Create app.py

app.py

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+import io
+import base64
+import numpy as np
+import cv2
+from PIL import Image as PILImage
+from ultralytics import YOLO
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import Optional
+from matplotlib import cm
+
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI()
+
+# Add CORS middleware to allow requests from anywhere
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allow all origins
+    allow_credentials=True,
+    allow_methods=["*"],  # Allow all HTTP methods
+    allow_headers=["*"],  # Allow all headers
+)
+
+class ImageRequest(BaseModel):
+    image: str  # Base64-encoded image string
+
+class ObjectDetectionSystem:
+    def __init__(self, model_path='Model/yolov8l.pt'):
+        self.yolo_model = YOLO(model_path)
+        self.color_map = cm.get_cmap("tab20", len(self.yolo_model.names))  # Generate distinct colors
+
+    def detect_objects(self, image_base64: str, confidence_threshold: float = 0.5):
+        try:
+            # Decode Base64 and convert to NumPy array
+            image_data = base64.b64decode(image_base64)
+            pil_image = PILImage.open(io.BytesIO(image_data)).convert("RGB")
+            image_np = np.array(pil_image)
+
+            # Resize image for consistent processing
+            input_size = 640  # Example size for YOLO models
+            height, width, _ = image_np.shape
+            scale = input_size / max(height, width)
+            resized_image = cv2.resize(image_np, (int(width * scale), int(height * scale)))
+
+            # Perform object detection
+            results = self.yolo_model(resized_image)
+
+            # Draw results on image
+            output_image = resized_image.copy()
+            for result in results:
+                boxes = result.boxes
+                for box in boxes:
+                    x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
+                    x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+                    confidence = float(box.conf[0])
+                    class_id = int(box.cls[0])
+                    class_name = self.yolo_model.names[class_id]
+
+                    if confidence > confidence_threshold:
+                        # Generate color for this class
+                        color = tuple(int(c * 255) for c in self.color_map(class_id)[:3])
+
+                        # Draw bounding box
+                        cv2.rectangle(output_image, (x1, y1), (x2, y2), color, 2)
+
+                        # Add label with confidence
+                        label = f'{class_name} ({confidence:.2f})'
+                        label_size, baseline = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+                        label_y = max(y1, label_size[1] + 10)
+                        cv2.rectangle(output_image, (x1, label_y - label_size[1] - 10),
+                                      (x1 + label_size[0], label_y + baseline - 10), color, -1)
+                        cv2.putText(output_image, label, (x1, label_y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+
+            # Convert back to Base64
+            output_pil = PILImage.fromarray(output_image)
+            buffered = io.BytesIO()
+            output_pil.save(buffered, format="PNG")
+            encoded_image = base64.b64encode(buffered.getvalue()).decode("utf-8")
+            
+            return encoded_image
+
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+# Initialize the detector with a more advanced model
+detector = ObjectDetectionSystem('Model/yolov8l.pt')
+
+@app.post("/detect")
+async def detect_objects(request: ImageRequest):
+    try:
+        result_image = detector.detect_objects(request.image)
+        return {"processed_image": result_image}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/")
+async def root():
+    return {"message": "Object Detection API is running"}