anjali06102004 / Gesture-Monitoring-System

Gesture-Monitoring-System

import cv2 import mediapipe as mp import numpy as np

Initialize Mediapipe Pose

mp_pose = mp.solutions.pose pose = mp_pose.Pose()

Initialize Video Capture

cap = cv2.VideoCapture(0)

def classify_pose(landmarks): """ Classify the pose based on keypoints. """ # Get coordinates left_hip = landmarks[mp_pose.PoseLandmark.LEFT_HIP.value] right_hip = landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value] left_shoulder = landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value] right_shoulder = landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value]

# Calculate the mid-point of hips and shoulders
mid_hip = np.array([(left_hip.x + right_hip.x) / 2, (left_hip.y + right_hip.y) / 2])
mid_shoulder = np.array([(left_shoulder.x + right_shoulder.x) / 2, (left_shoulder.y + right_shoulder.y) / 2])

# Calculate the vertical distance between hips and shoulders
vertical_distance = np.linalg.norm(mid_hip - mid_shoulder)

# Determine activity based on vertical distance
if vertical_distance < 0.2:
    return "Laying Down"
elif mid_hip[1] < mid_shoulder[1]:
    return "Standing"
else:
    return "Sitting"

while cap.isOpened(): ret, frame = cap.read() if not ret: break

# Convert the image to RGB
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Process the image and get the pose landmarks
results = pose.process(rgb_frame)

if results.pose_landmarks:
    # Draw pose landmarks
    mp.solutions.drawing_utils.draw_landmarks(frame, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)

    # Classify pose
    landmarks = results.pose_landmarks.landmark
    activity = classify_pose(landmarks)

    # Display the activity on the frame
    cv2.putText(frame, activity, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)

cv2.imshow('Human Activity Recognition', frame)

if cv2.waitKey(10) & 0xFF == ord('q'):
    break

cap.release() cv2.destroyAllWindows()