本文分享自華為雲社群《ModelBox-AI應用開發：動物目標檢測【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【影片教程】
二、應用開發
1. 建立工程
在ModelBox sdk目錄下使用create.bat建立yolov7_pet工程
(tensorflow) PS D:\modelbox-win10-x64-1.5.3> .\create.bat -t server -n yolov7_pet (tensorflow) D:\modelbox-win10-x64-1.5.3>set BASE_PATH=D:\modelbox-win10-x64-1.5.3\ (tensorflow) D:\modelbox-win10-x64-1.5.3>set PATH=D:\modelbox-win10-x64-1.5.3\\python-embed;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3\envs\tensorflow;C:\Users\yanso\miniconda3\envs\tensorflow\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\usr\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Scripts;C:\Users\yanso\miniconda3\envs\tensorflow\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\Library\usr\bin;C:\Users\yanso\miniconda3\Library\bin;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin (tensorflow) D:\modelbox-win10-x64-1.5.3>set PYTHONPATH= (tensorflow) D:\modelbox-win10-x64-1.5.3>set PYTHONHOME= (tensorflow) D:\modelbox-win10-x64-1.5.3>python.exe -u D:\modelbox-win10-x64-1.5.3\\create.py -t server -n yolov7_pet sdk version is modelbox-win10-x64-1.5.3 dos2unix: converting file D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet/graph\modelbox.conf to Unix format... dos2unix: converting file D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet/graph\yolov7_pet.toml to Unix format... dos2unix: converting file D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet/bin\mock_task.toml to Unix format... success: create yolov7_pet in D:\modelbox-win10-x64-1.5.3\workspace
create.bat工具的引數中，-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_pet (tensorflow) D:\modelbox-win10-x64-1.5.3>set BASE_PATH=D:\modelbox-win10-x64-1.5.3\ (tensorflow) D:\modelbox-win10-x64-1.5.3>set PATH=D:\modelbox-win10-x64-1.5.3\\python-embed;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3\envs\tensorflow;C:\Users\yanso\miniconda3\envs\tensorflow\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\usr\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Scripts;C:\Users\yanso\miniconda3\envs\tensorflow\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\Library\usr\bin;C:\Users\yanso\miniconda3\Library\bin;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin (tensorflow) D:\modelbox-win10-x64-1.5.3>set PYTHONPATH= (tensorflow) D:\modelbox-win10-x64-1.5.3>set PYTHONHOME= (tensorflow) D:\modelbox-win10-x64-1.5.3>python.exe -u D:\modelbox-win10-x64-1.5.3\\create.py -t infer -n yolov7_infer -p yolov7_pet sdk version is modelbox-win10-x64-1.5.3 success: create infer yolov7_infer in D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet/model/yolov7_infer
create.bat工具使用時，-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_infer 表示所建立的功能單元屬於yolov7_infer應用。
a. 下載轉換好的模型
執行此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_pet (tensorflow) D:\modelbox-win10-x64-1.5.3>set BASE_PATH=D:\modelbox-win10-x64-1.5.3\ (tensorflow) D:\modelbox-win10-x64-1.5.3>set PATH=D:\modelbox-win10-x64-1.5.3\\python-embed;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3\envs\tensorflow;C:\Users\yanso\miniconda3\envs\tensorflow\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\usr\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Scripts;C:\Users\yanso\miniconda3\envs\tensorflow\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\Library\usr\bin;C:\Users\yanso\miniconda3\Library\bin;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin (tensorflow) D:\modelbox-win10-x64-1.5.3>set PYTHONPATH= (tensorflow) D:\modelbox-win10-x64-1.5.3>set PYTHONHOME= (tensorflow) D:\modelbox-win10-x64-1.5.3>python.exe -u D:\modelbox-win10-x64-1.5.3\\create.py -t python -n yolov7_post -p yolov7_pet sdk version is modelbox-win10-x64-1.5.3 success: create python yolov7_post in D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet/etc/flowunit/yolov7_post
a. 修改配置檔案
# 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 = 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)} print(result) # 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'] 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. 修改流程圖
yolov7_pet工程graph目錄下存放流程圖，預設的流程圖yolov7_pet.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_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. 準備動物圖片和測試指令碼
a. 動物圖片
yolov7_pet工程data目錄下存放動物圖片資料夾test_imgs
b. 測試指令碼
yolov7_pet工程data目錄下存放測試指令碼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): '''畫框''' 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): '''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 = 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、執行應用
在yolov7_pet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_pet (tensorflow) PS D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet> .\bin\main.bat (tensorflow) D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet>set PATH=D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../../../python-embed;D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../../../modelbox-win10-x64/bin;D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../dependence/lib;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3\envs\tensorflow;C:\Users\yanso\miniconda3\envs\tensorflow\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\usr\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Library\bin;C:\Users\yanso\miniconda3\envs\tensorflow\Scripts;C:\Users\yanso\miniconda3\envs\tensorflow\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Users\yanso\miniconda3\envs\tensorflow\lib\site-packages\pywin32_system32;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Library\mingw-w64\bin;C:\Users\yanso\miniconda3\Library\usr\bin;C:\Users\yanso\miniconda3\Library\bin;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\bin;C:\Users\yanso\miniconda3\condabin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin;C:\Windows\System32\HWAudioDriverLibs;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0;C:\WINDOWS\System32\OpenSSH;C:\Program Files\Git\cmd;C:\Users\yanso\miniconda3;C:\Users\yanso\miniconda3\Scripts;C:\Users\yanso\miniconda3\Library\bin;.;C:\Program Files\Git LFS;C:\Users\yanso\AppData\Local\Microsoft\WindowsApps;.;C:\Users\yanso\AppData\Local\Programs\Microsoft VS Code\bin (tensorflow) D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet>modelbox.exe -c D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../graph/modelbox.conf [2024-06-10 06:42:50,922][ WARN][ iva_config.cc:143 ] update vas url failed. Fault, no vas projectid or iva endpoint open log file D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../hilens_data_dir/log/modelbox.log failed, No error input dims is:1,3,640,640, output dims is:1,25200,7,
HTTP服務啟動後可以在另一個終端進行請求測試，進入yolov7_pet工程目錄data資料夾中使用test_http.py指令碼發起HTTP請求進行測試：
(tensorflow) PS D:\modelbox-win10-x64-1.5.3> cd D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet\data (tensorflow) PS D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet\data> python .\test_http.py ================ Abyssinian_1.jpg ================ .\test_http.py:33: DeprecationWarning: tostring() is deprecated. Use tobytes() instead. img_str = cv2.imencode('.jpg', img_data)[1].tostring() response: {"det_result": "[[0.554308044910431, 0.1864600658416748, 0.7089953303337098, 0.3776256084442139, 0.82369065284729, 0.0]]"} ================ saint_bernard_143.jpg ================ response: {"det_result": "[[0.46182055473327643, 0.30239262580871584, 0.8193012714385988, 0.4969032764434815, 0.7603430151939392, 1.0]]"}
四、小結
本章我們介紹瞭如何使用ModelBox開發一個動物目標檢測的AI應用，我們只需要準備模型檔案以及簡單的配置即可建立一個HTTP服務。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~