Create app.py

app.py

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+import cv2
+import numpy as np
+import mediapipe as mp
+import gradio as gr
+
+# Initialize Mediapipe Pose
+mp_pose = mp.solutions.pose
+mp_drawing = mp.solutions.drawing_utils
+pose = mp_pose.Pose(static_image_mode=True, min_detection_confidence=0.5)
+
+def calculate_angle(a, b, c):
+    """Calculate angle between three points (for body extension)."""
+    a = np.array(a)  # Point A
+    b = np.array(b)  # Joint (Point B)
+    c = np.array(c)  # Point 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(image):
+    """Detect pose in an image and return annotated image + example angle."""
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    results = pose.process(image_rgb)
+
+    annotated = image.copy()
+    angle_text = "No person detected"
+
+    if results.pose_landmarks:
+        # Draw pose landmarks
+        mp_drawing.draw_landmarks(
+            annotated,
+            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: Calculate elbow angle (shoulder-elbow-wrist)
+        h, w, _ = image.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)
+        angle_text = f"Left Elbow Angle: {int(angle)}°"
+
+        cv2.putText(annotated, angle_text, (20, 40),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
+
+    return annotated[:, :, ::-1], angle_text  # Convert BGR → RGB for Gradio
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=detect_pose,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=[gr.Image(type="numpy", label="Pose Estimation"), gr.Textbox(label="Body Extension Angle")],
+    title="Human Pose Estimation with MediaPipe",
+    description="Upload an image with a person doing exercise. The app will detect body pose and calculate joint angles."
+)
+
+if __name__ == "__main__":
+    demo.launch()