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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| from mmcv.ops import contour_expand | |
| from mmocr.core import points2boundary | |
| from mmocr.models.builder import POSTPROCESSOR | |
| from .base_postprocessor import BasePostprocessor | |
| class PSEPostprocessor(BasePostprocessor): | |
| """Decoding predictions of PSENet to instances. This is partially adapted | |
| from https://github.com/whai362/PSENet. | |
| Args: | |
| text_repr_type (str): The boundary encoding type 'poly' or 'quad'. | |
| min_kernel_confidence (float): The minimal kernel confidence. | |
| min_text_avg_confidence (float): The minimal text average confidence. | |
| min_kernel_area (int): The minimal text kernel area. | |
| min_text_area (int): The minimal text instance region area. | |
| """ | |
| def __init__(self, | |
| text_repr_type='poly', | |
| min_kernel_confidence=0.5, | |
| min_text_avg_confidence=0.85, | |
| min_kernel_area=0, | |
| min_text_area=16, | |
| **kwargs): | |
| super().__init__(text_repr_type) | |
| assert 0 <= min_kernel_confidence <= 1 | |
| assert 0 <= min_text_avg_confidence <= 1 | |
| assert isinstance(min_kernel_area, int) | |
| assert isinstance(min_text_area, int) | |
| self.min_kernel_confidence = min_kernel_confidence | |
| self.min_text_avg_confidence = min_text_avg_confidence | |
| self.min_kernel_area = min_kernel_area | |
| self.min_text_area = min_text_area | |
| def __call__(self, preds): | |
| """ | |
| Args: | |
| preds (Tensor): Prediction map with shape :math:`(C, H, W)`. | |
| Returns: | |
| list[list[float]]: The instance boundary and its confidence. | |
| """ | |
| assert preds.dim() == 3 | |
| preds = torch.sigmoid(preds) # text confidence | |
| score = preds[0, :, :] | |
| masks = preds > self.min_kernel_confidence | |
| text_mask = masks[0, :, :] | |
| kernel_masks = masks[0:, :, :] * text_mask | |
| score = score.data.cpu().numpy().astype(np.float32) | |
| kernel_masks = kernel_masks.data.cpu().numpy().astype(np.uint8) | |
| region_num, labels = cv2.connectedComponents( | |
| kernel_masks[-1], connectivity=4) | |
| labels = contour_expand(kernel_masks, labels, self.min_kernel_area, | |
| region_num) | |
| labels = np.array(labels) | |
| label_num = np.max(labels) | |
| boundaries = [] | |
| for i in range(1, label_num + 1): | |
| points = np.array(np.where(labels == i)).transpose((1, 0))[:, ::-1] | |
| area = points.shape[0] | |
| score_instance = np.mean(score[labels == i]) | |
| if not self.is_valid_instance(area, score_instance, | |
| self.min_text_area, | |
| self.min_text_avg_confidence): | |
| continue | |
| vertices_confidence = points2boundary(points, self.text_repr_type, | |
| score_instance) | |
| if vertices_confidence is not None: | |
| boundaries.append(vertices_confidence) | |
| return boundaries | |