快速识别你家的猫猫狗狗，教你用ModelBox开发AI萌宠应用

华为云开发者社区 2024-06-13 09:13:00 阅读 89

本文介绍了如何使用ModelBox开发一个动物目标检测的AI应用，从而掌握图片标注、数据处理和模型训练方法，以及对应的推理应用逻辑。

本文分享自华为云社区《ModelBox-AI应用开发：动物目标检测【玩转华为云】》，作者：阳光大猫。

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ModelBoxsdk目录下使用create.bat创建yolov7_pet工程

(tensorflow) PS D:\modelbox-win10-x64-1.5.3> .\create.bat -t server -n yolov7_pet

(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 yolov7_pet

sdk version is modelbox-win10-x64-1.5.3

dos2unix: converting file D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet/graph\modelbox.conf to Unix format...

dos2unix: converting file D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet/graph\yolov7_pet.toml to Unix format...

dos2unix: converting file D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet/bin\mock_task.toml to Unix format...

success: create yolov7_pet 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ModelBoxsdk目录下使用create.bat创建yolov7_infer推理功能单元

(tensorflow) PS D:\modelbox-win10-x64-1.5.3> .\create.bat -t infer -n yolov7_infer -p yolov7_pet

(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 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 yolov7_infer -p yolov7_pet

sdk version is modelbox-win10-x64-1.5.3

success: create infer yolov7_infer in D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet/model/yolov7_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 yolov7_infer表示所创建的功能单元属于yolov7_infer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

屏幕截图 2024-06-10 062945.png

b. 修改模型配置文件

模型和配置文件保持在同级目录下

[base]

name = "yolov7_infer"

device = "cpu"

version = "1.0.0"

description = "your description"

entry = "./best.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ModelBoxsdk目录下使用create.bat创建yolov7_post后处理功能单元

(tensorflow) PS D:\modelbox-win10-x64-1.5.3> .\create.bat -t python -n yolov7_post -p yolov7_pet

(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 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 yolov7_post -p yolov7_pet

sdk version is modelbox-win10-x64-1.5.3

success: create python yolov7_post in D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet/etc/flowunit/yolov7_post

a. 修改配置文件

# Basic config

[base]

name = "yolov7_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 = "yolov7_post@yolov7_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]

net_h = 640

net_w = 640

num_classes = 2

conf_threshold = 0.5

iou_threshold = 0.45

# 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

#!/usr/bin/env python

# -*- coding: utf-8 -*-

import _flowunit as modelbox

import numpy as np

import json

import cv2

class yolov7_postFlowUnit(modelbox.FlowUnit):

# Derived from modelbox.FlowUnit

def __init__(self):

super().__init__()

# Open the flowunit to obtain configuration information

def open(self, config):

# 获取功能单元的配置参数

self.params = {}

self.params['net_h'] = config.get_int('net_h')

self.params['net_w'] = config.get_int('net_w')

self.params['num_classes'] = config.get_int('num_classes')

self.params['conf_thre'] = config.get_float('conf_threshold')

self.params['nms_thre'] = config.get_float('iou_threshold')

self.num_classes = config.get_int('num_classes')

return modelbox.Status.StatusCode.STATUS_SUCCESS

# Process the data

def process(self, data_context):

# 从DataContext中获取输入输出BufferList对象

in_feat = data_context.input("in_feat")

out_data = data_context.output("out_data")

# yolov7_post process code.

# 循环处理每一个输入Buffer数据

for buffer_feat in in_feat:

# 将输入Buffer转换为numpy对象

feat_data = np.array(buffer_feat.as_object(), copy=False)

feat_data = feat_data.reshape((-1, self.num_classes + 5))

# 业务处理：解码yolov7模型的输出数据，得到检测框，转化为json数据

bboxes = self.postprocess(feat_data, self.params)

result = {"det_result": str(bboxes)}

print(result)

# 将业务处理返回的结果数据转换为Buffer

result_str = json.dumps(result)

out_buffer = modelbox.Buffer(self.get_bind_device(), result_str)

# 将输出Buffer放入输出BufferList中

out_data.push_back(out_buffer)

return modelbox.Status.StatusCode.STATUS_SUCCESS

# model post-processing function

def postprocess(self, feat_data, params):

"""postprocess for yolo7 model"""

boxes = []

class_ids = []

confidences = []

for detection in feat_data:

scores = detection[5:]

class_id = np.argmax(scores)

if params['num_classes'] == 1:

confidence = detection[4]

else:

confidence = detection[4] * scores[class_id]

if confidence > params['conf_thre'] and detection[4] > params['conf_thre']:

center_x = detection[0] / params['net_w']

center_y = detection[1] / params['net_h']

width = detection[2] / params['net_w']

height = detection[3] / params['net_h']

left = center_x - width / 2

top = center_y - height / 2

class_ids.append(class_id)

confidences.append(confidence)

boxes.append([left, top, width, height])

# use nms algorithm in opencv

box_idx = cv2.dnn.NMSBoxes(

boxes, confidences, params['conf_thre'], params['nms_thre'])

detections = []

for i in box_idx:

boxes[i][0] = max(0.0, boxes[i][0]) # [0, 1]

boxes[i][1] = max(0.0, boxes[i][1]) # [0, 1]

boxes[i][2] = min(1.0, boxes[i][0] + boxes[i][2]) # [0, 1]

boxes[i][3] = min(1.0, boxes[i][1] + boxes[i][3]) # [0, 1]

dets = np.concatenate(

[boxes[i], np.array([confidences[i]]), np.array([class_ids[i]])], 0).tolist()

detections.append(dets)

return detections

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

yolov7_pet工程graph目录下存放流程图，默认的流程图yolov7_pet.toml与工程同名，其内容为（以Windows版ModelBox为例）：

[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 yolov7_pet {

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/yolov7_pet", 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=640, image_height=640]

image_transpose[type=flowunit, flowunit=packed_planar_transpose, device=cpu, deviceid=0]

normalize[type=flowunit flowunit=normalize device=cpu deviceid=0 standard_deviation_inverse="0.0039215686,0.0039215686,0.0039215686"]

yolov7_infer[type=flowunit, flowunit=yolov7_infer, device=cpu, deviceid=0, batch_size = 1]

yolov7_post[type=flowunit, flowunit=yolov7_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 -> image_transpose:in_image

image_transpose:out_image -> normalize:in_data

normalize:out_data -> yolov7_infer:Input

yolov7_infer:Output -> yolov7_post:in_feat

yolov7_post:out_data -> httpserver_sync_reply:in_reply_info

}"""

[flow]

desc = "yolov7_pet run in modelbox-win10-x64"

5. 准备动物图片和测试脚本

a. 动物图片

yolov7_pet工程data目录下存放动物图片文件夹test_imgs

b. 测试脚本

yolov7_pet工程data目录下存放测试脚本test_http.py

#!/usr/bin/env python

# -*- coding: utf-8 -*-

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].tostring()

img_bin = base64.b64encode(img_str)

img_base64_str = str(img_bin, encoding='utf8')

return img_data, img_base64_str

def decode_car_bboxes(bbox_str, input_shape):

try:

labels = [0, 1] # cat, dog

bboxes = json.loads(json.loads(bbox_str)['det_result'])

bboxes = list(filter(lambda x: int(x[5]) in labels, bboxes))

except Exception as ex:

print(str(ex))

return []

else:

for bbox in bboxes:

bbox[0] = int(bbox[0] * input_shape[1])

bbox[1] = int(bbox[1] * input_shape[0])

bbox[2] = int(bbox[2] * input_shape[1])

bbox[3] = int(bbox[3] * input_shape[0])

return bboxes

def draw_bboxes(img_data, bboxes):

'''画框'''

for bbox in bboxes:

x1, y1, x2, y2, score, label = bbox

color = (0, 0, 255)

names = ['cat', 'dog']

score = '%.2f' % score

label = '%s:%s' % (names[int(label)], score)

cv2.rectangle(img_data, (x1, y1), (x2, y2), color, 2)

cv2.putText(img_data, label, (x1, y1 - 10), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (0, 255, 0), thickness=1)

return img_data

def test_image(img_path, ip, port, url):

'''单张图片测试'''

img_data, 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)

bboxes = decode_car_bboxes(response, img_data.shape)

imt_out = draw_bboxes(img_data, bboxes)

cv2.imwrite('./result-' + os.path.basename(img_path), imt_out)

if __name__ == "__main__":

port = 8083

ip = "127.0.0.1"

url = "/v1/yolov7_pet"

img_path = "./test.jpg"

img_folder = './test_imgs'

file_list = os.listdir(img_folder)

for img_file in file_list:

print("\n================ {} ================".format(img_file))

img_path = os.path.join(img_folder, img_file)

test_image(img_path, ip, port, url)

三、运行应用

在yolov7_pet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\yolov7_pet

(tensorflow) PS D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet> .\bin\main.bat

(tensorflow) D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet>set PATH=D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../../../python-embed;D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../../../modelbox-win10-x64/bin;D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/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\yolov7_pet>modelbox.exe -c D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../graph/modelbox.conf

[2024-06-10 06:42:50,922][ 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/yolov7_pet/bin/../hilens_data_dir/log/modelbox.log failed, No error

input dims is:1,3,640,640,

output dims is:1,25200,7,

HTTP服务启动后可以在另一个终端进行请求测试，进入yolov7_pet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\yolov7_pet\data

(tensorflow) PS D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet\data> python .\test_http.py

================ Abyssinian_1.jpg ================

.\test_http.py:33: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.

img_str = cv2.imencode('.jpg', img_data)[1].tostring()

response: {"det_result": "[[0.554308044910431, 0.1864600658416748, 0.7089953303337098, 0.3776256084442139, 0.82369065284729, 0.0]]"}

================ saint_bernard_143.jpg ================

response: {"det_result": "[[0.46182055473327643, 0.30239262580871584, 0.8193012714385988, 0.4969032764434815, 0.7603430151939392, 1.0]]"}

屏幕截图 2024-06-10 064427.png

四、小结

本章我们介绍了如何使用ModelBox开发一个动物目标检测的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图片标注、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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