app.py

import cv2
import mediapipe as mp
import numpy as np
import gradio as gr

# Initialize MediaPipe Pose and drawing utils.
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

# Calculate angle between three points.
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

# Process the video and overlay pushup feedback.
def analyze_pushups(video_path):
    cap = cv2.VideoCapture(video_path)
    frame_width = int(cap.get(3))
    frame_height = int(cap.get(4))
    fps = cap.get(cv2.CAP_PROP_FPS) if cap.get(cv2.CAP_PROP_FPS) > 0 else 30

    output_video = "output_pushup.mp4"
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_video, fourcc, fps, (frame_width, frame_height))

    # Check pushup form based on elbow angles.
    def check_pushup_feedback(landmarks):
        def get_landmark(part):
            return [landmarks[part].x, landmarks[part].y]
        left_shoulder = get_landmark(mp_pose.PoseLandmark.LEFT_SHOULDER)
        left_elbow = get_landmark(mp_pose.PoseLandmark.LEFT_ELBOW)
        left_wrist = get_landmark(mp_pose.PoseLandmark.LEFT_WRIST)
        right_shoulder = get_landmark(mp_pose.PoseLandmark.RIGHT_SHOULDER)
        right_elbow = get_landmark(mp_pose.PoseLandmark.RIGHT_ELBOW)
        right_wrist = get_landmark(mp_pose.PoseLandmark.RIGHT_WRIST)
        left_elbow_angle = calculate_angle(left_shoulder, left_elbow, left_wrist)
        right_elbow_angle = calculate_angle(right_shoulder, right_elbow, right_wrist)
        avg_elbow_angle = (left_elbow_angle + right_elbow_angle) / 2
        accuracy = max(0, min(100, (1 - abs(avg_elbow_angle - 90) / 45) * 100))
        feedback = "Correct Push-Up" if 45 <= avg_elbow_angle <= 120 else "Incorrect Form"
        if avg_elbow_angle < 45:
            feedback += " - Go Higher"
        elif avg_elbow_angle > 120:
            feedback += " - Lower Your Chest"
        return feedback, int(accuracy)

    # Draw feedback and an accuracy bar on the frame.
    def draw_feedback(image, accuracy, feedback):
        bar_x, bar_y, bar_width, bar_height = 50, 400, 200, 20
        fill_width = int((accuracy / 100) * bar_width)
        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), (0, 255, 0), -1)
        cv2.putText(image, f"Accuracy: {accuracy}%", (bar_x, bar_y - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
        color = (0, 255, 0) if "Correct" in feedback else (0, 0, 255)
        cv2.putText(image, feedback, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 3)

    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        # Process frame with MediaPipe.
        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)
            feedback, accuracy = check_pushup_feedback(results.pose_landmarks.landmark)
            draw_feedback(image, accuracy, feedback)
        out.write(image)
    cap.release()
    out.release()
    return output_video

# Gradio Interface for pushup analysis.
gr.Interface(
    fn=analyze_pushups,
    inputs=gr.Video(),
    outputs=gr.Video(),
    title="Pushup Form Analyzer",
    description="Upload a video of your pushups, and get feedback on your form!",
).launch()