import logging from PIL import Image, ImageDraw from huggingface_hub import hf_hub_download from ultralytics import YOLO import shutil logger = logging.getLogger(__name__) shutil.rmtree("models/detection/weights", ignore_errors=True) class ObjectDetector: def __init__(self, model_key="yolov8n", device="cpu"): """ Initialize the Object Detection model using Ultralytics YOLO registry. Args: model_key (str): Model name supported by ultralytics, e.g. 'yolov5n', 'yolov8s', etc. device (str): 'cpu' or 'cuda' """ alias_map = { "yolov8s": "yolov8s", "yolov8l": "yolov8l", "yolov11b": "yolov11b", } raw_key = model_key.lower() resolved_key = alias_map.get(raw_key, raw_key) self.device = device self.model = YOLO(resolved_key) logger.info(f" Ultralytics YOLO model '{resolved_key}' initialized on {device}") def predict(self, image: Image.Image, conf_threshold=0.25): """ Run object detection. Args: image (PIL.Image.Image): Input image. Returns: List[Dict]: List of detected objects with class name, confidence, and bbox. """ logger.info("Running object detection") results = self.model(image) detections = [] for r in results: for box in r.boxes: detections.append({ "class_name": r.names[int(box.cls)], "confidence": float(box.conf), "bbox": box.xyxy[0].tolist() }) logger.info(f"Detected {len(detections)} objects") return detections def draw(self, image: Image.Image, detections, alpha=0.5): """ Draw bounding boxes on image. Args: image (PIL.Image.Image): Input image. detections (List[Dict]): Detection results. alpha (float): Blend strength. Returns: PIL.Image.Image: Image with bounding boxes drawn. """ overlay = image.copy() draw = ImageDraw.Draw(overlay) for det in detections: bbox = det["bbox"] label = f'{det["class_name"]} {det["confidence"]:.2f}' draw.rectangle(bbox, outline="red", width=2) draw.text((bbox[0], bbox[1]), label, fill="red") return Image.blend(image, overlay, alpha)