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app.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.responses import StreamingResponse, JSONResponse
+from io import BytesIO
+from PIL import Image
+import cv2
+import supervision as sv
+from inference import get_model
+
+app = FastAPI()
+
+# Load the pre-trained model once when the server starts
+model = get_model(model_id="yolov11n-640")
+
+def gen_frames():
+    # Open the default camera (index 0)
+    cap = cv2.VideoCapture(0)
+    if not cap.isOpened():
+        raise HTTPException(status_code=500, detail="Could not open video device")
+    
+    while True:
+        success, frame = cap.read()
+        if not success:
+            break
+
+        # Convert the frame from BGR (OpenCV default) to RGB
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        # Convert the numpy array to a PIL Image
+        pil_img = Image.fromarray(frame_rgb)
+        
+        # Run inference on the current frame (model.infer returns a list, so we take the first element)
+        try:
+            results = model.infer(pil_img)[0]
+        except Exception as e:
+            # If inference fails, skip this frame
+            print(f"Inference error: {e}")
+            continue
+
+        # Convert inference results to a Supervision detections object
+        detections = sv.Detections.from_inference(results)
+
+        # Create annotators for bounding boxes and labels
+        bounding_box_annotator = sv.BoxAnnotator()
+        label_annotator = sv.LabelAnnotator()
+
+        # Annotate the frame with bounding boxes and labels
+        annotated_image = bounding_box_annotator.annotate(scene=pil_img, detections=detections)
+        annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections)
+
+        # Save the annotated image to an in-memory buffer in JPEG format
+        buf = BytesIO()
+        annotated_image.save(buf, format="JPEG")
+        buf.seek(0)
+        frame_bytes = buf.read()
+
+        # Yield the frame in MJPEG format
+        yield (b'--frame\r\n'
+               b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
+    
+    # Release the camera when the loop ends
+    cap.release()
+
+@app.get("/live_feed")
+async def live_feed():
+    """
+    Streams a live feed from the camera with inference annotations.
+    Access via: http://localhost:8000/live_feed
+    """
+    return StreamingResponse(
+        gen_frames(),
+        media_type="multipart/x-mixed-replace; boundary=frame"
+    )
+
+@app.get("/detect_classes")
+async def detect_classes():
+    """
+    Detects and returns the labels of the detected classes in the current camera frame as JSON.
+    Updates every 30 frames.
+    Access via: http://localhost:8000/detect_classes
+    """
+    # Open the default camera (index 0)
+    cap = cv2.VideoCapture(0)
+    if not cap.isOpened():
+        raise HTTPException(status_code=500, detail="Could not open video device")
+
+    frame_count = 0
+    all_class_ids = []
+
+    while frame_count < 3:
+        success, frame = cap.read()
+        if not success:
+            cap.release()
+            raise HTTPException(status_code=500, detail="Failed to read frame from camera")
+
+        # Convert the frame from BGR (OpenCV default) to RGB
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        # Convert the numpy array to a PIL Image
+        pil_img = Image.fromarray(frame_rgb)
+        
+        # Run inference on the current frame (model.infer returns a list, so we take the first element)
+        try:
+            results = model.infer(pil_img)[0]
+        except Exception as e:
+            print(f"Inference error: {e}")
+            continue
+
+        # Convert inference results to a Supervision detections object
+        detections = sv.Detections.from_inference(results)
+
+        # Collect class IDs from the current frame
+        if detections.class_id is not None:
+            all_class_ids.extend(detections.class_id.tolist())
+
+        frame_count += 1
+
+    # Release the camera
+    cap.release()
+
+    # Extract unique detected class labels from all collected class IDs
+    unique_class_ids = set(all_class_ids)
+    class_labels = [model.class_names[class_id] for class_id in unique_class_ids]
+
+    # Return the detected class labels as JSON
+    return JSONResponse(content={"detected_classes": class_labels})
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

labels.txt

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+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+couch
+potted plant
+bed
+dining table
+toilet
+tv
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
+