app.py

import logging
import queue
from pathlib import Path
from typing import List, NamedTuple
import mediapipe as mp

import av
import cv2
import numpy as np
import streamlit as st
from streamlit_webrtc import WebRtcMode, webrtc_streamer

from sample_utils.download import download_file
from sample_utils.turn import get_ice_servers

# Logging setup
logger = logging.getLogger(__name__)

# Streamlit setup
st.set_page_config(page_title="AI Squat Detection", page_icon="🏋️", layout="wide")
st.markdown(
    """<style>
    .status-box {
        background: #f7f7f7;
        padding: 15px;
        border-radius: 8px;
        box-shadow: 2px 2px 5px rgba(0,0,0,0.1);
        margin-bottom: 20px;
        font-size: 18px;
    }
    .title {
        color: #2E86C1;
        font-size: 32px;
        font-weight: bold;
        text-align: center;
        margin-bottom: 10px;
    }
    .info {
        text-align: center;
        font-size: 18px;
        margin-bottom: 20px;
        color: #333;
    }
    </style>""", unsafe_allow_html=True)

st.markdown('<div class="title">AI Squat Detection</div>', unsafe_allow_html=True)
st.markdown('<div class="info">Use your webcam for real-time squat detection.</div>', unsafe_allow_html=True)

# Initialize MediaPipe components
mp_pose = mp.solutions.pose
mp_drawing = mp.solutions.drawing_utils

class Detection(NamedTuple):
    class_id: int
    label: str
    score: float
    box: np.ndarray

# Angle calculation function
def calculate_angle(a, b, c):
    a = np.array(a)
    b = np.array(b)
    c = np.array(c)
    radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])
    angle = np.abs(radians * 180.0 / np.pi)
    if angle > 180.0:
        angle = 360 - angle
    return angle


# Detection Queue
result_queue: "queue.Queue[List[Detection]]" = queue.Queue()

def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
    global counterL, correct, incorrect, stage
    if 'stage' not in globals():
        stage = 'up'
        correct = 0
        incorrect = 0

    image = frame.to_ndarray(format="bgr24")
    h, w = image.shape[:2]
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
        results = pose.process(image_rgb)
        landmarks = results.pose_landmarks.landmark if results.pose_landmarks else []

        # Corrected detection logic
        detections = [
            Detection(
                class_id=0,  # Assuming a generic class_id for pose detections
                label="Pose",
                score=0.5,  # Full confidence as pose landmarks were detected
                box=np.array([0, 0, image.shape[1], image.shape[0]])  # Full image as bounding box
            )  if landmarks else []

        
        if landmarks:
            hipL = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x, 
                   landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]
            kneeL = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x, 
                    landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]
            ankleL = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x, 
                     landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]
            shoulderL = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x, 
                        landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]

            # Calculate angles
            angleKneeL = calculate_angle(hipL, kneeL, ankleL)
            angleHipL = calculate_angle(shoulderL, hipL, [hipL[0], 0])

            if angleKneeL > 110 and stage == 'down':
                stage = 'up'
                if 18 < angleHipL < 40:
                    correct += 1

            if 80 < angleKneeL < 110 and stage == 'up':
                stage = 'down'

        # Overlay feedback messages
        overlay_text = f"Correct: {correct} | Stage: {stage}"
        cv2.rectangle(image, (0, 0), (500, 80), (245, 117, 16), -1)
        cv2.putText(image, overlay_text, (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)

        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,mp_drawing.DrawingSpec(color=(255, 175, 0), thickness=2, circle_radius=2),mp_drawing.DrawingSpec(color=(0, 255, 200), thickness=2, circle_radius=2))

    result_queue.put(detections)
    
    return av.VideoFrame.from_ndarray(image, format="bgr24")

webrtc_streamer(
    key="squat-detection",
    mode=WebRtcMode.SENDRECV,
    rtc_configuration={"iceServers": get_ice_servers(), "iceTransportPolicy": "relay"},
    media_stream_constraints={"video": True, "audio": False},
    video_frame_callback=video_frame_callback,
    async_processing=True,
)