final.py

import cv2
import numpy as np
import os
from matplotlib import pyplot as plt
import time
import mediapipe as mp
mp_holistic = mp.solutions.holistic # Holistic model
mp_drawing = mp.solutions.drawing_utils # Drawing utilities

mp_face_mesh = mp.solutions.face_mesh
holistic = mp_holistic.Holistic()
face_mesh = mp_face_mesh.FaceMesh()
def mediapipe_detection(image, model):
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # COLOR CONVERSION BGR 2 RGB
    image.flags.writeable = False                  # Image is no longer writeable
    results = model.process(image)                 # Make prediction
    image.flags.writeable = True                   # Image is now writeable 
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # COLOR COVERSION RGB 2 BGR
    return image, results
def draw_landmarks(image, results):
    # Draw Holistic pose, hand, and face landmarks
    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)  # Pose landmarks
    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)  # Left hand landmarks
    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)  # Right hand landmarks

    # Draw FaceMesh landmarks and connections
    if results.face_landmarks:
        mp_drawing.draw_landmarks(image, results.face_landmarks, mp_face_mesh.FACEMESH_TESSELATION)  # FaceMesh
def draw_styled_landmarks(image, results):
    # Draw face landmarks directly using Holistic
    if results.face_landmarks:
        mp_drawing.draw_landmarks(
            image,
            results.face_landmarks,
            mp_holistic.FACEMESH_TESSELATION,  # Use this for face mesh
            mp_drawing.DrawingSpec(color=(80, 110, 10), thickness=1, circle_radius=1),
            mp_drawing.DrawingSpec(color=(80, 256, 121), thickness=1, circle_radius=1)
        )
    if results.pose_landmarks:
        mp_drawing.draw_landmarks(
            image,
            results.pose_landmarks,
            mp_holistic.POSE_CONNECTIONS,
            mp_drawing.DrawingSpec(color=(80, 22, 10), thickness=2, circle_radius=4),
            mp_drawing.DrawingSpec(color=(80, 44, 121), thickness=2, circle_radius=2)
        )
    if results.left_hand_landmarks:
        mp_drawing.draw_landmarks(
            image,
            results.left_hand_landmarks,
            mp_holistic.HAND_CONNECTIONS,
            mp_drawing.DrawingSpec(color=(121, 22, 76), thickness=2, circle_radius=4),
            mp_drawing.DrawingSpec(color=(121, 44, 250), thickness=2, circle_radius=2)
        )
    if results.right_hand_landmarks:
        mp_drawing.draw_landmarks(
            image,
            results.right_hand_landmarks,
            mp_holistic.HAND_CONNECTIONS,
            mp_drawing.DrawingSpec(color=(245, 117, 66), thickness=2, circle_radius=4),
            mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2)
        )

def extract_keypoints(results):
    pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)
    face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(468*3)
    lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)
    rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)
    return np.concatenate([pose, face, lh, rh])