AI Workout Form Corrector

import cv2

import mediapipe as mp

import numpy as np

mp_drawing = mp.solutions.drawing_utils

mp_pose = mp.solutions.pose

# -----------------------

# Calculate angle between 3 points

# -----------------------

def calculate_angle(a, b, c):

    a = np.array(a)  # First

    b = np.array(b)  # Mid

    c = np.array(c)  # End

    

    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

# -----------------------

# Main workout tracker (Squats Example)

# -----------------------

cap = cv2.VideoCapture(0)

with mp_pose.Pose(min_detection_confidence=0.7, min_tracking_confidence=0.7) as pose:

    counter = 0

    stage = None

    

    while cap.isOpened():

        ret, frame = cap.read()

        if not ret:

            break

        

        # Recolor image

        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

        image.flags.writeable = False

        

        # Make detection

        results = pose.process(image)

        

        # Recolor back to BGR

        image.flags.writeable = True

        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

        

        try:

            landmarks = results.pose_landmarks.landmark

            

            # Get coordinates

            hip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,

                   landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]

            knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,

                    landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]

            ankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,

                     landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]

            

            # Calculate angle

            angle = calculate_angle(hip, knee, ankle)

            

            # Visualize angle

            cv2.putText(image, str(int(angle)),

                        tuple(np.multiply(knee, [640, 480]).astype(int)),

                        cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA

                        )

            

            # Squat counter logic

            if angle > 160:

                stage = "up"

            if angle < 90 and stage == "up":

                stage = "down"

                counter += 1

                print(f"✅ Squat count: {counter}")

            

            # Feedback

            if angle < 70:

                feedback = "Too Low! Go Higher"

            elif 70 <= angle <= 100:

                feedback = "Perfect Depth ✅"

            else:

                feedback = "Stand Tall"

            

            cv2.putText(image, feedback, (50,100),

                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2, cv2.LINE_AA)

            

        except:

            pass

        

        # Render detections

        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,

                                  mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2),

                                  mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)

                                 )               

        

        cv2.imshow('AI Workout Form Corrector - Squats', image)

        

        if cv2.waitKey(10) & 0xFF == ord('q'):

            break

    

    cap.release()

    cv2.destroyAllWindows()