Full Body Tracking Using Python Step By Step

#1 Installing Requirments

 Requirments

# Mediapipe

#Open Cv

Open CMD and Type

# Pip Install Mediapipe

then Press Enter

#2 Write Code

First, open any Code editor and create a file

Then Start Writing Code

import cv2

import mediapipe as mp

mp_drawing = mp.solutions.drawing_utils

mp_drawing_styles = mp.solutions.drawing_styles

mp_holistic = mp.solutions.holistic

# For static images:

IMAGE_FILES = []

with mp_holistic.Holistic(

    static_image_mode=True,

    model_complexity=2) as holistic:

  for idx, file in enumerate(IMAGE_FILES):

    image = cv2.imread(file)

    image_height, image_width, _ = image.shape

    # Convert the BGR image to RGB before processing.

    results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

    if results.pose_landmarks:

      print(

          f'Nose coordinates: ('

          f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '

          f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_height})'

      )

    # Draw pose, left and right hands, and face landmarks on the image.

    annotated_image = image.copy()

    mp_drawing.draw_landmarks(

        annotated_image,

        results.face_landmarks,

        mp_holistic.FACEMESH_TESSELATION,

        landmark_drawing_spec=None,

        connection_drawing_spec=mp_drawing_styles

        .get_default_face_mesh_tesselation_style())

    mp_drawing.draw_landmarks(

        annotated_image,

        results.pose_landmarks,

        mp_holistic.POSE_CONNECTIONS,

        landmark_drawing_spec=mp_drawing_styles.

        get_default_pose_landmarks_style())

    cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)

    # Plot pose world landmarks.

    mp_drawing.plot_landmarks(

        results.pose_world_landmarks, mp_holistic.POSE_CONNECTIONS)

# For webcam input:

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()

    if not success:

      print("Ignoring empty camera frame.")

      # If loading a video, use 'break' instead of 'continue'.

      continue

    # Flip the image horizontally for a later selfie-view display, and 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

    results = holistic.process(image)

    # Draw landmark annotation on the image.

    image.flags.writeable = True

    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

    mp_drawing.draw_landmarks(

        image,

        results.face_landmarks,

        mp_holistic.FACEMESH_CONTOURS,

        landmark_drawing_spec=None,

        connection_drawing_spec=mp_drawing_styles

        .get_default_face_mesh_contours_style())

    mp_drawing.draw_landmarks(

        image,

        results.pose_landmarks,

        mp_holistic.POSE_CONNECTIONS,

        landmark_drawing_spec=mp_drawing_styles

        .get_default_pose_landmarks_style())

    cv2.imshow('MediaPipe Holistic', image)

    if cv2.waitKey(5) & 0xFF == 27:

      break

cap.release()

#3 Run The Code

If you have any problem then contact me on telegram @odewithyash