jump_to_visual_looming.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implement jump in response to object detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tqdm import trange\n",
    "from gymnasium.utils.env_checker import check_env\n",
    "from gymnasium import spaces\n",
    "\n",
    "from flygym import Fly, SingleFlySimulation, Camera\n",
    "\n",
    "from flygym.examples.common import PreprogrammedSteps\n",
    "from flygym.arena import BaseArena\n",
    "from flygym import preprogrammed\n",
    "import dm_control.mjcf as mjcf\n",
    "from flygym.vision import save_video_with_vision_insets\n",
    "from typing import Union, Tuple, List, Optional\n",
    "from IPython.display import Video\n",
    "from pathlib import Path\n",
    "\n",
    "Path(\"./outputs\").mkdir(exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class VisionSimulation(SingleFlySimulation):\n",
    "    def __init__(\n",
    "        self, camera: Camera, obj_threshold=0.15, decision_interval=0.05, **kwargs\n",
    "    ):\n",
    "        super().__init__(cameras=[camera], **kwargs)\n",
    "\n",
    "        self.obj_threshold = obj_threshold\n",
    "        self.decision_interval = decision_interval\n",
    "        self.num_substeps = int(self.decision_interval / self.timestep)\n",
    "        self.visual_inputs_hist = []\n",
    "\n",
    "        self.coms = np.empty((self.fly.retina.num_ommatidia_per_eye, 2))\n",
    "        for i in range(self.fly.retina.num_ommatidia_per_eye):\n",
    "            mask = self.fly.retina.ommatidia_id_map == i + 1\n",
    "            self.coms[i, :] = np.argwhere(mask).mean(axis=0)\n",
    "\n",
    "        self.observation_space = spaces.Box(0, 1, shape=(6,))\n",
    "\n",
    "    def step(self, action):\n",
    "        raw_obs, _, _, _, info = super().step(action)\n",
    "        render_res = super().render()[0]\n",
    "        if render_res is not None:\n",
    "            # record visual inputs too because they will be played in the video\n",
    "            self.visual_inputs_hist.append(raw_obs[\"vision\"].copy())\n",
    "            \n",
    "        visual_features = self._process_visual_observation(raw_obs[\"vision\"])\n",
    "        return visual_features, 0, False, False, {}\n",
    "\n",
    "    def _process_visual_observation(self, vision_input):\n",
    "        features = np.zeros((2, 3))\n",
    "        for i, ommatidia_readings in enumerate(vision_input):\n",
    "            is_obj = ommatidia_readings.max(axis=1) < self.obj_threshold\n",
    "            is_obj_coords = self.coms[is_obj]\n",
    "            if is_obj_coords.shape[0] > 0:\n",
    "                features[i, :2] = is_obj_coords.mean(axis=0)\n",
    "            features[i, 2] = is_obj_coords.shape[0]\n",
    "        features[:, 0] /= self.fly.retina.nrows  # normalize y_center\n",
    "        features[:, 1] /= self.fly.retina.ncols  # normalize x_center\n",
    "        features[:, 2] /= self.fly.retina.num_ommatidia_per_eye  # normalize area\n",
    "        return features.ravel().astype(\"float32\")\n",
    "\n",
    "    def reset(self, seed=0, **kwargs):\n",
    "        raw_obs, _ = super().reset(seed=seed)\n",
    "        self.visual_inputs_hist = []\n",
    "        return self._process_visual_observation(raw_obs[\"vision\"]), {}\n",
    "\n",
    "\n",
    "class MovingObjArena(BaseArena):\n",
    "    def __init__(\n",
    "        self,\n",
    "        size=(300, 300),\n",
    "        friction=(1, 0.005, 0.0001),\n",
    "        obj_radius=1,\n",
    "        init_ball_pos=(12, 0),\n",
    "        move_speed=15,\n",
    "        move_direction=\"right\",\n",
    "    ):\n",
    "        self.ball_positions = []\n",
    "\n",
    "        self.init_ball_pos = (*init_ball_pos, obj_radius)\n",
    "        self.ball_pos = np.array(self.init_ball_pos, dtype=\"float32\")\n",
    "        self.friction = friction\n",
    "        self.move_speed = move_speed\n",
    "        self.curr_time = 0\n",
    "        self.move_direction = move_direction\n",
    "        if move_direction == \"left\":\n",
    "            self.y_mult = 1\n",
    "        elif move_direction == \"right\":\n",
    "            self.y_mult = -1\n",
    "        elif move_direction == \"random\":\n",
    "            self.y_mult = np.random.choice([-1, 1])\n",
    "        else:\n",
    "            raise ValueError(\"Invalid move_direction\")\n",
    "\n",
    "        self.root_element = mjcf.RootElement()\n",
    "        \n",
    "\n",
    "        # Add ground\n",
    "        ground_size = [*size, 1]\n",
    "        chequered = self.root_element.asset.add(\n",
    "            \"texture\",\n",
    "            type=\"2d\",\n",
    "            builtin=\"checker\",\n",
    "            width=300,\n",
    "            height=300,\n",
    "            rgb1=(0.4, 0.4, 0.4),\n",
    "            rgb2=(0.5, 0.5, 0.5),\n",
    "        )\n",
    "        grid = self.root_element.asset.add(\n",
    "            \"material\",\n",
    "            name=\"grid\",\n",
    "            texture=chequered,\n",
    "            texrepeat=(60, 60),\n",
    "            reflectance=0.1,\n",
    "        )\n",
    "        self.root_element.worldbody.add(\n",
    "            \"geom\",\n",
    "            type=\"plane\",\n",
    "            name=\"ground\",\n",
    "            material=grid,\n",
    "            size=ground_size,\n",
    "            friction=friction,\n",
    "        )\n",
    "        self.root_element.worldbody.add(\"body\", name=\"b_plane\")\n",
    "\n",
    "        # Add ball\n",
    "        obstacle = self.root_element.asset.add(\n",
    "            \"material\", name=\"obstacle\", reflectance=0.1\n",
    "        )\n",
    "        self.root_element.worldbody.add(\n",
    "            \"body\", name=\"ball_mocap\", mocap=True, pos=self.ball_pos, gravcomp=1\n",
    "        )\n",
    "        self.object_body = self.root_element.find(\"body\", \"ball_mocap\")\n",
    "        self.object_body.add(\n",
    "            \"geom\",\n",
    "            name=\"ball\",\n",
    "            type=\"sphere\",\n",
    "            size=(obj_radius, obj_radius),\n",
    "            rgba=(0.0, 0.0, 0.0, 1),\n",
    "            material=obstacle,\n",
    "        )\n",
    "\n",
    "        # Add camera\n",
    "        self.birdeye_cam = self.root_element.worldbody.add(\n",
    "            \"camera\",\n",
    "            name=\"birdeye_cam\",\n",
    "            mode=\"fixed\",\n",
    "            pos=(15, 0, 35),\n",
    "            euler=(0, 0, 0),     \n",
    "            fovy=45,\n",
    "        )\n",
    "        self.birdeye_cam_zoom = self.root_element.worldbody.add(\n",
    "            \"camera\",\n",
    "            name=\"birdeye_cam_zoom\",\n",
    "            mode=\"fixed\",\n",
    "            pos=(15, 0, 20),\n",
    "            euler=(0, 0, 0),\n",
    "            fovy=45,\n",
    "        )\n",
    "\n",
    "        self.tenta = self.root_element.worldbody.add(\n",
    "            \"camera\",\n",
    "            name=\"tenta\",\n",
    "            mode=\"fixed\",\n",
    "            pos=(2, -18, 9),\n",
    "            euler=(np.deg2rad(65), 0, 0),\n",
    "            fovy=45,\n",
    "        )\n",
    "\n",
    "    def get_spawn_position(self, rel_pos, rel_angle):\n",
    "        return rel_pos, rel_angle\n",
    "\n",
    "    def step(self, dt, physics):\n",
    "        ball_x, ball_y = self.ball_pos[:2]\n",
    "        self.ball_positions.append((ball_x, ball_y))\n",
    "        # Direct the movement strictly along the x-axis\n",
    "        heading_vec = np.array([-1, 0])  # Change direction vector to move left\n",
    "        self.ball_pos[:2] += self.move_speed * heading_vec * dt # Update x position\n",
    "        physics.bind(self.object_body).mocap_pos = self.ball_pos\n",
    "        self.curr_time += dt\n",
    "\n",
    "    def reset(self, physics):\n",
    "        if self.move_direction == \"random\":\n",
    "            self.y_mult = np.random.choice([-1, 1])\n",
    "        self.curr_time = 0\n",
    "        self.ball_pos = np.array(self.init_ball_pos, dtype=\"float32\")\n",
    "        physics.bind(self.object_body).mocap_pos = self.ball_pos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "contact_sensor_placements = [\n",
    "    f\"{leg}{segment}\"\n",
    "    for leg in [\"LF\", \"LM\", \"LH\", \"RF\", \"RM\", \"RH\"]\n",
    "    for segment in [\"Tibia\", \"Tarsus1\", \"Tarsus2\", \"Tarsus3\", \"Tarsus4\", \"Tarsus5\"]\n",
    "]\n",
    "\n",
    "\n",
    "fly = Fly(\n",
    "    contact_sensor_placements=contact_sensor_placements,\n",
    "    enable_vision=True,\n",
    "    render_raw_vision=True,\n",
    "    enable_adhesion =True,\n",
    "    draw_adhesion=True\n",
    ")\n",
    "\n",
    "arena=MovingObjArena()\n",
    "\n",
    "cam1= Camera(\n",
    "    fly=fly,\n",
    "    camera_id=\"birdeye_cam\",\n",
    "    play_speed=0.5,\n",
    "    window_size=(800,608)\n",
    ")\n",
    "\n",
    "cam3=Camera(\n",
    "    fly=fly,\n",
    "    camera_id=\"tenta\",\n",
    "    play_speed=0.2,\n",
    "    window_size=(800, 608),\n",
    ")\n",
    "\n",
    "sim = VisionSimulation(fly=fly, camera=cam3, arena=arena)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "data": {
      "text/html": [
       "<video src=\"kast.mp4\" controls  width=\"800\"  height=\"608\">\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprogrammed_steps = PreprogrammedSteps()\n",
    "swing_periods = preprogrammed_steps.swing_period\n",
    "legs = preprogrammed_steps.legs\n",
    "\n",
    "adhesion_action= np.zeros(len(legs))\n",
    "# Initialize standing action using preprogrammed steps for all legs\n",
    "standing_action = []\n",
    "for leg in legs:\n",
    "    standing_action.extend(\n",
    "        preprogrammed_steps.get_joint_angles(leg, swing_periods[leg][1])\n",
    "    )\n",
    "\n",
    "print(standing_action[fly.actuated_joints.index(\"joint_LHCoxa\")],)\n",
    "stand_action = {\"joints\": standing_action, \"adhesion\": adhesion_action}\n",
    "\n",
    "timestep = sim.timestep\n",
    "obj_threshold = 0.3\n",
    "detect_object=[]\n",
    "\n",
    "# Let the fly stand on the floor first\n",
    "for i in range(int(0.2 // timestep)):\n",
    "    detect_object.append(obs[2])\n",
    "    obs, reward, terminated, truncated, info =sim.step(stand_action)\n",
    "\n",
    "run_time1 =0.2\n",
    "run_time2 =0.5\n",
    "\n",
    "# Simulation parameters\n",
    "target_num_steps1 = int(run_time1 / timestep)\n",
    "target_num_steps2 = int(run_time2 / timestep)\n",
    "\n",
    "# Define group actions for the specific joints\n",
    "group_action = {\n",
    "    \"joint_LMTibia\": np.linspace(standing_action[fly.actuated_joints.index(\"joint_LMTibia\")], 2.60, target_num_steps1),\n",
    "    \"joint_RMTibia\": np.linspace(standing_action[fly.actuated_joints.index(\"joint_RMTibia\")], 2.60, target_num_steps1),\n",
    "    \"joint_LMTarsus1\": np.linspace(standing_action[fly.actuated_joints.index(\"joint_LMTarsus1\")], -1.6, target_num_steps1),\n",
    "    \"joint_RMTarsus1\": np.linspace(standing_action[fly.actuated_joints.index(\"joint_RMTarsus1\")], -1.6, target_num_steps1),\n",
    "    \"joint_LHTarsus1\": np.linspace(standing_action[fly.actuated_joints.index(\"joint_LHTarsus1\")], -1, target_num_steps1),\n",
    "    \"joint_RHTarsus1\": np.linspace(standing_action[fly.actuated_joints.index(\"joint_RHTarsus1\")], -1, target_num_steps1),\n",
    "    \"joint_LHCoxa\": np.linspace(standing_action[fly.actuated_joints.index(\"joint_LHCoxa\")], 0.2, target_num_steps1),\n",
    "    \"joint_RHCoxa\": np.linspace(standing_action[fly.actuated_joints.index(\"joint_RHCoxa\")], 0.2, target_num_steps1),\n",
    "    \"joint_LHTibia\": np.linspace(standing_action[fly.actuated_joints.index(\"joint_LHTibia\")], standing_action[fly.actuated_joints.index(\"joint_LHTibia\")], target_num_steps1),\n",
    "    \"joint_RHTibia\": np.linspace(standing_action[fly.actuated_joints.index(\"joint_RHTibia\")], standing_action[fly.actuated_joints.index(\"joint_RHTibia\")], target_num_steps1)\n",
    "}\n",
    "\n",
    "#define the action for the jump\n",
    "jumps_action = {\n",
    "    \"joint_LMTibia\": np.linspace(2.60, 0, target_num_steps2),\n",
    "    \"joint_RMTibia\": np.linspace(2.60, 0, target_num_steps2),\n",
    "    \"joint_LMTarsus1\": np.linspace(-1.6, 0, target_num_steps2),\n",
    "    \"joint_RMTarsus1\": np.linspace(-1.6, 0, target_num_steps2),\n",
    "    \"joint_LHTarsus1\": np.linspace(-1, 0, target_num_steps2),\n",
    "    \"joint_RHTarsus1\": np.linspace(-1, 0, target_num_steps2),\n",
    "    \"joint_LHCoxa\": np.linspace(0.2, 0, target_num_steps2),\n",
    "    \"joint_RHCoxa\": np.linspace(0.2, 0, target_num_steps2),\n",
    "    \"joint_LHTibia\": np.linspace(standing_action[fly.actuated_joints.index(\"joint_LHTibia\")], 1.5,target_num_steps2),\n",
    "    \"joint_RHTibia\": np.linspace(standing_action[fly.actuated_joints.index(\"joint_RHTibia\")], 1.5,target_num_steps2)\n",
    "\n",
    "}\n",
    "\n",
    "# Actuated joints and data block initialization\n",
    "actuated_joints = [\"joint_LMTibia\", \"joint_RMTibia\", \"joint_LMTarsus1\", \"joint_RMTarsus1\" ,\"joint_LHTarsus1\", \"joint_RHTarsus1\",\"joint_LHCoxa\", \"joint_RHCoxa\", \"joint_LHTibia\", \"joint_RHTibia\"]\n",
    "data_block1 = np.zeros((len(actuated_joints), target_num_steps1))\n",
    "output_t1 = np.arange(target_num_steps1) * timestep\n",
    "input_t1 = np.linspace(0, timestep * (target_num_steps1 - 1), num=target_num_steps1)\n",
    "\n",
    "data_block2 = np.zeros((len(actuated_joints), target_num_steps2))\n",
    "output_t2 = np.arange(target_num_steps2) * timestep\n",
    "input_t2 = np.linspace(0, timestep * (target_num_steps2 - 1), num=target_num_steps2)\n",
    "\n",
    "\n",
    "# Interpolation for the specified joints\n",
    "for i, joint in enumerate(actuated_joints):\n",
    "    data_block1[i, :] = np.interp(output_t1, input_t1, np.array(group_action[joint]))\n",
    "\n",
    "for i, joint in enumerate(actuated_joints):\n",
    "    data_block2[i, :] = np.interp(output_t2, input_t2, np.array(jumps_action[joint]))\n",
    "\n",
    "\n",
    "# Get the total number of actuated joints\n",
    "total_actuated_joints = len(fly.actuated_joints)\n",
    "\n",
    "# Lists to store joint angle values for plotting\n",
    "joint_LMTibia_values = []\n",
    "joint_RMTibia_values = []\n",
    "joint_LMTarsus_values = []\n",
    "joint_RMTarsus_values = []\n",
    "joint_LHTarsus_values = []\n",
    "joint_RHTarsus_values = []\n",
    "joint_LHCoxa_values = []\n",
    "joint_RHCoxa_values = []\n",
    "\n",
    "\n",
    "joint_angles=standing_action.copy()\n",
    "wait=standing_action.copy()\n",
    "\n",
    "for i in range(target_num_steps1):\n",
    "    detect_object.append(obs[2])\n",
    "    \n",
    "    # Update specific joints with interpolated values\n",
    "    joint_angles[fly.actuated_joints.index(\"joint_LMTibia\")] = data_block1[0, i]\n",
    "    joint_angles[fly.actuated_joints.index(\"joint_RMTibia\")] = data_block1[1, i]\n",
    "    joint_angles[fly.actuated_joints.index(\"joint_LMTarsus1\")] = data_block1[2, i]\n",
    "    joint_angles[fly.actuated_joints.index(\"joint_RMTarsus1\")] = data_block1[3, i]\n",
    "    joint_angles[fly.actuated_joints.index(\"joint_LHTarsus1\")] = data_block1[4, i]\n",
    "    joint_angles[fly.actuated_joints.index(\"joint_RHTarsus1\")] = data_block1[5, i]    \n",
    "    joint_angles[fly.actuated_joints.index(\"joint_LHCoxa\")] = data_block1[6, i]\n",
    "    joint_angles[fly.actuated_joints.index(\"joint_RHCoxa\")] = data_block1[7, i]\n",
    "\n",
    "        # Store values for plotting\n",
    "    joint_LMTibia_values.append(data_block1[0, i])\n",
    "    joint_RMTibia_values.append(data_block1[1, i])\n",
    "    joint_LMTarsus_values.append(data_block1[2, i])\n",
    "    joint_RMTarsus_values.append(data_block1[3, i])\n",
    "    joint_LHTarsus_values.append(data_block1[4, i])\n",
    "    joint_RHTarsus_values.append(data_block1[5, i])\n",
    "    joint_LHCoxa_values.append(data_block1[6, i])\n",
    "    joint_RHCoxa_values.append(data_block1[7, i])\n",
    "\n",
    "    action = {\"joints\": joint_angles, \"adhesion\": adhesion_action}\n",
    "\n",
    "    obs, reward, terminated, truncated, info = sim.step(action)\n",
    "    sim.render()\n",
    "\n",
    "\n",
    "for i in range (target_num_steps2):\n",
    "    detect_object.append(obs[2])\n",
    "    if obs[2] > obj_threshold:\n",
    "        if i < (target_num_steps2 - 3000):\n",
    "            adhesion_action = np.ones(len(legs))\n",
    "        else:\n",
    "            adhesion_action = np.zeros(len(legs))\n",
    "        \n",
    "        joint_angles[fly.actuated_joints.index(\"joint_LMTibia\")] = data_block2[0, i]\n",
    "        joint_angles[fly.actuated_joints.index(\"joint_RMTibia\")] = data_block2[1, i]\n",
    "        joint_angles[fly.actuated_joints.index(\"joint_LMTarsus1\")] = data_block2[2, i]\n",
    "        joint_angles[fly.actuated_joints.index(\"joint_RMTarsus1\")] = data_block2[3, i]\n",
    "        joint_angles[fly.actuated_joints.index(\"joint_LHTarsus1\")] = data_block2[4, i]\n",
    "        joint_angles[fly.actuated_joints.index(\"joint_RHTarsus1\")] = data_block2[5, i]   \n",
    "        joint_angles[fly.actuated_joints.index(\"joint_LHCoxa\")] = data_block2[6, i]\n",
    "        joint_angles[fly.actuated_joints.index(\"joint_RHCoxa\")] = data_block2[7, i]\n",
    "\n",
    "        # Store values for plotting\n",
    "        joint_LMTibia_values.append(data_block2[0, i])\n",
    "        joint_RMTibia_values.append(data_block2[1, i])\n",
    "        joint_LMTarsus_values.append(data_block2[2, i])\n",
    "        joint_RMTarsus_values.append(data_block2[3, i])\n",
    "        joint_LHTarsus_values.append(data_block2[4, i])\n",
    "        joint_RHTarsus_values.append(data_block2[5, i])\n",
    "        joint_LHCoxa_values.append(data_block2[6, i])\n",
    "        joint_RHCoxa_values.append(data_block2[7, i])\n",
    "\n",
    "        action = {\"joints\": joint_angles, \"adhesion\": adhesion_action}\n",
    "\n",
    "        obs, reward, terminated, truncated, info =sim.step(action)\n",
    "        sim.render()\n",
    "    else: \n",
    "        action = {\"joints\": wait, \"adhesion\": np.zeros(len(legs))}\n",
    "        obs, reward, terminated, truncated, info = sim.step(action)\n",
    "        sim.render()\n",
    "\n",
    "cam3.save_video('outputs/jump_object_detect.mp4')\n",
    "\n",
    "Video('outputs/jump_object_detect.mp4', width=800, height=608)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "detect_object = np.array(detect_object)\n",
    "print(detect_object)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(detect_object, label='Object Detected')\n",
    "plt.axhline(y=0.3, color='r', linestyle='-', label= 'detection threshold')\n",
    "#plt.plot(times, fly_x, label='fly position' )\n",
    "plt.title('Object Detection Over Time')\n",
    "plt.xlabel('Time Steps')\n",
    "plt.ylabel('Object Detected')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "flygym-v1",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}