python - 创建队列系统，按照请求的资历顺序依次处理请求？

Question

我有一个非常简单的 API，可以预测是否存在不安全的驾驶行为。除了识别行为的不规则性之外，API 还允许描述所犯的违规行为。

如何设置排队系统（例如使用 RabbitMQ）以按照请求的资历顺序依次处理图像？

main.py

from model import ClassificationModel
import argparse
import os


def predict_img(img_path, with_heatmap=False):
    model = ClassificationModel() <--- Aqui descargo el modelo cada vez que llamo a predict_img

    row = model.predict(img_path=img_path, row=None, img=None, load=True, original_image_path=img_path,
                        print_heatmap=with_heatmap)

    results = row.to_json()
    print(results)

    return results


if __name__ == "__main__":

    parser = argparse.ArgumentParser()
    parser.add_argument("--path", "-p", help="path of the image", required=True)
    parser.add_argument("--heatmap", "-heat", help="generate heatmap or not", required=True)

    args = parser.parse_args()

    if not(os.path.exists(args.path)):
        raise FileNotFoundError("{} does not exist.".format(args.path))

    elif args.path.split(".")[-1] not in ["png", "jpg", "jpeg"]:
        raise TypeError("The file is not a valid image file.")

    if args.heatmap.upper() == "TRUE":
        args.heatmap = True
    else:
        args.heatmap = False

    result = predict_img(img_path=args.path, with_heatmap=args.heatmap)

model.py

import numpy as np

np.random.seed(40)
from keras_preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import load_model

import settings as st

from predict import Predict


class ClassificationModel(Predict):
    def __init__(self):
        self.list_pos = [
            "safe driving",
            "texting - right",
            "talking to the phone - right",
            "texting - left",
            "talking on the phone - left",
            "operating the radio",
            "drinking",
            "reaching behind",
            "hair and makeup",
            "talking to passenger"
        ]

        self.image_size = 384
        self.generator = ImageDataGenerator(brightness_range=[0.7, 1.5],
                                            rotation_range=35,
                                            fill_mode='nearest',
                                            zoom_range=0.125,
                                            rescale=1. / 255)

        self.model = load_model(st.model_path)
        self.threshold = 0.5
        self.n_step = 5
        self.normal_index = 0

标签： pythonpython-3.xrabbitmqmessage-queuepriority-queue