def fcenet_decode(self,
                      preds,
                      fourier_degree,
                      num_reconstr_points,
                      scale,
                      alpha=1.0,
                      beta=2.0,
                      box_type='poly',
                      score_thr=0.3,
                      nms_thr=0.1):
        """Decoding predictions of FCENet to instances.

        Args:
            preds (list(Tensor)): The head output tensors.
            fourier_degree (int): The maximum Fourier transform degree k.
            num_reconstr_points (int): The points number of the polygon
                reconstructed from predicted Fourier coefficients.
            scale (int): The down-sample scale of the prediction.
            alpha (float) : The parameter to calculate final scores. Score_{final}
                    = (Score_{text region} ^ alpha)
                    * (Score_{text center region}^ beta)
            beta (float) : The parameter to calculate final score.
            box_type (str):  Boundary encoding type 'poly' or 'quad'.
            score_thr (float) : The threshold used to filter out the final
                candidates.
            nms_thr (float) :  The threshold of nms.

        Returns:
            boundaries (list[list[float]]): The instance boundary and confidence
                list.
        """
        assert isinstance(preds, list)
        assert len(preds) == 2
        assert box_type in ['poly', 'quad']

        cls_pred = preds[0][0]
        tr_pred = cls_pred[0:2]
        tcl_pred = cls_pred[2:]

        reg_pred = preds[1][0].transpose([1, 2, 0])
        x_pred = reg_pred[:, :, :2 * fourier_degree + 1]
        y_pred = reg_pred[:, :, 2 * fourier_degree + 1:]

        score_pred = (tr_pred[1]**alpha) * (tcl_pred[1]**beta)
        tr_pred_mask = (score_pred) > score_thr
        tr_mask = fill_hole(tr_pred_mask)

        tr_contours, _ = cv2.findContours(
            tr_mask.astype(np.uint8), cv2.RETR_TREE,
            cv2.CHAIN_APPROX_SIMPLE)  # opencv4

        mask = np.zeros_like(tr_mask)
        boundaries = []
        for cont in tr_contours:
            deal_map = mask.copy().astype(np.int8)
            cv2.drawContours(deal_map, [cont], -1, 1, -1)

            score_map = score_pred * deal_map
            score_mask = score_map > 0
            xy_text = np.argwhere(score_mask)
            dxy = xy_text[:, 1] + xy_text[:, 0] * 1j

            x, y = x_pred[score_mask], y_pred[score_mask]
            c = x + y * 1j
            c[:, fourier_degree] = c[:, fourier_degree] + dxy
            c *= scale

            polygons = fourier2poly(c, num_reconstr_points)
            score = score_map[score_mask].reshape(-1, 1)
            polygons = poly_nms(np.hstack((polygons, score)).tolist(), nms_thr)

            boundaries = boundaries + polygons

        boundaries = poly_nms(boundaries, nms_thr)

        if box_type == 'quad':
            new_boundaries = []
            for boundary in boundaries:
                poly = np.array(boundary[:-1]).reshape(-1, 2).astype(np.float32)
                score = boundary[-1]
                points = cv2.boxPoints(cv2.minAreaRect(poly))
                points = np.int0(points)
                new_boundaries.append(points.reshape(-1).tolist() + [score])
                boundaries = new_boundaries

        return boundaries
Decoding predictions of FCENet to instances.

        Args:
            preds (list(Tensor)): The head output tensors.
            fourier_degree (int): The maximum Fourier transform degree k.
            num_reconstr_points (int): The points number of the polygon
                reconstructed from predicted Fourier coefficients.
            scale (int): The down-sample scale of the prediction.
            alpha (float) : The parameter to calculate final scores. Score_{final}
                    = (Score_{text region} ^ alpha)
                    * (Score_{text center region}^ beta)
            beta (float) : The parameter to calculate final score.
            box_type (str):  Boundary encoding type 'poly' or 'quad'.
            score_thr (float) : The threshold used to filter out the final
                candidates.
            nms_thr (float) :  The threshold of nms.

        Returns:
            boundaries (list[list[float]]): The instance boundary and confidence
                list.
        
pytorchocr/postprocess/fce_postprocess.py