All posts by Adam Chan

Drone Programming – Swarm Contol, Video Streaming & Mission Pad

Drone Control

One of our main purposes for drone programming is achieving swarm control, that’s why we bought three Tello EDUs and today we are so excited that we can make this happen. Once again, thanks to DJITELLOPY, it saves us a lot of effort to develop our API and make this program done in a few lines. Once we understood the structure of TelloSwarm, it was not that hard to make the swarm control with video streaming and mission pad flying. We started with three drones, we want to control 10 or more for a Drones show.

Roadblock – Damaged Motor

But, there was a huge roadblock for us. It was one of the drones with a motor damaged, it smashed onto the wall when we tested the program. Since we didn’t get any warranty because we brought it from HK to the UK (even just 1.5 months..), we needed to order the spare part and did the motor replacement. The repair we did is quite similar as shown in this video – Simple Motor Replace. In case you need to replace the motor, it is the simplest way. We don’t want to damage the main board because of the poor soldering skills.

Drone Swarm

See below for the full codes. This time, we created two codes. We created the first one which is a very straightforward coding – manage the drone one by one with the parallel process, i.e. a beginner work but also easy for beginner to read . After we studied DJITELLOPY SWARM functions, we modified this. We put them together for a head to head comparison.

Light color – DJITELLPY SWARM version

from djitellopy import Tello, TelloSwarm
import cv2
import threading
import time, logging

flip = True
fly = True
video = False

telloswarm = TelloSwarm.fromIps(['192.168.3.81','192.168.3.82','192.168.3.83'])

for i, tello in zip(range(3), telloswarm):
    tello.LOGGER.setLevel(logging.ERROR) 
    tello.connect()
    print(f'Tello Battery {i+1}: {tello.get_battery()}')
    tello.change_vs_udp(8881+i)
    tello.set_video_resolution(Tello.RESOLUTION_480P)
    tello.set_video_bitrate(Tello.BITRATE_1MBPS)

landed = False   
def tello_video(tello, drone_number):
    while not landed:
        
        frame = tello.get_frame_read().frame
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 
        cv2.imshow(f'Tello {drone_number}' , frame)
        cv2.moveWindow(f'Tello {drone_number}', (drone_number - 1)*900, 50)
        if cv2.waitKey(40) & 0xFF == ord('q'):
            break
    
if video:    
    telloswarm.parallel(lambda drone, tello: tello.streamon())
    time.sleep(3)
    
    tello1_video = threading.Thread(target=tello_video, args=(telloswarm.tellos[0], 1), daemon=True)
    tello2_video = threading.Thread(target=tello_video, args=(telloswarm.tellos[1], 2), daemon=True)
    tello3_video = threading.Thread(target=tello_video, args=(telloswarm.tellos[2], 3), daemon=True)
    tello1_video.start()
    tello2_video.start()
    tello3_video.start()
    
if flip or fly:
    telloswarm.send_rc_control(0,0,0,0)
    telloswarm.takeoff()
    telloswarm.set_speed(10) 
    
if flip:
    fpath_1 = ['l', 'b', 'r']
    fpath_2 = ['r', 'f', 'l']
    telloswarm.parallel(lambda drone, tello: tello.flip(fpath_1[drone]))
    telloswarm.parallel(lambda drone, tello: tello.flip(fpath_2[drone]))
    
if fly:
    telloswarm.move_up(50)
    path_1 = [(-60, -60, 50, 20, 1), (60, -60, 100, 30, 2), (0, 120, 150, 40, 3)]
    path_2 = [(60, -60, 100, 30, 2), (0, 120, 150, 40, 3), (-60, -60, 50, 20, 1)]
    path_3 = [(0, 120, 150, 40, 3), (-60, -60, 50, 20, 1), (60, -60, 100, 30, 2)]
    path_4 = [(0, 0, 50, 20, 1), (0, 0, 50, 20, 2), (0, 0, 50, 20, 3)]
    telloswarm.parallel(lambda drone, tello: tello.enable_mission_pads)
    telloswarm.parallel(lambda drone, tello: tello.go_xyz_speed_mid(path_1[drone][0], path_1[drone][1], path_1[drone][2], path_1[drone][3], path_1[drone][4]))
    telloswarm.parallel(lambda drone, tello: tello.go_xyz_speed_mid(path_2[drone][0], path_2[drone][1], path_2[drone][2], path_2[drone][3], path_2[drone][4]))
    telloswarm.parallel(lambda drone, tello: tello.go_xyz_speed_mid(path_3[drone][0], path_3[drone][1], path_3[drone][2], path_3[drone][3], path_3[drone][4]))
    telloswarm.parallel(lambda drone, tello: tello.go_xyz_speed_mid(path_4[drone][0], path_4[drone][1], path_4[drone][2], path_4[drone][3], path_4[drone][4]))
    
if flip or fly:    
    telloswarm.land()
    landed = True
    
if video:
    tello1_video.join()
    tello2_video.join()
    tello3_video.join()
     
    telloswarm.parallel(lambda drone, tello: tello.streamoff())
    
telloswarm.end()
Python

Dark color – ‘Straight forward’ very beginner version

from djitellopy import Tello, TelloSwarm
import cv2
import threading
import time, logging

flip = True
fly = True
video = True

telloswarm = TelloSwarm.fromIps(['192.168.3.81','192.168.3.82','192.168.3.83'])
tello1 = telloswarm.tellos[0]
tello2 = telloswarm.tellos[1]
tello3 = telloswarm.tellos[2]

tello1.LOGGER.setLevel(logging.ERROR) 
tello2.LOGGER.setLevel(logging.ERROR) 
tello3.LOGGER.setLevel(logging.ERROR) 

tello1.connect()
tello2.connect()
tello3.connect()

print(f'Tello1 Battery : {tello1.get_battery()}')
print(f'Tello2 Battery : {tello2.get_battery()}')
print(f'Tello3 Battery : {tello3.get_battery()}')

tello1.change_vs_udp(8881)
tello1.set_video_resolution(Tello.RESOLUTION_480P)
tello1.set_video_bitrate(Tello.BITRATE_1MBPS)

tello2.change_vs_udp(8882)
tello2.set_video_resolution(Tello.RESOLUTION_480P)
tello2.set_video_bitrate(Tello.BITRATE_1MBPS)

tello3.change_vs_udp(8883)
tello3.set_video_resolution(Tello.RESOLUTION_480P)
tello3.set_video_bitrate(Tello.BITRATE_1MBPS)

landed = False   
def tello_video(tello, drone_number):
    while not landed:
        
        frame = tello.get_frame_read().frame
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 
        cv2.imshow(f'Tello {drone_number}' , frame)
        cv2.moveWindow(f'Tello {drone_number}', (drone_number - 1)*900, 50)
        if cv2.waitKey(40) & 0xFF == ord('q'):
            cv2.destroyWindow(f'Tello {drone_number}')
            break

def tello_flip(tello, direction):
    tello.flip(direction)
    
def tello_mpad(tello, x, y, z, speed, mpad):
    tello.enable_mission_pads
    tello.go_xyz_speed_mid(x, y, z, speed, mpad)
        
if video:
    
    tello1.streamon()
    tello1_video = threading.Thread(target=tello_video, args=(tello1, 1), daemon=True)
    tello1_video.start()

    tello2.streamon()
    tello2_video = threading.Thread(target=tello_video, args=(tello2, 2), daemon=True)
    tello2_video.start()
    
    tello3.streamon()
    tello3_video = threading.Thread(target=tello_video, args=(tello3, 3), daemon=True)
    tello3_video.start()

    time.sleep(3)

if flip or fly:
    telloswarm.send_rc_control(0,0,0,0)
    telloswarm.takeoff()
    telloswarm.set_speed(10) 
    
if flip:
    tello1_fpath = ['l','r']
    tello2_fpath = ['b','f']
    tello3_fpath = ['r','l']
    
    for i in range(2):
        flip1 = tello1_fpath[i]
        flip2 = tello2_fpath[i]
        flip3 = tello3_fpath[i]    
        tello1_flip = threading.Thread(target=tello_flip, args=(tello1, flip1), daemon=True)
        tello2_flip = threading.Thread(target=tello_flip, args=(tello2, flip2), daemon=True)
        tello3_flip = threading.Thread(target=tello_flip, args=(tello3, flip3), daemon=True)
        tello1_flip.start()
        tello2_flip.start()
        tello3_flip.start()
        tello1_flip.join()
        tello2_flip.join()
        tello3_flip.join()
    
if fly:
    telloswarm.move_up(50)
    tello1_path = [(-60, -60, 50, 20, 1), (60, -60, 100, 30, 2), (0, 120, 150, 40, 3), (0, 0, 50, 20, 1)]
    tello2_path = [(60, -60, 100, 30, 2), (0, 120, 150, 40, 3), (-60, -60, 50, 20, 1), (0, 0, 50, 20, 2)]
    tello3_path = [(0, 120, 150, 40, 3), (-60, -60, 50, 20, 1), (60, -60, 100, 30, 2), (0, 0, 50, 20, 3)]
    
    for i in range(4):
        x1, y1, z1, s1, mpad1 = tello1_path[i]
        x2, y2, z2, s2, mpad2 = tello2_path[i]
        x3, y3, z3, s3, mpad3 = tello3_path[i]
        
        tello1_mpad = threading.Thread(target=tello_mpad, args=(tello1, x1, y1, z1, s1, mpad1), daemon=True)
        tello2_mpad = threading.Thread(target=tello_mpad, args=(tello2, x2, y2, z2, s2, mpad2), daemon=True)
        tello3_mpad = threading.Thread(target=tello_mpad, args=(tello3, x3, y3, z3, s3, mpad3), daemon=True)
        tello1_mpad.start()
        tello2_mpad.start()
        tello3_mpad.start()
        tello1_mpad.join()
        tello2_mpad.join()
        tello3_mpad.join()
    
if flip or fly:    
    telloswarm.land()
    landed = True
    
if video:    
    tello1_video.join()
    tello2_video.join()
    tello3_video.join()

    tello1.streamoff()
    tello2.streamoff()
    tello3.streamoff()

telloswarm.end()
Python

Tello regular vs Tello EDU

Before we got our Tello EDU, we had two regular Tellos for years. When we popped up the idea of programming a drone, drone swarm control was our target. However, we just found that doing swarm with regular Tello is quite impossible because of the network connection constraint. It connect as access point instead of station mode, i.e. your PC or mobile get a fixed IP from Tello instead of Tello connecting to the network to get it’s IP. You will still get the same IP even connect to two regular Tellos with two wifi devices in your PC, someone worked this out with Linux, you may want to have a look – Adventures with DJI Ryze Tello: Controlling a Tello Swarm.

For us, we took the easier way, bought some Tello EDU and started our drone programming journey. The main different between Tello EDU and regular Tello is the network connection, supporting mission pad and SDK 3.0.

Setup your Tello EDU

Before you can do the swarm or multiple drone control, you need to change the Tello EDU network setup to the access point, you can do this from the Tello EDU APP, we recommend this because you can also check any firmware update. Otherwise, you may need the following codes, run it when your PC connects the Tello in station mode. Keep in mind that the SSID cannot include ‘.’, spaces, or other special characters, and you can always reset this by pressing the power button for 5 seconds.

from djitellopy import Tello

tello = Tello()
tello.connect()
tello.set_wifi_credentials(SSID, Password)
Python

Connect the drones

telloswarm = TelloSwarm.fromIps(['192.168.3.81','192.168.3.82','192.168.3.83'])
Python

Told TelloSwarm the drones’ IP, and we assigned fixed IP to our drones so that we don’t need to check it every time. Once TelloSwarm connects with the drones’ IP, it will put all drones under tellowarm.tellos.

Setup the drones

for i, tello in zip(range(3), telloswarm):
    tello.LOGGER.setLevel(logging.ERROR) 
    tello.connect()
    print(f'Tello Battery {i+1}: {tello.get_battery()}')
    tello.change_vs_udp(8881+i)
    tello.set_video_resolution(Tello.RESOLUTION_480P)
    tello.set_video_bitrate(Tello.BITRATE_1MBPS)
Python
tello1 = telloswarm.tellos[0]
tello2 = telloswarm.tellos[1]
tello3 = telloswarm.tellos[2]

tello1.LOGGER.setLevel(logging.ERROR) 
tello2.LOGGER.setLevel(logging.ERROR) 
tello3.LOGGER.setLevel(logging.ERROR) 

tello1.connect()
tello2.connect()
tello3.connect()

print(f'Tello1 Battery : {tello1.get_battery()}')
print(f'Tello2 Battery : {tello2.get_battery()}')
print(f'Tello3 Battery : {tello3.get_battery()}')

tello1.change_vs_udp(8881)
tello1.set_video_resolution(Tello.RESOLUTION_480P)
tello1.set_video_bitrate(Tello.BITRATE_1MBPS)

tello2.change_vs_udp(8882)
tello2.set_video_resolution(Tello.RESOLUTION_480P)
tello2.set_video_bitrate(Tello.BITRATE_1MBPS)

tello3.change_vs_udp(8883)
tello3.set_video_resolution(Tello.RESOLUTION_480P)
tello3.set_video_bitrate(Tello.BITRATE_1MBPS)
Python

Used a loop to setup each drones,

  1. Change the logging level to ERROR only, ignore all INFO feedback from DJITELLOPY.
  2. Connect the drone.
  3. Get and display the battery information.
  4. Change the video stream port to 888x, so that they will not be conflicting with each other, the original port 11111.
  5. Lower the resolution and bitrate, just want to ensure that we can get the video display. It get problems some times even with my AX6000 level router.

Swarm Action

if flip:
    fpath_1 = ['l', 'b', 'r']
    fpath_2 = ['r', 'f', 'l']
    telloswarm.parallel(lambda drone, tello: tello.flip(fpath_1[drone]))
    telloswarm.parallel(lambda drone, tello: tello.flip(fpath_2[drone]))
    
if fly:
    telloswarm.move_up(50)
    path_1 = [(-60, -60, 50, 20, 1), (60, -60, 100, 30, 2), (0, 120, 150, 40, 3)]
    path_2 = [(60, -60, 100, 30, 2), (0, 120, 150, 40, 3), (-60, -60, 50, 20, 1)]
    path_3 = [(0, 120, 150, 40, 3), (-60, -60, 50, 20, 1), (60, -60, 100, 30, 2)]
    path_4 = [(0, 0, 50, 20, 1), (0, 0, 50, 20, 2), (0, 0, 50, 20, 3)]
    telloswarm.parallel(lambda drone, tello: tello.enable_mission_pads)
    telloswarm.parallel(lambda drone, tello: tello.go_xyz_speed_mid(path_1[drone][0], path_1[drone][1], path_1[drone][2], path_1[drone][3], path_1[drone][4]))
    telloswarm.parallel(lambda drone, tello: tello.go_xyz_speed_mid(path_2[drone][0], path_2[drone][1], path_2[drone][2], path_2[drone][3], path_2[drone][4]))
    telloswarm.parallel(lambda drone, tello: tello.go_xyz_speed_mid(path_3[drone][0], path_3[drone][1], path_3[drone][2], path_3[drone][3], path_3[drone][4]))
    telloswarm.parallel(lambda drone, tello: tello.go_xyz_speed_mid(path_4[drone][0], path_4[drone][1], path_4[drone][2], path_4[drone][3], path_4[drone][4]))
Python
if flip:
    tello1_fpath = ['l','r']
    tello2_fpath = ['b','f']
    tello3_fpath = ['r','l']
    
    for i in range(2):
        flip1 = tello1_fpath[i]
        flip2 = tello2_fpath[i]
        flip3 = tello3_fpath[i]    
        tello1_flip = threading.Thread(target=tello_flip, args=(tello1, flip1), daemon=True)
        tello2_flip = threading.Thread(target=tello_flip, args=(tello2, flip2), daemon=True)
        tello3_flip = threading.Thread(target=tello_flip, args=(tello3, flip3), daemon=True)
        tello1_flip.start()
        tello2_flip.start()
        tello3_flip.start()
        tello1_flip.join()
        tello2_flip.join()
        tello3_flip.join()
    
if fly:
    telloswarm.move_up(50)
    tello1_path = [(-60, -60, 50, 20, 1), (60, -60, 100, 30, 2), (0, 120, 150, 40, 3), (0, 0, 50, 20, 1)]
    tello2_path = [(60, -60, 100, 30, 2), (0, 120, 150, 40, 3), (-60, -60, 50, 20, 1), (0, 0, 50, 20, 2)]
    tello3_path = [(0, 120, 150, 40, 3), (-60, -60, 50, 20, 1), (60, -60, 100, 30, 2), (0, 0, 50, 20, 3)]
    
    for i in range(4):
        x1, y1, z1, s1, mpad1 = tello1_path[i]
        x2, y2, z2, s2, mpad2 = tello2_path[i]
        x3, y3, z3, s3, mpad3 = tello3_path[i]
        
        tello1_mpad = threading.Thread(target=tello_mpad, args=(tello1, x1, y1, z1, s1, mpad1), daemon=True)
        tello2_mpad = threading.Thread(target=tello_mpad, args=(tello2, x2, y2, z2, s2, mpad2), daemon=True)
        tello3_mpad = threading.Thread(target=tello_mpad, args=(tello3, x3, y3, z3, s3, mpad3), daemon=True)
        tello1_mpad.start()
        tello2_mpad.start()
        tello3_mpad.start()
        tello1_mpad.join()
        tello2_mpad.join()
        tello3_mpad.join()
Python

We used telloswarm.parallel(lambda drone, tello: XXXXX) to command all drones at the same time, you can see from above. XXXXX can be any tello command or even a function.

  1. Line #48 & 50 : Flip drone #1 to left, drone #2 to backward and drone #3 to right
  2. Line #49 & 51 : Flip drone #1 to right, drone #2 to forward and drone #3 to left
  3. Line #55 & 59 : Fly drone #1 to -60, -60, 50 (x, y, z) away from mission pad 1 at speed of 20, drone #2 to 60, -60, 100 away from mission pad 2 at speed of 30 and drone #3 to 0, 120, 150 away from mission pad 3 at speed of 40
  4. Line #56 & 60, 57 & 61, 58 & 62, 59 & 63, fly to difficult positions according to the nearby mission pad to achieve drone rotations.

For telloswarm.parallel, it will wait for all actions to be done before moving to the next step.

Video streaming from drones

if video:    
    telloswarm.parallel(lambda drone, tello: tello.streamon())
    time.sleep(3)
    
    tello1_video = threading.Thread(target=tello_video, args=(telloswarm.tellos[0], 1), daemon=True)
    tello2_video = threading.Thread(target=tello_video, args=(telloswarm.tellos[1], 2), daemon=True)
    tello3_video = threading.Thread(target=tello_video, args=(telloswarm.tellos[2], 3), daemon=True)
    tello1_video.start()
    tello2_video.start()
    tello3_video.start()
Python
if video:
    
    tello1.streamon()
    tello1_video = threading.Thread(target=tello_video, args=(tello1, 1), daemon=True)
    tello1_video.start()

    tello2.streamon()
    tello2_video = threading.Thread(target=tello_video, args=(tello2, 2), daemon=True)
    tello2_video.start()
    
    tello3.streamon()
    tello3_video = threading.Thread(target=tello_video, args=(tello3, 3), daemon=True)
    tello3_video.start()

    time.sleep(3)
Python

Originally, we were using the telloswarm.parallel to execute this and the result is very good. However, as we mentioned before that the telloswarm.parallel wait until the action complete, it cannot serve our purpose, i.e. keep the video capture when flying. So, we used threading, just like what we did in the other project but running three drones by the same time.

It is quite easy to achieve drone swarm control and video streaming by using DJITELLOPY. However, I found it is a bit slow or long processing when using DJITELLOPY. Next project, we may want to communciate to the Tello SDK directly to see the result.

# of view 819

Drone Programming – Gesture Control

Drone Control, Features Projects

Once I did the face detection project, it made me easier to understand gesture recognition, you can do it too. It gives me more solid fundamentals to start with deep learning studying, I want to create my objection recognition model and have the drone fly with this. Before that, I like to try body detection and swap fly as my next project. OK, let’s see how we make this happen in our drone.

Basic Concept

  • Capture video frame from the drone
  • Use a hand detection tool to identify hands from the frame and identify the left hand.
  • Based on the hand-detected information (including wrist, finger, knuckles & phalanges) and the logic, we will achieve gesture control.

Understanding your hand

First of all, let’s understand our hands first… XD

I got this picture from sketchymedicine.com, it contains a lot of medical sketches and detailed information, very cool website.

Program with MediaPipe

See below for the full program

from djitellopy import Tello
import cv2
import mediapipe as mp
import threading
import math
import logging
import time

# Assign tello to the Tello class and set the information to error only
tello = Tello()
tello.LOGGER.setLevel(logging.ERROR) #Ignore INFO from Tello
fly = False #For debuggin purpose

# Assign the MediaPipe hands detection solution to mpHands and define the confidence level
mpHands = mp.solutions.hands
hands = mpHands.Hands(min_detection_confidence=0.8, min_tracking_confidence=0.8)

# When we detect the hand, we can use mp.solution to plot the location and connection
mpDraw = mp.solutions.drawing_utils

def hand_detection(tello):

    while True:
        
        global gesture
        
        # Read the frame from Tello
        frame = tello.get_frame_read().frame
        frame = cv2.flip(frame, 1)
        
        # Call hands from MediaPipe Solution for the hand detction, need to ensure the frame is RGB
        result = hands.process(frame)
        
        # Read frame width & height instead of using fixed number 960 & 720
        frame_height = frame.shape[0]
        frame_width = frame.shape[1]
        my_hand = []
        
        if result.multi_hand_landmarks:
            for handlms, handside in zip(result.multi_hand_landmarks, result.multi_handedness):
                if handside.classification[0].label == 'Right': # We will skip the right hand information
                    continue
                        
                # With mp.solutions.drawing_utils, plot the landmark location and connect them with define style        
                mpDraw.draw_landmarks(frame, handlms, mpHands.HAND_CONNECTIONS,\
                                        mp.solutions.drawing_styles.get_default_hand_landmarks_style(),\
                                        mp.solutions.drawing_styles.get_default_hand_connections_style())          
               
                # Convert all the hand information from a ratio into actual position according to the frame size.
                for i, landmark in enumerate(handlms.landmark):
                    x = int(landmark.x * frame_width)
                    y = int(landmark.y * frame_height)
                    my_hand.append((x, y))
                                        
                # Capture all the landmarks position and distance into hand[]
                # wrist = 0       
                # thumb = 1 - 4
                # index = 5 - 8
                # middle = 9 - 12
                # ring = 13 - 16
                # little = 17 - 20
                
                # Setup left hand control with the pre-defined logic. 
                # Besides thumb, we use finger tip y position compare with knuckle y position as an indicator
                # Thumb use the x position as the comparison.
                
                # Stop, a fist
                # Land, open hand
                # Right, only thumb open
                # Left, only little finger open
                # Up, only index finger open
                # Down, both thumb and index finger open
                # Come, both index and middle fingger open
                # Away, both index, middle and ring finger open            
                finger_on = []
                if my_hand[4][0] > my_hand[2][0]:
                    finger_on.append(1)                     
                else:
                    finger_on.append(0) 
                for i in range(1,5):
                    if my_hand[4 + i*4][1] < my_hand[2 + i*4][1]: 
                        finger_on.append(1)
                    else:
                        finger_on.append(0)
                
                gesture = 'Unknown'        
                if sum(finger_on) == 0:
                    gesture = 'Stop'
                elif sum(finger_on) == 5:
                    gesture = 'Land'
                elif sum(finger_on) == 1:
                    if finger_on[0] == 1:
                        gesture = 'Right'
                    elif finger_on[4] == 1:
                        gesture = 'Left'
                    elif finger_on[1] == 1:
                        gesture = 'Up'
                elif sum(finger_on) == 2:
                    if finger_on[0] == finger_on[1] == 1:
                        gesture = 'Down'
                    elif finger_on[1] == finger_on[2] == 1:
                        gesture = 'Come'
                elif sum(finger_on) == 3 and finger_on[1] == finger_on[2] == finger_on[3] == 1:
                    gesture = 'Away'
                
        cv2.putText(frame, gesture, (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 0), 3)
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 
        cv2.imshow('Tello Video Stream', frame)
        cv2.waitKey(1)
        if gesture == 'Landed':
            break      

########################
# Start of the program #
########################

# Connect to the drone via WIFI
tello.connect()

# Instrust Tello to start video stream and ensure first frame read
tello.streamon()

while True:
            frame = tello.get_frame_read().frame
            if frame is not None:
                break

# Start the hand detection thread when the drone is flying
gesture = 'Unknown'
video_thread = threading.Thread(target=hand_detection, args=(tello,), daemon=True)
video_thread.start()    

# Take off the drone
time.sleep(1)
if fly:
    tello.takeoff()
    tello.set_speed(10)
    time.sleep(2)
    tello.move_up(80)
    
while True:
    
    hV = dV = vV = rV = 0
    if gesture == 'Land':
        break
    elif gesture == 'Stop' or gesture == 'Unknown':
        hV = dV = vV = rV = 0
    elif gesture == 'Right':
        hV = -15
    elif gesture == 'Left':
        hV = 15
    elif gesture == 'Up':
        vV = 20
    elif gesture == 'Down':
        vV = -20
    elif gesture == 'Come':
        dV = 15
    elif gesture == 'Away':
        dV = -15
        
    tello.send_rc_control(hV, dV, vV, rV)

    
# Landing the drone
if fly: tello.land()
gesture = 'Landed'

# Stop the video stream
tello.streamoff()

# Show the battery level before ending the program
print("Battery :", tello.get_battery())
Python

Hand Detection

# Assign the MediaPipe hands detection solution to mpHands and define the confidence level
mpHands = mp.solutions.hands
hands = mpHands.Hands(min_detection_confidence=0.8, min_tracking_confidence=0.8)
Python

From MediaPipe, we are going to use MediaPipe Solution Hands for our hand detection, there are a few parameters that are important to us,

  • min_hand_detection_confidence – that’s the first step to identifying a hand in the single frame. At which score of detection is considered a success, the lower the number, the more detected objects can pass but the higher the chance of error. The higher the number, need a very high the score object, i.e. a very clear and precise hand image.
  • min_tracking_confidence – Once the hand is detected based on the min_hand_detection_confidence requirement, it will do the hand tracking with this number.

You can refer to the MediaPipe Official Website for more information. They also get a demonstration website for you to play with different models. However,

  def __init__(self,
               static_image_mode=False,
               max_num_hands=2,
               model_complexity=1,
               min_detection_confidence=0.5,
               min_tracking_confidence=0.5):
Python

I found that the documentation on the MediaPipe Official Website may not be updated, and min_hand_presence_confidence and running_mode no longer exist. What I checked from hands.py. It spent me 30 minutes playing with the demo and reading information to understand the difference between min_hand_detection_confidence and min_hand_presence_confidence.

gesture = 'Unknown'
video_thread = threading.Thread(target=hand_detection, args=(tello,), daemon=True)
video_thread.start()
Python

Just like what we did in face detection, we need to run the hand detection function in a thread (parallel processing) to capture and analyze the hand position, updating a global variable – gesture, so that the drone movement control can take corresponding actions according to this.

# Read the frame from Tello
        frame = tello.get_frame_read().frame
        frame = cv2.flip(frame, 1)
        
        # Call hands from MediaPipe Solution for the hand detction, need to ensure the frame is RGB
        result = hands.process(frame)
Python

Get the latest frame from the drone and flip it from the camera point of view to our point of view. Then, process the frame with hands, it was predefined in the beginning – line #11. Once the hand detection is done, the following information will be stored in result, it will contain,

  • result.multi_handedness – ‘Left’ or ‘Right’ hand
  • result.multi_hand_landmarks – an array containing 21 sets of data as show below, they call it landmarks, each landmark including the x, y & z position. But, the x and y coordinates are normalized to [0.0, 1.0] by the image width and height, respectively. The z coordinate represents the landmark depth, with the depth at the wrist being the origin.
  • result.multi_hand_world_landmarks – The 21 hand landmarks are also presented in world coordinates. Each landmark is composed of x, y, and z, representing real-world 3D coordinates in meters with the origin at the hand’s geometric center.
        if result.multi_hand_landmarks:
            for handlms, handside in zip(result.multi_hand_landmarks, result.multi_handedness):
                if handside.classification[0].label == 'Right': # We will skip the right hand information
                    continue
Python

If hand(s) are detected, the result will contain the necessary data. Since we only need to use the left hand to control the drone, we will skip reading the Right hand data.

mpDraw = mp.solutions.drawing_utils
Python
                mpDraw.draw_landmarks(frame, handlms, mpHands.HAND_CONNECTIONS, mp.solutions.drawing_styles.get_default_hand_landmarks_style(), mp.solutions.drawing_styles.get_default_hand_connections_style())
Python

Then, we use the drawing_utils from MediaPipe to highlight the landmarks and connect them.

                # Convert all the hand information from a ratio into actual position according to the frame size.
                for i, landmark in enumerate(handlms.landmark):
                    x = int(landmark.x * frame_width)
                    y = int(landmark.y * frame_height)
                    my_hand.append((x, y))
Python

Retreves result.multi_hand_landmarks x & y data and converts this into actual position to the frame size. Store into array my_hand for the gesture analysis.

Logic for different gestures – Finger open or close?

We just simply compare the y position of the fingertip (TIP) to the knuckle (MCP). For the thumb, we compare the x position of the fingertip (TIP) to the knuckle (MCP) as shown below,

When thumb is open, finger tip x is bigger then knuckle x. When the fingers are open, finger tip y is smaller than knuckle y.

                finger_on = []
                if my_hand[4][1] > my_hand[2][1]:
                    finger_on.append(1)                     
                else:
                    finger_on.append(0) 
                for i in range(1,5):
                    if my_hand[4 + i*4][3] > my_hand[2 + i*4][3]: 
                        finger_on.append(1)
                    else:
                        finger_on.append(0)
                
                gesture = 'Unknown'        
                if sum(finger_on) == 0:
                    gesture = 'Stop'
                elif sum(finger_on) == 5:
                    gesture = 'land'
                elif sum(finger_on) == 1:
                    if finger_on[0] == 1:
                        gesture = 'Right'
                    elif finger_on[4] == 1:
                        gesture = 'Left'
                    elif finger_on[1] == 1:
                        gesture = 'Up'
                elif sum(finger_on) == 2:
                    if finger_on[0] == finger_on[1] == 1:
                        gesture = 'Down'
                    elif finger_on[1] == finger_on[2] == 1:
                        gesture = 'Come'
                elif sum(finger_on) == 3 and finger_on[1] == finger_on[2] == finger_on[3] == 1:
                    gesture = 'Away'
Python

We compare each fingers one by one and record into array finger_on, 1 represent open and 0 represent to close. By mapping this to our target gesture,

  • Left – finger_on = [0, 0, 0, 0, 1]
  • Right – finger_on = [1, 0, 0, 0, 0]
  • Up – finger_on = [0, 1, 0, 0, 0]
  • Down – finger_on = [1, 1, 0, 0, 0]
  • Come – finger_on = [0, 1, 1, 0, 0]
  • Away – finger_on = [0, 1, 1, 1, 0]
  • Stop – finger_on = [0, 0, 0, 0, 0]
  • Land – finger_on = [1, 1, 1, 1, 1]

Then, we use if/then loops to determinate variable gesture according to the finger_on result.

It may be fun if we use binary numbers to represent the above gesture determination.

Drone Movement Control

while True:
    
    hV = dV = vV = rV = 0
    if gesture == 'Land':
        break
    elif gesture == 'Stop' or gesture == 'Unknown':
        hV = dV = vV = rV = 0
    elif gesture == 'Right':
        hV = -15
    elif gesture == 'Left':
        hV = 15
    elif gesture == 'Up':
        vV = 20
    elif gesture == 'Down':
        vV = -20
    elif gesture == 'Come':
        dV = 15
    elif gesture == 'Away':
        dV = -15
        
    tello.send_rc_control(hV, dV, vV, rV)
Python

With the real-time global variable gesture from the hand_detection(), we use this variable to command the drone movement, with the SEND_RC_CONTROL command.

Horizontal-100 – 0 to move left0 – 100 to move right
Depth -100 – 0 to move backward0 – 100 to move forward
Vertial-100 – 0 to move down0 -100 to move up
Rotation-100 – 0 to rotate anti-clockwsie 0 – 100 to rotate clockwise
SEND_RC_CONTROL(Horizontal velocity, Depth velocity, Vertial velocity, Rotation velocity)

Unlike the Face Detection project, we used hV to control the Left and Right movement instead of rotation- rV. Oh.. the ‘Left/Right’ in the program is from the user’s point of view, but the drone camera view, is reversed.

That’s all for the project. It is quite straightforward and similar to the Face Detection. Please leave a comment for any inputs and comment. I am now studying how to do swarm flying with 3 Tello Edu, just cleaned up some roadblockers…

New Command – Follow

Just pop-up that I can add a ‘follow’ command, like what was doing for the face detection and tracking. I believe that we just need to copy & paste the code from the Face Detection project with some modifications.

Above is the hand sign for ‘follow’, go ahead to modify the original code with belows,

        global gesture
        global hand_center # New line for hand follow
Python
                # New line for hand follow    
                elif sum(finger_on) == 3 and finger_on[0] == finger_on[1] == finger_on[2] == 1:
                    gesture = 'Follow'
                    
                    #Apply Shoelace formula to calculate the palm size
                    palm_vertexs = [0,1,2,5,9,13,17]
                    area = 0                    
                    for i in palm_vertexs:
                        x1, y1 = my_hand[i]
                        x2, y2 = my_hand[(i + 1) % 7]
                        area += (x1 * y2) - (x2 * y1)
                    area = 0.5 * abs(area)
                    
                    hand_center = my_hand[0][0], my_hand[0][1], area
Python
gesture = 'Unknown'
hand_center = 480, 360, 28000 # New line for hand follow
Python
    # New line for hand follow
    elif gesture == 'Follow':
        x, y, size = hand_center
    
        if x > 480:
            rV = -int((x - 480)/4.8)            
        elif x < 480:
            rV = +int((480 - x)/4.8)
        else:
            rV = 0
        
        if y > 360: 
            vV = -int((y - 360)/3.6)
        elif y < 360: 
            vV = int((360 - y)/3.6)
        else:
            vV = 0
            
        if size > 30000:
            dV = -15
        elif size < 26000:
            dV = 15
        else:
            dV = 0            
            
    tello.send_rc_control(hV, dV, vV, rV)
Python

That’s all!

Shoelace Formula

For the above codes, we use landmarks – 0, 1, 2, 5, 9, 13 & 17 to calculate the area of the palm, and this number determines how close the palm is to the drone, we target a range of 26000 – 30000. Then, we command it to fly toward or away from the hand, just similar to the face tracking in the last project.

But, what I want to highlight is the palm area calculation, it led me to learn about the Shoelace Formula, which is a very interesting and powerful formula. I don’t remember that I learned this before, maybe returned this to my teacher already 😎. Anyway, have a look at the below video, it’s worth watching and understanding the Shoelace Formula.

# of view 1,686

Drone Programming – Face Detection and Tracking

Drone Control, Features Projects

If I hadn’t tried, I never have known that doing face detection nowadays is such simple and easy. Even for a beginner like me, I can make it happen within a few hours… after I spent a few days learning and understanding the libraries. It is worth having a try, it will lead you to a new world and start to understand vision computing, deep learning, and AI development. Let’s take a look at what I did.

Basic Concept

  • Capture video frame from the drone
  • Use a face detection tool to identify the main face from the frame. Since we have not yet applied face recognition, we just picked the closet one as the main face.
  • Based on the face detected position (x, y) to move the drone and make the face at the center of the frame.

Program with CV2 model

See below for the full program

# Before you run this program, ensure to connect Tello with the WIFI

# Import Tello class from djitellopy library
from djitellopy import Tello

# Import additional library CV2 - OpenCV for image processing, threading for multi-tasking
import cv2
import threading
import time
import logging

# Assign tello to the Tello class and set the information to error only
tello = Tello()
tello.LOGGER.setLevel(logging.ERROR) #Ignore INFO from Tello
fly = True #For debuggin purpose

# Assign the pre-trained model - Haar Cascade classifier for CV2 face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
eyes_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml') 

# def a video capture and display function
def face_detection(tello):

    while True:
        # Change the face_center to be global, any changes will be read globally
        global face_center
                
        # Read the frame from Tello and convert the color from BGR to RGB
        frame = tello.get_frame_read().frame
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        
        # Convert the image to grayscale for face detection
        gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)

        # Perform face detection using the pre-train model - haarcascade_frontalface_default.xml
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=10, minSize=(80, 80))
        
        
        # Based on CV2 result, find the largest detected face and the position    
        largest_area = 0
        largest_face = None
                
        for (x, y, w, h) in faces:
            face_area = w * h
            if face_area > largest_area:
                largest_area = face_area
                largest_face = (x, y, w, h)
        
        # Confirm there are two eyes detected inside the face           
        if largest_face is not None:
            eyes = eyes_cascade.detectMultiScale(gray) # Using the default parameters
            eye_count = 0
            for (ex, ey, _, _) in eyes:
                if ex - x < w and ey - y < h:
                    eye_count += 1
            if eye_count < 2:
                continue
            
        # Highlight the largest face with a box and show the coordinates             
            x, y, w, h = largest_face
            face_center = (x + w/2), (y + h/2), w
            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
            position_text = f'Face : (x :{x}, y :{y}, w :{w}, h :{h})'
            center_text = f'{int(x + w/2)} , {int(y + h/2)}'
            rc_text = f'RC({hV}, {dV}, {vV}, {rV})'
            cv2.putText(frame, position_text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
            cv2.putText(frame, center_text, (int(x + w/2), int(y + h/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
            cv2.putText(frame, rc_text, (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
        else:
            face_center = 480, 360, 200
        
        # Display the face detected image and check whether 'q' is bing pressed or not
        cv2.imshow('Tello Video Stream', frame)              
        if cv2.waitKey(1) & 0xFF == ord('q'):
            face_center = False
            break

########################
# Start of the program #
########################

# Connect to the drone via WIFI
tello.connect()

# Instrust Tello to start video stream and ensure first frame read
tello.streamon()

while True:
            frame = tello.get_frame_read().frame
            if frame is not None:
                break

# Start the face detection thread when the drone is flying
face_center = 480, 360, 200
hV = vV = dV = rV = 0
video_thread = threading.Thread(target=face_detection, args=(tello,), daemon=True)
video_thread.start()

# Take off the drone
time.sleep(1)
if fly:
    tello.takeoff()
    tello.set_speed(10)
    time.sleep(2)
    tello.move_up(80)

# Use RC Control to control the movement of the drone
# send_rc_control(left_right_velocity, forward_backward_velocity, up_down_velocity, yaw_velocity) from -100 to 100

while face_center != False:
    
    x, y, w = face_center

    if x > 530:
        rV = +30           
    elif x < 430:
        rV = -30
    else:
        rV = 0
    
    if y > 410: 
        vV = -20 
    elif y < 310: 
        vV = 20 
    else:
        vV = 0
        
    if w > 300:
        dV = -15
    elif w < 200:
        dV = 15
    else:
        dV = 0
    
    tello.send_rc_control(hV, dV, vV, rV)
      
# Landing the drone
if fly: tello.land()

# Stop the video stream
tello.streamoff()

# Show the battery level before ending the program
print("Battery :", tello.get_battery())
Python

If you installed the DJITELLOPY package, CV2 is being installed as well. Otherwise, you need to do this with – PIP install djitellpy.

Face Detection

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
Python

Besides face detection, CV2 provides different models to support different purposes. All of them are already downloaded when you install the CV2 package. For face detection, we use haarcascade_frontalface_default.xml.

face_center = 480, 360, 200
hV = vV = dV = rV = 0
video_thread = threading.Thread(target=face_detection, args=(tello,), daemon=True)
video_thread.start()
Python

The program structure is very similar to the video-capturing project. We need to run the face detection function in a thread (parallel processing) to capture and analyze the face position, updating a global variable – face_center, so that the drone movement control can take corresponding actions.

        frame = tello.get_frame_read().frame
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        
        # Convert the image to grayscale for face detection
        gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)

        # Perform face detection using the pre-train model - haarcascade_frontalface_default.xml
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=10, minSize=(80, 80))
Python

Referring to the last project, we use get_frame_read() to get the latest frame from Tello’s camera. As we mentioned before, CV2 processes image data in BGR format but the image feed from Tello’s camera is in RGB format, the ‘R’ & ‘G’ are mis-mapped. We need to convert this into ‘RGB’ for a correct display. Then, we also need to create an image in grayscale because CV2 performs face detection in grayscale.

We use detectMultiScale to perform face detection based on the face_cascade setup and the grayscale image, the result will be stored in faces. There are three inputs to alter the detection result. Be short,

  • scaleFactor – controls the resizing of the image at each step to detect objects of different sizes. The higher the number, the faster the progress but more chance of missing faces.
  • minNeighbors – controls the sensitivity of the detector by requiring a certain number of overlapping detections to consider a region as a positive detection. Lower the number, more sensitive to potential detections, potentially resulting in more detections but also more false positives.
  • minSize – minimum size of the face detected, very straightforward
        largest_area = 0
        largest_face = None
                
        for (x, y, w, h) in faces:
            face_area = w * h
            if face_area > largest_area:
                largest_area = face_area
                largest_face = (x, y, w, h)
Python

Once the face detection is done, face position and sizes will be returned to the array variable faces, len of faces representing how many faces are detected and each faces[] contains the detected position x, position y, width, and height. We are using a for loop to read the x, y, w & h and identify the largest face as we mentioned before.

vvv Small tool to understanding Scale Factor and Min Neighbour vvv

# import the opencv library
import cv2
  
# Load the pre-trained Haar Cascade classifier for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
  
scale_factor = 1.1
min_neighbors = 10
      
while(True):
  
    frame = cv2.imread("people.jpg")
    
    # Convert the image to grayscale for face detection
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Perform face detection
    faces = face_cascade.detectMultiScale(gray, scaleFactor=scale_factor, minNeighbors=min_neighbors, minSize=(100, 100))
    biggest_face = [0, 0, 0, 0]
    
    # Draw rectangles around the detected faces
    for i, (x, y, w, h) in enumerate(faces):
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
        position_text = f'Face {i+1}: (x :{x}, y :{y}, w :{w}, h :{h})'
        center_text = f'{int(x + w/2)} , {int(y + h/2)}'
        cv2.putText(frame, position_text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
        cv2.putText(frame, center_text, (int(x + w/2), int(y + h/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
           
    cv2.putText(frame, f'scaleFactor = {scale_factor}, minNeighbors = {min_neighbors}', (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)        
        
    # Display the resulting frame
    cv2.imshow('People', frame)
      
    # q - quit
    # a/s - add or reduce scale factor by 0.05
    # z/x - add or reduce min neighors by 1
    # desired button of your choice
    key = cv2.waitKey(0)
    if key == ord('q'):
        break
    elif key == ord('a') and scale_factor > 1.05:
        scale_factor = round(scale_factor - 0.05, 2)
    elif key == ord('s'):
        scale_factor = round(scale_factor + 0.05, 2)
    elif key == ord('z') and min_neighbors > 1:
        min_neighbors -= 1
    elif key == ord('x'):
        min_neighbors += 1
    
# Destroy all the windows
cv2.destroyAllWindows()
Python

To better understand the above parameters, I also wrote a small tool to alter Scale Factor (with a & s key) and Min Neighbour (with z & x key), you can have a try with different photos.

Eyes Detection

When we developed the program with face detection only, we found that there was a chance to have ‘fault detection’ no ever what parameters we tried, which would interfere with our result and induce a ‘ghost’ face. We had added eye detection to ensure a face with eyes is correctly detected, it can greatly improve the detection result.

eyes_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
Python

Assigns haarcascade_eye.xml to eyes_cascade for eye detection.

        if largest_face is not None:
            eyes = eyes_cascade.detectMultiScale(gray) # Using the default parameters
            eye_count = 0
            for (ex, ey, _, _) in eyes:
                if ex - x < w and ey - y < h:
                    eye_count += 1
            if eye_count < 2:
                continue
Python

If the largest face is detected, we will do an eye detection to confirm the largest face with two eyes. Same as face detection, eye position, and size will be returned to the array of variable eyes. We need to compare that there are at least two eyes in the face box. Our logic is very simple, ensure that the eyes x & y position are within the face box, eye_x (ex) minus face_x (x) should be smaller than the width (w) and eye_y (ey) minus face_y (y) should be smaller than the height (h). Why at least two eyes? because it includes potential fault eye detection within the face box.

Face Position

        if largest_face is not None:
            eyes = eyes_cascade.detectMultiScale(gray) # Using the default parameters
            eye_count = 0
            for (ex, ey, _, _) in eyes:
                if ex - x < w and ey - y < h:
                    eye_count += 1
            if eye_count < 2:
                continue
            
        # Highlight the largest face with a box and show the coordinates             
            x, y, w, h = largest_face
            face_center = (x + w/2), (y + h/2), w
            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
            position_text = f'Face : (x :{x}, y :{y}, w :{w}, h :{h})'
            center_text = f'{int(x + w/2)} , {int(y + h/2)}'
            rc_text = f'RC({hV}, {dV}, {vV}, {rV})'
            cv2.putText(frame, position_text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
            cv2.putText(frame, center_text, (int(x + w/2), int(y + h/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
            cv2.putText(frame, rc_text, (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
        else:
            face_center = 480, 360, 200
Python

Once we confirm the largest face with eyes, we will get the face_center position ((x + w/2), (y + h/2)) and w for the drone movement. Since we are running the face_detection() in parallel, we make the face_center variable global, so that the drone can get the data in real-time and adjust the position. Then, we highlight the face with a blue box, the face position, and the drone movement (RC – we explain later) in the video for user information. If there is no face detected, the face_center will keep as 480, 360, 200.

Drone Movement Control

We target to position the largest face in the center of the camera. The resolution of Tello’s camera is 960 x 720, i.e. face center is (480, 360). It is not practical to position to a point, so we defined an area (480 +/- 50, 360 +/- 50).

With the real time face_center data from the face_detection(), we compare this with the box above.

  • if x > 530, we need to rotate the drone to right (view from the drone), i.e. clockwise
  • if x < 430, we need to rotate the drone to left (view from the drone, i.e anti-clockwise
  • if within the box, no movement is needed
  • if y > 410, we need to move up the drone
  • if y < 310, we need to move down the drone
  • if within the box, no movement is needed

Besides the x & y position, we also control how close the drone is to our face. We use the w (width) to make the judgment, we control the face size width between 300 – 200.

  • if w > 300, it too close, we need to move the drone away, i.e. backward
  • if w < 200, it too far, we need to move the drone closer, i.e. forward
  • if within the range, no movement is needed

DJITELLOPY SEND_RC_CONTROL

In our first project, we move the drone by using commands like move_up(), move_down(), rotate_clockwise(), etc.. Since this is a once-a-time command, the drone will be moving step by step, and also min. 20cm a time. The result will be lagging and unsmooth.

So, we use SEND_RC_CONTROL to control the drone movement. For RC_SEND_CONTROL, it can set the velocity of the drone in four dimensions a time.

* Left/Right is from drone’s view

Horizontal-100 – 0 to move left0 – 100 to move right
Depth -100 – 0 to move backward0 – 100 to move forward
Vertial-100 – 0 to move down0 -100 to move up
Rotation-100 – 0 to rotate anti-clockwsie 0 – 100 to rotate clockwise
SEND_RC_CONTROL(Horizontal velocity, Depth velocity, Vertial velocity, Rotation velocity)
# Use RC Control to control the movement of the drone
# send_rc_control(left_right_velocity, forward_backward_velocity, up_down_velocity, yaw_velocity) from -100 to 100

while face_center != False:
    
    x, y, w = face_center

    if x > 530:
        rV = +30           
    elif x < 430:
        rV = -30
    else:
        rV = 0
    
    if y > 410: 
        vV = -20 
    elif y < 310: 
        vV = 20 
    else:
        vV = 0
        
    if w > 300:
        dV = -15
    elif w < 200:
        dV = 15
    else:
        dV = 0
    
    tello.send_rc_control(hV, dV, vV, rV)
Python

As a result, we have the code above,

  • hV – Horizontal Velocity
  • dV – Depth Velocity
  • vV – Vertial Velocity
  • rV – Rotation Velocity

Since doing rotation is a better approach to adjusting the horizontal position, we used rV instead of hV. Once we send the velocity number to the drone, it will keep moving in the direction according to the velocity until the next change. So, the drone is flying smoothly to the face position and achieves face tracking.

That’s simple, right?

Face Detection with MediaPipe model?

Besides using CV2. haarcascade_frontalface_default.xml, we have tried to use the MediaPipe.blaze_face_short_range.tflite. We supposed the face detection good is better because it is a deep learning based model. And yes, it is better in accuracy and response. See below comparison,

However, blaze_face_short_range.tflite is a lightweight model for detecting single or multiple faces within selfie-like images from a smartphone camera or webcam. The model is optimized for front-facing phone camera images at short range. The result for our project is not ideal since it cannot detect a long-range face when I moved away from the drone, we will re-test this when the full-range blaze face is released.

See below for the full code with MediaPipe.

# Before you run this program, ensure to connect Tello with the WIFI

# Import Tello class from djitellopy library
from djitellopy import Tello

# Import additional library CV2 - OpenCV for image processing, threading for multi-tasking
# Import MediaPIPE for the face detection
import cv2
import threading
import time
import logging
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision

# Assign tello to the Tello class and set the information to error only
tello = Tello()
tello.LOGGER.setLevel(logging.ERROR) #Ignore INFO from Tello
fly = True #For debuggin purpose

# Upload the pre-trained model and setup the Face Detection Option for MediaPIPE
base_options = python.BaseOptions(model_asset_path='blaze_face_short_range.tflite')
options = vision.FaceDetectorOptions(base_options=base_options, min_detection_confidence = 0.8, min_suppression_threshold = 0.3)
detector = vision.FaceDetector.create_from_options(options)
  
# def a video capture and display function
def face_detection(tello):

    while True:
        # Change the face_center to be global, any changes will be read globally
        global face_center      
        
        # Read the frame from Tello and convert the color from BGR to RGB
        frame = tello.get_frame_read().frame
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        image = mp.Image(image_format = mp.ImageFormat.SRGB, data = frame)
        
        # Perform face detection using the pre-train model - blaze_face_short_range.tflite
        detection_result = detector.detect(image)
        
        # Based on the MediaPIPE result, find the largest detected face and the position    
        largest_area = 0
        largest_face = None
        
        #faces = len(face_position.detections)
        #if faces > 0:
        for face_position in detection_result.detections:
            x = face_position.bounding_box.origin_x
            y = face_position.bounding_box.origin_y
            w = face_position.bounding_box.width
            h = face_position.bounding_box.height
            face_area = w * h
            if face_area > largest_area:
                largest_area = face_area
                largest_face = (x, y, w, h)
        
        # Highlight the largest face with a box and show the coordinates        
        if largest_face is not None:
            x, y, w, h = largest_face
            face_center = (x + w/2), (y + h/2), w
            
            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
            position_text = f'Face : (x :{x}, y :{y}, w :{w}, h :{h})'
            center_text = f'{int(x + w/2)} , {int(y + h/2)}'
            rc_text = f'RC({hV}, {dV}, {vV}, {rV})'
            cv2.putText(frame, position_text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
            cv2.putText(frame, center_text, (int(x + w/2), int(y + h/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
            cv2.putText(frame, rc_text, (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
        else:
            face_center = 480, 360, 200
        
        # Display the face detected image and check whether 'q' is bing pressed or not
        cv2.imshow('Tello Video Stream', frame)              
        if cv2.waitKey(1) & 0xFF == ord('q'):
            face_center = False
            break

########################
# Start of the program #
########################

# Connect to the drone via WIFI
tello.connect()

# Instrust Tello to start video stream and ensure first frame read
tello.streamon()
while True:
            frame = tello.get_frame_read().frame
            if frame is not None:
                break

# Start the face detection thread when the drone is flying
face_center = 480, 360, 200
hV = vV = dV = rV = 0
video_thread = threading.Thread(target=face_detection, args=(tello,), daemon=True)
video_thread.start()

# Take off the drone
time.sleep(1)
if fly:
    tello.takeoff()
    tello.set_speed(10)
    time.sleep(2)
    tello.move_up(80)

# Use RC Control to control the movement of the drone
# send_rc_control(left_right_velocity, forward_backward_velocity, up_down_velocity, yaw_velocity) from -100 to 100
while face_center != False:
    
    x, y, w = face_center

    if x > 530:
        rV = +30           
    elif x < 430:
        rV = -30
    else:
        rV = 0
    
    if y > 410: 
        vV = -20 
    elif y < 310: 
        vV = 20 
    else:
        vV = 0
        
    if w > 250:
        dV = -15
    elif w < 150:
        dV = 15
    else:
        dV = 0
    
    tello.send_rc_control(hV, dV, vV, rV)
      
# Landing the drone
if fly: tello.land()

# Stop the video stream
tello.streamoff()

# Show the battery level before ending the program
print("Battery :", tello.get_battery())
Python

PID?

Thanks to Hacky from TelloPilots gave me the idea of PID, I started to study and am going to add this to the face detection project. To be frank, I got a failed mark and needed to redo the exam for the Feedback Control System when I was in college. However, I found it very important and useful when working… you may not know how useful what you were learning when you were a student. (sad..)

So, I need to do some revise first. See below for a basic PID concept,

As a result, I still have no clue how to implement PID into my program but change the speed from a constant value to a variable that varies according to the distance to the center. The result is much better and I can target the exact center (480,360) instead of a +/-50 box (480 +/- 50, 360 +/- 50), you can replace the following codes.

    if x > 480:
        rV = int((x - 480)/4.8)            
    elif x < 480:
        rV = -int((480 - x)/4.8)
    else:
        rV = 0
    
    if y > 360: 
        vV = -int((y - 360)/3.6)
    elif y < 360: 
        vV = int((360 - y)/3.6)
    else:
        vV = 0
Python

For safety reasons, I don’t implement this to the ‘Come’ and ‘Away’ speeds. I will keep studying how to implement the PID or you can give me an idea how to achieve this. Please leave me comment.

# of view 1,904

Drone Programming – Video Capturing

Drone Control

We introduced Drone movement in the last post, we are going to try the video capture. It is also very simple with the help of DJITELLOPY and CV2 API. With the basic movement control and video capture, we can start face detection and face tracking very soon. Let’s show you how to capture video from the Drone Tello.

CV2

CV2, stands for OpenCV (Open Source Computer Vision Library), which is a popular open-source computer vision and machine learning software library. It provides a wide range of functions and tools for various computer vision tasks, image and video processing, machine learning, and more. OpenCV is widely used in the fields of computer vision, robotics, image processing, and artificial intelligence.

As we mentioned in the previous post, CV2 already installed when we installing the DJITELLOPY 2.50 package. We just need to import the CV2 library when start the program.

Threading

As we need to capture and display the video from drone by the same time it is flying, we need parallel progressing. THREADING is basic and common used Python.

How’s the program working

# Before you run this program, ensure to connect Tello with the WIFI

# Import Tello class from djitellopy library
from djitellopy import Tello

# Import additional library CV2 - OpenCV for image processing, threading for multi-tasking
import cv2
import threading

# Assign tello to the Tello class
tello = Tello()

# def a video capture and display function
def capturing_video(tello):
    while True:
        frame = tello.get_frame_read().frame
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        cv2.imshow('Tello Video Stream', frame)
        cv2.moveWindow('Tello Video Stream',0,0)
        cv2.waitKey(1)

# Connect to the drone via WIFI
tello.connect()

# Instrust Tello to start video stream and ensure first frame read
tello.streamon()
while True:
            frame = tello.get_frame_read().frame
            if frame is not None:
                break

# Start the video capture thread when the drone is flying
video_thread = threading.Thread(target=capturing_video, args=(tello,), daemon=True)
video_thread.start()

# Take off the drone
tello.takeoff()

# Do combo action such as move up & down and rotating
tello.move_up(30)
tello.move_down(30)
tello.move_up(30)
tello.move_down(30)

tello.rotate_counter_clockwise(30)
tello.rotate_clockwise(60)
tello.rotate_counter_clockwise(30)

# Landing the drone
tello.land()

# Stop the video stream
tello.streamoff()

# Show the battery level before ending the program
print("Battery :", tello.get_battery())

# Stop the connection with the drone
tello.end()
Python

Video Stream from Tello

# Instrust Tello to start video stream and ensure first frame read
tello.streamon()
while True:
            frame = tello.get_frame_read().frame
            if frame is not None:
                break
Python

With DJITELLOPY to achieve the video stream is very simple, we use streamon() to instruct Tello to enable the video stream and get_frame_read() to read the existing video frame.

However, there may be a delay after calling streamon() in the DJITELLOPY library. When we call streamon(), we are initiating the video streaming from the Tello drone’s camera. The drone needs some time to establish the streaming connection and start sending video frames.

So, we setup a while loop to ensure the camera is ready and the first frame is being read before we proceed to the next step.

The get_frame_read() method in the djitellopy library returns a VideoFrame object that provides access to the current video frame from the Tello drone’s camera. Apart from the frame attribute, which contains the video frame data as a NumPy array, the VideoFrame object has other attributes that provide information about the frame. These attributes include:

  • frame: The actual video frame data as a NumPy array.
  • time: The timestamp of the frame in milliseconds.
  • frame_number: The sequential number of the frame.
  • h: The height of the video frame.
  • w: The width of the video frame.
  • channel: The number of color channels in the frame (usually 3 for RGB).

Video Capture and display

# def a video capture and display function
def capturing_video(tello):
    while True:
        frame = tello.get_frame_read().frame
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        cv2.imshow('Tello Video Stream', frame)
        cv2.moveWindow('Tello Video Stream',0,0)
        cv2.waitKey(1)
Python

We define a function to read the frame from the drone and show as image in a separated window by CV2. There are two highlights,

  1. The frame we capture from Tello is in RGB colors but cv2 processing image as ‘BGR’ order. If we show the image directly, it will result as something bluish (Smurfs?). We need to convert this from RGB to BGR before we can show this properly.
  2. cv2.waitKey() must be excuted after the cv2.imshow(), otherwise, the image will not be displayed.

Parallel Processing

Since we need to capture the frame and display during the drone flying, we need parallel processing for capture_video().

# Start the video capture thread when the drone is flying
video_thread = threading.Thread(target=capturing_video, args=(tello,), daemon=True)
video_thread.start()
Python

We started the video capture thread just before the drone takeoff, so that we can see the video window when the drone take off and flying.

Again, that’s cool and easy, we just add few more lines to the last program and make it fly with video capturing. We believe that we can start doing face detection and tracking now.

# of view 517

Drone Programming – Ryze Tello Introduction

Drone Control

We are starting a new page for Drone programming by Python, Ryze Tello is a very good price drone. With their SDK, which makes it very easy to program by different platforms such as Scratch and Python. We picked this to start our journey of Drone Programming, and hope that we can achieve Face recognition and Gesture-based control as our ultimate target.  Then, Swarm with Tello Edu – multiple drone control and performance.

SDK & DJITELLOPY API

Ryze Tello already provide SDK to connect the drone via a WIFI UDP port, allows users to control the drone with text command. You can refer to this link for their SDK 2.0 document and below is the example how’s the SDK working.

# Before you run this program, ensure to connect Tello with the WIFI

import socket
import time

# Tello IP and port
Tello_IP = '192.168.10.1'
Tello_PORT = 8889

# Create a UDP socket
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.bind(('', Tello_PORT))

# Function to send commands to the drone
def send_command(command):
    sock.sendto(command.encode(), (Tello_IP, Tello_PORT))
    
def receive_response():
    response, _ = sock.recvfrom(256)
    return response.decode()


# Connect to the Tello drone via WIFI
send_command('command')
receive_response()

# Take off the drone
send_command('takeoff')
receive_response()

# Do combo action such as move up & down and rotating
send_command('up 30')
receive_response()
send_command('down 30')
receive_response()
send_command('up 30')
receive_response()
send_command('down 30')
receive_response()

send_command('cw 30')
receive_response()
send_command('ccw 60')
receive_response()
send_command('cw 30')
receive_response()

# Landing the drone
send_command('land')
receive_response()

# Show the battery level before ending the program
send_command('battery?')
print("Battery :", receive_response())

# Stop the connection with the drone
send_command('End')
Python

To make life easier and the program easy to read, there are few third parties created library to support the SDK, such as EASYTELLO, TELLOPY, and DJITELLOPY. We picked DJITELLOPY from Murtaza’s Workshop – Robotics and AI (Thank you!) for our project.

First of all, go ahead to install DJITELLOPY into your Python by using command ‘PIP install djitellopy‘ in your terminal. The latest version I can download right now is 2.50 including the following libraries, DJITELLOPY, NUMPY, OPENCV-PYTHON, AV and PILLOW.

‘Hello World’ from Tello

OK, let start our first drone programming try with the code below.

# Before you run this program, ensure to connect Tello with the WIFI

# Import Tello class from djitellopy library
from djitellopy import Tello

# Assign tello to the Tello class
tello = Tello()

# Connect to the Tello drone via WIFI
tello.connect()

# Take off the drone
tello.takeoff()

# Do combo action such as move up & down, rotating and flipping
tello.move_up(30)
tello.move_down(30)
tello.move_up(30)
tello.move_down(30)

tello.rotate_counter_clockwise(30)
tello.rotate_clockwise(60)
tello.rotate_counter_clockwise(30)

# Landing the drone
tello.land()

# Show the battery level before ending the program
print("Battery :", tello.get_battery())

# Stop the connection with the drone
tello.end()
Python

It is pretty simple, right? Just import the DJITELLOPY API and you can use those command control the drone Tello. The first thing you must do is to ‘tello.connect()’ to the Tello, so that it is asked to be in SDK mode and accept different commands. Then, I just have the drone takeoff and do some combo as shown in the code. Finally, I get the battery information and display before complete the process.

Next step, we will learn how to capture video from the drone.

# of view 1,031

Mice Maze

Features Projects, Makecode & Scratch

My Second dream was ‘mice maze’, it was very popular when I was childhood, program a ‘mice’ to solve the maze automatically. Actually, my 25 years old final year project was ‘smart vacuum cleaner’, it is now very popular. But, it was very new concept at that time and I defined some ‘mice’ behavior. And now, I proof that it work based on Scratch.

Scratch, thanks a again!!

P.S. I like to stare the screen to see the mice to run the path. If you want to run it faster, hold shift when press the green flag.. 🙂

Basic idea for the mice’s logic

That’s a very basic and simple logic for the mice…. XD

  1. We make the mice in a square with dimension same as the maze path, we are using 48pt x 48pt.
  2. The mice is moving in a unit of it’s size, i.e. 48pt
  3. We define the priority of the movement for the mice to follow,
    • Always go forward
    • Go right if cannot go forward
    • Go left if both forward and right are blocked
    • Move backward if no more movement.
  4. Then, we need to record each movement and possible (not explore) directions.
  5. When the mice move backward, it will read the record and go to un-explore directions.

How does the program work

Prior I created the mice maze, I actually made a program by using WASD key to move the mice, solve the maze manually. So, keep in mind, W – up, D – right, S – down, A – left, it is too hard for you if you are FPS game player.. LOL

Sensor – detecting wall

Before the ‘mice’ do any movement, it will detect any wall in it’s four directions. In this program, I move the ‘mice’ to four directions in a certain distance. See if it will hit a wall (black in color) or not. If not hit, W A S & D will be put into the corresponding variable Wall U, D, R & L, tell the ‘mice’ that the direction is OK to move. Otherwise, keep the variable as X, means there is a wall.

Movement

Path Tracking

I am using a LIST called ‘Path’ to record each movement of the ‘mice’,

# of view 711

Frog vs Spider

Makecode & Scratch

Scratch made Adam’s dream came, create a game… Since my childhood, I was expert in Assembly, i.e. 6502, x86, 8051, modifying OS and even created a mini Chinese display system. But, never created a single game.. I tried to make game with Assembly, it was painful even make a character moving.. Thanks Scratch!!

# of view 659