import numpy as np import pyarrow as pa from dora import DoraStatus from ultralytics import YOLO pa.array([]) CAMERA_WIDTH = 960 CAMERA_HEIGHT = 540 model = YOLO("yolov8n.pt") class Operator: """ Infering object from images """ def on_event( self, dora_event, send_output, ) -> DoraStatus: if dora_event["type"] == "INPUT": frame = ( dora_event["value"].to_numpy().reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3)) ) frame = frame[:, :, ::-1] # OpenCV image (BGR to RGB) results = model(frame, verbose=False) # includes NMS boxes = np.array(results[0].boxes.xyxy.cpu()) conf = np.array(results[0].boxes.conf.cpu()) label = np.array(results[0].boxes.cls.cpu()) # concatenate them together arrays = np.concatenate((boxes, conf[:, None], label[:, None]), axis=1) send_output("bbox", pa.array(arrays.ravel()), dora_event["metadata"]) return DoraStatus.CONTINUE