Tag Archives: AI

AI for Everyone: Developing a Gesture Based Arcade Game Based on Mediapipe and Python

November 11, 2021 admin

In this video lesson we introduce the concept of using OpenCV and Mediapipe to create gesture based arcade games in Python. In this lesson we suggest creating a gesture based version of the classic Pong arcade game. We show enough to get started, and your homework is to complete the game.

import cv2
print(cv2.__version__)

class mpHands:
    import mediapipe as mp
    def __init__(self,maxHands=2,tol1=.5,tol2=.5):
        self.hands=self.mp.solutions.hands.Hands(False,maxHands,tol1,tol2)
    def Marks(self,frame):
        myHands=[]
        frameRGB=cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
        results=self.hands.process(frameRGB)
        if results.multi_hand_landmarks != None:
            for handLandMarks in results.multi_hand_landmarks:
                myHand=[]
                for landMark in handLandMarks.landmark:
                    myHand.append((int(landMark.x*width),int(landMark.y*height)))
                myHands.append(myHand)
        return myHands

width=1280
height=720
cam=cv2.VideoCapture(4,cv2.CAP_DSHOW)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)
cam.set(cv2.CAP_PROP_FPS, 30)
cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))
findHands=mpHands(2)
paddleWidth=125
paddleHeight=25
paddleColor=(0,255,0)
while True:
    ignore,  frame = cam.read()
    frame=cv2.resize(frame,(width,height))
    handData=findHands.Marks(frame)
    for hand in handData:
        cv2.rectangle(frame,(int(hand[8][0]-paddleWidth/2),0),(int(hand[8][0]+paddleWidth/2),paddleHeight),paddleColor,-1)
    cv2.imshow('my WEBcam', frame)
    cv2.moveWindow('my WEBcam',0,0)
    if cv2.waitKey(1) & 0xff ==ord('q'):
        break
cam.release()

import cv2

print(cv2.__version__)

class mpHands:

import mediapipe as mp

def __init__(self,maxHands=2,tol1=.5,tol2=.5):

self.hands=self.mp.solutions.hands.Hands(False,maxHands,tol1,tol2)

def Marks(self,frame):

myHands=[]

frameRGB=cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)

results=self.hands.process(frameRGB)

if results.multi_hand_landmarks != None:

for handLandMarks in results.multi_hand_landmarks:

myHand=[]

for landMark in handLandMarks.landmark:

myHand.append((int(landMark.x*width),int(landMark.y*height)))

myHands.append(myHand)

return myHands

width=1280

height=720

cam=cv2.VideoCapture(4,cv2.CAP_DSHOW)

cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)

cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)

cam.set(cv2.CAP_PROP_FPS, 30)

cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))

findHands=mpHands(2)

paddleWidth=125

paddleHeight=25

paddleColor=(0,255,0)

while True:

ignore, frame = cam.read()

frame=cv2.resize(frame,(width,height))

handData=findHands.Marks(frame)

for hand in handData:

cv2.rectangle(frame,(int(hand[8][0]-paddleWidth/2),0),(int(hand[8][0]+paddleWidth/2),paddleHeight),paddleColor,-1)

cv2.imshow('my WEBcam', frame)

cv2.moveWindow('my WEBcam',0,0)

if cv2.waitKey(1) & 0xff ==ord('q'):

break

cam.release()

Artificial Intelligence, OpenCV

AI for Everyone: Parsing Hand Position Data in Mediapipe

November 4, 2021 admin

In this video lesson we show how to properly parse the hand position data coming from the Mediapipe Library. Mediapipe returns a rather complex data structure, and in this video we show you how to crack into that data structure, and pull out useful data; specifically, the (x,y) position data of the various landmark data on the hand. We create a python class that can be reused in future programs, allowing you to simply call the python class in order to properly pull out hand landmark data. Enjoy!

import cv2
print(cv2.__version__)

class mpHands:
    import mediapipe as mp
    def __init__(self,maxHands=2,tol1=.5,tol2=.5):
        self.hands=self.mp.solutions.hands.Hands(False,maxHands,tol1,tol2)
    def Marks(self,frame):
        myHands=[]
        frameRGB=cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
        results=self.hands.process(frameRGB)
        if results.multi_hand_landmarks != None:
            for handLandMarks in results.multi_hand_landmarks:
                myHand=[]
                for landMark in handLandMarks.landmark:
                    myHand.append((int(landMark.x*width),int(landMark.y*height)))
                myHands.append(myHand)
        return myHands

width=1280
height=720
cam=cv2.VideoCapture(4,cv2.CAP_DSHOW)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)
cam.set(cv2.CAP_PROP_FPS, 30)
cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))
findHands=mpHands(2)
while True:
    ignore,  frame = cam.read()
    frame=cv2.resize(frame,(width,height))
    handData=findHands.Marks(frame)
    for hand in handData:
        for ind in [0,5,6,7,8]:
            cv2.circle(frame,hand[ind],25,(255,0,255),3)
    cv2.imshow('my WEBcam', frame)
    cv2.moveWindow('my WEBcam',0,0)
    if cv2.waitKey(1) & 0xff ==ord('q'):
        break
cam.release()

import cv2

print(cv2.__version__)

class mpHands:

import mediapipe as mp

def __init__(self,maxHands=2,tol1=.5,tol2=.5):

self.hands=self.mp.solutions.hands.Hands(False,maxHands,tol1,tol2)

def Marks(self,frame):

myHands=[]

frameRGB=cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)

results=self.hands.process(frameRGB)

if results.multi_hand_landmarks != None:

for handLandMarks in results.multi_hand_landmarks:

myHand=[]

for landMark in handLandMarks.landmark:

myHand.append((int(landMark.x*width),int(landMark.y*height)))

myHands.append(myHand)

return myHands

width=1280

height=720

cam=cv2.VideoCapture(4,cv2.CAP_DSHOW)

cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)

cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)

cam.set(cv2.CAP_PROP_FPS, 30)

cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))

findHands=mpHands(2)

while True:

ignore, frame = cam.read()

frame=cv2.resize(frame,(width,height))

handData=findHands.Marks(frame)

for hand in handData:

for ind in [0,5,6,7,8]:

cv2.circle(frame,hand[ind],25,(255,0,255),3)

cv2.imshow('my WEBcam', frame)

cv2.moveWindow('my WEBcam',0,0)

if cv2.waitKey(1) & 0xff ==ord('q'):

break

cam.release()

OpenCV, Python

AI FOR EVERYONE HOMEWORK SOLUTION: Face Recognition with Live Video in OpenCV

October 23, 2021 admin

In this video lesson we show you how to recognize faces in live video from your WEBcam. It is the homework solution for lesson #16. This builds on the earlier video lessons in this series, so if any of this does not make sense, go back and check out some of the earlier lessons.

import cv2
import face_recognition as FR
font=cv2.FONT_HERSHEY_SIMPLEX
width=640
height=360
cam=cv2.VideoCapture(0,cv2.CAP_DSHOW)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)
cam.set(cv2.CAP_PROP_FPS, 30)
cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))

paulFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Paul McWhorter.jpg')
faceLoc=FR.face_locations(paulFace)[0]
paulFaceEncode=FR.face_encodings(paulFace)[0]

gavFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Gavriella Joy.jpg')
faceLoc=FR.face_locations(gavFace)[0]
gavFaceEncode=FR.face_encodings(gavFace)[0]

knownEncodings=[paulFaceEncode,gavFaceEncode]
names=['Paul McWhorter','Gavriella Joy']

while True:
    ignore,  unknownFace = cam.read()

    unknownFaceRGB=cv2.cvtColor(unknownFace,cv2.COLOR_RGB2BGR)
    faceLocations=FR.face_locations(unknownFaceRGB)
    unknownEncodings=FR.face_encodings(unknownFaceRGB,faceLocations)

    for faceLocation,unknownEncoding in zip(faceLocations,unknownEncodings):
        top,right,bottom,left=faceLocation
        print(faceLocation)
        cv2.rectangle(unknownFace,(left,top),(right,bottom),(255,0,0),3)
        name='Unknown Person'
        matches=FR.compare_faces(knownEncodings,unknownEncoding)
        print(matches)
        if True in matches:
            matchIndex=matches.index(True)
            print(matchIndex)
            print(names[matchIndex])
            name=names[matchIndex]
        cv2.putText(unknownFace,name,(left,top),font,.75,(0,0,255),2)

    cv2.imshow('My Faces',unknownFace)
    if cv2.waitKey(1) & 0xff ==ord('q'):
        break
cam.release()
cv2.destroyAllWindows()

import cv2

import face_recognition as FR

font=cv2.FONT_HERSHEY_SIMPLEX

width=640

height=360

cam=cv2.VideoCapture(0,cv2.CAP_DSHOW)

cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)

cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)

cam.set(cv2.CAP_PROP_FPS, 30)

cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))

paulFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Paul McWhorter.jpg')

faceLoc=FR.face_locations(paulFace)[0]

paulFaceEncode=FR.face_encodings(paulFace)[0]

gavFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Gavriella Joy.jpg')

faceLoc=FR.face_locations(gavFace)[0]

gavFaceEncode=FR.face_encodings(gavFace)[0]

knownEncodings=[paulFaceEncode,gavFaceEncode]

names=['Paul McWhorter','Gavriella Joy']

while True:

ignore, unknownFace = cam.read()

unknownFaceRGB=cv2.cvtColor(unknownFace,cv2.COLOR_RGB2BGR)

faceLocations=FR.face_locations(unknownFaceRGB)

unknownEncodings=FR.face_encodings(unknownFaceRGB,faceLocations)

for faceLocation,unknownEncoding in zip(faceLocations,unknownEncodings):

top,right,bottom,left=faceLocation

print(faceLocation)

cv2.rectangle(unknownFace,(left,top),(right,bottom),(255,0,0),3)

name='Unknown Person'

matches=FR.compare_faces(knownEncodings,unknownEncoding)

print(matches)

if True in matches:

matchIndex=matches.index(True)

print(matchIndex)

print(names[matchIndex])

name=names[matchIndex]

cv2.putText(unknownFace,name,(left,top),font,.75,(0,0,255),2)

cv2.imshow('My Faces',unknownFace)

if cv2.waitKey(1) & 0xff ==ord('q'):

break

cam.release()

cv2.destroyAllWindows()

OpenCV, Python

AI for Everyone LESSON 16: Face Recognition with OpenCV

October 19, 2021 admin

Guys in this lesson we learn how to not only detect faces in an image, but also recognize who the person is. We start by training our model with known faces, and then find and identify those faces in other pictures. For your convenience, I include the code developed in this lesson below.

import cv2
import face_recognition as FR
font=cv2.FONT_HERSHEY_SIMPLEX

donFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Donald Trump.jpg')
faceLoc=FR.face_locations(donFace)[0]
donFaceEncode=FR.face_encodings(donFace)[0]


nancyFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Nancy Pelosi.jpg')
faceLoc=FR.face_locations(nancyFace)[0]
nancyFaceEncode=FR.face_encodings(nancyFace)[0]

penceFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Mike Pence.jpg')
faceLoc=FR.face_locations(penceFace)[0]
penceFaceEncode=FR.face_encodings(penceFace)[0]

knownEncodings=[donFaceEncode,nancyFaceEncode,penceFaceEncode]
names=['Donald Trump','Nancy Pelosi','Mike Pence']

unknownFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/unknown/u1.jpg')
unknownFaceBGR=cv2.cvtColor(unknownFace,cv2.COLOR_RGB2BGR)
faceLocations=FR.face_locations(unknownFace)
unknownEncodings=FR.face_encodings(unknownFace,faceLocations)

for faceLocation,unknownEncoding in zip(faceLocations,unknownEncodings):
    top,right,bottom,left=faceLocation
    print(faceLocation)
    cv2.rectangle(unknownFaceBGR,(left,top),(right,bottom),(255,0,0),3)
    name='Unknown Person'
    matches=FR.compare_faces(knownEncodings,unknownEncoding)
    print(matches)
    if True in matches:
        matchIndex=matches.index(True)
        print(matchIndex)
        print(names[matchIndex])
        name=names[matchIndex]
    cv2.putText(unknownFaceBGR,name,(left,top),font,.75,(0,0,255),2)

cv2.imshow('My Faces',unknownFaceBGR)

cv2.waitKey(10000)

import cv2

import face_recognition as FR

font=cv2.FONT_HERSHEY_SIMPLEX

donFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Donald Trump.jpg')

faceLoc=FR.face_locations(donFace)[0]

donFaceEncode=FR.face_encodings(donFace)[0]

nancyFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Nancy Pelosi.jpg')

faceLoc=FR.face_locations(nancyFace)[0]

nancyFaceEncode=FR.face_encodings(nancyFace)[0]

penceFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Mike Pence.jpg')

faceLoc=FR.face_locations(penceFace)[0]

penceFaceEncode=FR.face_encodings(penceFace)[0]

knownEncodings=[donFaceEncode,nancyFaceEncode,penceFaceEncode]

names=['Donald Trump','Nancy Pelosi','Mike Pence']

unknownFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/unknown/u1.jpg')

unknownFaceBGR=cv2.cvtColor(unknownFace,cv2.COLOR_RGB2BGR)

faceLocations=FR.face_locations(unknownFace)

unknownEncodings=FR.face_encodings(unknownFace,faceLocations)

for faceLocation,unknownEncoding in zip(faceLocations,unknownEncodings):

top,right,bottom,left=faceLocation

print(faceLocation)

cv2.rectangle(unknownFaceBGR,(left,top),(right,bottom),(255,0,0),3)

name='Unknown Person'

matches=FR.compare_faces(knownEncodings,unknownEncoding)

print(matches)

if True in matches:

matchIndex=matches.index(True)

print(matchIndex)

print(names[matchIndex])

name=names[matchIndex]

cv2.putText(unknownFaceBGR,name,(left,top),font,.75,(0,0,255),2)

cv2.imshow('My Faces',unknownFaceBGR)

cv2.waitKey(10000)

OpenCV, Python

AI for Everyone LESSON 15: Face Detection using OpenCV

October 12, 2021 admin

In this video lesson we show a simple method to detect faces in a WEB cam frame using openCV and Haar Cascades. We use pretrained models to find, box and track faces in a frame. Enjoy!

import cv2
print(cv2.__version__)
width=640
height=360
cam=cv2.VideoCapture(0,cv2.CAP_DSHOW)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)
cam.set(cv2.CAP_PROP_FPS, 30)
cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))

faceCascade=cv2.CascadeClassifier('C:\Users\Valued Customer\Documents\Python\haar\haarcascade_frontalface_default.xml')

while True:
    ignore,  frame = cam.read()
    frameGray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
    faces=faceCascade.detectMultiScale(frameGray,1.3,5)
    for face in faces:
        x,y,w,h=face
        cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),3)
    cv2.imshow('my WEBcam', frame)
    cv2.moveWindow('my WEBcam',0,0)
    if cv2.waitKey(1) & 0xff ==ord('q'):
        break
cam.release()

import cv2

print(cv2.__version__)

width=640

height=360

cam=cv2.VideoCapture(0,cv2.CAP_DSHOW)

cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)

cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)

cam.set(cv2.CAP_PROP_FPS, 30)

cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))

faceCascade=cv2.CascadeClassifier('C:\Users\Valued Customer\Documents\Python\haar\haarcascade_frontalface_default.xml')

while True:

ignore, frame = cam.read()

frameGray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)

faces=faceCascade.detectMultiScale(frameGray,1.3,5)

for face in faces:

x,y,w,h=face

cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),3)

cv2.imshow('my WEBcam', frame)

cv2.moveWindow('my WEBcam',0,0)

if cv2.waitKey(1) & 0xff ==ord('q'):

break

cam.release()

Technology Tutorials

Tag Archives: AI

AI for Everyone: Developing a Gesture Based Arcade Game Based on Mediapipe and Python

AI for Everyone: Parsing Hand Position Data in Mediapipe

AI FOR EVERYONE HOMEWORK SOLUTION: Face Recognition with Live Video in OpenCV

AI for Everyone LESSON 16: Face Recognition with OpenCV

AI for Everyone LESSON 15: Face Detection using OpenCV

Making The World a Better Place One High Tech Project at a Time. Enjoy!