def test_normalize(self):
img = np.arange(0, 9).reshape(3, 3)
value_range_in = (0, 10)
value_range_out = (0, 1)
expected = np.array([[0, .1, .2],
[.3, .4, .5],
[.6, .7, .8]])
calculated = ImageTools.normalize(img,
value_range_in=value_range_in,
value_range_out=value_range_out)
np.testing.assert_array_almost_equal(expected, calculated)
def test_normalize_0to255(self):
img = np.arange(-4, 5).reshape(3, 3) / 5
value_range_in = (-1, 1)
value_range_out = (0, 255)
expected = np.array([[25.5, 51., 76.5],
[102., 127.5, 153.],
[178.5, 204., 229.5]])
calculated = ImageTools.normalize(img,
value_range_in=value_range_in,
value_range_out=value_range_out)
np.testing.assert_array_almost_equal(expected, calculated)
def test_normalize_clip(self):
img = np.arange(0, 9).reshape(3, 3)
value_range_in = (0, 5)
value_range_out = (0, 1)
expected = np.array([[0, .2, .4],
[.6, .8, 1],
[1, 1, 1]])
calculated = ImageTools.normalize(img,
value_range_in=value_range_in,
value_range_out=value_range_out,
clip=True)
np.testing.assert_array_almost_equal(expected, calculated)
def get_ndvi(red, nir, vrange=(-1, 1), dtype=np.float32):
"""
Calculate the NDVI (Normalized-Difference Vegetation Index)
:param red: The red band dataset
:type red: :class:`Common.GDalDatasetWrapper.GDalDatasetWrapper`
:param nir: The nir band dataset
:type nir: :class:`Common.GDalDatasetWrapper.GDalDatasetWrapper`
:param vrange: The range of output values as tuple. By default: (-1, 1).
:type vrange: tuple of int
:param dtype: The output dtype.
:type dtype: :class`np.dtype`
:return: The NDVI as numpy array.
:rtype: :class:`Common.GDalDatasetWrapper.GDalDatasetWrapper`
"""
if nir.extent != red.extent or nir.epsg != red.epsg:
raise ValueError("Cannot calculate NDSI on two different extents.")
if nir.resolution != red.resolution:
# Resize to nir resolution in this case.
tr = " ".join([str(i) for i in nir.resolution])
ds_red = ImageTools.gdal_translate(red, tr=tr, r="cubic")
else:
ds_red = red
# TODO Add new test
img_red = np.array(ds_red.array, dtype=np.float32)
img_nir = np.array(nir.array, dtype=np.float32)
# Compensate for nan:
np.seterr(divide='ignore', invalid='ignore')
img_ndvi = np.where((img_red + img_nir) != 0, (img_red - img_nir) / (img_red + img_nir), -1)
# Scale to vrange
img_ndvi_scaled = ImageTools.normalize(img_ndvi, value_range_out=vrange, value_range_in=(-1, 1),
dtype=dtype, clip=True)
return GDalDatasetWrapper(ds=nir.get_ds(), array=img_ndvi_scaled)