HYTOS

            cursor = conn.cursor()

            if not all:

                sql = """SELECT a.UID,a.Name,b.Type,a.Threshold,a.MinMatchPoint,a.IsDetectOrigin,a.RotationCount,

                a.IsExceptDetect,a.HasInstrumentLabel,a.flip,a.TextArea,b.UID as DB_UID FROM Symbol a inner join SymbolType b on 

                a.SymbolType_UID=b.UID WHERE a.IsExceptDetect = 0 order by width * height desc"""

            else:

                sql = """SELECT a.UID,a.Name,b.Type,a.Threshold,a.MinMatchPoint,a.IsDetectOrigin,a.RotationCount,

                a.IsExceptDetect,a.HasInstrumentLabel,a.flip,a.TextArea,b.UID as DB_UID FROM Symbol a inner join SymbolType b on 

                a.SymbolType_UID=b.UID order by width * height desc"""

            try:

                                                row['ConnectionPoint'], row['BaseSymbol'],

                                                row['AdditionalSymbol'], row['IsExceptDetect'],

                                                row['HasInstrumentLabel'], row['UID'],

                                                detectFlip=row['flip'], text_area=row['TextArea'])

                    targetSymbolList.append(sym)

            except Exception as ex:

                    worker.displayTitle.emit(worker.tr('Detecting symbols...'))

                    configs = app_doc_data.getConfigs('Engine', 'Symbol')

                    # get symbol original way

                        # detect only equipments

                    r_w, r_h = roiItem.shape[::-1]

                    if r_w < sw or r_h < sh:

                        continue

                    tmRes = cv2.matchTemplate(roiItem, symGray, cv2.TM_CCOEFF_NORMED)

                    _, max_val, __, max_loc = cv2.minMaxLoc(tmRes)

                    #maxIndex = tmRes.argmax()

    def __init__(self, sName, sType, threshold = None, minMatchCount = 0

                 , isDetectOnOrigin = False, rotationCount = 4, ocrOption = OCR_OPTION_NOT_EXEC, isContainChild = 0

                 , originalPoint = None, connectionPoint = None, baseSymbol = None, additionalSymbol = None

        self.uid = uid ## Auto increased Unique Id

        self.sName = sName

        self.sType = sType

        #    classes.append(label[0])

        symbol_list = app_doc_data.getTargetSymbolList(all=True)

        for symbol in symbol_list:

        app_web_service = AppWebService()

        res = app_web_service.request_execute_training(project.name, classes, bigs)

            self.ui.progressBar.setValue(0)

            self.ui.progressBar.setMaximum(len(rootList))

            maxsize = self.ui.spinBox_2.value()

            for root in rootList:

                images = []

                rootPath = os.path.join(tilePath, root)

                imgList = os.listdir(rootPath)

                for img in imgList:

                    filename, fileExtension = os.path.splitext(img)

                grid_size[1] = grid_size[1] + space * 2

                dimension = [None, None]

                pages = int(len(images) / (dimension[0] * dimension[1])) + 1

                for page in range(pages):

                    done = False

                    for j in range(dimension[1]):

                        for i in range(dimension[0]):

        <number>2048</number>

       </property>

       <property name="value">

       </property>

      </widget>

     </item>

        <number>2024</number>

       </property>

       <property name="value">

       </property>

      </widget>

     </item>

        boxes = test_doftech_all_images.get_symbol(imgs, data['name'], root_path=data_path, trained_model1=os.path.join(data_path, 'checkpoint', data['name'] + "_only_params.pth"), \

                                                   trained_model2=os.path.dirname(os.path.realpath(

        return jsonify({'symbol_box': boxes[0]})

        boxes_list = boxes

        '''

            os.path.realpath(__file__)) + '\\..\\..\\CRAFT_pytorch_master\\weights\\craft_mlt_25k.pth', imgs]]]

        boxes = streamer.predict(infos)

        boxes_list = boxes[0][0]

from app import app

if __name__ == '__main__':

        print("=========> "+ img_name)

                save_dir_tile1 = save_dir + "a/"

                save_dir_tile2 = save_dir + "b/"

                save_dir_tile1 = None

                save_dir_tile2 = None

        if save:

            text_file = open(save_dir+'test_result.txt', mode='wt', encoding='utf-8')