| # 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) | |