HYTOS

    if True:#QLicenseDialog.check_license_key():

'''

'''

def get_text_box_batch(infos):

    boxes_list = get_text_box(infos[3], infos[0], infos[1], infos[2])

    return boxes_list

def get_text_box_stream(imgs, img_path=None, score_path=None, trained_model=None):

    parser = argparse.ArgumentParser(description='CRAFT Text Detection')

    parser.add_argument('--trained_model', default=trained_model, type=str, help='pretrained model')

    parser.add_argument('--text_threshold', default=0.7, type=float, help='text confidence threshold')

    parser.add_argument('--low_text', default=0.4, type=float, help='text low-bound score')

    parser.add_argument('--link_threshold', default=0.4, type=float, help='link confidence threshold')

    parser.add_argument('--cuda', default=True, type=str2bool, help='Use cuda for inference')

    parser.add_argument('--canvas_size', default=1000, type=int, help='image size for inference')

    parser.add_argument('--mag_ratio', default=1, type=float, help='image magnification ratio')

    parser.add_argument('--poly', default=False, action='store_true', help='enable polygon type')

    parser.add_argument('--show_time', default=False, action='store_true', help='show processing time')

    parser.add_argument('--test_folder', default='E:\Projects\DTIPID_GERRIT\DTI_PID\DTI_PID\CRAFT_pytorch_master/image/', type=str, help='folder path to input images')

    parser.add_argument('--refine', default=False, action='store_true', help='enable link refiner')

    parser.add_argument('--refiner_model', default='E:\Projects\DTIPID_GERRIT\DTI_PID\DTI_PID\CRAFT_pytorch_master/weights/craft_refiner_CTW1500.pth', type=str, help='pretrained refiner model')

    args = parser.parse_args()

    # load net

    net = CRAFT()     # initialize

    print('Loading weights from checkpoint (' + args.trained_model + ')')

    if args.cuda:

        net.load_state_dict(copyStateDict(torch.load(args.trained_model)))

    else:

        net.load_state_dict(copyStateDict(torch.load(args.trained_model, map_location='cpu')))

    if args.cuda:

        net = net.cuda()

        net = torch.nn.DataParallel(net)

        cudnn.benchmark = False

    net.eval()

    # LinkRefiner

    refine_net = None

    if args.refine:

        from refinenet import RefineNet

        refine_net = RefineNet()

        print('Loading weights of refiner from checkpoint (' + args.refiner_model + ')')

        if args.cuda:

            refine_net.load_state_dict(copyStateDict(torch.load(args.refiner_model)))

            refine_net = refine_net.cuda()

            refine_net = torch.nn.DataParallel(refine_net)

        else:

            refine_net.load_state_dict(copyStateDict(torch.load(args.refiner_model, map_location='cpu')))

        refine_net.eval()

        args.poly = True

    box_craft_list = []

    for img in imgs:

        if img.shape[0] == 2: img = img[0]

        if len(img.shape) == 2 : img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)

        if img.shape[2] == 4:   img = img[:,:,:3]

        image = np.array(img)

        t = time.time()

        bboxes, polys, score_text = test_net(net, image, args.text_threshold, args.link_threshold, args.low_text, args.cuda, args.poly, refine_net, args)

        # save score text

        if score_path:

            cv2.imwrite(score_path, score_text)

        box_craft = []

        for i, box in enumerate(polys):

            poly = np.array(box).astype(np.int32).reshape((-1))

            box_craft.append([int(p) for p in poly])

        #file_utils.saveResult(image_path, image[:,:,::-1], polys, dirname=result_folder)

        img = np.array(image[:,:,::-1])

        # save result image

        for i, box in enumerate(polys):

            poly = np.array(box).astype(np.int32).reshape((-1))

            poly = poly.reshape(-1, 2)

            cv2.polylines(img, [poly.reshape((-1, 1, 2))], True, color=(0, 0, 255), thickness=2)

        if img_path:

            cv2.imwrite(img_path, img)

        print("elapsed time : {}s".format(time.time() - t))

        box_craft_list.append(box_craft)

    return box_craft_list

        '''

            # faster

            #boxes = streamer.predict([[img, None, None, os.path.dirname(os.path.realpath(__file__)) + '\\CRAFT_pytorch_master\\weights\\craft_ic15_20k.pth']])

            # More accurate

        '''

        '''

        infos = []

        for img in imgs:

            infos.append([img, None, None, os.path.dirname(os.path.realpath(__file__)) + '\\CRAFT_pytorch_master\\weights\\craft_mlt_25k.pth'])

        boxes = streamer.predict(infos)

        boxes_list = boxes

        '''

        infos = [None, None, os.path.dirname(os.path.realpath(__file__)) + '\\CRAFT_pytorch_master\\weights\\craft_mlt_25k.pth', imgs]

        boxes = streamer.predict(infos)

        boxes_list = boxes