feat: add route to count volume using math

app.py

@@ -50,4 +50,111 @@ async def inference(file: UploadFile):
     return JSONResponse({
         "image": img_base64,
         "detections": detections
-    })
+    })
+
+
+AVERAGE_TUMOR_VOLUME = 523.6 
+
+def calculate_sphere_volume(width, height):
+    """
+    Menghitung volume tumor menggunakan rumus bola
+    Volume = (4/3) * π * r³
+    Diameter = rata-rata dari width dan height
+    """
+    try:
+        # Diameter sebagai rata-rata dari width dan height (dalam pixel)
+        diameter = (width + height) / 2
+        radius = diameter / 2
+        
+        # Rumus volume bola
+        volume = (4/3) * math.pi * (radius ** 3)
+        
+        return round(volume, 2)
+    except:
+        return None
+
+@app.post("/inference_volume")
+async def inference_volume(file: UploadFile):
+    """
+    Endpoint untuk deteksi tumor dengan volume, return image dengan anotasi
+    """
+    if file.content_type not in ["image/jpeg", "image/png", "image/jpg"]:
+        return JSONResponse(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            content={"error": "Invalid file format"}
+        )
+    
+    image_bytes = await file.read()
+    img = np.frombuffer(image_bytes, dtype=np.uint8)
+    img = cv2.imdecode(img, cv2.IMREAD_COLOR)
+    
+    results = model.predict(
+        source=img, 
+        conf=0.5,
+        iou=0.2
+    )
+    
+    total_volume = 0
+    detection_count = 0
+    
+    for r in results: 
+        boxes = r.boxes
+        
+        for box in boxes: 
+            x1, y1, x2, y2 = box.xyxy[0]
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+            
+            width = x2 - x1
+            height = y2 - y1
+            
+            volume = calculate_sphere_volume(width, height)
+            if volume is None:
+                volume = AVERAGE_TUMOR_VOLUME
+            
+            total_volume += volume
+            detection_count += 1
+            
+            label = int(box.cls[0].item())
+            label_name = model.names[label]
+            confidence = box.conf[0].item()
+            
+            # Draw bounding box
+            cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 255), 2)
+            
+            # Label dengan volume
+            text = f"{label_name} {confidence:.2f}"
+            vol_text = f"Vol: {volume:.1f}mm3"
+            
+            (text_width, text_height), baseline = cv2.getTextSize(
+                text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2
+            )
+            
+            cv2.rectangle(
+                img, 
+                (x1, y1 - text_height - baseline - 25), 
+                (x1 + max(text_width, 120), y1), 
+                (255, 0, 255), 
+                -1
+            )
+            
+            cv2.putText(
+                img, text, (x1, y1 - 20), 
+                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2
+            )
+            cv2.putText(
+                img, vol_text, (x1, y1 - 5), 
+                cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1
+            )
+    
+    summary_text = f"Total: {detection_count} tumor(s) | Vol: {total_volume:.1f}mm3"
+    cv2.rectangle(img, (10, 10), (400, 40), (0, 0, 0), -1)
+    cv2.putText(img, summary_text, (15, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
+    
+    resp_img_bytes = cv2.imencode('.jpg', img)[1].tobytes()
+    resp_filename = f"volume_{file.filename}" if file.filename else "volume_image.jpg"
+    
+    return StreamingResponse(
+        BytesIO(resp_img_bytes), 
+        media_type="image/jpeg", 
+        headers={"Content-Disposition": f"attachment; filename={resp_filename}"}
+    )