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ModelBox-AI应用开发：美食分类【玩转华为云】

阳光大猫发表于 2024/06/07 02:21:38 2024/06/07

【摘要】本章我们介绍了如何使用ModelBox开发一个美食图片分类的AI应用，我们只需要准备模型文件以及简单的配置即可创建一个HTTP服务。同时我们可以了解到CNN网络的基本结构、数据处理和模型训练方法，以及对应的推理应用逻辑。

ModelBox-AI应用开发：美食分类

一、准备环境

ModelBox端云协同AI开发套件（Windows）环境准备【视频教程】

二、应用开发

1. 创建工程

在ModelBox sdk目录下使用create.bat创建food_mbv2工程

(tensorflow) PS D:\modelbox-win10-x64-1.5.3> .\create.bat -t server -n food_mbv2                 

(tensorflow) D:\modelbox-win10-x64-1.5.3>set BASE_PATH=D:\modelbox-win10-x64-1.5.3\ 

(tensorflow) D:\modelbox-win10-x64-1.5.3>set PATH=D:\modelbox-win10-x64-1.5.3\\python-embed;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3\envs\tensorflow;C:\Users\yanso\miniconda3\envs\tensorflow\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\usr\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Scripts;C:\Users\yanso\miniconda3\envs\tensorflow\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\Library\usr\bin;C:\Users\yanso\miniconda3\Library\bin;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin 

(tensorflow) D:\modelbox-win10-x64-1.5.3>set PYTHONPATH= 

(tensorflow) D:\modelbox-win10-x64-1.5.3>set PYTHONHOME= 

(tensorflow) D:\modelbox-win10-x64-1.5.3>python.exe -u D:\modelbox-win10-x64-1.5.3\\create.py -t server -n food_mbv2      
sdk version is modelbox-win10-x64-1.5.3
dos2unix: converting file D:\modelbox-win10-x64-1.5.3\workspace\food_mbv2/graph\food_mbv2.toml to Unix format...
dos2unix: converting file D:\modelbox-win10-x64-1.5.3\workspace\food_mbv2/graph\modelbox.conf to Unix format...
dos2unix: converting file D:\modelbox-win10-x64-1.5.3\workspace\food_mbv2/bin\mock_task.toml to Unix format...
success: create food_mbv2 in D:\modelbox-win10-x64-1.5.3\workspace

create.bat工具的参数中，-t表示所创建实例的类型，包括server（ModelBox工程）、python（Python功能单元）、c++（C++功能单元）、infer（推理功能单元）等；-n表示所创建实例的名称，开发者自行命名。

2. 创建推理功能单元

在ModelBox sdk目录下使用create.bat创建mbv2_infer推理功能单元

(tensorflow) PS D:\modelbox-win10-x64-1.5.3> .\create.bat -t infer -n mbv2_infer -p food_mbv2

(tensorflow) D:\modelbox-win10-x64-1.5.3>set BASE_PATH=D:\modelbox-win10-x64-1.5.3\ 

(tensorflow) D:\modelbox-win10-x64-1.5.3>set PATH=D:\modelbox-win10-x64-1.5.3\\python-embed;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3\envs\tensorflow;C:\Users\yanso\miniconda3\envs\tensorflow\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\usr\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Scripts;C:\Users\yanso\miniconda3\envs\tensorflow\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\Library\usr\bin;C:\Users\yanso\miniconda3\Library\bin;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin

(tensorflow) D:\modelbox-win10-x64-1.5.3>set PYTHONPATH=

(tensorflow) D:\modelbox-win10-x64-1.5.3>set PYTHONHOME=

(tensorflow) D:\modelbox-win10-x64-1.5.3>python.exe -u D:\modelbox-win10-x64-1.5.3\\create.py -t infer -n mbv2_infer -p food_mbv2
sdk version is modelbox-win10-x64-1.5.3
success: create infer mbv2_infer in D:\modelbox-win10-x64-1.5.3\workspace\food_mbv2/model/mbv2_infer

create.bat工具使用时，-t infer 即表示创建的是推理功能单元；-n xxx_infer 表示创建的功能单元名称为xxx_infer；-p food_mbv2 表示所创建的功能单元属于food_mbv2应用。

a. 下载转换好的模型

运行此Notebook下载转换好的ONNX格式模型

b. 修改模型配置文件

模型和配置文件保持在同级目录下

# Copyright (C) 2020 Huawei Technologies Co., Ltd. All rights reserved.

[base]
name = "mbv2_infer"
device = "cpu"
version = "1.0.0"
description = "your description"
entry = "./mbv2.onnx"  # model file path, use relative path
type = "inference" 
virtual_type = "onnx" # inference engine type: win10 now only support onnx
group_type = "Inference"  # flowunit group attribution, do not change

# Input ports description
[input]
[input.input1]  # input port number, Format is input.input[N]
name = "Input"  # input port name
type = "float"  # input port data type ,e.g. float or uint8
device = "cpu"  # input buffer type: cpu, win10 now copy input from cpu

# Output ports description
[output]
[output.output1] # output port number, Format is output.output[N]
name = "Output"  # output port name
type = "float"   # output port data type ,e.g. float or uint8

3. 创建后处理功能单元

在ModelBox sdk目录下使用create.bat创建mbv2_post后处理功能单元

(tensorflow) PS D:\modelbox-win10-x64-1.5.3> .\create.bat -t python -n mbv2_post -p food_mbv2

(tensorflow) D:\modelbox-win10-x64-1.5.3>set BASE_PATH=D:\modelbox-win10-x64-1.5.3\ 

(tensorflow) D:\modelbox-win10-x64-1.5.3>set PATH=D:\modelbox-win10-x64-1.5.3\\python-embed;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3\envs\tensorflow;C:\Users\yanso\miniconda3\envs\tensorflow\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\usr\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Scripts;C:\Users\yanso\miniconda3\envs\tensorflow\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\Library\usr\bin;C:\Users\yanso\miniconda3\Library\bin;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin

(tensorflow) D:\modelbox-win10-x64-1.5.3>set PYTHONPATH=

(tensorflow) D:\modelbox-win10-x64-1.5.3>set PYTHONHOME=

(tensorflow) D:\modelbox-win10-x64-1.5.3>python.exe -u D:\modelbox-win10-x64-1.5.3\\create.py -t python -n mbv2_post -p food_mbv2
sdk version is modelbox-win10-x64-1.5.3
success: create python mbv2_post in D:\modelbox-win10-x64-1.5.3\workspace\food_mbv2/etc/flowunit/mbv2_post

a. 修改配置文件

# Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.

# Basic config
[base]
name = "mbv2_post" # The FlowUnit name
device = "cpu" # The flowunit runs on cpu
version = "1.0.0" # The version of the flowunit
type = "python" # Fixed value, do not change
description = "description" # The description of the flowunit
entry = "mbv2_post@mbv2_postFlowUnit" # Python flowunit entry function
group_type = "Generic"  # flowunit group attribution, change as Input/Output/Image/Generic ...

# Flowunit Type
stream = false # Whether the flowunit is a stream flowunit
condition = false # Whether the flowunit is a condition flowunit
collapse = false # Whether the flowunit is a collapse flowunit
collapse_all = false # Whether the flowunit will collapse all the data
expand = false #  Whether the flowunit is a expand flowunit

# The default Flowunit config
[config]
num_classes = 23

# Input ports description
[input]
[input.input1] # Input port number, the format is input.input[N]
name = "in_feat" # Input port name
type = "float" # Input port type

# Output ports description
[output]
[output.output1] # Output port number, the format is output.output[N]
name = "out_data" # Output port name
type = "string" # Output port type

b. 修改逻辑代码

# Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import _flowunit as modelbox
import numpy as np
import json

class mbv2_postFlowUnit(modelbox.FlowUnit):
    # Derived from modelbox.FlowUnit
    def __init__(self):
        super().__init__()

    def open(self, config):
        # Open the flowunit to obtain configuration information
        self.params = {}
        self.params['num_classes'] = config.get_int('num_classes')

        return modelbox.Status.StatusCode.STATUS_SUCCESS

    def process(self, data_context):
        # Process the data
        in_feat = data_context.input("in_feat")
        out_data = data_context.output("out_data")

        # mbv2_post process code.
        # Remove the following code and add your own code here.
        for buffer_feat in in_feat:
            feat_data = np.array(buffer_feat.as_object(), copy=False)
            result = np.argmax(feat_data)
            result = {"det_result": str(result)}
            result_str = json.dumps(result)
            out_buffer = modelbox.Buffer(self.get_bind_device(), result_str)
            out_data.push_back(out_buffer)

        return modelbox.Status.StatusCode.STATUS_SUCCESS

    def close(self):
        # Close the flowunit
        return modelbox.Status()

    def data_pre(self, data_context):
        # Before streaming data starts
        return modelbox.Status()

    def data_post(self, data_context):
        # After streaming data ends
        return modelbox.Status()

    def data_group_pre(self, data_context):
        # Before all streaming data starts
        return modelbox.Status()

    def data_group_post(self, data_context):
        # After all streaming data ends
        return modelbox.Status()

4. 修改流程图

food_mbv2工程graph目录下存放流程图，默认的流程图food_mbv2.toml与工程同名，其内容为（以Windows版ModelBox为例）：

# Copyright (C) 2020 Huawei Technologies Co., Ltd. All rights reserved.

[driver]
dir = ["${HILENS_APP_ROOT}/etc/flowunit",
"${HILENS_APP_ROOT}/etc/flowunit/cpp",
"${HILENS_APP_ROOT}/model",
"${HILENS_MB_SDK_PATH}/flowunit"]
skip-default = true
[profile]
profile=false
trace=false
dir="${HILENS_DATA_DIR}/mb_profile"
[graph]
format = "graphviz"
graphconf = """digraph food_mbv2 {
    node [shape=Mrecord]
    queue_size = 4
    batch_size = 1
    input1[type=input,flowunit=input,device=cpu,deviceid=0]
    httpserver_sync_receive[type=flowunit, flowunit=httpserver_sync_receive_v2, device=cpu, deviceid=0, time_out_ms=5000, endpoint="http://0.0.0.0:8083/v1/food_mbv2", max_requests=100]
    image_decoder[type=flowunit, flowunit=image_decoder, device=cpu, key="image_base64", queue_size=4]
    image_resize[type=flowunit, flowunit=resize, device=cpu, deviceid=0, image_width=224, image_height=224]
    normalize[type=flowunit, flowunit=normalize, device=cpu, deviceid=0, standard_deviation_inverse="0.003921568627450,0.003921568627450,0.003921568627450"]
    mbv2_infer[type=flowunit, flowunit=mbv2_infer, device=cpu, deviceid=0, batch_size=1]
    mbv2_post[type=flowunit, flowunit=mbv2_post, device=cpu, deviceid=0]
    httpserver_sync_reply[type=flowunit, flowunit=httpserver_sync_reply_v2, device=cpu, deviceid=0]
    
    input1:input -> httpserver_sync_receive:in_url
    httpserver_sync_receive:out_request_info -> image_decoder:in_encoded_image
    image_decoder:out_image -> image_resize:in_image
    image_resize:out_image -> normalize:in_data
    normalize:out_data -> mbv2_infer:Input
    mbv2_infer:Output -> mbv2_post:in_feat
    mbv2_post:out_data -> httpserver_sync_reply:in_reply_info
}"""
[flow]
desc = "food_mbv2 run in modelbox-win10-x64"

5. 准备美食图片和测试脚本

a. 美食图片

food_mbv2工程data目录下存放美食图片test.jpg

b. 测试脚本

food_mbv2工程data目录下存放测试脚本test_http.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.

import os
import cv2
import json
import base64
import http.client


class HttpConfig:
    '''http调用的参数配置'''
    def __init__(self, host_ip, port, url, img_base64_str):
        self.hostIP = host_ip
        self.Port = port

        self.httpMethod = "POST"
        self.requstURL = url
        self.headerdata = {
            "Content-Type": "application/json"
        }
        self.test_data = {
            "image_base64": img_base64_str
        }
        self.body = json.dumps(self.test_data)


def read_image(img_path):
    '''读取图片数据并转为base64编码的字符串'''
    img_data = cv2.imread(img_path)
    img_str = cv2.imencode('.jpg', img_data)[1].tobytes()
    img_bin = base64.b64encode(img_str)
    img_base64_str = str(img_bin, encoding='utf8')
    return img_base64_str

    
def decode_result_str(result_str):
    try:
        result = json.loads(json.loads(result_str)['det_result'])
    except Exception as ex:
        print(str(ex))
        return []
    else:
        return result
    
    
index_to_name = {0: '美食_八宝玫瑰镜糕',
                 1: '美食_凉皮',
                 2: '美食_凉鱼',
                 3: '美食_德懋恭水晶饼',
                 4: '美食_搅团',
                 5: '美食_枸杞炖银耳',
                 6: '美食_柿子饼',
                 7: '美食_浆水面',
                 8: '美食_灌汤包',
                 9: '美食_烧肘子',
                 10: '美食_石子饼',
                 11: '美食_神仙粉',
                 12: '美食_粉汤羊血',
                 13: '美食_羊肉泡馍',
                 14: '美食_肉夹馍',
                 15: '美食_荞面饸饹',
                 16: '美食_菠菜面',
                 17: '美食_蜂蜜凉粽子',
                 18: '美食_蜜饯张口酥饺',
                 19: '美食_西安油茶',
                 20: '美食_贵妃鸡翅',
                 21: '美食_醪糟',
                 22: '美食_金线油塔'}


def test_image(img_path, ip, port, url):
    '''单张图片测试'''
    img_base64_str = read_image(img_path)
    http_config = HttpConfig(ip, port, url, img_base64_str)

    conn = http.client.HTTPConnection(host=http_config.hostIP, port=http_config.Port)
    conn.request(method=http_config.httpMethod, url=http_config.requstURL,
                 body=http_config.body, headers=http_config.headerdata)

    response = conn.getresponse().read().decode()
    print('response: ', response)

    result = decode_result_str(response)
    print(index_to_name.get(result))


if __name__ == "__main__":
    port = 8083
    ip = "127.0.0.1"
    url = "/v1/food_mbv2"
    img_path = "test.jpg"
    test_image(img_path, ip, port, url)

三、运行应用

在food_mbv2工程目录下执行.\bin\main.bat运行应用：

(tensorflow) PS D:\modelbox-win10-x64-1.5.3> cd D:\modelbox-win10-x64-1.5.3\workspace\food_mbv2
(tensorflow) PS D:\modelbox-win10-x64-1.5.3\workspace\food_mbv2> .\bin\main.bat 

(tensorflow) D:\modelbox-win10-x64-1.5.3\workspace\food_mbv2>set PATH=D:/modelbox-win10-x64-1.5.3/workspace/food_mbv2/bin/../../../python-embed;D:/modelbox-win10-x64-1.5.3/workspace/food_mbv2/bin/../../../modelbox-win10-x64/bin;D:/modelbox-win10-x64-1.5.3/workspace/food_mbv2/bin/../dependence/lib;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3\envs\tensorflow;C:\Users\yanso\miniconda3\envs\tensorflow\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\usr\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Scripts;C:\Users\yanso\miniconda3\envs\tensorflow\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\Library\usr\bin;C:\Users\yanso\miniconda3\Library\bin;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin 

(tensorflow) D:\modelbox-win10-x64-1.5.3\workspace\food_mbv2>modelbox.exe -c D:/modelbox-win10-x64-1.5.3/workspace/food_mbv2/bin/../graph/modelbox.conf 
[2024-06-07 01:56:35,801][ WARN][    iva_config.cc:143 ] update vas url failed. Fault, no vas projectid or iva endpoint
open log file D:/modelbox-win10-x64-1.5.3/workspace/food_mbv2/bin/../hilens_data_dir/log/modelbox.log failed, No error
input dims is:1,224,224,3,
output dims is:1,23,

HTTP服务启动后可以在另一个终端进行请求测试，进入food_mbv2工程目录data文件夹中使用test_http.py脚本发起HTTP请求进行测试：

(tensorflow) PS D:\modelbox-win10-x64-1.5.3> cd D:\modelbox-win10-x64-1.5.3\workspace\food_mbv2\data
(tensorflow) PS D:\modelbox-win10-x64-1.5.3\workspace\food_mbv2\data> python test_http.py                                                                    
response:  {"det_result": "1"}
美食_凉皮

四、小结

本章我们介绍了如何使用ModelBox开发一个美食图片分类的AI应用，我们只需要准备模型文件以及简单的配置即可创建一个HTTP服务。同时我们可以了解到CNN网络的基本结构、数据处理和模型训练方法，以及对应的推理应用逻辑。

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