def test_gdal_translate(self):
img = np.ones((self.height, self.width, 2), np.int16)
path = os.path.join(os.getcwd(), "test_gdal_translate.tif")
scaled = os.path.join(os.getcwd(), "scaled.tif")
out_resolution = (20, -20)
ImageIO.write_geotiff(img, path, self.projection, self.coordinates)
self.assertTrue(os.path.exists(path))
ds = ImageTools.gdal_translate(path, scaled,
tr=" ".join([str(i) for i in out_resolution]),
scale="0 1 0 255")
self.assertEqual(ds.resolution, out_resolution)
self.assertEqual(ds.array.shape, (self.height // 2, self.width // 2, 2))
np.testing.assert_almost_equal(ds.array, 255)
FileSystem.remove_file(path)
FileSystem.remove_file(scaled)
def test_merge_then_translate(self):
datasets = []
init = np.zeros((2, 2), np.int16)
path = os.path.join(os.getcwd(), "test_gdal_merge.tif")
ImageIO.write_geotiff(init, path, self.projection, self.coordinates)
ds_in = GDalDatasetWrapper.from_file(path)
for i in range(1, 3, 1):
img = np.ones((i*2, i*2), np.int16) * i
ds_n = GDalDatasetWrapper(ds=ds_in.get_ds(), array=img)
self.assertTrue(os.path.exists(path))
datasets.append(ds_n)
ds_merged = ImageTools.gdal_merge(*datasets, dst="out.tif",
separate=True,
q=True,
a_nodata=0)
# Array of shape (4, 4, 2):
expected = np.array([[[1, 2],
[1, 2],
[0, 2],
[0, 2]],
[[1, 2],
[1, 2],
[0, 2],
[0, 2]],
[[0, 2],
[0, 2],
[0, 2],
[0, 2]],
[[0, 2],
[0, 2],
[0, 2],
[0, 2]]], dtype=np.int16)
FileSystem.remove_file("out.tif")
np.testing.assert_equal(expected.dtype, ds_merged.array.dtype)
np.testing.assert_almost_equal(expected, ds_merged.array)
self.assertEqual(ds_merged.nodata_value, 0)
self.assertEqual(ds_merged.epsg, 32631)
ds_translate = ImageTools.gdal_translate(ds_merged,
a_nodata=0)
FileSystem.remove_file(path)
np.testing.assert_equal(expected.dtype, ds_translate.array.dtype)
np.testing.assert_almost_equal(ds_translate.array, expected)
self.assertEqual(ds_translate.nodata_value, 0)
self.assertEqual(ds_translate.epsg, 32631)
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)
def get_synthetic_band(self, synthetic_band, **kwargs):
wdir = kwargs.get("wdir", self.fpath)
output_folder = os.path.join(wdir, self.base)
output_bname = "_".join([self.base.split(".")[0], synthetic_band.upper() + ".tif"])
output_filename = kwargs.get("output_filename", os.path.join(output_folder, output_bname))
max_value = kwargs.get("max_value", 10000.)
# Skip existing:
if os.path.exists(output_filename):
return output_filename
if synthetic_band.lower() == "ndvi":
FileSystem.create_directory(output_folder)
b4 = self.find_file(pattern=r"*B0?4(_10m)?.jp2$")[0]
b8 = self.find_file(pattern=r"*B0?8(_10m)?.jp2$")[0]
ds_red = GDalDatasetWrapper.from_file(b4)
ds_nir = GDalDatasetWrapper.from_file(b8)
ds_ndvi = ImageApps.get_ndvi(ds_red, ds_nir, vrange=(0, max_value), dtype=np.int16)
ds_ndvi.write(output_filename, options=["COMPRESS=DEFLATE"])
elif synthetic_band.lower() == "ndsi":
FileSystem.create_directory(output_folder)
b3 = self.find_file(pattern=r"*B0?3(_10m)?.jp2$")[0]
b11 = self.find_file(pattern=r"*B11(_20m)?.jp2$")[0]
ds_green = ImageTools.gdal_translate(b3, tr="20 20", r="cubic")
ds_swir = GDalDatasetWrapper.from_file(b11)
ds_ndsi = ImageApps.get_ndsi(ds_green, ds_swir, vrange=(0, max_value), dtype=np.int16)
ds_ndsi.write(output_filename, options=["COMPRESS=DEFLATE"])
elif synthetic_band.lower() == "mca_sim":
FileSystem.create_directory(output_folder)
b4 = self.find_file(pattern=r"*B0?4(_10m)?.jp2$")[0]
b3 = self.find_file(pattern=r"*B0?3(_10m)?.jp2$")[0]
img_red, drv = ImageIO.tiff_to_array(b4, array_only=False)
img_green = ImageIO.tiff_to_array(b3)
img_mcasim = (img_red + img_green) / 2
ImageIO.write_geotiff_existing(img_mcasim, output_filename, drv, options=["COMPRESS=DEFLATE"])
else:
raise ValueError("Unknown synthetic band %s" % synthetic_band)
return output_filename