HYTOS

    def request_symbol_box(self, name, img, classes):

            data = { 'classes':classes, 'name':name, 'img':None }

        classes = 0

        symbolTypeList = app_doc_data.getSymbolTypeList()

        for symbolType in symbolTypeList:

            if not symbolType[1]: continue  # skip if category is empty

            symbolList = app_doc_data.getSymbolListByType('UID', symbolType[0])

            for symbol in symbolList:

                classes += 1

        symbols = app_web_service.request_symbol_box(project.name, area.img, classes)

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

def get_args():

    parser = argparse.ArgumentParser("You Only Look Once: Unified, Real-Time Object Detection")

    parser.add_argument("--image_size", type=int, default=448, help="The common width and height for all images")

    parser.add_argument("--conf_threshold", type=float, default=0.5)

    parser.add_argument("--nms_threshold", type=float, default=0.6)

    parser.add_argument("--pre_trained_model_type", type=str, choices=["model", "params"], default="params")

    parser.add_argument("--pre_trained_model_path", type=str, default="E:\Projects\DTIPID_GERRIT\DTI_PID\WebServer\symbol_recognition\MODEL/f_doftech_all_class_only_params.pth")

    parser.add_argument("--pre_trained_model_path2", type=str, default="E:\Projects\DTIPID_GERRIT\DTI_PID\WebServer\symbol_recognition\MODEL/doftech_all_class_only_params_opc.pth")

    #parser.add_argument("--pre_trained_model_path3", type=str, default="trained_models/only_params_trained_yolo_voc")

    parser.add_argument("--input", type=str, default="test_images")

    parser.add_argument("--output", type=str, default="result_images")

    parser.add_argument("--data_path_test", type=str, default="E:\Projects\DTIPID_GERRIT\DTI_PID\WebServer\symbol_recognition\pid_images/", help="the root folder of dataset") #Doftech VOCdevkit

    args = parser.parse_args()

    return args

def get_symbol(imgs, classes, trained_model1=None, trained_model2=None):

def test(opt):

    global colors

    if torch.cuda.is_available():

        model1 = Yolo(35).cuda()

        model1.load_state_dict(torch.load(opt.pre_trained_model_path))

        model2 = Yolo(35).cuda()

        model2.load_state_dict(torch.load(opt.pre_trained_model_path2))

    else:

        return None

    colors = pickle.load(open("E:\Projects\DTIPID_GERRIT\DTI_PID\WebServer\symbol_recognition/src/pallete", "rb"))

    print(opt.input)

    img_list = sorted(glob.glob(os.path.join(opt.data_path_test, '*.png')))

    start = time.time()

    print(img_list)

    # ----------------------------------------------

    # Get Patch arguments : (img list, patch_size, overlap_size)

    # ----------------------------------------------

    for idx_img in range(len(img_list)):

        print("=========> CROP PATCH")

        small_object_patch_list = []

        large_object_patch_list = []

        total_symbole_list = []

        small_object_patch_list.append(get_patch(img_list[idx_img], 500, 250))

        large_object_patch_list.append(get_patch(img_list[idx_img], 800, 200))

        img_name = img_list[idx_img].split('\\')[1].split('.')[0]

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

        save_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'save', img_name)

        if not os.path.isdir(save_dir):

            os.mkdir(save_dir)

            os.mkdir(save_dir+"a/")

            os.mkdir(save_dir+"b/")

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

        text_file.write(img_name+" -> Detection Reesult\n")

        text_file.write("================================\n")

            # Text File 만들기

        # ----------------------------------------------

        # Small Object Detection (Valve, Sensor, etc.)

        # ---------------------------------------------

        print("=========> SMALL OBJECT DETECTION")

        for idx_small in range(len(small_object_patch_list)):

            patchs = small_object_patch_list[idx_small]

            total_symbole_list.append(detection_object(patchs, model1, save_dir+"a/", opc=False, opt=opt))

        # ----------------------------------------------

        # Large Object Detection (OPC etc.)

        # ----------------------------------------------

        print("=========> LARGE OBJECT DETECTION")

        for idx_large in range(len(large_object_patch_list)):

            patchs = large_object_patch_list[idx_large]

            total_symbole_list.append(detection_object(patchs, model2, save_dir+"b/", opc=True, opt=opt))

        count_result = merge_fn(img_list[idx_img], total_symbole_list, save_dir)

        for idx, value in enumerate(count_result):

            text_file.write(DOFTECH_CLASSES[idx]+ " : "+ str(value) + "\n")

        text_file.close()

    print("time :", time.time() - start)  # 현재시각 - 시작시간 = 실행 시간

def get_args():

    parser = argparse.ArgumentParser("You Only Look Once: Unified, Real-Time Object Detection")

    parser.add_argument("--image_size", type=int, default=448, help="The common width and height for all images")

    parser.add_argument("--batch_size", type=int, default=10, help="The number of images per batch")

    # Training 기본 Setting

    parser.add_argument("--momentum", type=float, default=0.9)

    parser.add_argument("--decay", type=float, default=0.0005)

    parser.add_argument("--dropout", type=float, default=0.5)

    parser.add_argument("--num_epoches", type=int, default=1000)

    parser.add_argument("--test_interval", type=int, default=20, help="Number of epoches between testing phases")

    parser.add_argument("--object_scale", type=float, default=1.0)

    parser.add_argument("--noobject_scale", type=float, default=0.5)

    parser.add_argument("--class_scale", type=float, default=1.0)

    parser.add_argument("--coord_scale", type=float, default=5.0)

    parser.add_argument("--reduction", type=int, default=32)

    parser.add_argument("--es_min_delta", type=float, default=0.0,

                        help="Early stopping's parameter: minimum change loss to qualify as an improvement")

    parser.add_argument("--es_patience", type=int, default=0,

                        help="Early stopping's parameter: number of epochs with no improvement after which training will be stopped. Set to 0 to disable this technique.")

    # 확인해야 하는 PATH

    parser.add_argument("--data_path", type=str, default="D:/data/DATA_Doftech/small_symbol/training", help="the root folder of dataset") # 학습 데이터 경로 -> image와 xml의 상위 경로 입력

    parser.add_argument("--data_path_test", type=str, default="./data/Test", help="the root folder of dataset") # 테스트 데이터 경로 -> test할 이미지만 넣으면 됨

    #parser.add_argument("--pre_trained_model_type", type=str, choices=["model", "params"], default="model")

    parser.add_argument("--pre_trained_model_path", type=str, default="trained_models/only_params_trained_yolo_voc") # Pre-training 된 모델 경로

    parser.add_argument("--saved_path", type=str, default="./checkpoint") # training 된 모델 저장 경로

    parser.add_argument("--conf_threshold", type=float, default=0.35)

    parser.add_argument("--nms_threshold", type=float, default=0.5)

    args = parser.parse_args()

    return args

print(len(DOFTECH_CLASSES))