| from typing import Optional |
|
|
| import cv2 |
| import numpy as np |
| import torch |
| from torch import Tensor |
| from shapely.geometry import Polygon |
|
|
| from mmengine.structures import InstanceData |
| from mmocr.structures import TextDetDataSample |
| from mmocr.registry import MODELS |
| from mmocr.models.textdet.postprocessors import DBPostprocessor |
|
|
| from seghist.utils import unstretch_kernel |
|
|
| @MODELS.register_module() |
| class IterExpandPostprocessor(DBPostprocessor): |
| """Implementation for Iterative Expansion Distance Post-Processor. |
| |
| Args: |
| shrink_ratio: r<1 |
| stretch_ratio: s>=1 |
| min_text_area: min regional area in origin scale. |
| refine: refine or unclip kernel only once. |
| unclip_ratio: u>0, used when refine is false. |
| """ |
| def __init__(self, |
| shrink_ratio: float = 0., |
| stretch_ratio: float = 2.0, |
| min_text_area: int = 200, |
| refine: bool = True, |
| unclip_ratio: Optional[float] = None, |
| **kwargs): |
| super().__init__(**kwargs) |
| self.stretch_ratio = stretch_ratio |
| self.shrink_ratio = shrink_ratio |
| self.min_text_area = min_text_area |
| self.refine = refine |
| if not refine: |
| assert unclip_ratio > 0, 'must set unclip ratio u when not refine' |
| self.unclip_ratio = unclip_ratio |
|
|
| def get_text_instances(self, prob_map: Tensor, |
| data_sample: TextDetDataSample |
| ) -> TextDetDataSample: |
| """Get text instance predictions of one image. |
| |
| Args: |
| pred_result (Tensor): DBNet's output ``prob_map`` of shape |
| :math:`(H, W)`. |
| data_sample (TextDetDataSample): Datasample of an image. |
| |
| Returns: |
| TextDetDataSample: A new DataSample with predictions filled in. |
| Polygons and results are saved in |
| ``TextDetDataSample.pred_instances.polygons``. The confidence |
| scores are saved in ``TextDetDataSample.pred_instances.scores``. |
| """ |
| prob_map = prob_map[..., :data_sample.valid_shape[0], :data_sample.valid_shape[1]] |
|
|
| data_sample.pred_instances = InstanceData() |
| data_sample.pred_instances.polygons = [] |
| data_sample.pred_instances.scores = [] |
|
|
| text_mask = prob_map > self.mask_thr |
|
|
| score_map = prob_map.data.cpu().numpy().astype(np.float32) |
| text_mask = text_mask.data.cpu().numpy() * 255 |
| text_mask = text_mask.astype(np.uint8) |
| |
| contours, _ = cv2.findContours(text_mask, |
| cv2.RETR_EXTERNAL, |
| cv2.CHAIN_APPROX_SIMPLE) |
|
|
| for i, poly in enumerate(contours): |
| if i > self.max_candidates: |
| break |
| epsilon = self.epsilon_ratio * cv2.arcLength(poly, True) |
| approx = cv2.approxPolyDP(poly, epsilon, True) |
| poly_pts = approx.reshape(-1, 2) |
| if poly_pts.shape[0] < 4: |
| continue |
| score = self._get_bbox_score(score_map, poly_pts) |
| if score < self.min_text_score: |
| continue |
| |
| |
| try: |
| poly = unstretch_kernel(poly_pts, |
| self.shrink_ratio, |
| self.stretch_ratio, |
| refinement=self.refine, |
| unclip_ratio=self.unclip_ratio) |
| except Exception as e: |
| print(f'Error {e} find when unstretching kernel {poly_pts}.') |
| |
| |
| |
| if len(poly) == 0: |
| continue |
| poly = poly.reshape(-1, 2) |
|
|
| if self.text_repr_type == 'quad': |
| rect = cv2.minAreaRect(poly.astype(np.int32)) |
| vertices = cv2.boxPoints(rect) |
| poly = vertices.flatten() if min( |
| rect[1]) >= self.min_text_width else [] |
| elif self.text_repr_type == 'poly': |
| scale = data_sample.scale_factor[0] * data_sample.scale_factor[1] |
| poly = poly.flatten() if Polygon( |
| poly).area / scale > self.min_text_area else [] |
|
|
| if len(poly) < 8: |
| poly = np.array([], dtype=np.float32) |
|
|
| if len(poly) > 0: |
| data_sample.pred_instances.polygons.append(poly) |
| data_sample.pred_instances.scores.append(score) |
|
|
| data_sample.pred_instances.scores = torch.FloatTensor( |
| data_sample.pred_instances.scores) |
|
|
| return data_sample |
