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