app.py

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

mp_pose = mp.solutions.pose
mp_drawing = mp.solutions.drawing_utils

pose = mp_pose.Pose(static_image_mode=False, min_detection_confidence=0.5, min_tracking_confidence=0.5)

def calculate_angle(a, b, c):
    a = np.array(a)
    b = np.array(b)
    c = np.array(c)
    ba = a - b
    bc = c - b
    cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
    angle = np.arccos(cosine_angle)
    return np.degrees(angle)

def detect_pose_video(video_path):
    cap = cv2.VideoCapture(video_path)
    output_frames = []

    while True:
        ret, frame = cap.read()
        if not ret:
            break

        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        results = pose.process(frame_rgb)

        if results.pose_landmarks:
            mp_drawing.draw_landmarks(
                frame,
                results.pose_landmarks,
                mp_pose.POSE_CONNECTIONS,
                mp_drawing.DrawingSpec(color=(0,255,0), thickness=2, circle_radius=2),
                mp_drawing.DrawingSpec(color=(0,0,255), thickness=2)
            )

            # Example: left elbow angle
            h, w, _ = frame.shape
            landmarks = results.pose_landmarks.landmark
            shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x * w,
                        landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y * h]
            elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x * w,
                     landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y * h]
            wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x * w,
                     landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y * h]

            angle = calculate_angle(shoulder, elbow, wrist)
            cv2.putText(frame, f"Left Elbow: {int(angle)} deg", (20,40),
                        cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,0), 2)

        output_frames.append(frame)

    cap.release()

    # Convert frames to video file
    out_path = "annotated_video.mp4"
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(out_path, fourcc, 20.0, (frame.shape[1], frame.shape[0]))
    for f in output_frames:
        out.write(f)
    out.release()

    return out_path

# Gradio interface
demo = gr.Interface(
    fn=detect_pose_video,
    inputs=gr.Video(label="Upload Video"),
    outputs=gr.Video(label="Annotated Video"),
    title="Human Pose Estimation on Video",
    description="Upload a video and see pose landmarks & joint angles detected in real-time."
)

if __name__ == "__main__":
    demo.launch()