HYTOS

            # get text box original way

            not_containing_bbox, binary_image = self.getTextBox(ocr_image, imgGray, maxTextSize, minSize)

            # using craft

            #not_containing_bbox, binary_image = self.getTextBox_craft(ocr_image, maxTextSize, minSize)

    def getTextBox_craft(self, ocr_image, maxTextSize, minSize):

        pass

        '''

        from CRAFT_pytorch_master import text_craft

        from AppDocData import AppDocData

        app_doc_data = AppDocData.instance()

        project = app_doc_data.getCurrentProject()

        binary_image = cv2.threshold(ocr_image, 200, 255, cv2.THRESH_BINARY)[1]

        score_path = os.path.join(project.getTempPath(), 'OCR_CRAFT_SCORE_{}.png'.format(app_doc_data.imgName))

        img_path = os.path.join(project.getTempPath(), 'OCR_CRAFT_{}.png'.format(app_doc_data.imgName))

        boxes = text_craft.get_text_box(ocr_image, img_path, score_path)

        rects = []

        for box in boxes:

            rects.append(QRect(box[0], box[1], box[4] - box[0], box[5] - box[1]))

        return rects, binary_image

        '''

    def getTextBox(self, ocr_image, imgGray, maxTextSize, minSize):

        """ get text box """

        from AppDocData import AppDocData

        app_doc_data = AppDocData.instance()

        project = app_doc_data.getCurrentProject()

        cv2.rectangle(ocr_image, (0, 0), ocr_image.shape[::-1], (255, 255, 255), -1)

        mask = cv2.threshold(imgGray, 200, 255, cv2.THRESH_BINARY)[1]

        contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

        for contour in contours:

            # remove too big one or horizontal/vertical line

            [x, y, w, h] = cv2.boundingRect(contour)

            area = cv2.contourArea(contour, True)

            # skip one which size is greater than max size or less then minimum size

            if (w > maxTextSize or h > maxTextSize) or (w <= minSize and h <= minSize):

                #cv2.drawContours(ocr_image, [contour], -1, (255, 255, 255), -1)

                continue

            if area >= 0:

                cv2.drawContours(ocr_image, [contour], -1, (0, 0, 0), -1)

                #cv2.drawContours(ocr_image, [contour], -1, (255, 255, 255), 1)

            #else:

            #    cv2.drawContours(ocr_image, [contour], -1, (255, 255, 255), -1)

        path = os.path.join(project.getTempPath(), 'OCR_{}.png'.format(app_doc_data.imgName))

        cv2.imwrite(path, ocr_image)

        """

        east = False

        if east:

            # define the two output layer names for the EAST detector model that

            # we are interested -- the first is the output probabilities and the

            # second can be used to derive the bounding box coordinates of text

            layerNames = [

                "feature_fusion/Conv_7/Sigmoid",

                "feature_fusion/concat_3"]

            # load the pre-trained EAST text detector

            net = cv2.dnn.readNet("C:\\ProgramData\\Digital PID\\frozen_east_text_detection.pb")

            (H, W) = ocr_image.shape[:2]

            # construct a blob from the image and then perform a forward pass of

            # the model to obtain the two output layer sets

            blob = cv2.dnn.blobFromImage(ocr_image, 1.0, (W, H), (123.68, 116.78, 103.94), swapRB=True, crop=False)

            net.setInput(blob)

            (scores, geometry) = net.forward(layerNames)

            # decode the predictions, then  apply non-maxima suppression to

            # suppress weak, overlapping bounding boxes

            (rects, confidences) = self.decode_predictions(scores, geometry)

            boxes = non_max_suppression(np.array(rects), probs=confidences)

            # loop over the bounding boxes

            for (startX, startY, endX, endY) in boxes:

                pass

        else:

        """

        configs = app_doc_data.getConfigs('Text Recognition', 'Expand Size')

        expand_size = int(configs[0].value) if 1 == len(configs) else 10

        configs = app_doc_data.getConfigs('Text Recognition', 'Shrink Size')

        shrinkSize = int(configs[0].value) if 1 == len(configs) else 0

        binary_image = cv2.threshold(ocr_image, 200, 255, cv2.THRESH_BINARY)[1]

        eroded = cv2.erode(binary_image, np.ones((expand_size, expand_size), np.uint8))

        eroded = cv2.bitwise_not(eroded)

        bboxes = []

        contours, hierarchy = cv2.findContours(eroded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        for contour in contours:

            area = cv2.contourArea(contour, True)

            if area < 0:

                [x, y, w, h] = cv2.boundingRect(contour)

                bboxes.append(QRect(x, y, w, h))

        # exclude bounding boxes contains child bounding box

        not_containing_bbox = []

        for bbox in bboxes:

            matches = [_bbox for _bbox in bboxes if bbox != _bbox and bbox.contains(_bbox)]

            if not matches:

                not_containing_bbox.append(bbox)

        # up to here

        return not_containing_bbox, binary_image