def test_resize_local_mean3d_keep(channel_axis):
# keep 3rd dimension
nch = 3
x = np.zeros((5, 5, nch), dtype=np.double)
x[1, 1, :] = 1
# move channels to expected dimension
x = np.moveaxis(x, -1, channel_axis)
resized = resize_local_mean(x, (10, 10), channel_axis=channel_axis)
# move channels back to last axis to match the reference image
resized = np.moveaxis(resized, channel_axis, -1)
with pytest.raises(ValueError):
# output_shape too short
resize_local_mean(x, (10, ))
ref = np.zeros((10, 10, nch))
ref[2:4, 2:4, :] = 1
assert_array_almost_equal(resized, ref)
channel_axis = channel_axis % x.ndim
spatial_shape = (10, 10)
out_shape = (spatial_shape[:channel_axis] + (nch, ) +
spatial_shape[channel_axis:])
resized = resize_local_mean(x, out_shape)
# move channels back to last axis to match the reference image
resized = np.moveaxis(resized, channel_axis, -1)
assert_array_almost_equal(resized, ref)
def test_resize_local_mean3d_resize():
# resize 3rd dimension
x = np.zeros((5, 5, 3), dtype=np.double)
x[1, 1, :] = 1
resized = resize_local_mean(x, (10, 10, 1))
ref = np.zeros((10, 10, 1))
ref[2:4, 2:4] = 1
assert_array_almost_equal(resized, ref)
# can't resize along specified channel axis
with pytest.raises(ValueError):
resize_local_mean(x, (10, 10, 1), channel_axis=-1)
def test_resize_local_mean3d():
x = np.zeros((5, 5, 2), dtype=np.double)
x[1, 1, 0] = 0
x[1, 1, 1] = 1
resized = resize_local_mean(x, (10, 10, 1))
ref = np.zeros((10, 10, 1))
ref[2:4, 2:4, :] = 0.5
assert_array_almost_equal(resized, ref)
resized = resize_local_mean(x, (10, 10, 1), grid_mode=False)
ref[1, 1, :] = 0.0703125
ref[2, 2, :] = 0.5
ref[3, 3, :] = 0.3828125
ref[1, 2, :] = ref[2, 1, :] = 0.1875
ref[1, 3, :] = ref[3, 1, :] = 0.1640625
ref[2, 3, :] = ref[3, 2, :] = 0.4375
assert_array_almost_equal(resized, ref)
def test_resize_local_mean2d():
x = np.zeros((5, 5), dtype=np.double)
x[1, 1] = 1
resized = resize_local_mean(x, (10, 10))
ref = np.zeros((10, 10))
ref[2:4, 2:4] = 1
assert_array_almost_equal(resized, ref)
def test_resize_local_mean_nd(dim):
shape = 2 + np.arange(dim) * 2
x = np.ones(shape)
out_shape = (np.asarray(shape) * 1.5).astype(int)
resized = resize_local_mean(x, out_shape)
expected_shape = 1.5 * shape
assert_array_equal(resized.shape, expected_shape)
assert_array_equal(resized, 1)
def test_resize_local_mean3d_2din_3dout():
# 3D output with 2D input
x = np.zeros((5, 5), dtype=np.double)
x[1, 1] = 1
resized = resize_local_mean(x, (10, 10, 1))
ref = np.zeros((10, 10, 1))
ref[2:4, 2:4] = 1
assert_array_almost_equal(resized, ref)
def test_resize_local_mean2d_4d():
# resize with extra output dimensions
x = np.zeros((5, 5), dtype=np.double)
x[1, 1] = 1
out_shape = (10, 10, 1, 1)
resized = resize_local_mean(x, out_shape)
ref = np.zeros(out_shape)
ref[2:4, 2:4, ...] = 1
assert_array_almost_equal(resized, ref)
def test_resize_local_mean_dtype():
x = np.zeros((5, 5))
x_f32 = x.astype(np.float32)
x_u8 = x.astype(np.uint8)
x_b = x.astype(bool)
assert resize_local_mean(x, (10, 10),
preserve_range=False).dtype == x.dtype
assert resize_local_mean(x, (10, 10), preserve_range=True).dtype == x.dtype
assert resize_local_mean(x_u8, (10, 10),
preserve_range=False).dtype == np.double
assert resize_local_mean(x_u8, (10, 10),
preserve_range=True).dtype == np.double
assert resize_local_mean(x_b, (10, 10),
preserve_range=False).dtype == np.double
assert resize_local_mean(x_b, (10, 10),
preserve_range=True).dtype == np.double
assert resize_local_mean(x_f32, (10, 10),
preserve_range=False).dtype == x_f32.dtype
assert resize_local_mean(x_f32, (10, 10),
preserve_range=True).dtype == x_f32.dtype
