Drone programming with python face recognition tracking

Drone Programming with Python — Face Recognition & Tracking

The course Drone Programming with Python – Face Recognition & Tracking is an online class provided by Udemy. It may be possible to receive a verified certification or use the course to prepare for a degree.

Operating drone with network programming, face recognition using OpenCV, automatic tracking, implementing web camera

• Drone Programming with Python – Face Recognition & Tracking

• Remote automatic drone operation using Python
• Face recognition programming using OpenCV
• How to implement a web camera app using Flask’s web framework
• Automatic tracking using face recognition with drone camera

• Basic level of python programming.
• Good to have knowledge of network, WiFi and IP address, as we will be connecting the drones through WiFi
• Better to have knowledge on building development environment with IDE

• 5 hours on-demand video
• 1 article
• 1 downloadable resource
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion
• 酒井 潤 （さかい じゅん）

Prerequisites & Facts

Drone Programming with Python – Face Recognition & Tracking

University, College, Institution

Online, self-paced (see curriculum for more information)

Degree & Cost

Drone Programming with Python – Face Recognition & Tracking

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ZzombieE0/Face-Recognition-and-Tracking-System-with-Drone-with-Python

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README.md

Put the pictures of the faces you want the drone to recognize in the «Faces» folder with the extensions «.jpg» or «.jpeg». Then change the names of the pictures to face1, face2, face3, face4. If you want to customize the names of the pictures. You should also change only the names face1, face2, face3, and face4 in the «Face_Recognition.py» file.

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Drone programming with python face recognition tracking

Udemy – Drone Programming with Python – Face Recognition & Tracking 2019-11

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Description

Drone Programming with Python – Face Recognition & Tracking is a programming training course for remote control aircraft that uses face recognition technology using OpenCV, autopilot and web camera. As you know, robot technology is very advanced today. In this course, you will learn a variety of topics using a drone that can fly indoors. Getting started with a drone can be a great way to start your robot programming experience using Python. This is also an interesting and easy way to attract children to coding.

What you will learn in the course Drone Programming with Python – Face Recognition & Tracking:

• Remote operation of the drone using Python
• Face recognition programming using OpenCV
• Using a webcam app using the Flask web framework
• Automatic tracking using face recognition with the help of UAV camera

Course specifications

Publisher: Udemy
Instructors: 酒井 潤 （さかい じゅん） And Sara Freixas
Language: English
Level: Introductory to Advanced
Number of Courses: 45
Duration: 4 hours and 45 minutes

Course topics:

Prerequisites for Drone Programming with Python – Face Recognition & Tracking:

Basic level of python programming.
Good to have knowledge of network, WiFi and IP address, as we will be connecting the drones through WiFi
Better to have knowledge on building development environment with IDE

Pictures:

Drone Programming with Python Introduction Video – Face Recognition & Tracking:

Installation guide

After Extract, watch with your favorite Player.

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Tello Drone Python Programming, Face Tracking From Drone Camera! Using Python Module OpenCV and PyGame!

Introduction: Tello Drone Python Programming, Face Tracking From Drone Camera! Using Python Module OpenCV and PyGame!

About: My name is Muhammad Irsyad Yunus. I like to build many related Arduino project from multiple hardware. Instagram account: @techwithbob Don’t Forget To Follow For More DIY Content And Tech Development! More About BobWithTech »

In this tutorial, I will show you how you can program a face tracking drone through the use of Python programming languages with OpenCV library.

Supplies

Gather The Required Material:

Tello Drone (1x Battery, 1x Drone, 8x Propeller, No Multiple Battery Charger)

Tello Drone Boost Combo Pack (3x Battery, 1x Drone, 8x Propeller, 1x Multiple Battery Charger)

Amazon:

Ebay:

Software:

• Any IDE that support Python
• Python Programming Language

Required Python Version:

Required Python Library:

Attachments

Step 1: Create Python Virtual Environment (Optional)

Setting Up Virtual Environment On Python:

If you already set up the virtual environment on your computer, just skip this step and move on to step 2.

Windows:

For windows, please refer to this link to set up the virtual environment.

Linux:

First install the required module virtualenv in the system.

Create a folder for your project.

mkdir projectA 
cd projectA

Setup the virtual environment with the given python version that you currently use.

Activate that virtual environment.

To deactivate the virtual environment just enter the command:

Step 2: Install the Required Python Module

Installing the Python Module:

Step 3: Programming

Firstly, create the drone controller python scripts and name it «controller.py» or what ever name you want.

You can also look up in my first tello tutorial.

Write this lines of codes in the «controller.py»:

import pygame
def init():
#initialize pygame library 
pygame.init()
#Set Control Display as 400x400 pixel 
windows = pygame.display.set_mode((400,400))
if __name__ == '__main__': 
init() 
while True: 
main() 

Secondly, create the facial recognition python scripts and name it «main.py» or what ever name you want.

Write this lines of codes in the «main.py» (with explanation):

import cv2 #import opencv library
import numpy as np #import numpy library
from djitellopy import tello #import djitellopy library
import time #import time library

me = tello.Tello() #initialise the djitello module classes on variable
me.connect() #establish wifi connection to the tello drone
print(me.get_battery()) #print the battery available on the tello drone

me.streamon() #start streaming the tello drone camera

w, h = 540, 360 #initialise the display dimension for the camera
MAX_STATE = 5

fbRange = [15 * 1000, 20 * 1000] #the range area of the detected face for forward and backward movement

#Range of threshold values of the detected face center point in y-axis 
udRange = [(h/2)-30, (h/2)] # MIN DOWN, MAX UP
udMax = [(h/2), (h/2)+30]
udMin = [(h/2)-30, (h/2)-60]
udMotion = [0, -10, 10, -30, 30] # neutral, min, max, MIN , MAX

#Range of thresholds values of the detected face center point in x-axis
lrRange = [(w/2)-30, (w/2)+30] # MIN DOWN, MAX UP
lrMax = [(w/2)+30, (w/2)+60]
lrMin = [(w/2)-30 , (w/2)-60]
lrMotion = [0, 8, -8, 16, -16] #motion for left and right movement variables

#pid tuning controller
pid = [0.5, 0.5, 0]

pError = 0 #initial error value of PID

def findFace(img):
 
faceCascade = cv2.CascadeClassifier("haarcascades/haarcascade_frontalface_default.xml") # the file location for frontal face detection.
 
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
faces = faceCascade.detectMultiScale(imgGray, 1.2, 5)
 
myFaceListC = []
 
myFaceListArea = []
 
for (x, y, w, h) in faces:
 
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
 
cx = x + w // 2
 
cy = y + h // 2
 
area = w * h
 
cv2.circle(img, (cx, cy), 5, (0, 255, 0), cv2.FILLED)
 
myFaceListC.append([cx, cy])
 
myFaceListArea.append(area)
 
if len(myFaceListArea) != 0:
 
i = myFaceListArea.index(max(myFaceListArea))
 
return img, [myFaceListC[i], myFaceListArea[i]]
 
else:
 
return img, [[0, 0], 0]
 
def trackFace( info, w, pid, pError): 
global x, y, area
 
area = info[1]
 
x, y = info[0]
 
fb, ud , lr = 0, 0, 0
 
error = x - w // 2
 
speed = pid[0] * error + pid[1] * (error - pError)
 
speed = int(np.clip(speed, -100, 100))
 
udState = [y > udRange[0] and y < udRange[1], y >= udMax[0], y = udMax[1], y lrState = [x > lrRange[0] and x < lrRange[1], x >= lrMax[0], x = lrMax[1], x  
if area > fbRange[0] and area < fbRange[1]:
 
fb = 0
 
elif area >= fbRange[1]:
 
fb = -20
 
elif area  
fb = 20
 
for index in range(MAX_STATE): #up down 
if (udState[index]): 
ud = udMotion[index]
 
for index in range(MAX_STATE): #left right 
if (lrState[index]): 
lr = lrMotion[index]
 
###################################### 
if x == 0:
 
speed = 0
 
error = 0
 
#print(speed, fb)
 
me.send_rc_control(lr, fb, ud, speed)
 
return error
 
#cap = cv2.VideoCapture(1)
 
me.takeoff() 
me.send_rc_control(0, 0, 15, 0) 
time.sleep(1.5)

while True:
 
#_, img = cap.read()
 
img = me.get_frame_read().frame
 
img = cv2.resize(img, (w, h))
 
img, info = findFace(img)
 
pError = trackFace( info, w, pid, pError)
 
#print("Center", info[0], "Area", info[1])
 
cv2.imshow("Output", img)
 
if cv2.waitKey(1) & 0xFF == ord('q'):
 
me.land() 
me.streamoff()
 
break

cv2.destroyAllWindows()
exit()

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