def test_opencv(self, device):
data = torch.tensor(
[[[0.3944633, 0.8597369, 0.1670904, 0.2825457, 0.0953912],
[0.1251704, 0.8020709, 0.8933256, 0.9170977, 0.1497008],
[0.2711633, 0.1111478, 0.0783281, 0.2771807, 0.5487481],
[0.0086008, 0.8288748, 0.9647092, 0.8922020, 0.7614344],
[0.2898048, 0.1282895, 0.7621747, 0.5657831, 0.9918593]],
[[0.5414237, 0.9962701, 0.8947155, 0.5900949, 0.9483274],
[0.0468036, 0.3933847, 0.8046577, 0.3640994, 0.0632100],
[0.6171775, 0.8624780, 0.4126036, 0.7600935, 0.7279997],
[0.4237089, 0.5365476, 0.5591233, 0.1523191, 0.1382165],
[0.8932794, 0.8517839, 0.7152701, 0.8983801, 0.5905426]],
[[0.2869580, 0.4700376, 0.2743714, 0.8135023, 0.2229074],
[0.9306560, 0.3734594, 0.4566821, 0.7599275, 0.7557513],
[0.7415742, 0.6115875, 0.3317572, 0.0379378, 0.1315770],
[0.8692724, 0.0809556, 0.7767404, 0.8742208, 0.1522012],
[0.7708948, 0.4509611, 0.0481175, 0.2358997, 0.6900532]]])
data = data.to(device)
expected = torch.tensor(
[[0.4485849, 0.8233618, 0.6262833, 0.6218331, 0.6341921],
[0.3200093, 0.4340172, 0.7107211, 0.5454938, 0.2801398],
[0.6149265, 0.7018101, 0.3503231, 0.4891168, 0.5292346],
[0.5096100, 0.4336508, 0.6704276, 0.4525143, 0.2134447],
[0.7878902, 0.6494595, 0.5211386, 0.6623823, 0.6660464]])
expected = expected.to(device)
img_gray = kornia.bgr_to_grayscale(data)
assert_allclose(img_gray, expected)
def test_cardinality(self, device, dtype, batch_size, height, width):
img = torch.ones(batch_size,
3,
height,
width,
device=device,
dtype=dtype)
assert kornia.bgr_to_grayscale(img).shape == (batch_size, 1, height,
width)
def test_module(self):
data = torch.tensor([[[[100., 73.], [200., 22.]],
[[50., 10.], [
148,
14,
]], [[225., 255.], [48., 8.]]]])
assert_allclose(kornia.bgr_to_grayscale(data / 255),
kornia.color.BgrToGrayscale()(data / 255))
def test_bgr_to_grayscale_batch(self, device):
batch_size, channels, height, width = 2, 3, 4, 5
img = torch.ones(batch_size, channels, height, width).to(device)
assert kornia.bgr_to_grayscale(img).shape == \
(batch_size, 1, height, width)
def test_bgr_to_grayscale(self, device):
channels, height, width = 3, 4, 5
img = torch.ones(channels, height, width).to(device)
assert kornia.bgr_to_grayscale(img).shape == (1, height, width)
def test_smoke(self, device, dtype):
C, H, W = 3, 4, 5
img = torch.rand(C, H, W, device=device, dtype=dtype)
assert kornia.bgr_to_grayscale(img) is not None
