Update readme

README.md

@@ -6,7 +6,7 @@ The repository containing the code for **real** environment on a **real** DJI Te
 
 
 ![Cover Photo](/images/cover.png)
-
+![Cover Photo](/images/depth.gif)
 ![Cover Photo](/images/envs.png)
 
 

aux_functions.py

@@ -2,7 +2,7 @@
 # Created: 10/14/2019, 12:50 PM
 # Email: aqeel.anwar@gatech.edu
 import numpy as np
-import os, subprocess
+import os, subprocess, psutil
 import math
 import random
 import time
@@ -11,6 +11,15 @@ import pygame
 from configs.read_cfg import read_cfg
 import matplotlib.pyplot as plt
 
+
+def close_env(env_process):
+    process = psutil.Process(env_process.pid)
+    for proc in process.children(recursive=True):
+        proc.kill()
+    process.kill()
+
+
+
 def save_network_path(cfg):
     # Save the network to the directory network_path
     weights_type = 'Imagenet'
@@ -28,17 +37,21 @@ def save_network_path(cfg):
 def start_environment(env_name):
     env_folder = os.path.dirname(os.path.abspath(__file__)) + "/unreal_envs/" + env_name + "/"
     path = env_folder + env_name + ".exe"
-    # print(path)
     env_process = subprocess.Popen(path)
-    time.sleep(6)
+    time.sleep(5)
     print("Successfully loaded environment: " + env_name)
 
     return env_process, env_folder
 
 
 def initialize_infer(env_cfg, client, env_folder):
-    c_z = (env_cfg.ceiling_z - env_cfg.player_start_z) / 100
-    f_z = (env_cfg.floor_z - env_cfg.player_start_z) / 100
+
+    if not os.path.exists(env_folder+'results'):
+        os.makedirs(env_folder+'results')
+
+    # Mapping floor to 0 height
+    c_z = (env_cfg.ceiling_z-env_cfg.floor_z)/100
+    p_z = (env_cfg.player_start_z-env_cfg.floor_z)/100
 
     plt.ion()
     fig_z = plt.figure()
@@ -47,11 +60,11 @@ def initialize_infer(env_cfg, client, env_folder):
     ax_z.set_ylim(0, c_z)
     plt.title("Altitude variation")
 
-    start_posit = client.simGetVehiclePose()
+    # start_posit = client.simGetVehiclePose()
 
     fig_nav = plt.figure()
     ax_nav = fig_nav.add_subplot(111)
-    img = plt.imread(env_folder+ "floor.png")
+    img = plt.imread(env_folder+ env_cfg.floorplan)
     ax_nav.imshow(img)
     plt.axis('off')
     plt.title("Navigational map")
@@ -59,7 +72,7 @@ def initialize_infer(env_cfg, client, env_folder):
     nav, = ax_nav.plot(env_cfg.o_x, env_cfg.o_y)
 
 
-    return f_z, fig_z, ax_z, line_z, fig_nav, ax_nav, nav
+    return p_z, fig_z, ax_z, line_z, fig_nav, ax_nav, nav
 
 def translate_action(action, num_actions):
     # action_word = ['Forward', 'Right', 'Left', 'Sharp Right', 'Sharp Left']
@@ -185,7 +198,7 @@ def connect_drone(ip_address='127.0.0.0', phase='infer'):
     old_posit = client.simGetVehiclePose()
     if phase == 'train':
         client.simSetVehiclePose(
-            airsim.Pose(airsim.Vector3r(0, 0, -2.2), old_posit.orientation),
+            airsim.Pose(airsim.Vector3r(0, 0, 0), old_posit.orientation),
             ignore_collison=True)
     elif phase == 'infer':
         client.enableApiControl(True)

configs/config.cfg

@@ -3,7 +3,7 @@ run_name:                   Tello_indoor
 custom_load:                True
 custom_load_path:           models/trained/Indoor/indoor_cloud/Imagenet/e2e/e2e
 env_type:                   Indoor
-env_name:                   indoor_cloud
+env_name:                   indoor_frogeyes
 phase:                      infer
 
 

configs/read_cfg.py

@@ -13,7 +13,7 @@ def read_env_cfg(config_filename = 'configs/main.cfg'):
     config.read(config_filename)
 
     cfg.run_name = config.get('general_params', 'env_name')
-    cfg.custom_load_path = str(config.get('general_params', 'floorplan'))
+    cfg.floorplan = str(config.get('general_params', 'floorplan'))
     cfg.o_x = float(config.get('general_params', 'o_x').split(',')[0])
     cfg.o_y = float(config.get('general_params', 'o_y').split(',')[0])
     cfg.alpha = float(config.get('general_params', 'alpha').split(',')[0])
@@ -69,7 +69,7 @@ def read_cfg(config_filename = 'configs/main.cfg', verbose = False):
     cfg.update_target_interval = int(config.get('RL_params', 'update_target_interval').split(',')[0])
 
 
-    if verbose:
+    if verbose and cfg.phase=='train':
         print('------------------------------ Config File ------------------------------')
         for param in cfg:
             spaces = ' '*(30-len(param))

environments/initial_positions.py

@@ -79,7 +79,7 @@ def indoor_complex():
     ]
     level_name = ['Complex1', 'Complex2', 'Complex3', 'Complex4']
     crash_threshold = 0.07
-    initZ = -2
+    initZ = 0
     return orig_ip, level_name, crash_threshold, initZ
 
 # Test condo indoor initial positions
@@ -115,7 +115,7 @@ def indoor_vanleer():
     # The environment can be downloaded from
     # https://drive.google.com/drive/u/2/folders/1u5teth6l4JW2IXAkZAg1CbDGR6zE-v6Z
     orig_ip = [
-        [-3080, -4280, 90],  #Player Start
+        [-3100, -4530, 90],  #Player Start
         [-1340, -2240, -90],
         [-3790, -5450, 180],
         [-3980, -1760, -90]

main.py

@@ -46,7 +46,7 @@ elif cfg.phase == 'infer':
     nav_x=[]
     nav_y=[]
     altitude=[]
-    f_z, fig_z, ax_z, line_z, fig_nav, ax_nav, nav = initialize_infer(env_cfg=env_cfg, client=client, env_folder=env_folder)
+    p_z, fig_z, ax_z, line_z, fig_nav, ax_nav, nav = initialize_infer(env_cfg=env_cfg, client=client, env_folder=env_folder)
     nav_text = ax_nav.text(0, 0, '')
 
 # Initialize variables
@@ -239,14 +239,14 @@ while active:
                     client.moveByVelocityAsync(vx=0, vy=0, vz=0, duration=1).join()
                     ax_nav.plot(nav_x.pop(), nav_y.pop(), 'r*', linewidth=20)
                     file_path = env_folder + 'results/'
-                    fig_z.savefig(file_path + 'altitude_variation.png', dpi=1000)
-                    fig_nav.savefig(file_path + 'navigation.png', dpi=1000)
-
+                    fig_z.savefig(file_path + 'altitude_variation.png', dpi=500)
+                    fig_nav.savefig(file_path + 'navigation.png', dpi=500)
+                    close_env(env_process)
                     print('Figures saved')
                 else:
                     posit = agent.client.simGetVehiclePose()
                     distance = distance + np.linalg.norm(np.array([old_posit.position.x_val-posit.position.x_val,old_posit.position.y_val-posit.position.y_val]))
-                    altitude.append(-posit.position.z_val-f_z)
+                    altitude.append(-posit.position.z_val+p_z)
 
                     x_val = posit.position.x_val
                     y_val = posit.position.y_val