def _read_envi(path: Path) -> Tuple[ndarray, BandInfo, dict]:
"""Read array data from ENVI binary and header file.
This function uses imageio to read the image and adds the
BandInfo and metadata from the ENVI header file.
Args:
path: Path to ENVI binary file (usually .raw or .img).
Returns:
image, bandinfo, meta
Tuple containing ndarray of the image data, BandInfo object and
metadata dictionary.
"""
hsi = imageio.imread(path, format='gdal')
if hsi.ndim == 2:
hsi = hsi[..., np.newaxis]
# Transpose axis as imageio uses (ch, x, y) shape for spectral images
hsi = np.transpose(hsi, (1, 2, 0))
meta = SpectralArray.read_envi_header(path.with_suffix('.hdr'))
bandinfo = BandInfo.from_envi_header(meta)
return hsi, bandinfo, meta
def test_band_info_envi():
"""Test ``from_envi`` class method.
"""
testing.needs_internet()
envi_file = testing.get_remote_file(TEST_ENVI_HDR)
d = SpectralArray.read_envi_header(envi_file)
band_info = BandInfo.from_envi_header(d)
centers_exp = np.asarray([400.6, 450.3, 500, 551.7, 600.666, 760.9])
bandwiths_exp = np.asarray([1.1, 1.2, 1.3, 1.4, 1.5, 1.6])
assert 6 == band_info.num_channels
assert band_info.type is None
assert band_info.centers_std is None
assert band_info.bandwidths_std is None
assert np.array_equal(centers_exp, band_info.centers)
assert np.array_equal(bandwiths_exp, band_info.bandwidths)
return