Create app.py

app.py

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+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()