MediaPipe is a cross-platform framework for building multimodal applied machine learning pipelines. MediaPipe Python package is available on PyPI for Linux, macOS, and Windows.
Today we will write a simple code for hand joint detection using OpenCV.
At first, import the necessary packages –
import cv2
import mediapipe as mp
import time
Initialize the classes-
cap = cv2.VideoCapture(0)
mpHands = mp.solutions.hands
hands = mpHands.Hands()
mpDraw = mp.solutions.drawing_utils
Final code-
pTime = 0
cTime = 0
while True:
success, image = cap.read()
imgRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = hands.process(imgRGB)
#rint(results.multi_hand_landmarks)
if results.multi_hand_landmarks:
for handLms in results.multi_hand_landmarks:
for id, lm in enumerate (handLms.landmark):
#print(id,lm)
h, w, c = image.shape
cx, cy = int(lm.x*w), int(lm.y*h)
print(id, cx, cy)
if id == 4:
cv2.circle(image,(cx,cy),15, (255,0,255),cv2.FILLED)
mpDraw.draw_landmarks(image, handLms, mpHands.HAND_CONNECTIONS)
cTime = time.time()
fps = 1/(cTime-pTime)
pTime = cTime
cv2.putText(image, str(int(fps)),(10,60), cv2.FONT_HERSHEY_PLAIN,3, (255,0,255),4)
cv2.imshow("Results", image)
if cv2.waitKey(1) & 0XFF == ord('q'):
break
And the final output-
