python对象的赋值缓慢

Question

import socket
import time

import info

HOST = '127.0.0.1'  # 监听的地址
PORT = 8888  # 监听的端口号
# 创建套接字并监听
server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_socket.bind((HOST, PORT))
server_socket.listen(1)

while True:
    try:
        # 等待客户端连接
        print('Waiting for Java client...')
        client_socket, address = server_socket.accept()
        print('Connected by', address)
        client_socket.settimeout(0.5)
        # 接收Java客户端发送的数据并返回
        result = ""
        while True:
            data = client_socket.recv(1024)
            if len(data) < 1024:
                break
            result += data.decode()
        print('Received from Java client' + str(time.time()))
        result1 = info.fun(result)
        print("encode" + str(time.time()))
        result1 = result1.encode()
        print("send" + str(time.time()))
        client_socket.send(result1)
        print("done" + str(time.time()))
        client_socket.close()
    except Exception as err:
        server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        server_socket.bind((HOST, PORT))
        server_socket.listen(1)
        continue

import time

from visionUtils.videoDemo import vinoModel
import base64
from PIL import Image, ImageFile
import numpy as np
from io import BytesIO

ImageFile.LOAD_TRUNCATED_IMAGES = True


def decoder(image_data):
    # 对字节数据进行解码
    decoded_image_data = base64.b64decode(image_data)
    image = Image.open(BytesIO(decoded_image_data))
    img_np = np.array(image)
    # image.show()
    return img_np


def encoder(det_img):
    image = Image.fromarray(det_img)
    # 将PIL Image对象转换为字节数据
    buffer = BytesIO()
    image.save(buffer, format='JPEG')
    image_data = buffer.getvalue()
    # 将字节数据编码为base64字符串
    base64_image_data = base64.b64encode(image_data).decode('utf-8')

    return base64_image_data


def fun(data):
    ## 用openvino加速
    model_path = 'visionUtils/data/onnx/yolop-384-640.onnx'
    detector = vinoModel(model_path)

    ## 更精确的模型但是没有加速
    # model_path='visionUtils/data/weights/yolopv2.pt'
    # detector = detectModel(model_path)

    # base64转图片
    image_data = data
    img = decoder(image_data)

    print("det_img = detector(img)" + str(time.time()))
    # 用模型处理图片
    det_img = detector(img)  # return detect result

    print("after_det_img = detector(img)" + str(time.time()))
    # 图片转base64
    base64_img = encoder(det_img)
    print("base" + str(time.time()))
    return base64_img

输出：

Waiting for Java client...
Connected by ('127.0.0.1', 50083)
Received from Java client1688277171.867513
det_img = detector(img)1688277172.934858
inf : (0.1666s/frame)   nms : (0.0010s/frame)
after_det_img = detector(img)1688277173.2231426
base1688277173.2291653
encode1688277187.638751
send1688277187.638751
done1688277187.638751
Waiting for Java client...
Connected by ('127.0.0.1', 50085)
Received from Java client1688277187.6485023
det_img = detector(img)1688277188.7829428
inf : (0.1550s/frame)   nms : (0.0010s/frame)
after_det_img = detector(img)1688277189.0648327
base1688277189.0708199
encode1688277203.6613748
send1688277203.6613748
done1688277203.6613748
Waiting for Java client...
Connected by ('127.0.0.1', 50087)
Received from Java client1688277203.669413
det_img = detector(img)1688277204.7575696
inf : (0.1895s/frame)   nms : (0.0010s/frame)
after_det_img = detector(img)1688277205.0817413
base1688277205.0887675
encode1688277224.2824495
send1688277224.2824495
done1688277224.2824495

python进行图像处理，Java把图片base64编码后发给python，python处理好后发给Java，两个平台用socket通信，通信，编解码几乎不耗时
就是不知道为什么那中间要隔这么久

Answer 1

这个time.time输出的是时间戳，不是代码执行的时间