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