import cv2 import mediapipe as mp import numpy as np
mp_pose = mp.solutions.pose pose = mp_pose.Pose()
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()