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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
import mmengine
from mmocr.datasets.icdar_dataset import IcdarDataset
class TestIcdarDataset(TestCase):
def _create_dummy_icdar_json(self, json_name):
image_1 = {
'id': 0,
'width': 640,
'height': 640,
'file_name': 'fake_name.jpg',
}
image_2 = {
'id': 1,
'width': 640,
'height': 640,
'file_name': 'fake_name1.jpg',
}
annotation_1 = {
'id': 1,
'image_id': 0,
'category_id': 0,
'area': 400,
'bbox': [50, 60, 20, 20],
'iscrowd': 0,
'segmentation': [[50, 60, 70, 60, 70, 80, 50, 80]]
}
annotation_2 = {
'id': 2,
'image_id': 0,
'category_id': 0,
'area': 900,
'bbox': [100, 120, 30, 30],
'iscrowd': 0,
'segmentation': [[100, 120, 130, 120, 120, 150, 100, 150]]
}
annotation_3 = {
'id': 3,
'image_id': 0,
'category_id': 0,
'area': 1600,
'bbox': [150, 160, 40, 40],
'iscrowd': 1,
'segmentation': [[150, 160, 190, 160, 190, 200, 150, 200]]
}
annotation_4 = {
'id': 4,
'image_id': 0,
'category_id': 0,
'area': 10000,
'bbox': [250, 260, 100, 100],
'iscrowd': 1,
'segmentation': [[250, 260, 350, 260, 350, 360, 250, 360]]
}
annotation_5 = {
'id': 5,
'image_id': 1,
'category_id': 0,
'area': 10000,
'bbox': [250, 260, 100, 100],
'iscrowd': 1,
'segmentation': [[250, 260, 350, 260, 350, 360, 250, 360]]
}
annotation_6 = {
'id': 6,
'image_id': 1,
'category_id': 0,
'area': 0,
'bbox': [0, 0, 0, 0],
'iscrowd': 1,
'segmentation': [[250, 260, 350, 260, 350, 360, 250, 360]]
}
annotation_7 = {
'id': 7,
'image_id': 1,
'category_id': 2,
'area': 10000,
'bbox': [250, 260, 100, 100],
'iscrowd': 1,
'segmentation': [[250, 260, 350, 260, 350, 360, 250, 360]]
}
annotation_8 = {
'id': 8,
'image_id': 1,
'category_id': 0,
'area': 10000,
'bbox': [250, 260, 100, 100],
'iscrowd': 1,
'segmentation': [[250, 260, 350, 260, 350, 360, 250, 360]]
}
categories = [{
'id': 0,
'name': 'text',
'supercategory': 'text',
}]
fake_json = {
'images': [image_1, image_2],
'annotations': [
annotation_1, annotation_2, annotation_3, annotation_4,
annotation_5, annotation_6, annotation_7, annotation_8
],
'categories':
categories
}
self.metainfo = dict(classes=('text'))
mmengine.dump(fake_json, json_name)
def test_icdar_dataset(self):
tmp_dir = tempfile.TemporaryDirectory()
# create dummy data
fake_json_file = osp.join(tmp_dir.name, 'fake_data.json')
self._create_dummy_icdar_json(fake_json_file)
# test initialization
dataset = IcdarDataset(
ann_file=fake_json_file,
data_prefix=dict(img_path='imgs'),
metainfo=self.metainfo,
pipeline=[])
self.assertEqual(dataset.metainfo['classes'], self.metainfo['classes'])
dataset.full_init()
self.assertEqual(len(dataset), 2)
self.assertEqual(len(dataset.load_data_list()), 2)
# test load_data_list
anno = dataset.load_data_list()[0]
self.assertEqual(len(anno['instances']), 4)
self.assertTrue('ignore' in anno['instances'][0])
self.assertTrue('bbox' in anno['instances'][0])
self.assertEqual(anno['instances'][0]['bbox_label'], 0)
self.assertTrue('polygon' in anno['instances'][0])
tmp_dir.cleanup()
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