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| import cv2 | |
| import mediapipe as mp | |
| import numpy as np | |
| import gradio as gr | |
| # Initialize MediaPipe pose and drawing utilities | |
| 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 | |
| # Common angle calculation function | |
| 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_leg_raise_feedback(landmarks): | |
| # Using left leg landmarks as reference | |
| 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] | |
| angle = calculate_angle(hip, knee, ankle) | |
| leg_lift = 1 - knee[1] # approximate vertical lift (adjust as needed) | |
| accuracy = max(0, min(100, (1 - abs(angle - 180) / 50) * 100)) | |
| feedback = "Correct Leg Raise" if angle > 160 and leg_lift > 0.4 else "Incorrect Leg Raise" | |
| if angle < 160: | |
| feedback += " - Keep Legs Straight" | |
| if leg_lift < 0.4: | |
| feedback += " - Raise Legs Higher" | |
| return feedback, int(accuracy) | |
| def draw_accuracy_bar(image, accuracy): | |
| bar_x, bar_y = 50, image.shape[0] - 50 | |
| bar_width, bar_height = 200, 20 | |
| fill_width = int((accuracy / 100) * bar_width) | |
| color = (0, 255, 0) if accuracy >= 80 else (0, 0, 255) if accuracy < 50 else (0, 255, 255) | |
| cv2.rectangle(image, (bar_x, bar_y), (bar_x + bar_width, bar_y + bar_height), (200, 200, 200), 2) | |
| cv2.rectangle(image, (bar_x, bar_y), (bar_x + fill_width, bar_y + bar_height), color, -1) | |
| cv2.putText(image, f"Accuracy: {accuracy}%", (bar_x, bar_y - 10), | |
| cv2.FONT_HERSHEY_DUPLEX, 0.6, (255, 255, 255), 2) | |
| def analyze_leg_raises(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 | |
| output_video = "output_leg_raises.mp4" | |
| fourcc = cv2.VideoWriter_fourcc(*'mp4v') | |
| 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_leg_raise_feedback(landmarks) | |
| draw_accuracy_bar(image, accuracy) | |
| color = (0, 255, 0) if "Correct" 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 | |
| gr.Interface( | |
| fn=analyze_leg_raises, | |
| inputs=gr.Video(), | |
| outputs=gr.Video(), | |
| title="Leg Raises Form Analyzer", | |
| description="Upload a video of your leg raises and receive form feedback!" | |
| ).launch() | |