本文分享自华为云社区《ModelBox-AI应用开发：动物目标检测【玩转华为云】》，作者：阳光大猫。
一、准备环境
ModelBox端云协同AI开发套件（Windows）环境准备【视频教程】
二、应用开发
1. 创建工程
在ModelBox sdk目录下使用create.bat创建yolov7_pet工程
(tensorflow) PS D:modelbox-win10-x64-1.5.3> .create.bat -t server -n yolov7_pet (tensorflow) D:modelbox-win10-x64-1.5.3>set BASE_PATH=D:modelbox-win10-x64-1.5.3 (tensorflow) D:modelbox-win10-x64-1.5.3>set PATH=D:modelbox-win10-x64-1.5.3\python-embed;C:Usersyansominiconda3envstensorflowlibsite-packagespywin32_system32;C:Usersyansominiconda3envstensorflow;C:Usersyansominiconda3envstensorflowLibrarymingw-w64bin;C:Usersyansominiconda3envstensorflowLibraryusrbin;C:Usersyansominiconda3envstensorflowLibrarybin;C:Usersyansominiconda3envstensorflowScripts;C:Usersyansominiconda3envstensorflowbin;C:Usersyansominiconda3condabin;C:WindowsSystem32HWAudioDriverLibs;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:UsersAdministratorAppDataLocalMicrosoftWindowsApps;C:WINDOWSsystem32;C:WINDOWS;C:WINDOWSSystem32Wbem;C:WINDOWSSystem32WindowsPowerShellv1.0;C:WINDOWSSystem32OpenSSH;C:Program FilesGitcmd;C:Usersyansominiconda3;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3Librarybin;.;C:Program FilesGit LFS;C:UsersyansoAppDataLocalMicrosoftWindowsApps;.;C:UsersyansoAppDataLocalProgramsMicrosoft VS Codebin;C:Usersyansominiconda3envstensorflowlibsite-packagespywin32_system32;C:Usersyansominiconda3;C:Usersyansominiconda3Librarymingw-w64bin;C:Usersyansominiconda3Libraryusrbin;C:Usersyansominiconda3Librarybin;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3bin;C:Usersyansominiconda3condabin;C:WindowsSystem32HWAudioDriverLibs;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:UsersAdministratorAppDataLocalMicrosoftWindowsApps;C:WINDOWSsystem32;C:WINDOWS;C:WINDOWSSystem32Wbem;C:WINDOWSSystem32WindowsPowerShellv1.0;C:WINDOWSSystem32OpenSSH;C:Program FilesGitcmd;C:Usersyansominiconda3;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3Librarybin;.;C:Program FilesGit LFS;C:UsersyansoAppDataLocalMicrosoftWindowsApps;.;C:UsersyansoAppDataLocalProgramsMicrosoft VS Codebin;C:WindowsSystem32HWAudioDriverLibs;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:UsersAdministratorAppDataLocalMicrosoftWindowsApps;C:WINDOWSsystem32;C:WINDOWS;C:WINDOWSSystem32Wbem;C:WINDOWSSystem32WindowsPowerShellv1.0;C:WINDOWSSystem32OpenSSH;C:Program FilesGitcmd;C:Usersyansominiconda3;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3Librarybin;.;C:Program FilesGit LFS;C:UsersyansoAppDataLocalMicrosoftWindowsApps;.;C:UsersyansoAppDataLocalProgramsMicrosoft VS Codebin (tensorflow) D:modelbox-win10-x64-1.5.3>set PYTHONPATH= (tensorflow) D:modelbox-win10-x64-1.5.3>set PYTHONHOME= (tensorflow) D:modelbox-win10-x64-1.5.3>python.exe -u D:modelbox-win10-x64-1.5.3\create.py -t server -n yolov7_pet sdk version is modelbox-win10-x64-1.5.3 dos2unix: converting file D:modelbox-win10-x64-1.5.3workspaceyolov7_pet/graphmodelbox.conf to Unix format... dos2unix: converting file D:modelbox-win10-x64-1.5.3workspaceyolov7_pet/graphyolov7_pet.toml to Unix format... dos2unix: converting file D:modelbox-win10-x64-1.5.3workspaceyolov7_pet/binmock_task.toml to Unix format... success: create yolov7_pet in D:modelbox-win10-x64-1.5.3workspace
create.bat工具的参数中，-t表示所创建实例的类型，包括server（ModelBox工程）、python（Python功能单元）、c++（C++功能单元）、infer（推理功能单元）等；-n表示所创建实例的名称，开发者自行命名。
2. 创建推理功能单元
在ModelBox sdk目录下使用create.bat创建yolov7_infer推理功能单元
(tensorflow) PS D:modelbox-win10-x64-1.5.3> .create.bat -t infer -n yolov7_infer -p yolov7_pet (tensorflow) D:modelbox-win10-x64-1.5.3>set BASE_PATH=D:modelbox-win10-x64-1.5.3 (tensorflow) D:modelbox-win10-x64-1.5.3>set PATH=D:modelbox-win10-x64-1.5.3\python-embed;C:Usersyansominiconda3envstensorflowlibsite-packagespywin32_system32;C:Usersyansominiconda3envstensorflow;C:Usersyansominiconda3envstensorflowLibrarymingw-w64bin;C:Usersyansominiconda3envstensorflowLibraryusrbin;C:Usersyansominiconda3envstensorflowLibrarybin;C:Usersyansominiconda3envstensorflowScripts;C:Usersyansominiconda3envstensorflowbin;C:Usersyansominiconda3condabin;C:WindowsSystem32HWAudioDriverLibs;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:UsersAdministratorAppDataLocalMicrosoftWindowsApps;C:WINDOWSsystem32;C:WINDOWS;C:WINDOWSSystem32Wbem;C:WINDOWSSystem32WindowsPowerShellv1.0;C:WINDOWSSystem32OpenSSH;C:Program FilesGitcmd;C:Usersyansominiconda3;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3Librarybin;.;C:Program FilesGit LFS;C:UsersyansoAppDataLocalMicrosoftWindowsApps;.;C:UsersyansoAppDataLocalProgramsMicrosoft VS Codebin;C:Usersyansominiconda3envstensorflowlibsite-packagespywin32_system32;C:Usersyansominiconda3;C:Usersyansominiconda3Librarymingw-w64bin;C:Usersyansominiconda3Libraryusrbin;C:Usersyansominiconda3Librarybin;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3bin;C:Usersyansominiconda3condabin;C:WindowsSystem32HWAudioDriverLibs;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:UsersAdministratorAppDataLocalMicrosoftWindowsApps;C:WINDOWSsystem32;C:WINDOWS;C:WINDOWSSystem32Wbem;C:WINDOWSSystem32WindowsPowerShellv1.0;C:WINDOWSSystem32OpenSSH;C:Program FilesGitcmd;C:Usersyansominiconda3;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3Librarybin;.;C:Program FilesGit LFS;C:UsersyansoAppDataLocalMicrosoftWindowsApps;.;C:UsersyansoAppDataLocalProgramsMicrosoft VS Codebin;C:WindowsSystem32HWAudioDriverLibs;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:UsersAdministratorAppDataLocalMicrosoftWindowsApps;C:WINDOWSsystem32;C:WINDOWS;C:WINDOWSSystem32Wbem;C:WINDOWSSystem32WindowsPowerShellv1.0;C:WINDOWSSystem32OpenSSH;C:Program FilesGitcmd;C:Usersyansominiconda3;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3Librarybin;.;C:Program FilesGit LFS;C:UsersyansoAppDataLocalMicrosoftWindowsApps;.;C:UsersyansoAppDataLocalProgramsMicrosoft VS Codebin (tensorflow) D:modelbox-win10-x64-1.5.3>set PYTHONPATH= (tensorflow) D:modelbox-win10-x64-1.5.3>set PYTHONHOME= (tensorflow) D:modelbox-win10-x64-1.5.3>python.exe -u D:modelbox-win10-x64-1.5.3\create.py -t infer -n yolov7_infer -p yolov7_pet sdk version is modelbox-win10-x64-1.5.3 success: create infer yolov7_infer in D:modelbox-win10-x64-1.5.3workspaceyolov7_pet/model/yolov7_infer
create.bat工具使用时，-t infer 即表示创建的是推理功能单元；-n xxx_infer 表示创建的功能单元名称为xxx_infer；-p yolov7_infer 表示所创建的功能单元属于yolov7_infer应用。
a. 下载转换好的模型
运行此Notebook下载转换好的ONNX格式模型
b. 修改模型配置文件
模型和配置文件保持在同级目录下
# Copyright (C) 2020 Huawei Technologies Co., Ltd. All rights reserved. [base] name = "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ModelBox sdk目录下使用create.bat创建yolov7_post后处理功能单元
(tensorflow) PS D:modelbox-win10-x64-1.5.3> .create.bat -t python -n yolov7_post -p yolov7_pet (tensorflow) D:modelbox-win10-x64-1.5.3>set BASE_PATH=D:modelbox-win10-x64-1.5.3 (tensorflow) D:modelbox-win10-x64-1.5.3>set PATH=D:modelbox-win10-x64-1.5.3\python-embed;C:Usersyansominiconda3envstensorflowlibsite-packagespywin32_system32;C:Usersyansominiconda3envstensorflow;C:Usersyansominiconda3envstensorflowLibrarymingw-w64bin;C:Usersyansominiconda3envstensorflowLibraryusrbin;C:Usersyansominiconda3envstensorflowLibrarybin;C:Usersyansominiconda3envstensorflowScripts;C:Usersyansominiconda3envstensorflowbin;C:Usersyansominiconda3condabin;C:WindowsSystem32HWAudioDriverLibs;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:UsersAdministratorAppDataLocalMicrosoftWindowsApps;C:WINDOWSsystem32;C:WINDOWS;C:WINDOWSSystem32Wbem;C:WINDOWSSystem32WindowsPowerShellv1.0;C:WINDOWSSystem32OpenSSH;C:Program FilesGitcmd;C:Usersyansominiconda3;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3Librarybin;.;C:Program FilesGit LFS;C:UsersyansoAppDataLocalMicrosoftWindowsApps;.;C:UsersyansoAppDataLocalProgramsMicrosoft VS Codebin;C:Usersyansominiconda3envstensorflowlibsite-packagespywin32_system32;C:Usersyansominiconda3;C:Usersyansominiconda3Librarymingw-w64bin;C:Usersyansominiconda3Libraryusrbin;C:Usersyansominiconda3Librarybin;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3bin;C:Usersyansominiconda3condabin;C:WindowsSystem32HWAudioDriverLibs;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:UsersAdministratorAppDataLocalMicrosoftWindowsApps;C:WINDOWSsystem32;C:WINDOWS;C:WINDOWSSystem32Wbem;C:WINDOWSSystem32WindowsPowerShellv1.0;C:WINDOWSSystem32OpenSSH;C:Program FilesGitcmd;C:Usersyansominiconda3;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3Librarybin;.;C:Program FilesGit LFS;C:UsersyansoAppDataLocalMicrosoftWindowsApps;.;C:UsersyansoAppDataLocalProgramsMicrosoft VS Codebin;C:WindowsSystem32HWAudioDriverLibs;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:UsersAdministratorAppDataLocalMicrosoftWindowsApps;C:WINDOWSsystem32;C:WINDOWS;C:WINDOWSSystem32Wbem;C:WINDOWSSystem32WindowsPowerShellv1.0;C:WINDOWSSystem32OpenSSH;C:Program FilesGitcmd;C:Usersyansominiconda3;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3Librarybin;.;C:Program FilesGit LFS;C:UsersyansoAppDataLocalMicrosoftWindowsApps;.;C:UsersyansoAppDataLocalProgramsMicrosoft VS Codebin (tensorflow) D:modelbox-win10-x64-1.5.3>set PYTHONPATH= (tensorflow) D:modelbox-win10-x64-1.5.3>set PYTHONHOME= (tensorflow) D:modelbox-win10-x64-1.5.3>python.exe -u D:modelbox-win10-x64-1.5.3\create.py -t python -n yolov7_post -p yolov7_pet sdk version is modelbox-win10-x64-1.5.3 success: create python yolov7_post in D:modelbox-win10-x64-1.5.3workspaceyolov7_pet/etc/flowunit/yolov7_post
a. 修改配置文件
# 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 = 2 conf_threshold = 0.5 iou_threshold = 0.45 # Input ports description [input] [input.input1] # Input port number, the format is input.input[N] name = "in_feat" # Input port name type = "float" # Input port type # Output ports description [output] [output.output1] # Output port number, the format is output.output[N] name = "out_data" # Output port name type = "string" # Output port type
b. 修改逻辑代码
# 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): # 获取功能单元的配置参数 self.params = {} self.params['net_h'] = config.get_int('net_h') self.params['net_w'] = config.get_int('net_w') self.params['num_classes'] = config.get_int('num_classes') self.params['conf_thre'] = config.get_float('conf_threshold') self.params['nms_thre'] = config.get_float('iou_threshold') self.num_classes = config.get_int('num_classes') return modelbox.Status.StatusCode.STATUS_SUCCESS # Process the data def process(self, data_context): # 从DataContext中获取输入输出BufferList对象 in_feat = data_context.input("in_feat") out_data = data_context.output("out_data") # yolov7_post process code. # 循环处理每一个输入Buffer数据 for buffer_feat in in_feat: # 将输入Buffer转换为numpy对象 feat_data = np.array(buffer_feat.as_object(), copy=False) feat_data = feat_data.reshape((-1, self.num_classes + 5)) # 业务处理：解码yolov7模型的输出数据，得到检测框，转化为json数据 bboxes = self.postprocess(feat_data, self.params) result = {"det_result": str(bboxes)} print(result) # 将业务处理返回的结果数据转换为Buffer result_str = json.dumps(result) out_buffer = modelbox.Buffer(self.get_bind_device(), result_str) # 将输出Buffer放入输出BufferList中 out_data.push_back(out_buffer) return modelbox.Status.StatusCode.STATUS_SUCCESS # model post-processing function def postprocess(self, feat_data, params): """postprocess for yolo7 model""" boxes = [] class_ids = [] confidences = [] for detection in feat_data: scores = detection[5:] class_id = np.argmax(scores) if params['num_classes'] == 1: confidence = detection[4] else: confidence = detection[4] * scores[class_id] if confidence > params['conf_thre'] and detection[4] > params['conf_thre']: center_x = detection[0] / params['net_w'] center_y = detection[1] / params['net_h'] width = detection[2] / params['net_w'] height = detection[3] / params['net_h'] left = center_x - width / 2 top = center_y - height / 2 class_ids.append(class_id) confidences.append(confidence) boxes.append([left, top, width, height]) # use nms algorithm in opencv box_idx = cv2.dnn.NMSBoxes( boxes, confidences, params['conf_thre'], params['nms_thre']) detections = [] for i in box_idx: boxes[i][0] = max(0.0, boxes[i][0]) # [0, 1] boxes[i][1] = max(0.0, boxes[i][1]) # [0, 1] boxes[i][2] = min(1.0, boxes[i][0] + boxes[i][2]) # [0, 1] boxes[i][3] = min(1.0, boxes[i][1] + boxes[i][3]) # [0, 1] dets = np.concatenate( [boxes[i], np.array([confidences[i]]), np.array([class_ids[i]])], 0).tolist() detections.append(dets) return detections def close(self): # Close the flowunit return modelbox.Status() def data_pre(self, data_context): # Before streaming data starts return modelbox.Status() def data_post(self, data_context): # After streaming data ends return modelbox.Status() def data_group_pre(self, data_context): # Before all streaming data starts return modelbox.Status() def data_group_post(self, data_context): # After all streaming data ends return modelbox.Status()
4. 修改流程图
yolov7_pet工程graph目录下存放流程图，默认的流程图yolov7_pet.toml与工程同名，其内容为（以Windows版ModelBox为例）：
# 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_pet { node [shape=Mrecord] queue_size = 4 batch_size = 1 input1[type=input,flowunit=input,device=cpu,deviceid=0] httpserver_sync_receive[type=flowunit, flowunit=httpserver_sync_receive_v2, device=cpu, deviceid=0, time_out_ms=5000, endpoint="http://0.0.0.0:8083/v1/yolov7_pet", max_requests=100] image_decoder[type=flowunit, flowunit=image_decoder, device=cpu, key="image_base64", queue_size=4] image_resize[type=flowunit, flowunit=resize, device=cpu, deviceid=0, image_width=640, image_height=640] image_transpose[type=flowunit, flowunit=packed_planar_transpose, device=cpu, deviceid=0] normalize[type=flowunit flowunit=normalize device=cpu deviceid=0 standard_deviation_inverse="0.0039215686,0.0039215686,0.0039215686"] yolov7_infer[type=flowunit, flowunit=yolov7_infer, device=cpu, deviceid=0, batch_size = 1] yolov7_post[type=flowunit, flowunit=yolov7_post, device=cpu, deviceid=0] httpserver_sync_reply[type=flowunit, flowunit=httpserver_sync_reply_v2, device=cpu, deviceid=0] input1:input -> httpserver_sync_receive:in_url httpserver_sync_receive:out_request_info -> image_decoder:in_encoded_image image_decoder:out_image -> image_resize:in_image image_resize:out_image -> image_transpose:in_image image_transpose:out_image -> normalize:in_data normalize:out_data -> yolov7_infer:Input yolov7_infer:Output -> yolov7_post:in_feat yolov7_post:out_data -> httpserver_sync_reply:in_reply_info }""" [flow] desc = "yolov7_pet run in modelbox-win10-x64"
5. 准备动物图片和测试脚本
a. 动物图片
yolov7_pet工程data目录下存放动物图片文件夹test_imgs
b. 测试脚本
yolov7_pet工程data目录下存放测试脚本test_http.py
#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved. import os import cv2 import json import base64 import http.client class HttpConfig: '''http调用的参数配置''' def __init__(self, host_ip, port, url, img_base64_str): self.hostIP = host_ip self.Port = port self.httpMethod = "POST" self.requstURL = url self.headerdata = { "Content-Type": "application/json" } self.test_data = { "image_base64": img_base64_str } self.body = json.dumps(self.test_data) def read_image(img_path): '''读取图片数据并转为base64编码的字符串''' img_data = cv2.imread(img_path) img_str = cv2.imencode('.jpg', img_data)[1].tostring() img_bin = base64.b64encode(img_str) img_base64_str = str(img_bin, encoding='utf8') return img_data, img_base64_str def decode_car_bboxes(bbox_str, input_shape): try: labels = [0, 1] # cat, dog bboxes = json.loads(json.loads(bbox_str)['det_result']) bboxes = list(filter(lambda x: int(x[5]) in labels, bboxes)) except Exception as ex: print(str(ex)) return [] else: for bbox in bboxes: bbox[0] = int(bbox[0] * input_shape[1]) bbox[1] = int(bbox[1] * input_shape[0]) bbox[2] = int(bbox[2] * input_shape[1]) bbox[3] = int(bbox[3] * input_shape[0]) return bboxes def draw_bboxes(img_data, bboxes): '''画框''' for bbox in bboxes: x1, y1, x2, y2, score, label = bbox color = (0, 0, 255) names = ['cat', 'dog'] score = '%.2f' % score label = '%s:%s' % (names[int(label)], score) cv2.rectangle(img_data, (x1, y1), (x2, y2), color, 2) cv2.putText(img_data, label, (x1, y1 - 10), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (0, 255, 0), thickness=1) return img_data def test_image(img_path, ip, port, url): '''单张图片测试''' img_data, img_base64_str = read_image(img_path) http_config = HttpConfig(ip, port, url, img_base64_str) conn = http.client.HTTPConnection(host=http_config.hostIP, port=http_config.Port) conn.request(method=http_config.httpMethod, url=http_config.requstURL, body=http_config.body, headers=http_config.headerdata) response = conn.getresponse().read().decode() print('response: ', response) bboxes = decode_car_bboxes(response, img_data.shape) imt_out = draw_bboxes(img_data, bboxes) cv2.imwrite('./result-' + os.path.basename(img_path), imt_out) if __name__ == "__main__": port = 8083 ip = "127.0.0.1" url = "/v1/yolov7_pet" img_path = "./test.jpg" img_folder = './test_imgs' file_list = os.listdir(img_folder) for img_file in file_list: print("n================ {} ================".format(img_file)) img_path = os.path.join(img_folder, img_file) test_image(img_path, ip, port, url)
三、运行应用
在yolov7_pet工程目录下执行.binmain.bat运行应用：
(tensorflow) PS D:modelbox-win10-x64-1.5.3> cd D:modelbox-win10-x64-1.5.3workspaceyolov7_pet (tensorflow) PS D:modelbox-win10-x64-1.5.3workspaceyolov7_pet> .binmain.bat (tensorflow) D:modelbox-win10-x64-1.5.3workspaceyolov7_pet>set PATH=D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../../../python-embed;D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../../../modelbox-win10-x64/bin;D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../dependence/lib;C:Usersyansominiconda3envstensorflowlibsite-packagespywin32_system32;C:Usersyansominiconda3envstensorflow;C:Usersyansominiconda3envstensorflowLibrarymingw-w64bin;C:Usersyansominiconda3envstensorflowLibraryusrbin;C:Usersyansominiconda3envstensorflowLibrarybin;C:Usersyansominiconda3envstensorflowScripts;C:Usersyansominiconda3envstensorflowbin;C:Usersyansominiconda3condabin;C:WindowsSystem32HWAudioDriverLibs;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:UsersAdministratorAppDataLocalMicrosoftWindowsApps;C:WINDOWSsystem32;C:WINDOWS;C:WINDOWSSystem32Wbem;C:WINDOWSSystem32WindowsPowerShellv1.0;C:WINDOWSSystem32OpenSSH;C:Program FilesGitcmd;C:Usersyansominiconda3;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3Librarybin;.;C:Program FilesGit LFS;C:UsersyansoAppDataLocalMicrosoftWindowsApps;.;C:UsersyansoAppDataLocalProgramsMicrosoft VS Codebin;C:Usersyansominiconda3envstensorflowlibsite-packagespywin32_system32;C:Usersyansominiconda3;C:Usersyansominiconda3Librarymingw-w64bin;C:Usersyansominiconda3Libraryusrbin;C:Usersyansominiconda3Librarybin;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3bin;C:Usersyansominiconda3condabin;C:WindowsSystem32HWAudioDriverLibs;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:UsersAdministratorAppDataLocalMicrosoftWindowsApps;C:WINDOWSsystem32;C:WINDOWS;C:WINDOWSSystem32Wbem;C:WINDOWSSystem32WindowsPowerShellv1.0;C:WINDOWSSystem32OpenSSH;C:Program FilesGitcmd;C:Usersyansominiconda3;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3Librarybin;.;C:Program FilesGit LFS;C:UsersyansoAppDataLocalMicrosoftWindowsApps;.;C:UsersyansoAppDataLocalProgramsMicrosoft VS Codebin;C:WindowsSystem32HWAudioDriverLibs;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:UsersAdministratorAppDataLocalMicrosoftWindowsApps;C:WINDOWSsystem32;C:WINDOWS;C:WINDOWSSystem32Wbem;C:WINDOWSSystem32WindowsPowerShellv1.0;C:WINDOWSSystem32OpenSSH;C:Program FilesGitcmd;C:Usersyansominiconda3;C:Usersyansominiconda3Scripts;C:Usersyansominiconda3Librarybin;.;C:Program FilesGit LFS;C:UsersyansoAppDataLocalMicrosoftWindowsApps;.;C:UsersyansoAppDataLocalProgramsMicrosoft VS Codebin (tensorflow) D:modelbox-win10-x64-1.5.3workspaceyolov7_pet>modelbox.exe -c D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../graph/modelbox.conf [2024-06-10 06:42:50,922][ WARN][ iva_config.cc:143 ] update vas url failed. Fault, no vas projectid or iva endpoint open log file D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../hilens_data_dir/log/modelbox.log failed, No error input dims is:1,3,640,640, output dims is:1,25200,7,
HTTP服务启动后可以在另一个终端进行请求测试，进入yolov7_pet工程目录data文件夹中使用test_http.py脚本发起HTTP请求进行测试：
(tensorflow) PS D:modelbox-win10-x64-1.5.3> cd D:modelbox-win10-x64-1.5.3workspaceyolov7_petdata (tensorflow) PS D:modelbox-win10-x64-1.5.3workspaceyolov7_petdata> python .test_http.py ================ Abyssinian_1.jpg ================ .test_http.py:33: DeprecationWarning: tostring() is deprecated. Use tobytes() instead. img_str = cv2.imencode('.jpg', img_data)[1].tostring() response: {"det_result": "[[0.554308044910431, 0.1864600658416748, 0.7089953303337098, 0.3776256084442139, 0.82369065284729, 0.0]]"} ================ saint_bernard_143.jpg ================ response: {"det_result": "[[0.46182055473327643, 0.30239262580871584, 0.8193012714385988, 0.4969032764434815, 0.7603430151939392, 1.0]]"}
四、小结
本章我们介绍了如何使用ModelBox开发一个动物目标检测的AI应用，我们只需要准备模型文件以及简单的配置即可创建一个HTTP服务。同时我们可以了解到图片标注、数据处理和模型训练方法，以及对应的推理应用逻辑。
