AI For Everyone: Parsing Pose and Hand Data Landmarks from Mediapipe

In this video lesson we show you how to create simple Python classes to parse the data from mediapipe hand Landmarks and pose estimation. With these two classes, it is very easy to parse the most important data generated by mediapipe.

import cv2
print(cv2.__version__)

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=[]
        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)
findPose=mpPose()
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)
    poseData=findPose.Marks(frame)
    if len(poseData)!=0:
        cv2.circle(frame,poseData[0],5,(0,255,0),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__)

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=[]

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)

findPose=mpPose()

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)

poseData=findPose.Marks(frame)

if len(poseData)!=0:

cv2.circle(frame,poseData[0],5,(0,255,0),3)

cv2.imshow('my WEBcam', frame)

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

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

break

cam.release()

Technology Tutorials

AI For Everyone: Parsing Pose and Hand Data Landmarks from Mediapipe

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