| | 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 |
| |
|
| |
|
