| |
| import cv2 |
| import numpy as np |
| import torch |
| from mmcv.ops import pixel_group |
|
|
| from mmocr.core import points2boundary |
| from mmocr.models.builder import POSTPROCESSOR |
| from .base_postprocessor import BasePostprocessor |
|
|
|
|
| @POSTPROCESSOR.register_module() |
| class PANPostprocessor(BasePostprocessor): |
| """Convert scores to quadrangles via post processing in PANet. This is |
| partially adapted from https://github.com/WenmuZhou/PAN.pytorch. |
| |
| Args: |
| text_repr_type (str): The boundary encoding type 'poly' or 'quad'. |
| min_text_confidence (float): The minimal text confidence. |
| min_kernel_confidence (float): The minimal kernel confidence. |
| min_text_avg_confidence (float): The minimal text average confidence. |
| min_text_area (int): The minimal text instance region area. |
| """ |
|
|
| def __init__(self, |
| text_repr_type='poly', |
| min_text_confidence=0.5, |
| min_kernel_confidence=0.5, |
| min_text_avg_confidence=0.85, |
| min_text_area=16, |
| **kwargs): |
| super().__init__(text_repr_type) |
|
|
| self.min_text_confidence = min_text_confidence |
| self.min_kernel_confidence = min_kernel_confidence |
| self.min_text_avg_confidence = min_text_avg_confidence |
| 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[:2, :, :] = torch.sigmoid(preds[:2, :, :]) |
| preds = preds.detach().cpu().numpy() |
|
|
| text_score = preds[0].astype(np.float32) |
| text = preds[0] > self.min_text_confidence |
| kernel = (preds[1] > self.min_kernel_confidence) * text |
| embeddings = preds[2:].transpose((1, 2, 0)) |
|
|
| region_num, labels = cv2.connectedComponents( |
| kernel.astype(np.uint8), connectivity=4) |
| contours, _ = cv2.findContours((kernel * 255).astype(np.uint8), |
| cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) |
| kernel_contours = np.zeros(text.shape, dtype='uint8') |
| cv2.drawContours(kernel_contours, contours, -1, 255) |
| text_points = pixel_group(text_score, text, embeddings, labels, |
| kernel_contours, region_num, |
| self.min_text_avg_confidence) |
|
|
| boundaries = [] |
| for text_point in text_points: |
| text_confidence = text_point[0] |
| text_point = text_point[2:] |
| text_point = np.array(text_point, dtype=int).reshape(-1, 2) |
| area = text_point.shape[0] |
|
|
| if not self.is_valid_instance(area, text_confidence, |
| self.min_text_area, |
| self.min_text_avg_confidence): |
| continue |
|
|
| vertices_confidence = points2boundary(text_point, |
| self.text_repr_type, |
| text_confidence) |
| if vertices_confidence is not None: |
| boundaries.append(vertices_confidence) |
|
|
| return boundaries |
|
|
