smurar/leg_raises_analysis

1import cv22import mediapipe as mp3import numpy as np4import gradio as gr5 6# Initialize MediaPipe pose and drawing utilities7mp_pose = mp.solutions.pose8pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)9mp_drawing = mp.solutions.drawing_utils10 11# Common angle calculation function12def calculate_angle(a, b, c):13    a, b, c = np.array(a), np.array(b), np.array(c)14    radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])15    angle = np.abs(radians * 180.0 / np.pi)16    return angle17 18def check_leg_raise_feedback(landmarks):19    # Using left leg landmarks as reference20    hip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,21           landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]22    knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,23            landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]24    ankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,25             landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]26    27    angle = calculate_angle(hip, knee, ankle)28    leg_lift = 1 - knee[1]  # approximate vertical lift (adjust as needed)29    accuracy = max(0, min(100, (1 - abs(angle - 180) / 50) * 100))30    31    feedback = "Correct Leg Raise" if angle > 160 and leg_lift > 0.4 else "Incorrect Leg Raise"32    if angle < 160:33        feedback += " - Keep Legs Straight"34    if leg_lift < 0.4:35        feedback += " - Raise Legs Higher"36    return feedback, int(accuracy)37 38def draw_accuracy_bar(image, accuracy):39    bar_x, bar_y = 50, image.shape[0] - 5040    bar_width, bar_height = 200, 2041    fill_width = int((accuracy / 100) * bar_width)42    color = (0, 255, 0) if accuracy >= 80 else (0, 0, 255) if accuracy < 50 else (0, 255, 255)43    cv2.rectangle(image, (bar_x, bar_y), (bar_x + bar_width, bar_y + bar_height), (200, 200, 200), 2)44    cv2.rectangle(image, (bar_x, bar_y), (bar_x + fill_width, bar_y + bar_height), color, -1)45    cv2.putText(image, f"Accuracy: {accuracy}%", (bar_x, bar_y - 10),46                cv2.FONT_HERSHEY_DUPLEX, 0.6, (255, 255, 255), 2)47 48def analyze_leg_raises(video_path):49    cap = cv2.VideoCapture(video_path)50    frame_width, frame_height = int(cap.get(3)), int(cap.get(4))51    fps = cap.get(cv2.CAP_PROP_FPS) if cap.get(cv2.CAP_PROP_FPS) > 0 else 3052 53    output_video = "output_leg_raises.mp4"54    fourcc = cv2.VideoWriter_fourcc(*'mp4v')55    out = cv2.VideoWriter(output_video, fourcc, fps, (frame_width, frame_height))56 57    while cap.isOpened():58        ret, frame = cap.read()59        if not ret:60            break61        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)62        results = pose.process(image)63        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)64        if results.pose_landmarks:65            mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)66            landmarks = results.pose_landmarks.landmark67            feedback, accuracy = check_leg_raise_feedback(landmarks)68            draw_accuracy_bar(image, accuracy)69            color = (0, 255, 0) if "Correct" in feedback else (0, 0, 255)70            cv2.putText(image, feedback, (50, 50),71                        cv2.FONT_HERSHEY_COMPLEX, 1, color, 3)72        out.write(image)73    cap.release()74    out.release()75    return output_video76 77gr.Interface(78    fn=analyze_leg_raises,79    inputs=gr.Video(),80    outputs=gr.Video(),81    title="Leg Raises Form Analyzer",82    description="Upload a video of your leg raises and receive form feedback!"83).launch()84