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Update app.py
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import streamlit as st
import cv2
import joblib
import mediapipe as mp
import numpy as np
# Load trained model and label encoder
model = joblib.load("pose_classifier.joblib")
label_encoder = joblib.load("label_encoder.joblib")
# Initialize MediaPipe Pose
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
# Streamlit UI
st.title("Live Pose Classification")
st.write("Real-time pose detection using OpenCV and MediaPipe.")
# OpenCV Video Capture
cap = cv2.VideoCapture(0)
# Streamlit Image Display
frame_placeholder = st.empty()
while cap.isOpened():
ret, frame = cap.read()
if not ret:
st.warning("Failed to capture video. Check your camera.")
break
# Convert frame to RGB
img_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Process frame with MediaPipe Pose
results = pose.process(img_rgb)
if results.pose_landmarks:
landmarks = results.pose_landmarks.landmark
pose_data = [j.x for j in landmarks] + [j.y for j in landmarks] + \
[j.z for j in landmarks] + [j.visibility for j in landmarks]
pose_data = np.array(pose_data).reshape(1, -1)
# Predict pose
y_pred = model.predict(pose_data)
predicted_label = label_encoder.inverse_transform(y_pred)[0]
# Display predicted label
cv2.putText(frame, f"Pose: {predicted_label}", (20, 50),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3)
# Display frame in Streamlit
frame_placeholder.image(frame, channels="BGR")
# Break loop if user stops execution
if st.button("Stop Camera"):
break
cap.release()
cv2.destroyAllWindows()