File size: 1,365 Bytes
b98ffbb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import cv2
import numpy as np
from ultralytics import YOLO
from dora import Node
import pyarrow as pa
model = YOLO("yolov8n.pt")
node = Node()
for event in node:
event_type = event["type"]
if event_type == "INPUT":
event_id = event["id"]
if event_id == "image":
print("[object detection] received image input")
frame = event["value"].to_numpy()
frame = cv2.imdecode(frame, -1)
frame = frame[:, :, ::-1] # OpenCV image (BGR to RGB)
results = model(frame) # includes NMS
# Process results
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)
node.send_output("bbox", pa.array(arrays.ravel()), event["metadata"])
else:
print("[object detection] ignoring unexpected input:", event_id)
elif event_type == "STOP":
print("[object detection] received stop")
elif event_type == "ERROR":
print("[object detection] error: ", event["error"])
else:
print("[object detection] received unexpected event:", event_type)
|