app.py

import cv2
import mediapipe as mp
import numpy as np
import streamlit as st

# Initialize MediaPipe Pose
mp_pose = mp.solutions.pose
pose = mp_pose.Pose(static_image_mode=False, model_complexity=1, enable_segmentation=False, min_detection_confidence=0.5)
mp_drawing = mp.solutions.drawing_utils

# Streamlit web interface
st.title("Yoga Pose Detector")
st.text("Using Mediapipe and Streamlit")

run = st.checkbox('Run')
FRAME_WINDOW = st.image([])

cap = cv2.VideoCapture(0)

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

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

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

    # Draw pose landmarks
    if results.pose_landmarks:
        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)

    # Convert the image color back for OpenCV
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

    # Display the image in the web interface
    FRAME_WINDOW.image(image)

cap.release()
cv2.destroyAllWindows()