def initializeTello():
myDrone = djitellopy.Tello()
myDrone.connect()
myDrone.for_back_velocity = 0
myDrone.left_right_velocity = 0
myDrone.up_down_velocity = 0
myDrone.yaw_velocity = 0
print(myDrone.get_battery())
myDrone.streamoff()
myDrone.streamon()
return myDrone
self.event = Event()
self.thread = Thread(target=self._target)
self.thread.start()
def _target(self):
while not self.event.wait(self._time):
self.function(*self.args, **self.kwargs)
@property
def _time(self):
return self.interval - ((time.time() - self.start) % self.interval)
def stop(self):
self.stopped = True
self.event.set()
self.thread.join()
me = tello.Tello()
me.connect()
w,h = 360, 240
thresh = 0.5# Threshold to detect object
classNames = []
classFile = 'coco.names'
with open(classFile, 'rt') as f:
classNames = f.read().rstrip('\n').split('\n')
configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
weightPath = 'frozen_inference_graph.pb'
net = cv2.dnn_DetectionModel(weightPath, configPath)
net.setInputSize(320, 320)
net.setInputScale(1.0/127.5)
finally:
# drone.quit()
pass
if __name__ == '__main__':
def handler(event, sender, data, **args):
drone = sender
if event is drone.EVENT_FLIGHT_DATA:
print(data)
face_cascad = cv2.CascadeClassifier(cv2.data.haarcascades +
'haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +
'haarcascade_eye.xml')
drone = djitellopy.Tello()
time.sleep(2)
drone.connect()
time.sleep(2)
threadl = []
t1 = mythread1()
t2 = mythread2()
threadl.append(t1)
threadl.append(t2)
t1.start()
time.sleep(2)
t2.start()
print('Ending now %s' % time.ctime())
def __init__(self):
# FlowDroNet Configuration
self.pwcnet_ckpt_path = localpath + '/models/pwc-net/pwcnet.ckpt-11000'
self.dronet_model_path = localpath + '/models/FlowDroNet/model_graph_final.pb'
self.output_folder = '/recordings/test_0'
self.logfile = "log.json"
self.FPS = 10
self.hud_scale = 1.5
self.starting_speed = 100
# Configuration of controllable parameters
# initial values
self.params = {
'v_max': 50,
'r_max': 100,
'r_scale': 0,
'prev_len': 3,
'prev_coll': 0.6,
'prev_steer': 0.3,
'reverse_thresh': 0.6,
'reverse_offset': 0.7,
}
# stepsize
self.params_d = {
'v_max': 5,
'r_max': 5,
'r_scale': 50,
'prev_len': 1,
'prev_coll': 0.1,
'prev_steer': 0.1,
'reverse_thresh': 0.1,
'reverse_offset': 0.1,
}
# max (min is 0)
self.params_m = {
'v_max': 100,
'r_max': 100,
'r_scale': 1000,
'prev_len': 10,
'prev_coll': 1,
'prev_steer': 1,
'reverse_thresh': 1,
'reverse_offset': 1,
}
# Logging
self.log_info = {'cur_v': [], 'cur_s': [], 'prd_c': [], 'prd_s': []}
# Init internal variables (do not change anything below here)
self.for_back_velocity = 0 # Drone velocities between -100~100
self.left_right_velocity = 0
self.up_down_velocity = 0
self.yaw_velocity = 0
self.internalSpeed = 100
self.send_rc_control = False
self.v_old = [
0,
]
self.s_old = [
0,
]
self.was_dronet = True
self.last_pred_col = 0
self.last_pred_ang = 0
self.last_time = time.time()
self.is_armed = False
self.battery_percentage = 0
self.should_stop = False
self.record_data = False
self.show_raw_data = False
self.frame = None
self.current_parameter = 0
self.param_keys = list(self.params.keys())
self.log_count = 0
# Configure the pwc-net model for inference, starting with the default options
self.nn_opts = deepcopy(_DEFAULT_FLOWDRONET_OPTS)
self.nn_opts['verbose'] = False
self.nn_opts['batch_size'] = 1
self.nn_opts['ckpt_path'] = self.pwcnet_ckpt_path
self.nn_opts['dronet_mode'] = 'rgb'
self.nn_opts['dronet_model_path'] = self.dronet_model_path
self.target_size = (int(self.nn_opts['y_shape'][1]),
int(self.nn_opts['y_shape'][0]))
# Create output folder if not exists
if not os.path.exists(
os.path.join(self.output_folder,
str(self.log_count).zfill(2))):
os.makedirs(
os.path.join(self.output_folder,
str(self.log_count).zfill(2)))
# Init pygame with display
pygame.init()
pygame.display.init()
pygame.display.set_caption("FlowDroNeTello")
self.screen = pygame.display.set_mode([
int(self.hud_scale * self.target_size[0]) + 44,
int(self.hud_scale * self.target_size[1]) + 36
])
# Show bootscreen
loadimg = cv2.imread(localpath + "/../misc/images/startuplogo.png")
loadimg = np.fliplr(loadimg)
loadimg = np.rot90(loadimg)
loadimg = pygame.surfarray.make_surface(loadimg)
self.screen.fill([0, 0, 0])
self.screen.blit(loadimg, (0, 0))
pygame.display.update()
pygame.time.set_timer(USEREVENT + 1, int(1. / self.FPS * 1000))
# Instantiate the model in inference mode and display the model configuration
self.nn = ModelFlowDroNet(mode='test', options=self.nn_opts)
self.nn.print_config()
# Init Tello object that interacts with the Tello drone
self.tello = djitellopy.Tello()
