def ga_predict_button_interaction(self):
mouse_pos = pygame.mouse.get_pos()
if (self.ga_predict_button.coords[0] < mouse_pos[0] < self.ga_predict_button.coords[0] + self.ga_predict_button.dimensions[0] and
self.ga_predict_button.coords[1] < mouse_pos[1] < self.ga_predict_button.coords[1] + self.ga_predict_button.dimensions[1]):
self.ga_predict_button.button_light(self.screen, (110, -3))
mouse_click = pygame.mouse.get_pressed()
if mouse_click[0] == 1:
self.pause_background_video = True
questions = ['Model name']
input_box = InputBoxMenu(self.screen, len(questions),
(self.ga_predict_button.coords[0] + 25, self.ga_predict_button.coords[1] + 75),
questions, ['name'])
input_box.help()
inputs = input_box.ask_boxes()
if os.path.exists('./used_models/ga/' + inputs[0] + '.h5') is False:
self.display_error_message("Model doesn't exists in /used_models/ga/. Loading default model.")
# replace with standard_model after training a good model ↓
inputs[0] = 'model_2000'
agent = NeuroEvolutionary(self.screen, self.screen_width, self.screen_height, 0, False, False, False,
None, None, False)
agent.kinematic_ga.load_model(inputs[0])
while pygame.key.get_pressed() != pygame.K_ESCAPE:
agent.ga_sim.run_ga(agent.kinematic_ga.model)
quit()
else:
self.ga_predict_button.draw_button(self.screen, (110, -3))
def drl_predict_button_interaction(self):
mouse_pos = pygame.mouse.get_pos()
if (self.drl_predict_button.coords[0] < mouse_pos[0] < self.drl_predict_button.coords[0] + self.drl_predict_button.dimensions[0] and
self.drl_predict_button.coords[1] < mouse_pos[1] < self.drl_predict_button.coords[1] + self.drl_predict_button.dimensions[1]):
self.drl_predict_button.button_light(self.screen, (110, -3))
mouse_click = pygame.mouse.get_pressed()
if mouse_click[0] == 1:
self.pause_background_video = True
questions = ['Rays_nr']
input_box = InputBoxMenu(self.screen, len(questions),
(self.drl_predict_button.coords[0] + 25, self.drl_predict_button.coords[1] + 75),
questions, [int])
input_box.help()
inputs = input_box.ask_boxes()
check = input_box.check_inputbox_input()
error_message_pos = [20, 20]
while check in input_box.errors:
self.display_error_message('Error ' + check, position=tuple(error_message_pos), sleep_time=0)
error_message_pos[1] += 40
inputs = input_box.ask_boxes()
check = input_box.check_inputbox_input()
agent = DqnSimulator(self.screen, self.screen_width, self.screen_height, 0, False, False, False,
None, None, False, rays_nr=int(inputs[0]))
agent.predict_conv_dqn()
quit()
else:
self.drl_predict_button.draw_button(self.screen, (110, -3))
def replay_button_interaction(self):
mouse_pos = pygame.mouse.get_pos()
if (self.replay_button.coords[0] < mouse_pos[0] < self.replay_button.coords[0] + self.replay_button.dimensions[0] and
self.replay_button.coords[1] < mouse_pos[1] < self.replay_button.coords[1] + self.replay_button.dimensions[1]):
self.replay_button.button_light(self.screen, (125, -3))
mouse_click = pygame.mouse.get_pressed()
if mouse_click[0] == 1:
questions = ['Sensor size', 'Replay_data path']
input_box = InputBoxMenu(self.screen, len(questions),
(self.replay_button.coords[0] + 25, self.replay_button.coords[1] + 75),
questions, [int, 'path + csv'])
input_box.help()
inputs = input_box.ask_boxes()
check = input_box.check_inputbox_input()
error_message_pos = [20, 20]
while check in input_box.errors:
self.display_error_message('Error ' + check, position=tuple(error_message_pos), sleep_time=0)
error_message_pos[1] += 40
inputs = input_box.ask_boxes()
check = input_box.check_inputbox_input()
replay = Replay(self.screen, self.screen_width, self.screen_height,
activations=self.activation_cbox.isChecked(),
traffic=self.traffic_cbox.isChecked(),
sensors=self.sensors_cbox.isChecked(),
distance_sensor=self.distance_sensor_cbox.isChecked(),
sensor_size=int(inputs[0]),
enabled_menu=True)
replay.replay(inputs[1], enable_trajectory=True)
quit()
else:
self.replay_button.draw_button(self.screen, (125, -3))
def ga_train_button_interaction(self):
mouse_pos = pygame.mouse.get_pos()
if (self.ga_train_button.coords[0] < mouse_pos[0] < self.ga_train_button.coords[0] + self.ga_train_button.dimensions[0] and
self.ga_train_button.coords[1] < mouse_pos[1] < self.ga_train_button.coords[1] + self.ga_train_button.dimensions[1]):
self.ga_train_button.button_light(self.screen, (110, -3))
mouse_click = pygame.mouse.get_pressed()
if mouse_click[0] == 1:
self.pause_background_video = True
questions = ['No_population', 'No_generations', 'Rays_nr']
input_box = InputBoxMenu(self.screen, len(questions),
(self.ga_train_button.coords[0] + 25, self.ga_train_button.coords[1] + 75),
questions, [int, int, int])
input_box.help()
inputs = input_box.ask_boxes()
check = input_box.check_inputbox_input()
error_message_pos = [20, 20]
while check in input_box.errors:
self.display_error_message('Error ' + check, position=tuple(error_message_pos), sleep_time=0)
error_message_pos[1] += 40
inputs = input_box.ask_boxes()
check = input_box.check_inputbox_input()
agent = NeuroEvolutionary(self.screen, self.screen_width, self.screen_height, 0, False, False, False,
None, None, False,
population_size=int(inputs[0]),
num_generations=int(inputs[1]),
shape=int(inputs[2]))
agent.neuro_trainer.train(agent.kinematic_ga.neuro_eval)
quit()
else:
self.ga_train_button.draw_button(self.screen, (110, -3))
def manual_driving_button_interaction(self):
mouse_pos = pygame.mouse.get_pos()
if (self.manual_driving_button.coords[0] < mouse_pos[0] < self.manual_driving_button.coords[0] + self.manual_driving_button.dimensions[0] and
self.manual_driving_button.coords[1] < mouse_pos[1] < self.manual_driving_button.coords[1] + self.manual_driving_button.dimensions[1]):
self.manual_driving_button.button_light(self.screen, (75, -3))
mouse_click = pygame.mouse.get_pressed()
if mouse_click[0] == 1:
self.pause_background_video = True
questions = ['Sensor size']
input_box = InputBoxMenu(self.screen, len(questions),
(self.manual_driving_button.coords[0] + 25, self.manual_driving_button.coords[1] + 75),
questions, [int])
input_box.help()
inputs = input_box.ask_boxes()
check = input_box.check_inputbox_input()
error_message_pos = [20, 20]
while check in input_box.errors:
self.display_error_message('Error ' + check, position=tuple(error_message_pos), sleep_time=0)
error_message_pos[1] += 40
inputs = input_box.ask_boxes()
check = input_box.check_inputbox_input()
sim = CitySimulator(self.screen, self.screen_width, self.screen_height,
activations=self.activation_cbox.isChecked(), record_data=False,
sensor_size=int(inputs[0]),
traffic=self.traffic_cbox.isChecked(),
sensors=self.sensors_cbox.isChecked(),
distance_sensor=self.distance_sensor_cbox.isChecked(),
enabled_menu=True)
sim.run()
quit()
else:
self.manual_driving_button.draw_button(self.screen, (75, -3))