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12x.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:682ce8dadee004dbe964950f1bf3eda451671815a6ed62db80b398916b9b7c6f
+size 119322638

app.py

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+from fastapi import FastAPI, WebSocket
+import cv2
+import numpy as np
+from ultralytics import YOLO
+import base64
+import os
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load YOLO model (Ensure 12x.pt exists)
+model_path = "12x.pt"
+if not os.path.exists(model_path):
+    raise FileNotFoundError(f"Model file '{model_path}' not found. Please place it in the project directory.")
+
+model = YOLO(model_path)
+
+
+def process_frame(frame):
+    results = model(frame)
+    predictions = []
+    object_count = {}
+
+    for result in results:
+        for box in result.boxes:
+            class_name = result.names[int(box.cls)]
+            predictions.append({
+                "class": class_name,
+                "confidence": float(box.conf),
+                "bbox": [float(x) for x in box.xyxy[0]]
+            })
+
+            # Count objects
+            object_count[class_name] = object_count.get(class_name, 0) + 1
+
+    return predictions, object_count
+
+
+@app.websocket("/ws")
+async def websocket_endpoint(websocket: WebSocket):
+    await websocket.accept()
+
+    cap = cv2.VideoCapture(0)  # Open webcam
+    if not cap.isOpened():
+        await websocket.send_json({"error": "Could not open webcam"})
+        await websocket.close()
+        return
+
+    try:
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            predictions, object_count = process_frame(frame)
+
+            # Draw bounding boxes on the frame
+            for pred in predictions:
+                x1, y1, x2, y2 = map(int, pred["bbox"])
+                label = f"{pred['class']} ({pred['confidence']:.2f})"
+                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+            # Encode the frame to send over WebSocket
+            _, buffer = cv2.imencode('.jpg', frame)
+            frame_base64 = base64.b64encode(buffer).decode('utf-8')
+
+            # Send frame and object count to frontend
+            await websocket.send_json({"frame": frame_base64, "object_count": object_count})
+
+    except Exception as e:
+        print(f"WebSocket Error: {e}")
+        await websocket.send_json({"error": str(e)})
+    finally:
+        cap.release()
+        await websocket.close()
+
+
+@app.get("/")
+def home():
+    return {"message": "Real-Time Object Detection API using 12x.pt"}