養豬大戶必備！教你用ModelBox開發一個AI數豬應用

本文分享自華為雲社群《ModelBox-影片應用開發：AI智慧數豬【玩轉華為雲】》，作者： 陽光大貓。

一、準備環境

ModelBox端雲協同AI開發套件（Windows）環境準備【ModelArts+ModelBox 端雲協同AI應用開發實訓課程】

二、應用開發

1. 建立工程

在ModelBox sdk目錄下使用create.bat建立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工具的引數中，-t表示所建立例項的型別，包括server（ModelBox工程）、python（Python功能單元）、c++（C++功能單元）、infer（推理功能單元）等；-n表示所建立例項的名稱，開發者自行命名。

2. 建立推理功能單元

在ModelBox sdk目錄下使用create.bat建立yolov7_infer推理功能單元：

(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 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 yolov7_infer -p yolov7_pig
sdk version is modelbox-win10-x64-1.5.3

create.bat工具使用時，-t infer 即表示建立的是推理功能單元；-n xxx_infer 表示建立的功能單元名稱為xxx_infer；-p yolov7_pig 表示所建立的功能單元屬於yolov7_pig應用。

a. 下載轉換好的模型

執行Notebook下載轉換好的ONNX格式模型

b. 修改模型配置檔案

模型和配置檔案保持在同級目錄下

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

[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. 建立後處理功能單元

在ModelBox sdk目錄下使用create.bat建立yolov7_post後處理功能單元

(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 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 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. 修改配置檔案

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

# 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. 修改邏輯程式碼

# 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
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):
        # 獲取功能單元的配置引數
        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):
        # 從DataContext中獲取輸入輸出BufferList物件
        in_feat = data_context.input("in_feat")
        out_data = data_context.output("out_data")

        # yolov7_post process code.
        # 迴圈處理每一個輸入Buffer資料
        for buffer_feat in in_feat:
            # 將輸入Buffer轉換為numpy物件
            feat_data = np.array(buffer_feat.as_object(), copy=False)
            feat_data = feat_data.reshape((-1, self.num_classes + 5))

            # 業務處理：解碼yolov7模型的輸出資料，得到檢測框，轉化為json資料
            bboxes = self.postprocess(feat_data, self.params)
            result = {"det_result": str(bboxes)}

            # 將業務處理返回的結果資料轉換為Buffer
            result_str = json.dumps(result)
            out_buffer = modelbox.Buffer(self.get_bind_device(), result_str)

            # 將輸出Buffer放入輸出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. 建立繪圖功能單元

在ModelBox sdk目錄下使用create.bat建立draw_pig_bbox繪圖功能單元：

a. 修改配置檔案

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

# 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. 修改邏輯程式碼

# 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
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):
        '''畫框'''
        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. 修改流程圖

yolov7_pig工程graph目錄下存放流程圖，預設的流程圖yolov7_pig.toml與工程同名，其內容為（以Windows版ModelBox為例）：

# 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 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目錄下存放流程圖，新建流程圖yolov7_pig_http.toml，其內容為（以Windows版ModelBox為例）：

# 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 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、執行應用

準備測試影片和測試圖片，在yolov7_pet工程目錄下修改bin/mock_task.toml配置檔案：

# 用於本地mock檔案讀取任務，指令碼中已經配置了IVA_SVC_CONFIG環境變數, 新增了此檔案路徑
########### 請確定使用linux的路徑型別，比如在windows上要用  D:/xxx/xxx  不能用D:\xxx\xxx  ###########
# 任務的引數為一個壓縮並轉義後的json字串
# 直接寫需要轉義雙引號， 也可以用 content_file 新增一個json檔案，如果content和content_file都存在content會被覆蓋
# content_file支援絕對路徑或者相對路徑，不支援解析環境變數(包括${HILENS_APP_ROOT}、${HILENS_DATA_DIR}等)
[common]
content = "{\"param_str\":\"string param\",\"param_int\":10,\"param_float\":10.5}"

# 任務輸入配置，mock模擬目前僅支援一路rtsp或者本地url, 當前支援以下幾種輸入方式：
# 1. rtsp攝像頭或rtsp影片流：type="rtsp", url="rtsp://xxx.xxx"  (type為rtsp的時候，支援影片中斷自動重連)
# 2. 裝置自帶攝像頭或者USB攝像頭：type="url"，url="攝像頭編號,比如 0 或者 1 等" (需配合local_camera功能單元使用)
# 3. 本地影片檔案：type="url"，url="影片檔案路徑" (可以是相對路徑 -- 相對這個mock_task.toml檔案, 也支援從環境變數${HILENS_APP_ROOT}所在目錄檔案輸入)
# 4. http服務：type="url", url="http://xxx.xxx"(指的是任務作為http服務啟動，此處需填寫對外暴露的http服務地址，需配合httpserver類的功能單元使用)
# 5. 支援多輸入[input] [input1] [input2] ...，對應的輸出為[output] [output1] [output2] ...，如果使用videoout功能單元輸出，則輸入和輸出個數必須匹配，同時url不能重名
[input]
type = "url"
url = "${HILENS_APP_ROOT}/data/pig.mp4"

# 任務輸出配置，當前支援以下幾種輸出方式：
# 1. rtsp影片流：type="local", url="rtsp://xxx.xxx" 
# 2. 本地螢幕：type="local", url="0:xxx" (裝置需要接顯示器，系統需要安裝桌面)
# 3. 本地影片檔案：type="local"，url="影片檔案路徑" (可以是相對路徑——相對這個mock_task.toml檔案, 也支援輸出到環境變數${HILENS_DATA_DIR}所在目錄或子目錄)
# 4. http服務：type="webhook", url="http://xxx.xxx" (指的是任務產生的資料上報給某個http服務，此處需填寫上傳的http服務地址)
[output]
type = "local"
# url = "0:pig_det"  
url = "${HILENS_APP_ROOT}/hilens_data_dir/pig_detection_result.mp4"

在yolov7_pig工程目錄下執行.\bin\main.bat執行應用：

(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 . . . 

生成的影片儲存在yolov7_pig工程目錄下hilens_data_dir資料夾中：

在yolov7_pig工程目錄下執行.\bin\main.bat http開啟HTTP服務：

(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>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 19:23:53,655][ 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服務啟動後可以在另一個終端進行請求測試，進入yolov7_pig工程目錄data資料夾中建立test_http.py指令碼然後發起HTTP請求進行測試：

#!/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呼叫的引數配置'''
    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):
    '''讀取圖片資料並轉為base64編碼的字串'''
    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):
    '''繪製檢測框'''
    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):
    '''單張圖片測試'''
    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 ================
response:  {"det_result": "[[0.33151587247848513, 0.7472196042537689, 0.4520726799964905, 0.944657951593399, 0.9549262523651123, 0.0], [0.6966262102127075, 0.38143407106399535, 0.8187609434127808, 0.5275840640068055, 0.9383824467658997, 0.0], [0.3309335768222809, 0.38504688739776616, 0.5029547631740571, 0.5163029432296753, 0.9308131337165833, 0.0], [0.22159602940082548, 0.2502309620380402, 0.2970270365476608, 0.4074728190898895, 0.9295130372047424, 0.0], [0.52587131857872, 0.6116060733795166, 0.6969760477542877, 0.7392594814300537, 0.9274167418479919, 0.0], [0.8019156157970428, 0.5839392662048339, 0.9134944260120392, 0.6862605094909667, 0.9207823276519775, 0.0], [0.22548656463623046, 0.7824307978153229, 0.3473879337310791, 0.9226225316524506, 0.9075026512145996, 0.0], [0.7616031408309937, 0.23449447751045227, 0.8773191690444947, 0.30470640063285825, 0.8723774552345276, 0.0], [0.47462679147720344, 0.7428544700145722, 0.659464204311371, 0.8985731422901153, 0.8692379593849182, 0.0], [0.7726404637098313, 0.047815185785293576, 0.8582356959581375, 0.16538638472557066, 0.857752799987793, 0.0], [0.04095276296138763, 0.35726237297058105, 0.09002236425876617, 0.4370449066162109, 0.8512980937957764, 0.0], [0.5121502339839935, 0.16153932213783262, 0.5878590166568756, 0.35389813780784607, 0.8481522798538208, 0.0], [0.6813990414142609, 0.5552682638168336, 0.8241379916667939, 0.7315007448196412, 0.8349569439888, 0.0], [0.7848798453807831, 0.2997883439064026, 0.8487448036670685, 0.4219695448875427, 0.8328686952590942, 0.0], [0.675277727842331, 0.5038471221923828, 0.788842660188675, 0.5971584320068359, 0.8305914998054504, 0.0], [0.36567584276199344, 0.1846299886703491, 0.4232009768486023, 0.32693817615509035, 0.8237928748130798, 0.0], [0.8332049459218979, 0.15594483613967897, 0.9077650934457778, 0.21918100118637085, 0.8095088005065918, 0.0], [0.8188231199979782, 0.5099769771099091, 0.9065980225801468, 0.5991565525531769, 0.789814293384552, 0.0], [0.16333829164505004, 0.7263919234275817, 0.23472834825515745, 0.8557828545570373, 0.77839595079422, 0.0], [0.09923059344291688, 0.5434492200613021, 0.2318732798099518, 0.6342397600412368, 0.7709190249443054, 0.0], [0.04113772809505463, 0.6769565641880035, 0.09518382847309112, 0.7874909341335297, 0.7675528526306152, 0.0], [0.12286571264266968, 0.7784929722547531, 0.18941720724105837, 0.8611520320177078, 0.7538224458694458, 0.0], [0.8486063361167908, 0.1046980857849121, 0.8987706780433655, 0.17449078559875486, 0.6860058307647705, 0.0], [0.8597557455301285, 0.3714954495429993, 0.9395408242940902, 0.5046039462089539, 0.6594332456588745, 0.0], [0.725972306728363, 0.2879889786243439, 0.8093014359474182, 0.4364859759807587, 0.6504802703857422, 0.0], [0.80337233543396, 0.43979210257530216, 0.9095016956329346, 0.5416744768619538, 0.5330303907394409, 0.0], [0.0, 0.44415642619132994, 0.03889097571372986, 0.5607487976551055, 0.5000762939453125, 0.0]]"}
================ pig_count: 27 ================

================ 20190515143432.jpg ================
response:  {"det_result": "[[0.6373811900615692, 0.4556077837944031, 0.7505947887897492, 0.6163338780403137, 0.958651065826416, 0.0], [0.7536835551261902, 0.19262979030609134, 0.853804886341095, 0.3675990819931031, 0.9556296467781067, 0.0], [0.24914194345474241, 0.1605188548564911, 0.37957080602645876, 0.2712996304035187, 0.9410728812217712, 0.0], [0.5784417808055878, 0.6893238723278046, 0.7007974922657013, 0.8144921600818634, 0.9311926364898682, 0.0], [0.4877449214458466, 0.838113933801651, 0.6275949299335479, 0.9414070546627045, 0.9248373508453369, 0.0], [0.7326700747013092, 0.5087914645671845, 0.8424749791622161, 0.6524371922016144, 0.9235878586769104, 0.0], [0.6557062178850174, 0.04749650359153747, 0.7422844856977463, 0.22362623810768129, 0.8662278056144714, 0.0], [0.7520786702632904, 0.37903311252593996, 0.8365497171878815, 0.5275002241134643, 0.8632115721702576, 0.0], [0.4560465335845947, 0.7834814339876175, 0.5788590908050537, 0.875244876742363, 0.8591019511222839, 0.0], [0.8486663192510605, 0.24308437108993527, 0.9209946304559707, 0.38057256937026973, 0.8481018543243408, 0.0], [0.06989501118659974, 0.21171223521232607, 0.14719852805137634, 0.2901284992694855, 0.8403106927871704, 0.0], [0.07931529581546784, 0.7274754464626313, 0.14586327970027924, 0.8284106314182282, 0.8276875019073486, 0.0], [0.8199215471744538, 0.5190264046192169, 0.916180557012558, 0.601987999677658, 0.8218949437141418, 0.0], [0.7753406018018723, 0.6247795641422271, 0.869887974858284, 0.7028177678585052, 0.8103967905044556, 0.0], [0.5350410103797912, 0.2904348909854889, 0.7034945845603943, 0.47202824950218203, 0.7742581367492676, 0.0], [0.7052563428878784, 0.594559383392334, 0.7930361032485962, 0.802627182006836, 0.7661027312278748, 0.0], [0.2078638195991516, 0.836474758386612, 0.3327731490135193, 0.9146723210811616, 0.7656165361404419, 0.0], [0.35651546716690063, 0.8356708973646164, 0.48673042058944704, 0.9123004466295243, 0.7651214599609375, 0.0], [0.04286686778068543, 0.651385509967804, 0.1189558446407318, 0.7819379687309265, 0.7318003177642822, 0.0], [0.16353363096714021, 0.7001014947891235, 0.23111434876918796, 0.8483508825302124, 0.7219746112823486, 0.0], [0.8943167269229888, 0.465467381477356, 0.9480641782283782, 0.5696653127670288, 0.7132480144500732, 0.0], [0.13475828468799592, 0.7123962700366974, 0.19173202216625213, 0.8635941207408905, 0.6760420203208923, 0.0], [0.9031854838132858, 0.37944490909576417, 0.9443069249391556, 0.4960151433944702, 0.6697810292243958, 0.0], [0.7169572085142135, 0.051375272870063785, 0.7699337750673294, 0.11804214417934418, 0.5670255422592163, 0.0]]"}
================ pig_count: 24 ================

================ 20190621141141.jpg ================
response:  {"det_result": "[[0.2858048617839813, 0.6920524835586548, 0.40033505558967586, 0.876273512840271, 0.9693846702575684, 0.0], [0.8160727888345719, 0.3245592772960663, 0.9153503984212876, 0.44209216237068183, 0.9652171730995178, 0.0], [0.3276993572711945, 0.2825296759605408, 0.48299114108085633, 0.4778394341468811, 0.9459359049797058, 0.0], [0.7524076730012894, 0.6082898855209351, 0.8520412176847458, 0.7410279512405396, 0.9435095191001892, 0.0], [0.52833012342453, 0.6118248403072357, 0.7453569531440735, 0.7360025942325592, 0.9292019009590149, 0.0], [0.7283130764961242, 0.24003291130065915, 0.8490503191947937, 0.41690392494201656, 0.9274832010269165, 0.0], [0.5753984153270721, 0.7113316237926484, 0.7098467171192169, 0.8695607483386993, 0.9142341613769531, 0.0], [0.3426991164684296, 0.160414856672287, 0.502489310503006, 0.24934423565864564, 0.8892913460731506, 0.0], [0.21955735087394712, 0.14540990591049194, 0.34493497014045715, 0.25439044237136843, 0.8848840594291687, 0.0], [0.17622598409652712, 0.7975501120090485, 0.28576570749282837, 0.894774717092514, 0.8471149206161499, 0.0], [0.4498146414756775, 0.7951198756694794, 0.602517569065094, 0.8975848972797394, 0.8465115427970886, 0.0], [0.336152708530426, 0.7672465980052948, 0.4467101454734802, 0.9145124733448029, 0.8383660912513733, 0.0], [0.8808505088090897, 0.5050277709960938, 0.9662985771894455, 0.6021172523498535, 0.8287425637245178, 0.0], [0.5983243703842164, 0.05242392122745514, 0.7141552209854126, 0.10514067113399506, 0.8067843317985535, 0.0], [0.03119544088840484, 0.5027143061161041, 0.09562594592571258, 0.6602520406246185, 0.8055742383003235, 0.0], [0.3866620540618897, 0.8470147281885146, 0.5265138149261475, 0.9368914455175399, 0.8049367666244507, 0.0], [0.8214336901903153, 0.2318374812602997, 0.8985568493604661, 0.337394791841507, 0.7954773902893066, 0.0], [0.6170122742652893, 0.07954724133014679, 0.7388171553611755, 0.14763021171092988, 0.7868005633354187, 0.0], [0.7230192244052887, 0.07258228063583373, 0.8153064668178558, 0.15148414373397828, 0.7648471593856812, 0.0], [0.050417631864547736, 0.6464603543281555, 0.11656169295310975, 0.7629135966300964, 0.7563851475715637, 0.0], [0.8766493797302246, 0.25733983516693115, 0.929527759552002, 0.373953652381897, 0.731341540813446, 0.0], [0.8231545478105545, 0.10986250042915345, 0.8790049523115159, 0.19118310809135436, 0.7140998840332031, 0.0], [0.10614479780197145, 0.7547796964645386, 0.18728572130203247, 0.8611701250076294, 0.6874741911888123, 0.0], [0.9011492043733597, 0.40590643882751465, 0.9454408377408982, 0.5230727672576905, 0.6780690550804138, 0.0], [0.0784837692975998, 0.7619574457406998, 0.13632536232471468, 0.8540185064077378, 0.602765679359436, 0.0]]"}
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================ 20190621141249.jpg ================
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================ 20190621141553.jpg ================
response:  {"det_result": "[[0.25505531430244444, 0.1648145318031311, 0.36948043704032896, 0.319969117641449, 0.9663053750991821, 0.0], [0.6804072797298432, 0.6567524552345275, 0.7649277269840241, 0.8261979460716247, 0.9214552640914917, 0.0], [0.5162991523742676, 0.1550707459449768, 0.6786110877990723, 0.26980427503585813, 0.89951092004776, 0.0], [0.04029585719108582, 0.39804551005363464, 0.09393433928489686, 0.5120114624500275, 0.8869109749794006, 0.0], [0.2331909120082855, 0.7339452266693115, 0.35462484955787654, 0.8563205242156983, 0.8849216103553772, 0.0], [0.8875470131635665, 0.35947387814521786, 0.9421267539262771, 0.5058832347393035, 0.8830375671386719, 0.0], [0.35890615582466123, 0.8096627533435822, 0.5082674205303193, 0.9209826171398163, 0.8803096413612366, 0.0], [0.22905958890914918, 0.32565538287162776, 0.3996945500373841, 0.466394692659378, 0.8800082206726074, 0.0], [0.3235291659832001, 0.09731526970863341, 0.46806238293647767, 0.2023240029811859, 0.8659636378288269, 0.0], [0.7194619923830032, 0.07266746759414673, 0.8077177256345749, 0.12683988809585572, 0.8515225648880005, 0.0], [0.5173878550529479, 0.8031063377857208, 0.6822316288948058, 0.9041860282421113, 0.8419069051742554, 0.0], [0.7942464396357537, 0.09258331954479218, 0.8421867802739144, 0.17845857441425322, 0.8290656208992004, 0.0], [0.8780211091041564, 0.5133154094219208, 0.9525304198265075, 0.6248787701129913, 0.8264977335929871, 0.0], [0.1312229812145233, 0.23110288381576538, 0.26738985180854796, 0.32273465394973755, 0.8258971571922302, 0.0], [0.1273337364196777, 0.7012694180011749, 0.24159545898437498, 0.8553301990032196, 0.8090124130249023, 0.0], [0.869355183839798, 0.2267285704612732, 0.9228637874126435, 0.37382589578628544, 0.7690571546554565, 0.0], [0.1645243227481842, 0.16016942262649536, 0.23051308989524844, 0.2507018446922302, 0.7572027444839478, 0.0], [0.4552234292030334, 0.06282475888729096, 0.6467123389244078, 0.14914280474185942, 0.7095248699188232, 0.0], [0.6207995533943176, 0.07808120846748351, 0.7318315386772155, 0.17110306620597837, 0.6961165070533752, 0.0], [0.08310246467590332, 0.7593006700277328, 0.16655261516571046, 0.8486085325479508, 0.6946851015090942, 0.0], [0.0550129920244217, 0.6377000391483307, 0.10035936534404755, 0.7601681172847748, 0.6907160878181458, 0.0], [0.1823414504528046, 0.8208630412817001, 0.2753485023975373, 0.8927535206079482, 0.6773250102996826, 0.0], [0.03336356282234192, 0.33990943133831025, 0.09926139712333679, 0.4163350373506546, 0.6322934031486511, 0.0], [0.09137872457504273, 0.6876684784889221, 0.1576262593269348, 0.7882758498191833, 0.5370994806289673, 0.0]]"}
================ pig_count: 24 ================

================ 20190621141702.jpg ================
response:  {"det_result": "[[0.22350141406059268, 0.6762887954711914, 0.3731423199176789, 0.8184487342834472, 0.9548922181129456, 0.0], [0.3554376900196075, 0.8033478677272797, 0.5178024947643279, 0.916188532114029, 0.9342366456985474, 0.0], [0.41963348984718324, 0.10559765100479125, 0.5215966165065765, 0.31351484060287477, 0.9148365259170532, 0.0], [0.24928047060966493, 0.327079701423645, 0.40166056752204893, 0.4416670560836792, 0.8924790620803833, 0.0], [0.5149345755577087, 0.17052462100982668, 0.6779902100563049, 0.29205095767974854, 0.8909578323364258, 0.0], [0.5388258635997771, 0.7447358965873718, 0.6867108643054961, 0.8399172902107239, 0.8849926590919495, 0.0], [0.71777563393116, 0.06494015455245972, 0.7983715027570725, 0.12339491844177246, 0.8744017481803894, 0.0], [0.7954665750265122, 0.5785638570785522, 0.8826254278421403, 0.7036791086196899, 0.8737918138504028, 0.0], [0.6637972176074982, 0.6482859671115876, 0.8199613273143769, 0.7434307992458344, 0.8732413649559021, 0.0], [0.8661074936389923, 0.22600043416023252, 0.9228401839733124, 0.3657041251659393, 0.8627683520317078, 0.0], [0.7602462828159332, 0.11995237469673156, 0.8734313905239105, 0.23398701548576356, 0.8567670583724976, 0.0], [0.8956452071666717, 0.3657357215881347, 0.9450255692005157, 0.5135135650634766, 0.8445569276809692, 0.0], [0.14634484350681304, 0.7393070816993714, 0.23839896619319914, 0.8807906508445741, 0.8305044770240784, 0.0], [0.8794899225234986, 0.5026386678218842, 0.9294854879379273, 0.634317833185196, 0.8270094394683838, 0.0], [0.5092974722385407, 0.815280893445015, 0.6741485536098482, 0.9056802958250046, 0.8193835616111755, 0.0], [0.07616996467113496, 0.21829421520233155, 0.15875621140003204, 0.32190282344818116, 0.8052489757537842, 0.0], [0.6853458523750305, 0.07137364149093627, 0.7411078333854676, 0.2028606295585632, 0.7820446491241455, 0.0], [0.07674590349197388, 0.7687113344669342, 0.14894877672195433, 0.8493100583553314, 0.7341012954711914, 0.0], [0.039960877597332, 0.3126288890838623, 0.08451076298952104, 0.4022897243499756, 0.7196946144104004, 0.0], [0.06600799262523652, 0.6542474508285523, 0.16344936192035675, 0.8114694833755494, 0.6840709447860718, 0.0], [0.5587529957294464, 0.06180830597877503, 0.7073469340801238, 0.2052306354045868, 0.5386325716972351, 0.0], [0.6123762965202332, 0.05824828445911408, 0.7150348782539367, 0.12364704310894013, 0.5344389081001282, 0.0]]"}
================ pig_count: 22 ================

四、本章小結

本章我們介紹瞭如何使用ModelBox開發一個AI智慧數豬的原創應用，我們只需要在之前的基礎上重新訓練一個豬隻目標檢測模型進行替換，之後修改配置檔案和工程的流程圖即可進行影片推理。同時我們可以瞭解到圖片標註、資料處理和模型訓練方法，以及對應的推理應用邏輯。

點選關注，第一時間瞭解華為雲新鮮技術~