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| import cv2 | |
| import mediapipe as mp | |
| import numpy as np | |
| import gradio as gr | |
| mp_pose = mp.solutions.pose | |
| pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) | |
| mp_drawing = mp.solutions.drawing_utils | |
| def calculate_angle(a, b, c): | |
| a, b, c = np.array(a), np.array(b), 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) | |
| return angle | |
| def check_squat_feedback(landmarks): | |
| left_hip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x, landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y] | |
| left_knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y] | |
| left_ankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x, landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y] | |
| right_hip = [landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y] | |
| right_knee = [landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y] | |
| right_ankle = [landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y] | |
| left_angle = calculate_angle(left_hip, left_knee, left_ankle) | |
| right_angle = calculate_angle(right_hip, right_knee, right_ankle) | |
| avg_angle = (left_angle + right_angle) / 2 | |
| if avg_angle < 80: | |
| feedback = "Too Low! Raise Your Hips" | |
| elif 80 <= avg_angle <= 110: | |
| feedback = "Perfect Squat!" | |
| elif 110 < avg_angle <= 140: | |
| feedback = "Almost There! Go Lower" | |
| else: | |
| feedback = "Too High! Lower Your Hips" | |
| accuracy = max(0, min(100, (1 - abs(avg_angle - 95) / 50) * 100)) | |
| return feedback, int(accuracy) | |
| def analyze_squats(video_path): | |
| cap = cv2.VideoCapture(video_path) | |
| frame_width, frame_height = int(cap.get(3)), int(cap.get(4)) | |
| fps = cap.get(cv2.CAP_PROP_FPS) if cap.get(cv2.CAP_PROP_FPS) > 0 else 30 | |
| fourcc = cv2.VideoWriter_fourcc(*'mp4v') | |
| output_video = "output_squat.mp4" | |
| out = cv2.VideoWriter(output_video, fourcc, fps, (frame_width, frame_height)) | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| results = pose.process(image) | |
| image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) | |
| if results.pose_landmarks: | |
| mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS) | |
| landmarks = results.pose_landmarks.landmark | |
| feedback, accuracy = check_squat_feedback(landmarks) | |
| color = (0, 255, 0) if "Perfect" in feedback else (0, 0, 255) | |
| cv2.putText(image, feedback, (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, color, 3) | |
| out.write(image) | |
| cap.release() | |
| out.release() | |
| return output_video | |
| # Gradio Interface | |
| gr.Interface( | |
| fn=analyze_squats, | |
| inputs=gr.Video(), | |
| outputs=gr.Video(), | |
| title="Squat Form Analyzer", | |
| description="Upload a video of your squats, and get feedback on your form!", | |
| ).launch() | |