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# import numpy as np
# import pyarrow as pa
# from dora import Node
# from dora import DoraStatus
# from ultralytics import YOLO
# import cv2
# pa.array([])
# CAMERA_WIDTH = 720
# CAMERA_HEIGHT = 1280
# model = YOLO("/home/peiji/yolov8n.pt")
# node = Node()
# # 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
# for event in node:
# print("djieoajdsaosijoi")
# event_type = event["type"]
# if event_type == "INPUT":
# event_id = event["id"]
# if event_id == "image":
# print("[object detection] received image input")
# image = event["value"].to_numpy().reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3))
# frame = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# frame = frame[:, :, ::-1] # OpenCV image (BGR to RGB)
# results = model(frame) # includes NMS
# # Process results
# boxes = np.array(results[0].boxes.xywh.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)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import cv2
import numpy as np
from ultralytics import YOLO
from dora import Node
import pyarrow as pa
node = Node()
model = YOLO("/home/peiji/yolov8n.pt")
IMAGE_WIDTH = int(os.getenv("IMAGE_WIDTH", 1280))
IMAGE_HEIGHT = int(os.getenv("IMAGE_HEIGHT", 720))
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")
image = event["value"].to_numpy().reshape((IMAGE_HEIGHT, IMAGE_WIDTH, 3))
frame = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
frame = frame[:, :, ::-1] # OpenCV image (BGR to RGB)
results = model(frame) # includes NMS
# Process results
boxes = np.array(results[0].boxes.xywh.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)
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