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import csv
import os.path as osp
import tempfile
import mmcv
import numpy as np
import pytest
from mmdet.datasets import OpenImagesChallengeDataset, OpenImagesDataset
def _create_ids_error_oid_csv(
label_file,
fake_csv_file,
):
label_description = ['/m/000002', 'Football']
# `newline=''` is used to avoid index error of out of bounds
# in Windows system
with open(label_file, 'w', newline='') as f:
f_csv = csv.writer(f)
f_csv.writerow(label_description)
header = [
'ImageID', 'Source', 'LabelName', 'Confidence', 'XMin', 'XMax', 'YMin',
'YMax', 'IsOccluded', 'IsTruncated', 'IsGroupOf', 'IsDepiction',
'IsInside'
]
annotations = [[
'color', 'xclick', '/m/000002', '1', '0.022673031', '0.9642005',
'0.07103825', '0.80054647', '0', '0', '0', '0', '0'
],
[
'000595fe6fee6369', 'xclick', '/m/000000', '1', '0',
'1', '0', '1', '0', '0', '1', '0', '0'
]]
# `newline=''` is used to avoid index error of out of bounds
# in Windows system
with open(fake_csv_file, 'w', newline='') as f:
f_csv = csv.writer(f)
f_csv.writerow(header)
f_csv.writerows(annotations)
def _create_oid_style_ann(label_file, csv_file, label_level_file):
label_description = [['/m/000000', 'Sports equipment'],
['/m/000001', 'Ball'], ['/m/000002', 'Football'],
['/m/000004', 'Bicycle']]
with open(label_file, 'w', newline='') as f:
f_csv = csv.writer(f)
f_csv.writerows(label_description)
header = [
'ImageID', 'Source', 'LabelName', 'Confidence', 'XMin', 'XMax', 'YMin',
'YMax', 'IsOccluded', 'IsTruncated', 'IsGroupOf', 'IsDepiction',
'IsInside'
]
annotations = [
[
'color', 'xclick', '/m/000002', 1, 0.0333333, 0.1, 0.0333333, 0.1,
0, 0, 1, 0, 0
],
[
'color', 'xclick', '/m/000002', 1, 0.1, 0.166667, 0.1, 0.166667, 0,
0, 0, 0, 0
],
]
# `newline=''` is used to avoid index error of out of bounds
# in Windows system
with open(csv_file, 'w', newline='') as f:
f_csv = csv.writer(f)
f_csv.writerow(header)
f_csv.writerows(annotations)
header = ['ImageID', 'Source', 'LabelName', 'Confidence']
annotations = [['color', 'xclick', '/m/000002', '1'],
['color', 'xclick', '/m/000004', '0']]
# `newline=''` is used to avoid index error of out of bounds
# in Windows system
with open(label_level_file, 'w', newline='') as f:
f_csv = csv.writer(f)
f_csv.writerow(header)
f_csv.writerows(annotations)
def _create_hierarchy_json(hierarchy_name):
fake_hierarchy = \
{'LabelName': '/m/0bl9f', # entity label
'Subcategory': [
{
'LabelName': '/m/000000',
'Subcategory':
[
{'LabelName': '/m/000001',
'Subcategory':
[
{
'LabelName': '/m/000002'
}
]
},
{
'LabelName': '/m/000004'
}
]
}
]
}
mmcv.dump(fake_hierarchy, hierarchy_name)
def _create_hierarchy_np(hierarchy_name):
fake_hierarchy = np.array([[0, 1, 0, 0, 0], [0, 1, 1, 0,
0], [0, 1, 1, 1, 0],
[0, 1, 0, 0, 1], [0, 0, 0, 0, 0]])
with open(hierarchy_name, 'wb') as f:
np.save(f, fake_hierarchy)
def _create_dummy_results():
boxes = [
np.zeros((0, 5)),
np.zeros((0, 5)),
np.array([[10, 10, 15, 15, 1.0], [15, 15, 30, 30, 0.98],
[10, 10, 25, 25, 0.98], [28, 28, 35, 35, 0.97],
[30, 30, 51, 51, 0.96], [100, 110, 120, 130, 0.15]]),
np.array([[30, 30, 50, 50, 0.51]]),
]
return [boxes]
def _creat_oid_challenge_style_ann(txt_file, label_file, label_level_file):
bboxes = [
'validation/color.jpg\n',
'4 29\n',
'2\n',
'1 0.0333333 0.1 0.0333333 0.1 1\n',
'1 0.1 0.166667 0.1 0.166667 0\n',
]
# `newline=''` is used to avoid index error of out of bounds
# in Windows system
with open(txt_file, 'w', newline='') as f:
f.writelines(bboxes)
f.close()
label_description = [['/m/000000', 'Sports equipment', 1],
['/m/000001', 'Ball', 2],
['/m/000002', 'Football', 3],
['/m/000004', 'Bicycle', 4]]
# `newline=''` is used to avoid index error of out of bounds
# in Windows system
with open(label_file, 'w', newline='') as f:
f_csv = csv.writer(f)
f_csv.writerows(label_description)
header = ['ImageID', 'LabelName', 'Confidence']
annotations = [['color', '/m/000001', '1'], ['color', '/m/000000', '0']]
# `newline=''` is used to avoid index error of out of bounds
# in Windows system
with open(label_level_file, 'w', newline='') as f:
f_csv = csv.writer(f)
f_csv.writerow(header)
f_csv.writerows(annotations)
def _create_metas(meta_file):
fake_meta = [{
'filename': 'data/OpenImages/OpenImages/validation/color.jpg',
'ori_shape': (300, 300, 3)
}]
mmcv.dump(fake_meta, meta_file)
def test_oid_annotation_ids_unique():
# create fake ann files
tmp_dir = tempfile.TemporaryDirectory()
fake_label_file = osp.join(tmp_dir.name, 'fake_label.csv')
fake_ann_file = osp.join(tmp_dir.name, 'fake_ann.csv')
_create_ids_error_oid_csv(fake_label_file, fake_ann_file)
# test annotation ids not unique error
with pytest.raises(AssertionError):
OpenImagesDataset(
ann_file=fake_ann_file, label_file=fake_label_file, pipeline=[])
tmp_dir.cleanup()
def test_openimages_dataset():
# create fake ann files
tmp_dir = tempfile.TemporaryDirectory()
label_file = osp.join(tmp_dir.name, 'label_file.csv')
ann_file = osp.join(tmp_dir.name, 'ann_file.csv')
label_level_file = osp.join(tmp_dir.name, 'label_level_file.csv')
_create_oid_style_ann(label_file, ann_file, label_level_file)
hierarchy_json = osp.join(tmp_dir.name, 'hierarchy.json')
_create_hierarchy_json(hierarchy_json)
# test whether hierarchy_file is not None when set
# get_parent_classes is True
with pytest.raises(AssertionError):
OpenImagesDataset(
ann_file=ann_file,
label_file=label_file,
image_level_ann_file=label_level_file,
pipeline=[])
dataset = OpenImagesDataset(
ann_file=ann_file,
label_file=label_file,
image_level_ann_file=label_level_file,
hierarchy_file=hierarchy_json,
pipeline=[])
ann = dataset.get_ann_info(0)
# two legal detection bboxes with `group_of` parameter
assert ann['bboxes'].shape[0] == ann['labels'].shape[0] == \
ann['gt_is_group_ofs'].shape[0] == 2
# test load metas from pipeline
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(128, 128),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
dataset = OpenImagesDataset(
ann_file=ann_file,
img_prefix='tests/data',
label_file=label_file,
image_level_ann_file=label_level_file,
load_from_file=False,
hierarchy_file=hierarchy_json,
pipeline=test_pipeline)
dataset.prepare_test_img(0)
assert len(dataset.test_img_metas) == 1
result = _create_dummy_results()
dataset.evaluate(result)
# test get hierarchy for classes
hierarchy_json = osp.join(tmp_dir.name, 'hierarchy.json')
_create_hierarchy_json(hierarchy_json)
# test with hierarchy file wrong suffix
with pytest.raises(AssertionError):
fake_path = osp.join(tmp_dir.name, 'hierarchy.csv')
OpenImagesDataset(
ann_file=ann_file,
img_prefix='tests/data',
label_file=label_file,
image_level_ann_file=label_level_file,
load_from_file=False,
hierarchy_file=fake_path,
pipeline=test_pipeline)
# test load hierarchy file succseefully
hierarchy = dataset.get_relation_matrix(hierarchy_json)
hierarchy_gt = np.array([[1, 0, 0, 0], [1, 1, 0, 0], [1, 1, 1, 0],
[1, 0, 0, 1]])
assert np.equal(hierarchy, hierarchy_gt).all()
# test evaluation
# create fake metas
meta_file = osp.join(tmp_dir.name, 'meta.pkl')
_create_metas(meta_file)
dataset = OpenImagesDataset(
ann_file=ann_file,
label_file=label_file,
image_level_ann_file=label_level_file,
hierarchy_file=hierarchy_json,
meta_file=meta_file,
pipeline=[])
# test evaluation with using group_of, adding father classes to
# GT and annotations, and considering image_level_image,
# In the first label (Sports equipment): tp = [0, 1, 0, 0, 1],
# fp = [1, 0, 1, 1, 0]
# In the second label (Ball), tp = [0, 1, 0, 1], fp = [1, 0, 1, 0].
# In the third label (Football), tp = [0, 1, 0, 1], fp = [1, 0, 1, 0].
# In the forth label (Bicycle), tp = [0], fp = [1].
result = _create_dummy_results()
parsed_results = dataset.evaluate(result)
assert np.isclose(parsed_results['mAP'], 0.8333, 1e-4)
dataset = OpenImagesDataset(
ann_file=ann_file,
label_file=label_file,
load_image_level_labels=False,
image_level_ann_file=label_level_file,
hierarchy_file=hierarchy_json,
meta_file=meta_file,
pipeline=[])
# test evaluation with using group_of, adding father classes to
# GT and annotations, and not considering image_level_image,
# In the first label (Sports equipment): tp = [0, 1, 0, 0, 1],
# fp = [1, 0, 1, 1, 0]
# In the second label (Ball), tp = [0, 1, 0, 1], fp = [1, 0, 1, 0].
# In the third label (Football), tp = [0, 1, 0, 1], fp = [1, 0, 1, 0].
# In the forth label (Bicycle), tp = [], fp = [].
result = _create_dummy_results()
parsed_results = dataset.evaluate(result)
assert np.isclose(parsed_results['mAP'], 0.8333, 1e-4)
tmp_dir.cleanup()
def test_openimages_challenge_dataset():
# create fake ann files
tmp_dir = tempfile.TemporaryDirectory()
ann_file = osp.join(tmp_dir.name, 'ann_file.txt')
label_file = osp.join(tmp_dir.name, 'label_file.csv')
label_level_file = osp.join(tmp_dir.name, 'label_level_file.csv')
_creat_oid_challenge_style_ann(ann_file, label_file, label_level_file)
dataset = OpenImagesChallengeDataset(
ann_file=ann_file,
label_file=label_file,
load_image_level_labels=False,
get_supercategory=False,
pipeline=[])
ann = dataset.get_ann_info(0)
# two legal detection bboxes with `group_of` parameter
assert ann['bboxes'].shape[0] == ann['labels'].shape[0] == \
ann['gt_is_group_ofs'].shape[0] == 2
dataset.prepare_train_img(0)
dataset.prepare_test_img(0)
meta_file = osp.join(tmp_dir.name, 'meta.pkl')
_create_metas(meta_file)
result = _create_dummy_results()
with pytest.raises(AssertionError):
fake_json = osp.join(tmp_dir.name, 'hierarchy.json')
OpenImagesChallengeDataset(
ann_file=ann_file,
label_file=label_file,
image_level_ann_file=label_level_file,
hierarchy_file=fake_json,
meta_file=meta_file,
pipeline=[])
hierarchy_file = osp.join(tmp_dir.name, 'hierarchy.np')
_create_hierarchy_np(hierarchy_file)
dataset = OpenImagesChallengeDataset(
ann_file=ann_file,
label_file=label_file,
image_level_ann_file=label_level_file,
hierarchy_file=hierarchy_file,
meta_file=meta_file,
pipeline=[])
dataset.evaluate(result)
tmp_dir.cleanup()
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