养猪大户必备！教你用ModelBox开发一个AI数猪应用

华为云开发者社区 2024-06-20 14:13:00 阅读 76

本章主要介绍了如何使用ModelBox开发一个AI智能数猪的原创应用，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。

本文分享自华为云社区《ModelBox-视频应用开发：AI智能数猪【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【ModelArts+ModelBox 端云协同AI应用开发实训课程】

二、应用开发

1. 创建工程

在ModelBoxsdk目录下使用create.bat创建yolov7_pig工程：

(tensorflow) PS D:\modelbox-win10-x64-1.5.3> .\create.bat -t server -n yolov7_pig

(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_pig

sdk version is modelbox-win10-x64-1.5.3

dos2unix: converting file D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pig/graph\modelbox.conf to Unix format...

dos2unix: converting file D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pig/graph\yolov7_pig.toml to Unix format...

dos2unix: converting file D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pig/bin\mock_task.toml to Unix format...

success: create yolov7_pig 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_pig

(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_pig

sdk version is modelbox-win10-x64-1.5.3

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_pig表示所创建的功能单元属于yolov7_pig应用。

a. 下载转换好的模型

运行Notebook下载转换好的ONNX格式模型

b. 修改模型配置文件

屏幕截图 2024-06-18 175317.png

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_pig

(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_pig

sdk version is modelbox-win10-x64-1.5.3

success: create python yolov7_post in D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pig/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 = 1

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)}

# 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']:

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. 创建绘图功能单元

在ModelBoxsdk目录下使用create.bat创建draw_pig_bbox绘图功能单元：

a. 修改配置文件

# Basic config

[base]

name = "draw_pig_bbox" # 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 = "draw_pig_bbox@draw_pig_bboxFlowUnit" # 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]

item = "value"

# Input ports description

[input]

[input.input1] # Input port number, the format is input.input[N]

name = "in_image" # Input port name

type = "uint8" # Input port type

[input.input2] # Input port number, the format is input.input[N]

name = "in_box" # Input port name

type = "string" # Input port type

# Output ports description

[output]

[output.output1] # Output port number, the format is output.output[N]

name = "out_image" # Output port name

type = "uint8" # 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 draw_pig_bboxFlowUnit(modelbox.FlowUnit):

# Derived from modelbox.FlowUnit

def __init__(self):

super().__init__()

def open(self, config):

# Open the flowunit to obtain configuration information

return modelbox.Status.StatusCode.STATUS_SUCCESS

def process(self, data_context):

# Process the data

in_image = data_context.input("in_image")

in_box = data_context.input("in_box")

out_image = data_context.output("out_image")

# draw_image process code.

# Remove the following code and add your own code here.

for buffer_img, buffer_box in zip(in_image, in_box):

width = buffer_img.get("width")

height = buffer_img.get("height")

channel = buffer_img.get("channel")

img_data = np.array(buffer_img.as_object(), copy=False)

img_data = img_data.reshape((height, width, channel))

bbox_str = buffer_box.as_object()

bboxes = self.decode_car_bboxes(bbox_str, (height, width))

img_out = self.draw_bboxes(img_data, bboxes)

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

out_buffer.copy_meta(buffer_img)

out_image.push_back(out_buffer)

return modelbox.Status.StatusCode.STATUS_SUCCESS

def decode_car_bboxes(self, bbox_str, input_shape):

try:

labels = [0] # pig

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(self, img_data, bboxes):

'''画框'''

count = len(bboxes)

cv2.putText(img_data, 'pig_count: '+str(count), (20, 40), cv2.FONT_HERSHEY_TRIPLEX, 1.5, (255, 0, 0), thickness=2)

for bbox in bboxes:

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

color = (0, 0, 255)

names = ['pig']

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 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()

5. 修改流程图

yolov7_pig工程graph目录下存放流程图，默认的流程图yolov7_pig.toml与工程同名，其内容为（以Windows版ModelBox为例）：

屏幕截图 2024-06-18 193941.png

[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_pig {

node [shape=Mrecord]

queue_size = 4

batch_size = 1

input1[type=input,flowunit=input,device=cpu,deviceid=0]

data_source_parser[type=flowunit, flowunit=data_source_parser, device=cpu, deviceid=0]

video_demuxer[type=flowunit, flowunit=video_demuxer, device=cpu, deviceid=0]

video_decoder[type=flowunit, flowunit=video_decoder, device=cpu, deviceid=0, pix_fmt=rgb]

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]

draw_pig_bbox[type=flowunit, flowunit=draw_pig_bbox, device=cpu, deviceid=0]

video_out[type=flowunit, flowunit=video_out, device=cpu, deviceid=0]

input1:input -> data_source_parser:in_data

data_source_parser:out_video_url -> video_demuxer:in_video_url

video_demuxer:out_video_packet -> video_decoder:in_video_packet

video_decoder:out_video_frame -> 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

video_decoder:out_video_frame -> draw_pig_bbox:in_image

yolov7_post:out_data -> draw_pig_bbox:in_box

draw_pig_bbox:out_image -> video_out:in_video_frame

}"""

[flow]

desc = "yolov7_pig run in modelbox-win10-x64"

yolov7_pig工程graph目录下存放流程图，新建流程图yolov7_pig_http.toml，其内容为（以Windows版ModelBox为例）：

屏幕截图 2024-06-18 194152.png

[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_pig {

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_pig", 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_pig run in modelbox-win10-x64"

三、运行应用

准备测试视频和测试图片，在yolov7_pet工程目录下修改bin/mock_task.toml配置文件：

# 用于本地mock文件读取任务，脚本中已经配置了IVA_SVC_CONFIG环境变量, 添加了此文件路径

########### 请确定使用linux的路径类型，比如在windows上要用 D:/xxx/xxx 不能用D:\xxx\xxx ###########

# 任务的参数为一个压缩并转义后的json字符串

# 直接写需要转义双引号，也可以用 content_file 添加一个json文件，如果content和content_file都存在content会被覆盖

# content_file支持绝对路径或者相对路径，不支持解析环境变量(包括${HILENS_APP_ROOT}、${HILENS_DATA_DIR}等)

[common]

content = "{\"param_str\":\"string param\",\"param_int\":10,\"param_float\":10.5}"

# 任务输入配置，mock模拟目前仅支持一路rtsp或者本地url, 当前支持以下几种输入方式：

# 1. rtsp摄像头或rtsp视频流：type="rtsp", url="rtsp://xxx.xxx" (type为rtsp的时候，支持视频中断自动重连)

# 2. 设备自带摄像头或者USB摄像头：type="url"，url="摄像头编号,比如 0 或者 1 等" (需配合local_camera功能单元使用)

# 3. 本地视频文件：type="url"，url="视频文件路径" (可以是相对路径 -- 相对这个mock_task.toml文件, 也支持从环境变量${HILENS_APP_ROOT}所在目录文件输入)

# 4. http服务：type="url", url="http://xxx.xxx"(指的是任务作为http服务启动，此处需填写对外暴露的http服务地址，需配合httpserver类的功能单元使用)

# 5. 支持多输入[input] [input1] [input2] ...，对应的输出为[output] [output1] [output2] ...，如果使用videoout功能单元输出，则输入和输出个数必须匹配，同时url不能重名

[input]

type = "url"

url = "${HILENS_APP_ROOT}/data/pig.mp4"

# 任务输出配置，当前支持以下几种输出方式：

# 1. rtsp视频流：type="local", url="rtsp://xxx.xxx"

# 2. 本地屏幕：type="local", url="0:xxx" (设备需要接显示器，系统需要安装桌面)

# 3. 本地视频文件：type="local"，url="视频文件路径" (可以是相对路径——相对这个mock_task.toml文件, 也支持输出到环境变量${HILENS_DATA_DIR}所在目录或子目录)

# 4. http服务：type="webhook", url="http://xxx.xxx" (指的是任务产生的数据上报给某个http服务，此处需填写上传的http服务地址)

[output]

type = "local"

# url = "0:pig_det"

url = "${HILENS_APP_ROOT}/hilens_data_dir/pig_detection_result.mp4"

在yolov7_pig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_pig

(tensorflow) PS D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pig> .\bin\main.bat

(tensorflow) D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pig>set PATH=D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pig/bin/../../../python-embed;D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pig/bin/../../../modelbox-win10-x64/bin;D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pig/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_pig>modelbox.exe -c D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pig/bin/../graph/modelbox.conf

[2024-06-18 19:16:51,441][ 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_pig/bin/../hilens_data_dir/log/modelbox.log failed, No error

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

output dims is:1,25200,6,

[h264_mf @ 0000000046bab040] MFT name: 'H264 Encoder MFT'

[2024-06-18 19:17:44,535][ WARN][ffmpeg_video_muxer.cc:78 ] Success: video stream has been written to D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pig/hilens_data_dir/pig_detection_result.mp4

[2024-06-18 19:17:44,788][ERROR][flow_scheduler.cc:438 ] the scheduler caught an error : Stop operation

Press any key to continue . . .

生成的视频保存在yolov7_pig工程目录下hilens_data_dir文件夹中：

屏幕截图 2024-06-18 191854.png

在yolov7_pig工程目录下执行.\bin\main.bat http开启HTTP服务：

(tensorflow) PS D:\modelbox-win10-x64-1.5.3> cd D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pig

(tensorflow) PS D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pig> .\bin\main.bat http

(tensorflow) D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pig>modelbox.exe -c D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pig/bin/../graph/modelbox.conf

[2024-06-18 19:23:53,655][ 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_pig/bin/../hilens_data_dir/log/modelbox.log failed, No error

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

output dims is:1,25200,6,

HTTP服务启动后可以在另一个终端进行请求测试，进入yolov7_pig工程目录data文件夹中创建test_http.py脚本然后发起HTTP请求进行测试：

#!/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):

'''绘制检测框'''

count = len(bboxes)

cv2.putText(img_data, 'pig_count: '+str(count), (20, 40), cv2.FONT_HERSHEY_TRIPLEX, 1.5, (255, 0, 0), thickness=2)

for bbox in bboxes:

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

color = (0, 0, 255)

names = ['pig']

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, count

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, count = draw_bboxes(img_data, bboxes)

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

return count

if __name__ == "__main__":

port = 8083

ip = "127.0.0.1"

url = "/v1/yolov7_pig"

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)

count = test_image(img_path, ip, port, url)

print("================ pig_count: {} ================".format(count))

(tensorflow) PS D:\modelbox-win10-x64-1.5.3> cd D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pig\data

(tensorflow) PS D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pig\data> D:\modelbox-win10-x64-1.5.3\python-embed\python.exe .\test_http.py

================ 20190515142012.jpg ================

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

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

response: {"det_result": "[[0.5314995586872101, 0.41453492641448975, 0.6529222667217255, 0.6227057695388794, 0.9563100934028625, 0.0], [0.5491283953189849, 0.6111114203929902, 0.6829706609249114, 0.7607279121875763, 0.9505243301391602, 0.0], [0.7092265635728836, 0.32529172897338865, 0.7929001301527023, 0.5090352058410644, 0.9247245192527771, 0.0], [0.7478243410587311, 0.24942492246627807, 0.8540475308895111, 0.3306758046150208, 0.8886529207229614, 0.0], [0.2863448441028595, 0.7783691227436066, 0.40429784655570983, 0.9388670146465301, 0.8832182288169861, 0.0], [0.3198540091514588, 0.7068635761737824, 0.469990885257721, 0.8155526340007783, 0.8750752806663513, 0.0], [0.8573761910200118, 0.1112288475036621, 0.9163458853960037, 0.24985072612762452, 0.8558010458946228, 0.0], [0.19583110213279725, 0.4393650829792023, 0.3086510717868805, 0.5697859942913055, 0.8494003415107727, 0.0], [0.6343106091022491, 0.031079763174057015, 0.7757764041423798, 0.11533512473106385, 0.8335027098655701, 0.0], [0.32979671955108647, 0.24954813718795776, 0.41185748577117925, 0.4911258816719055, 0.8318935036659241, 0.0], [0.42157947421073916, 0.6686352252960205, 0.5486672222614288, 0.8406521320343017, 0.8014721274375916, 0.0], [0.036364993453025816, 0.3792011857032776, 0.11506653130054473, 0.5173661589622498, 0.7864469289779663, 0.0], [0.18477005362510682, 0.7536652505397796, 0.2830569803714752, 0.9396657049655914, 0.7493076324462891, 0.0], [0.7877487927675247, 0.0820700079202652, 0.8679187029600144, 0.16506505906581878, 0.7153295278549194, 0.0], [0.8873884886503219, 0.4463221430778504, 0.968685844540596, 0.6051429867744447, 0.6919704675674438, 0.0], [0.08014355301856996, 0.73122136592865, 0.14829849600791933, 0.8797563791275025, 0.6634459495544434, 0.0], [0.2530277907848358, 0.7759395956993104, 0.31589220166206355, 0.9465934395790101, 0.6256685853004456, 0.0], [0.059241372346878055, 0.7150145590305328, 0.1196309506893158, 0.8592026650905609, 0.6242099404335022, 0.0], [0.5020054757595062, 0.7558817148208619, 0.6267922461032867, 0.9292884588241578, 0.6164543032646179, 0.0], [0.12215411663055419, 0.6416930139064789, 0.2372045278549194, 0.7511061728000641, 0.5967950224876404, 0.0], [0.5455536484718323, 0.7130935192108154, 0.6122338652610779, 0.8517275333404541, 0.5695550441741943, 0.0]]"}

================ pig_count: 21 ================

================ 20190515142128.jpg ================

response: {"det_result": "[[0.2530054211616516, 0.3259969651699066, 0.37694138288497925, 0.5073411047458649, 0.9552642107009888, 0.0], [0.7655192792415619, 0.37133423089981077, 0.8948265612125397, 0.5411525130271911, 0.946380078792572, 0.0], [0.7068361639976501, 0.6438457131385803, 0.8532623887062073, 0.7564051985740662, 0.9442737698554993, 0.0], [0.4689387619495392, 0.6953599870204925, 0.6093841254711152, 0.8904086172580719, 0.9394132494926453, 0.0], [0.5610995948314667, 0.41046096086502076, 0.7043379127979279, 0.5980172038078309, 0.9185187816619873, 0.0], [0.1679925262928009, 0.7986599743366241, 0.27840816378593447, 0.9098593890666961, 0.9116157293319702, 0.0], [0.6167296946048737, 0.47176125049591067, 0.7882755696773529, 0.5939736127853393, 0.9113425016403198, 0.0], [0.8483672857284545, 0.558952933549881, 0.9509815454483032, 0.6832078635692597, 0.9013326168060303, 0.0], [0.6976672232151032, 0.05890357494354248, 0.7643234193325044, 0.14790127277374268, 0.8907486200332642, 0.0], [0.3439214825630188, 0.764359337091446, 0.48462504148483276, 0.9410788953304292, 0.8897976875305176, 0.0], [0.9087600618600845, 0.41939428448677063, 0.9657777875661849, 0.5442929446697236, 0.8668227791786194, 0.0], [0.5307900249958039, 0.013786908984184262, 0.6927152812480927, 0.10742981731891632, 0.8617266416549683, 0.0], [0.06923194527626038, 0.2071500480175018, 0.1422653615474701, 0.354218989610672, 0.8515089154243469, 0.0], [0.46218110322952266, 0.543884563446045, 0.5642802119255066, 0.7718154907226563, 0.8315367102622986, 0.0], [0.12014133036136627, 0.7533746421337127, 0.20710271298885347, 0.8859859764575958, 0.8161348700523376, 0.0], [0.6099689096212387, 0.6311885595321655, 0.6959998518228531, 0.8344843149185179, 0.8158949613571167, 0.0], [0.47475093603134155, 0.15836940407752992, 0.5509012818336487, 0.3299554765224457, 0.7964221239089966, 0.0], [0.7360249996185303, 0.20219690799713136, 0.8462663173675538, 0.2739208459854126, 0.7939417958259583, 0.0], [0.8505313932895661, 0.15010038614273072, 0.905955308675766, 0.22253469228744507, 0.766402542591095, 0.0], [0.8721131294965744, 0.3546452701091766, 0.9455597907304764, 0.4159835159778595, 0.7579628825187683, 0.0], [0.8202457576990128, 0.07152213156223297, 0.8842441409826278, 0.16832662522792816, 0.7537699937820435, 0.0], [0.4672187626361847, 0.8035521984100341, 0.6121010005474091, 0.9495066165924071, 0.7330396771430969, 0.0], [0.7931241631507874, 0.07484636306762696, 0.8552763342857361, 0.2041161298751831, 0.7252987623214722, 0.0], [0.05245172083377838, 0.34656997919082644, 0.11882493197917937, 0.4487423300743103, 0.711035966873169, 0.0], [0.039842239022254936, 0.6570722877979278, 0.10100120007991789, 0.7980956733226776, 0.7003825902938843, 0.0], [0.07937039136886598, 0.6993482947349549, 0.14970344305038452, 0.808335554599762, 0.6464911699295044, 0.0], [0.0408034861087799, 0.24992489218711852, 0.08359875082969664, 0.36381874680519105, 0.6194641590118408, 0.0]]"}

================ pig_count: 27 ================

================ 20190515143224.jpg ================

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四、本章小结

本章我们介绍了如何使用ModelBox开发一个AI智能数猪的原创应用，我们只需要在之前的基础上重新训练一个猪只目标检测模型进行替换，之后修改配置文件和工程的流程图即可进行视频推理。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。

点击关注，第一时间了解华为云新鲜技术~

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下一篇：探索AI工具的巅峰：个人体验与深度剖析

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