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from collections import OrderedDict |
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import torch |
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import numpy as np |
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from concern.config import Configurable, State |
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from .data_process import DataProcess |
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import cv2 |
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class MakeICDARData(DataProcess): |
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shrink_ratio = State(default=0.4) |
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def __init__(self, debug=False, cmd={}, **kwargs): |
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self.load_all(**kwargs) |
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self.debug = debug |
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if 'debug' in cmd: |
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self.debug = cmd['debug'] |
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def process(self, data): |
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polygons = [] |
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ignore_tags = [] |
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annotations = data['polys'] |
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for annotation in annotations: |
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polygons.append(np.array(annotation['points'])) |
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ignore_tags.append(annotation['ignore']) |
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ignore_tags = np.array(ignore_tags, dtype=np.uint8) |
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filename = data.get('filename', data['data_id']) |
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if self.debug: |
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self.draw_polygons(data['image'], polygons, ignore_tags) |
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shape = np.array(data['shape']) |
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return OrderedDict(image=data['image'], |
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polygons=polygons, |
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ignore_tags=ignore_tags, |
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shape=shape, |
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filename=filename, |
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is_training=data['is_training']) |
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def draw_polygons(self, image, polygons, ignore_tags): |
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for i in range(len(polygons)): |
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polygon = polygons[i].reshape(-1, 2).astype(np.int32) |
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ignore = ignore_tags[i] |
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if ignore: |
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color = (255, 0, 0) |
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else: |
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color = (0, 0, 255) |
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cv2.polylines(image, [polygon], True, color, 1) |
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polylines = staticmethod(draw_polygons) |
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class ICDARCollectFN(Configurable): |
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def __init__(self, *args, **kwargs): |
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pass |
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def __call__(self, batch): |
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data_dict = OrderedDict() |
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for sample in batch: |
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for k, v in sample.items(): |
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if k not in data_dict: |
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data_dict[k] = [] |
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if isinstance(v, np.ndarray): |
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v = torch.from_numpy(v) |
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data_dict[k].append(v) |
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data_dict['image'] = torch.stack(data_dict['image'], 0) |
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return data_dict |
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