holisticDetection.py

import cv2
import mediapipe as mp
import time


mp_drawing = mp.solutions.drawing_utils
mp_holistic = mp.solutions.holistic



cap = cv2.VideoCapture(0)


with mp_holistic.Holistic(
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5) as holistic:

    while cap.isOpened():

        success, image = cap.read()
       
        start = time.time()


        # Flip the image horizontally for a later selfie-view display
        # Convert the BGR image to RGB.
        image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)

        # To improve performance, optionally mark the image as not writeable to
        # pass by reference.
        image.flags.writeable = False

        # Process the image and detect the holistic
        results = holistic.process(image)

        # Draw landmark annotation on the image.
        image.flags.writeable = True

        # Convert the image color back so it can be displayed
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)



        print(results.pose_landmarks)

        mp_drawing.draw_landmarks(
            image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS)
        mp_drawing.draw_landmarks(
            image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
        mp_drawing.draw_landmarks(
            image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
        mp_drawing.draw_landmarks(
            image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)




        end = time.time()
        totalTime = end - start

        fps = 1 / totalTime
        print("FPS: ", fps)

        cv2.putText(image, f'FPS: {int(fps)}', (20,70), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0,255,0), 2)

        cv2.imshow('MediaPipe Holistic', image)


        if cv2.waitKey(5) & 0xFF == 27:
            break

cap.release()