| |
|
|
| 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 |
|
|
|
|
| @POSTPROCESSOR.register_module() |
| 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) |
|
|
| 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 |
|
|
