def _init_files_and_annotations(self, *args, **kwargs):
"""Create files and annotations."""
if self.subset == "train":
subset = "train_validation"
elif self.subset == "validation" or self.subset == "test":
subset = "test"
if subset == "train_validation":
pascalvoc_2007 = Pascalvoc2007(subset=subset,
skip_difficult=self.skip_difficult,
*args,
**kwargs)
pascalvoc_2012 = Pascalvoc2012(subset=subset,
skip_difficult=self.skip_difficult,
*args,
**kwargs)
self.files = pascalvoc_2007.files + pascalvoc_2012.files
self.annotations = pascalvoc_2007.annotations + pascalvoc_2012.annotations
elif subset == "test":
pascalvoc_2007 = Pascalvoc2007(subset=subset,
skip_difficult=self.skip_difficult,
*args,
**kwargs)
self.files = pascalvoc_2007.files
self.annotations = pascalvoc_2007.annotations
def test_pascalvoc_2007():
dataset = Pascalvoc2007()
assert len(dataset.classes) == 20
assert dataset.available_subsets == [
'train', 'validation', 'test', 'train_validation'
]
assert dataset._files_and_annotations() == ([
'unit/fixtures/datasets/PASCALVOC_2007/VOCdevkit/VOC2007/JPEGImages/000085.jpg'
], [[[5, 103, 212, 174, 8], [21, 69, 232, 257, 14]]])
def test_hue():
batch_size = 3
image_size = [256, 512]
dataset = Pascalvoc2007(
batch_size=batch_size, pre_processor=ResizeWithGtBoxes(image_size),
augmentor=Hue((-10, 10)),
)
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
_show_images_with_boxes(images, labels)
def test_filp_top_bottom():
batch_size = 3
image_size = [256, 512]
dataset = Pascalvoc2007(
batch_size=batch_size, pre_processor=ResizeWithGtBoxes(image_size),
augmentor=FlipTopBottom(),
)
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
_show_images_with_boxes(images, labels)
def test_ssd_random_crop():
batch_size = 3
image_size = [256, 512]
dataset = Pascalvoc2007(
batch_size=batch_size, pre_processor=ResizeWithGtBoxes(image_size),
augmentor=SSDRandomCrop(),
)
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
_show_images_with_boxes(images, labels)
assert np.all(labels[:, :, 2] <= 512)
assert np.all(labels[:, :, 3] <= 256)
def test_sequence():
batch_size = 3
image_size = [256, 512]
augmentor = Sequence([
FlipLeftRight(),
FlipTopBottom(),
SSDRandomCrop(),
])
dataset = Pascalvoc2007(
batch_size=batch_size, pre_processor=ResizeWithGtBoxes(image_size),
augmentor=augmentor,
)
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
_show_images_with_boxes(images, labels)