    "psm3.move(PyKDL.Frame(DOWN_JAW_ORIENTATION, dest), blocking=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ========================================================================================================== \n",
    "# Runs PSM 3\n",
    "# ========================================================================================================== \n",
    "\n",
    "VECTOR_EPS = 0.005\n",
    "\n",
    "DOWN_JAW_ORIENTATION = PyKDL.Rotation.RPY(math.pi, 0, - math.pi / 2.0)\n",
    "\n",
    "PSM_HOME_POS = PyKDL.Vector(0., 0., -0.1)\n",
    "\n",
    "obj_dest = world.bowl.pos + PyKDL.Vector(0, 0, 0.05)\n",
    "\n",
    "# OPEN JAW\n",
    "psm3.open_jaw()\n",
    "\n",
    "# APPROACH OBJECT\n",
    "# send dest to child scripts\n",
    "res, retInts, retFloats, retStrings, retBuffer = \\\n",
    "vrep.simxCallScriptFunction(clientID, 'remoteApiCommandServer', vrep.sim_scripttype_childscript, 'visualizePath', \\\n",
    "                            [robotHandle, 255, 0, 255], path, [], emptyBuff, vrep.simx_opmode_oneshot_wait)\n",
    "# while loop\n",
    "# send dest to child script\n",
    "# get path from child script\n",
    "dest = tf_world_to_psm3_base * objects[1].pos\n",
    "psm3.move(PyKDL.Frame(DOWN_JAW_ORIENTATION, dest), blocking=False)\n",
    "\n",
    "\n",
    "# grab object\n",
    "obj_pos = tf_world_to_psm3_base * objects[1].pos\n",
    "app_vec = tf_world_to_psm3_base.M * approach_vec\n",
    "dest = (obj_pos + app_vec)\n",
    "psm3.move(PyKDL.Frame(DOWN_JAW_ORIENTATION, dest), blocking=False)\n",
    "\n",
    "# close jaw\n",
    "psm3.close_jaw()\n",
    "\n",
    "# approach bowl\n",
    "\n",
    "# open jaw\n",
    "psm3.open_jaw()\n",
    "\n",
    "# home"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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