快速识别你家的猫猫狗狗，教你用 ModelBox 开发 AI 萌宠应用

(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_petsdk version is modelbox-win10-x64-1.5.3dos2unix: 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

(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-x64-1.5.3success: create infer yolov7_infer in D:\modelbox-win10-x64-1.5.3\workspace\yolov7_pet/model/yolov7_infer

# 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 pathtype = "inference" virtual_type = "onnx" # inference engine type: win10 now only support onnxgroup_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 nametype = "float"  # input port data type ,e.g. float or uint8device = "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 nametype = "float"   # output port data type ,e.g. float or uint8

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

# Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
# Basic config[base]name = "yolov7_post" # The FlowUnit namedevice = "cpu" # The flowunit runs on cpuversion = "1.0.0" # The version of the flowunittype = "python" # Fixed value, do not changedescription = "description" # The description of the flowunitentry = "yolov7_post@yolov7_postFlowUnit" # Python flowunit entry functiongroup_type = "Generic"  # flowunit group attribution, change as Input/Output/Image/Generic ...
# Flowunit Typestream = false # Whether the flowunit is a stream flowunitcondition = false # Whether the flowunit is a condition flowunitcollapse = false # Whether the flowunit is a collapse flowunitcollapse_all = false # Whether the flowunit will collapse all the dataexpand = false #  Whether the flowunit is a expand flowunit
# The default Flowunit config[config]net_h = 640net_w = 640num_classes = 2conf_threshold = 0.5iou_threshold = 0.45
# Input ports description[input][input.input1] # Input port number, the format is input.input[N]name = "in_feat" # Input port nametype = "float" # Input port type
# Output ports description[output][output.output1] # Output port number, the format is output.output[N]name = "out_data" # Output port nametype = "string" # Output port type

# Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
#!/usr/bin/env python# -*- coding: utf-8 -*-import _flowunit as modelboximport numpy as npimport jsonimport 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()

# 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=falsetrace=falsedir="${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"

#!/usr/bin/env python# -*- coding: utf-8 -*-
# Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
import osimport cv2import jsonimport base64import http.clientclass 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_strdef 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 bboxesdef 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_datadef 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)

(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 endpointopen log file D:/modelbox-win10-x64-1.5.3/workspace/yolov7_pet/bin/../hilens_data_dir/log/modelbox.log failed, No errorinput dims is:1,3,640,640,output dims is:1,25200,7,

(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]]"}