def images():
image1 = ImageIOLoader().load(
fname=Path(__file__).parent / "resources" / "images" / "1_mask.png"
)[0]
image2 = ImageIOLoader().load(
fname=Path(__file__).parent / "resources" / "images" / "2_mask.png"
)[0]
return [image1, image2]
def images():
image1 = ImageIOLoader().load(
fname=Path(__file__).parent / 'resources' / 'images' / '1_mask.png'
)[0]
image2 = ImageIOLoader().load(
fname=Path(__file__).parent / 'resources' / 'images' / '2_mask.png'
)[0]
return [image1, image2]
def test_image_io_loader():
fname = Path(__file__).parent / 'resources' / 'images' / '1_mask.png'
o = ImageIOLoader.load(fname=fname)
assert o['path'] == fname
assert o['img'].shape == (584, 565)
with pytest.raises(FileLoaderError):
non_image_fname = (Path(__file__).parent / 'resources' / 'csv' /
'algorithm_result.csv')
ImageIOLoader.load(fname=non_image_fname)
def __init__(self):
self._metrics_output = {}
self._total_G = 0
self._total_S = 0
super().__init__(
file_loader=ImageIOLoader(),
validators=(
NumberOfCasesValidator(num_cases=4),
UniquePathIndicesValidator(),
UniqueImagesValidator(),
),
aggregates={
"mean",
},
)
def test_image_io_loader():
fname = Path(__file__).parent / "resources" / "images" / "1_mask.png"
loader = ImageIOLoader()
o = loader.load(fname=fname)[0]
img = loader.load_image(fname)
assert o["path"] == fname
assert o["hash"] == loader.hash_image(img)
assert img.shape == (584, 565)
with pytest.raises(FileLoaderError):
non_image_fname = (Path(__file__).parent / "resources" / "csv" /
"algorithm_result.csv")
ImageIOLoader().load(fname=non_image_fname)