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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import mmcv
import numpy as np
from mmocr.datasets.icdar_dataset import IcdarDataset
def _create_dummy_icdar_json(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]]
}
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],
'categories':
categories
}
mmcv.dump(fake_json, json_name)
def test_icdar_dataset():
tmp_dir = tempfile.TemporaryDirectory()
# create dummy data
fake_json_file = osp.join(tmp_dir.name, 'fake_data.json')
_create_dummy_icdar_json(fake_json_file)
# test initialization
dataset = IcdarDataset(ann_file=fake_json_file, pipeline=[])
assert dataset.CLASSES == ('text')
assert dataset.img_ids == [0, 1]
assert dataset.select_first_k == -1
# test _parse_ann_info
ann = dataset.get_ann_info(0)
assert np.allclose(ann['bboxes'],
[[50., 60., 70., 80.], [100., 120., 130., 150.]])
assert np.allclose(ann['labels'], [0, 0])
assert np.allclose(ann['bboxes_ignore'],
[[150., 160., 190., 200.], [250., 260., 350., 360.]])
assert np.allclose(ann['masks'],
[[[50, 60, 70, 60, 70, 80, 50, 80]],
[[100, 120, 130, 120, 120, 150, 100, 150]]])
assert np.allclose(ann['masks_ignore'],
[[[150, 160, 190, 160, 190, 200, 150, 200]],
[[250, 260, 350, 260, 350, 360, 250, 360]]])
assert dataset.cat_ids == [0]
tmp_dir.cleanup()
# test rank output
# result = [[]]
# out_file = tempfile.NamedTemporaryFile().name
# with pytest.raises(AssertionError):
# dataset.output_ranklist(result, out_file)
# result = [{'hmean': 1}, {'hmean': 0.5}]
# output = dataset.output_ranklist(result, out_file)
# assert output[0]['hmean'] == 0.5
# test get_gt_mask
# output = dataset.get_gt_mask()
# assert np.allclose(output[0][0],
# [[50, 60, 70, 60, 70, 80, 50, 80],
# [100, 120, 130, 120, 120, 150, 100, 150]])
# assert output[0][1] == []
# assert np.allclose(output[1][0],
# [[150, 160, 190, 160, 190, 200, 150, 200],
# [250, 260, 350, 260, 350, 360, 250, 360]])
# assert np.allclose(output[1][1],
# [[250, 260, 350, 260, 350, 360, 250, 360]])
# test evluation
metrics = ['hmean-iou', 'hmean-ic13']
results = [{
'boundary_result': [[50, 60, 70, 60, 70, 80, 50, 80, 1],
[100, 120, 130, 120, 120, 150, 100, 150, 1]]
}, {
'boundary_result': []
}]
output = dataset.evaluate(results, metrics)
assert output['hmean-iou:hmean'] == 1
assert output['hmean-ic13:hmean'] == 1
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