FastAIoT 平台，自建 service

 不用 central broker 版本:

import pika, sys, os

import numpy as np

import cv2

import torch

from torch import nn

import torchvision

os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"

# load the COCO dataset category names

# we will use the same list for this notebook

COCO_INSTANCE_CATEGORY_NAMES = [

    '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',

    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',

    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',

    'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A',

    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',

    'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',

    'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',

    'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',

    'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table',

    'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',

    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book',

    'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'

]

model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)

model.eval()

class PersonDetector():

    def __init__(self) -> None:

        self.connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.56.1', port=5672))

        self.channel = self.connection.channel()

        self.channel.exchange_declare(exchange="PersonDetector", exchange_type="topic", auto_delete=True, arguments={"output":["PersonDetector_output_text"]})

        self.channel.queue_declare(queue='PersonDetector_input_image', exclusive=True)

        self.channel.queue_bind(queue="PersonDetector_input_image", exchange="PersonDetector", routing_key=f"*.*.*.image")

        

        # load model

        print("Start loading model")

        #model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)

        #model.eval()

        print("Load model successfully")

    

    def __callback(self, ch, method, properties, body):

        if "PersonDetector" in method.routing_key:

            pass

        else:

            routing_key_tokens = method.routing_key.split(".")

            app_name = routing_key_tokens[0]

            client_id = routing_key_tokens[1]

            

            # preprocessing

            img_bytes = np.frombuffer(body, dtype=np.uint8)

            img = cv2.imdecode(img_bytes, 1)

            cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

            img = img.astype(np.float32) / 255.0

            img = torch.tensor(img)

            img = img.permute(2, 0, 1)

            

            # detect

            pred = model([img])

            pred_class = [COCO_INSTANCE_CATEGORY_NAMES[i] for i in list(pred[0]['labels'].numpy())]

            pred_boxes = [[(i[0], i[1]), (i[2], i[3])] for i in list(pred[0]['boxes'].detach().numpy())]

            pred_score = list(pred[0]['scores'].detach().numpy())

            pred_t = [pred_score.index(x) for x in pred_score if x>0.7][-1]

            pred_boxes = pred_boxes[:pred_t+1]

            pred_class = pred_class[:pred_t+1]

            for cls in pred_class:

                if cls == "person":

                    print(f"person detect!!!")

                

    def run(self):

        self.channel.basic_consume(queue='PersonDetector_input_image', on_message_callback=self.__callback, auto_ack=True)

        print(' [*] Waiting for messages. To exit press CTRL+C')

        self.channel.start_consuming()

        

        

if __name__ == '__main__':

    try:

        detector = PersonDetector()

        detector.run()

    except KeyboardInterrupt:

        print('Interrupted')

        try:

            sys.exit(0)

        except SystemExit:

            os._exit(0)

==========

import pika

import pandas as pd

import numpy as np

connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.56.1', port=5672))

channel = connection.channel()

with open(f"C:\\Users\\Cherry\\Downloads\\image.jpg", "rb") as f:

    data = f.read()

channel.basic_publish(exchange='PersonDetector',

                        routing_key=f'PersonDetection.client0.null.image',

                        body=data)

connection.close()

print("finish")