def test_icp_opencv(self):
warnings.simplefilter("error")
# Do a fast an dirty 3 iteration ICP just to make sure it doesn't error out.
icp = coreg.ICP(max_iterations=3)
icp.fit(**self.fit_params)
aligned_dem = icp.apply(self.tba.data, self.ref.transform)
assert aligned_dem.shape == self.ref.data.squeeze().shape
def test_all_nans(self):
"""Check that the coregistration approaches fail gracefully when given only nans."""
dem1 = np.ones((50, 50), dtype=float)
dem2 = dem1.copy() + np.nan
affine = rio.transform.from_origin(0, 0, 1, 1)
biascorr = coreg.BiasCorr()
icp = coreg.ICP()
pytest.raises(ValueError, biascorr.fit, dem1, dem2, transform=affine)
pytest.raises(ValueError, icp.fit, dem1, dem2, transform=affine)
dem2[[3, 20, 40], [2, 21, 41]] = 1.2
biascorr.fit(dem1, dem2, transform=affine)
pytest.raises(ValueError, icp.fit, dem1, dem2, transform=affine)
# Apply the bias to a DEM
corrected_dem = bias_corr.apply(tba_data,
transform=dem_to_be_aligned.transform)
# Use median bias instead
bias_median = coreg.BiasCorr(bias_func=np.median)
# bias_median.fit(... # etc.
##############
# SECTION: ICP
##############
# Instantiate the object with default parameters
icp = coreg.ICP()
# Fit the data to a suitable transformation.
icp.fit(ref_data, tba_data, transform=transform, inlier_mask=inlier_mask)
# Apply the transformation matrix to the data (or any other data)
aligned_dem = icp.apply(tba_data, transform=transform)
###################
# SECTION: Pipeline
###################
pipeline = coreg.CoregPipeline([coreg.BiasCorr(), coreg.ICP()])
# pipeline.fit(... # etc.
# This works identically to the syntax above