Tag Archives: AI

In this video lesson we show you how you can improve the accuracy of your gesture recognition program developed in the last lesson. We do this by normalizing the hand landmarks distance matrix to a standard size. By doing this, you get accurate results independent of the distance your hand is from the camera. For your convenience, I include the code below which we develop in this lesson. Enjoy!

import time
import cv2
print(cv2.__version__)
import numpy as np

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
def findDistances(handData):
    distMatrix=np.zeros([len(handData),len(handData)],dtype='float')
    palmSize=((handData[0][0]-handData[9][0])**2+(handData[0][1]-handData[9][1])**2)**(1./2.)
    for row in range(0,len(handData)):
        for column in range(0,len(handData)):
            distMatrix[row][column]=(((handData[row][0]-handData[column][0])**2+(handData[row][1]-handData[column][1])**2)**(1./2.))/palmSize
    return distMatrix

def findError(gestureMatrix,unknownMatrix,keyPoints):
    error=0
    for row in keyPoints:
        for column in keyPoints:
            error=error+abs(gestureMatrix[row][column]-unknownMatrix[row][column])
    print(error)
    return error
def findGesture(unknownGesture,knownGestures,keyPoints,gestNames,tol):
    errorArray=[]
    for i in range(0,len(gestNames),1):
        error=findError(knownGestures[i],unknownGesture,keyPoints)
        errorArray.append(error)
    errorMin=errorArray[0]
    minIndex=0
    for i in range(0,len(errorArray),1):
        if errorArray[i]<errorMin:
            errorMin=errorArray[i]
            minIndex=i
    if errorMin<tol:
        gesture=gestNames[minIndex]
    if errorMin>=tol:
        gesture='Unknown'
    return gesture


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(1)
time.sleep(5)
keyPoints=[0,4,5,9,13,17,8,12,16,20]
train=True
tol=10
trainCnt=0
knownGestures=[]

numGest=int(input('How Many Gestures Do You Want? '))

gestNames=[]

for i in range(0,numGest,1):
    prompt='Name of Gesture #'+str(i+1)+' '
    name=input(prompt)
    gestNames.append(name)
print(gestNames)

while True:
    ignore,  frame = cam.read()
    frame=cv2.resize(frame,(width,height))
    handData=findHands.Marks(frame)
    if train==True:
        if handData!=[]:
            print('Please Show Gesture ',gestNames[trainCnt],': Press t when Ready')
            if cv2.waitKey(1) & 0xff==ord('t'):
                knownGesture=findDistances(handData[0])
                knownGestures.append(knownGesture)
                trainCnt=trainCnt+1
                if trainCnt==numGest:
                    train=False
    if train == False:
        if handData!=[]:
            unknownGesture=findDistances(handData[0])
            myGesture=findGesture(unknownGesture,knownGestures,keyPoints,gestNames,tol)
            #error=findError(knownGesture,unknownGesture,keyPoints)
            cv2.putText(frame,myGesture,(100,175),cv2.FONT_HERSHEY_SIMPLEX,3,(255,0,0),8)
    for hand in handData:
        for ind in keyPoints:
            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()

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import time

import cv2

print(cv2.__version__)

import numpy as np

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

def findDistances(handData):

distMatrix=np.zeros([len(handData),len(handData)],dtype='float')

palmSize=((handData[0][0]-handData[9][0])**2+(handData[0][1]-handData[9][1])**2)**(1./2.)

for row in range(0,len(handData)):

for column in range(0,len(handData)):

distMatrix[row][column]=(((handData[row][0]-handData[column][0])**2+(handData[row][1]-handData[column][1])**2)**(1./2.))/palmSize

return distMatrix

def findError(gestureMatrix,unknownMatrix,keyPoints):

error=0

for row in keyPoints:

for column in keyPoints:

error=error+abs(gestureMatrix[row][column]-unknownMatrix[row][column])

print(error)

return error

def findGesture(unknownGesture,knownGestures,keyPoints,gestNames,tol):

errorArray=[]

for i in range(0,len(gestNames),1):

error=findError(knownGestures[i],unknownGesture,keyPoints)

errorArray.append(error)

errorMin=errorArray[0]

minIndex=0

for i in range(0,len(errorArray),1):

if errorArray[i]<errorMin:

errorMin=errorArray[i]

minIndex=i

if errorMin<tol:

gesture=gestNames[minIndex]

if errorMin>=tol:

gesture='Unknown'

return gesture

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(1)

time.sleep(5)

keyPoints=[0,4,5,9,13,17,8,12,16,20]

train=True

tol=10

trainCnt=0

knownGestures=[]

numGest=int(input('How Many Gestures Do You Want? '))

gestNames=[]

for i in range(0,numGest,1):

prompt='Name of Gesture #'+str(i+1)+' '

name=input(prompt)

gestNames.append(name)

print(gestNames)

while True:

ignore, frame = cam.read()

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

handData=findHands.Marks(frame)

if train==True:

if handData!=[]:

print('Please Show Gesture ',gestNames[trainCnt],': Press t when Ready')

if cv2.waitKey(1) & 0xff==ord('t'):

knownGesture=findDistances(handData[0])

knownGestures.append(knownGesture)

trainCnt=trainCnt+1

if trainCnt==numGest:

train=False

if train == False:

if handData!=[]:

unknownGesture=findDistances(handData[0])

myGesture=findGesture(unknownGesture,knownGestures,keyPoints,gestNames,tol)

#error=findError(knownGesture,unknownGesture,keyPoints)

cv2.putText(frame,myGesture,(100,175),cv2.FONT_HERSHEY_SIMPLEX,3,(255,0,0),8)

for hand in handData:

for ind in keyPoints:

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()

s lesson. Enjoy!

Artificial Intelligence, Python

AI for Everyone LESSON 26: Accurate Gesture Recognition using Python and MediaPipe

February 3, 2022 admin

In this lesson we demonstrate how to use mediaPipe and Python to create an AI system that can accurately recognize hand gestures. This is follow on work to what we developed in Lesson 25. The code we developed is presented below.

import time
import cv2
print(cv2.__version__)
import numpy as np

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
def findDistances(handData):
    distMatrix=np.zeros([len(handData),len(handData)],dtype='float')
    for row in range(0,len(handData)):
        for column in range(0,len(handData)):
            distMatrix[row][column]=((handData[row][0]-handData[column][0])**2+(handData[row][1]-handData[column][1])**2)**(1./2.)
    return distMatrix

def findError(gestureMatrix,unknownMatrix,keyPoints):
    error=0
    for row in keyPoints:
        for column in keyPoints:
            error=error+abs(gestureMatrix[row][column]-unknownMatrix[row][column])
    return error
def findGesture(unknownGesture,knownGestures,keyPoints,gestNames,tol):
    errorArray=[]
    for i in range(0,len(gestNames),1):
        error=findError(knownGestures[i],unknownGesture,keyPoints)
        errorArray.append(error)
    errorMin=errorArray[0]
    minIndex=0
    for i in range(0,len(errorArray),1):
        if errorArray[i]<errorMin:
            errorMin=errorArray[i]
            minIndex=i
    if errorMin<tol:
        gesture=gestNames[minIndex]
    if errorMin>=tol:
        gesture='Unknown'
    return gesture


width=1280
height=720
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'))
findHands=mpHands(1)
time.sleep(5)
keyPoints=[0,4,5,9,13,17,8,12,16,20]
train=True
tol=1500
trainCnt=0
knownGestures=[]

numGest=int(input('How Many Gestures Do You Want? '))

gestNames=[]

for i in range(0,numGest,1):
    prompt='Name of Gesture #'+str(i+1)+' '
    name=input(prompt)
    gestNames.append(name)
print(gestNames)

while True:
    ignore,  frame = cam.read()
    frame=cv2.resize(frame,(width,height))
    handData=findHands.Marks(frame)
    if train==True:
        if handData!=[]:
            print('Please Show Gesture ',gestNames[trainCnt],': Press t when Ready')
            if cv2.waitKey(1) & 0xff==ord('t'):
                knownGesture=findDistances(handData[0])
                knownGestures.append(knownGesture)
                trainCnt=trainCnt+1
                if trainCnt==numGest:
                    train=False
    if train == False:
        if handData!=[]:
            unknownGesture=findDistances(handData[0])
            myGesture=findGesture(unknownGesture,knownGestures,keyPoints,gestNames,tol)
            #error=findError(knownGesture,unknownGesture,keyPoints)
            cv2.putText(frame,myGesture,(100,175),cv2.FONT_HERSHEY_SIMPLEX,3,(255,0,0),8)
    for hand in handData:
        for ind in keyPoints:
            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()

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import time

import cv2

print(cv2.__version__)

import numpy as np

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

def findDistances(handData):

distMatrix=np.zeros([len(handData),len(handData)],dtype='float')

for row in range(0,len(handData)):

for column in range(0,len(handData)):

distMatrix[row][column]=((handData[row][0]-handData[column][0])**2+(handData[row][1]-handData[column][1])**2)**(1./2.)

return distMatrix

def findError(gestureMatrix,unknownMatrix,keyPoints):

error=0

for row in keyPoints:

for column in keyPoints:

error=error+abs(gestureMatrix[row][column]-unknownMatrix[row][column])

return error

def findGesture(unknownGesture,knownGestures,keyPoints,gestNames,tol):

errorArray=[]

for i in range(0,len(gestNames),1):

error=findError(knownGestures[i],unknownGesture,keyPoints)

errorArray.append(error)

errorMin=errorArray[0]

minIndex=0

for i in range(0,len(errorArray),1):

if errorArray[i]<errorMin:

errorMin=errorArray[i]

minIndex=i

if errorMin<tol:

gesture=gestNames[minIndex]

if errorMin>=tol:

gesture='Unknown'

return gesture

width=1280

height=720

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'))

findHands=mpHands(1)

time.sleep(5)

keyPoints=[0,4,5,9,13,17,8,12,16,20]

train=True

tol=1500

trainCnt=0

knownGestures=[]

numGest=int(input('How Many Gestures Do You Want? '))

gestNames=[]

for i in range(0,numGest,1):

prompt='Name of Gesture #'+str(i+1)+' '

name=input(prompt)

gestNames.append(name)

print(gestNames)

while True:

ignore, frame = cam.read()

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

handData=findHands.Marks(frame)

if train==True:

if handData!=[]:

print('Please Show Gesture ',gestNames[trainCnt],': Press t when Ready')

if cv2.waitKey(1) & 0xff==ord('t'):

knownGesture=findDistances(handData[0])

knownGestures.append(knownGesture)

trainCnt=trainCnt+1

if trainCnt==numGest:

train=False

if train == False:

if handData!=[]:

unknownGesture=findDistances(handData[0])

myGesture=findGesture(unknownGesture,knownGestures,keyPoints,gestNames,tol)

#error=findError(knownGesture,unknownGesture,keyPoints)

cv2.putText(frame,myGesture,(100,175),cv2.FONT_HERSHEY_SIMPLEX,3,(255,0,0),8)

for hand in handData:

for ind in keyPoints:

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()

Artificial Intelligence, Python

AI for Everyone LESSON 25: First Attempt at Using MediaPipe for Gesture Detection

January 27, 2022 admin

In this lesson we describe what our approach will be to creating a gesture detection and recognition algorithm using mediaPipe. We will demonstrate the algorithm with some simple code demos. In future lessons we will further develop the algorithm. The code below is the initial algorithm developed in this video. Enjoy!

import cv2
print(cv2.__version__)
import numpy as np

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
def findDistances(handData):
    distMatrix=np.zeros([len(handData),len(handData)],dtype='float')
    for row in range(0,len(handData)):
        for column in range(0,len(handData)):
            distMatrix[row][column]=((handData[row][0]-handData[column][0])**2+(handData[row][1]-handData[column][1])**2)**(1./2.)
    return distMatrix

def findError(gestureMatrix,unknownMatrix,keyPoints):
    error=0
    for row in keyPoints:
        for column in keyPoints:
            error=error+abs(gestureMatrix[row][column]-unknownMatrix[row][column])
    return error


width=1280
height=720
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'))
findHands=mpHands(1)

keyPoints=[0,4,5,9,13,17,8,12,16,20]
train=True
while True:
    ignore,  frame = cam.read()
    frame=cv2.resize(frame,(width,height))
    handData=findHands.Marks(frame)
    if train==True:
        if handData!=[]:
            print('Show your Gesture, press t when Ready')
            if cv2.waitKey(1) & 0xff==ord('t'):
                knownGesture=findDistances(handData[0])
                train=False
                print(knownGesture)
    if train == False:
        if handData!=[]:
            unknownGesture=findDistances(handData[0])
            error=findError(knownGesture,unknownGesture,keyPoints)
            cv2.putText(frame,str(int(error,)),(100,175),cv2.FONT_HERSHEY_SIMPLEX,3,(255,0,0),8)
    for hand in handData:
        for ind in keyPoints:
            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__)

import numpy as np

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

def findDistances(handData):

distMatrix=np.zeros([len(handData),len(handData)],dtype='float')

for row in range(0,len(handData)):

for column in range(0,len(handData)):

distMatrix[row][column]=((handData[row][0]-handData[column][0])**2+(handData[row][1]-handData[column][1])**2)**(1./2.)

return distMatrix

def findError(gestureMatrix,unknownMatrix,keyPoints):

error=0

for row in keyPoints:

for column in keyPoints:

error=error+abs(gestureMatrix[row][column]-unknownMatrix[row][column])

return error

width=1280

height=720

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'))

findHands=mpHands(1)

keyPoints=[0,4,5,9,13,17,8,12,16,20]

train=True

while True:

ignore, frame = cam.read()

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

handData=findHands.Marks(frame)

if train==True:

if handData!=[]:

print('Show your Gesture, press t when Ready')

if cv2.waitKey(1) & 0xff==ord('t'):

knownGesture=findDistances(handData[0])

train=False

print(knownGesture)

if train == False:

if handData!=[]:

unknownGesture=findDistances(handData[0])

error=findError(knownGesture,unknownGesture,keyPoints)

cv2.putText(frame,str(int(error,)),(100,175),cv2.FONT_HERSHEY_SIMPLEX,3,(255,0,0),8)

for hand in handData:

for ind in keyPoints:

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()

Artificial Intelligence, OpenCV, Python

Parsing Mediapipe Data for Pose Landmarks, Hand Landmarks and Face Bounding Box

December 2, 2021 admin

In this lesson we show how to create python classes to parse the data coming from Mediapipe for hand Landmarks, Pose Landmarks and the bounding boxes for found faces. Creating these classes allows the difficult parsing to be done in the class, and then have a simple way to parse and use all the data.

import cv2
print(cv2.__version__)

class mpFace:
    import mediapipe as mp 
    def __init__(self):
        self.myFace=self.mp.solutions.face_detection.FaceDetection()
    def Marks(self,frame):
        frameRGB=cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
        results=self.myFace.process(frameRGB)
        faceBoundBoxs=[]
        if results.detections != None:
            for face in results.detections:
                bBox=face.location_data.relative_bounding_box
                topLeft=(int(bBox.xmin*width),int(bBox.ymin*height))
                bottomRight=(int((bBox.xmin+bBox.width)*width),int((bBox.ymin+bBox.height)*height))
                faceBoundBoxs.append((topLeft,bottomRight))
        return faceBoundBoxs

class mpPose:
    import mediapipe as mp
    def __init__(self,still=False,upperBody=False, smoothData=True, tol1=.5, tol2=.5):
        self.myPose=self.mp.solutions.pose.Pose(still,upperBody,smoothData,tol1,tol2)
    def Marks(self,frame):
        frameRGB=cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
        results=self.myPose.process(frameRGB)
        poseLandmarks=[]
        if results.pose_landmarks:
            for lm in results.pose_landmarks.landmark:            
                poseLandmarks.append((int(lm.x*width),int(lm.y*height)))
        return poseLandmarks

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=[]
        handsType=[]
        frameRGB=cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
        results=self.hands.process(frameRGB)
        if results.multi_hand_landmarks != None:
            #print(results.multi_handedness)
            for hand in results.multi_handedness:
                #print(hand)
                #print(hand.classification)
                #print(hand.classification[0])
                handType=hand.classification[0].label
                handsType.append(handType)
            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,handsType

width=1280
height=720
cam=cv2.VideoCapture(3,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)
findFace=mpFace()
findPose=mpPose()

font=cv2.FONT_HERSHEY_SIMPLEX
fontColor=(0,0,255)

while True:
    ignore,  frame = cam.read()
    frame=cv2.resize(frame,(width,height))
    handsLM,handsType=findHands.Marks(frame)
    faceLoc=findFace.Marks(frame)
    poseLM=findPose.Marks(frame)
    if poseLM != []:
        for ind in [13,14,15,16]:
            cv2.circle(frame,poseLM[ind],20,(0,255,0),-1)

    for face in faceLoc:
        cv2.rectangle(frame,face[0],face[1],(255,0,0),3)
    for hand,handType in zip(handsLM,handsType):
        if handType=='Right':
            lbl='Right'
        if handType=='Left':
            lbl='Left'
        cv2.putText(frame,lbl,hand[8],font,2,fontColor,2)

    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 mpFace:

import mediapipe as mp

def __init__(self):

self.myFace=self.mp.solutions.face_detection.FaceDetection()

def Marks(self,frame):

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

results=self.myFace.process(frameRGB)

faceBoundBoxs=[]

if results.detections != None:

for face in results.detections:

bBox=face.location_data.relative_bounding_box

topLeft=(int(bBox.xmin*width),int(bBox.ymin*height))

bottomRight=(int((bBox.xmin+bBox.width)*width),int((bBox.ymin+bBox.height)*height))

faceBoundBoxs.append((topLeft,bottomRight))

return faceBoundBoxs

class mpPose:

import mediapipe as mp

def __init__(self,still=False,upperBody=False, smoothData=True, tol1=.5, tol2=.5):

self.myPose=self.mp.solutions.pose.Pose(still,upperBody,smoothData,tol1,tol2)

def Marks(self,frame):

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

results=self.myPose.process(frameRGB)

poseLandmarks=[]

if results.pose_landmarks:

for lm in results.pose_landmarks.landmark:

poseLandmarks.append((int(lm.x*width),int(lm.y*height)))

return poseLandmarks

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=[]

handsType=[]

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

results=self.hands.process(frameRGB)

if results.multi_hand_landmarks != None:

#print(results.multi_handedness)

for hand in results.multi_handedness:

#print(hand)

#print(hand.classification)

#print(hand.classification[0])

handType=hand.classification[0].label

handsType.append(handType)

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,handsType

width=1280

height=720

cam=cv2.VideoCapture(3,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)

findFace=mpFace()

findPose=mpPose()

font=cv2.FONT_HERSHEY_SIMPLEX

fontColor=(0,0,255)

while True:

ignore, frame = cam.read()

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

handsLM,handsType=findHands.Marks(frame)

faceLoc=findFace.Marks(frame)

poseLM=findPose.Marks(frame)

if poseLM != []:

for ind in [13,14,15,16]:

cv2.circle(frame,poseLM[ind],20,(0,255,0),-1)

for face in faceLoc:

cv2.rectangle(frame,face[0],face[1],(255,0,0),3)

for hand,handType in zip(handsLM,handsType):

if handType=='Right':

lbl='Right'

if handType=='Left':

lbl='Left'

cv2.putText(frame,lbl,hand[8],font,2,fontColor,2)

cv2.imshow('my WEBcam', frame)

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

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

break

cam.release()

Artificial Intelligence, OpenCV, Python

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()

Technology Tutorials

Tag Archives: AI

Improved Gesture Recognition in Python and MediaPipe

AI for Everyone LESSON 26: Accurate Gesture Recognition using Python and MediaPipe

AI for Everyone LESSON 25: First Attempt at Using MediaPipe for Gesture Detection

Parsing Mediapipe Data for Pose Landmarks, Hand Landmarks and Face Bounding Box

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

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