def test_load():
image = Image("tests/images/lenna.png")
assert image.loaded
image = Image("tests/images/lenna.png", lazy=True)
assert not image.loaded
image.load()
assert image.loaded
def test_compute():
image = Image("tests/images/lenna.png", lazy=True)
image = image.apply(GrayScale())
assert image.pending.num_transforms() == 1
computed = image.compute()
assert computed.pending.num_transforms() == 0
image.compute(in_place=True)
assert image.pending.num_transforms() == 0
def test_image():
image = Image("tests/images/lenna.png")
assert image.array is not None
image = Image("https://images.dog.ceo/breeds/komondor/n02105505_2699.jpg")
assert image.array is not None
def test_apply():
image = Image("tests/images/lenna.png")
pipe = Pipeline([Blur(), GrayScale()])
image2 = image.apply(Blur()).apply(GrayScale())
image = image.apply(pipe)
assert image == image2
def test_width():
image = Image("tests/images/lenna.png")
assert image.width == 512
def test_height():
image = Image("tests/images/lenna.png")
assert image.height == 512
def test_pending():
image = Image("tests/images/lenna.png", lazy=True)
image = image.apply(FilterChannels(channels=[0, 1]))
image.apply(GrayScale(), in_place=True)
assert image.pending.num_transforms() == 2