def test_cog_overviews():
with TemporaryDirectory() as dir_name:
source = os.path.join(dir_name, 'source.tif')
dest = os.path.join(dir_name, 'dest.tif')
image = sample_raster_image(height=800, width=900)
tl.GeoRaster2(deepcopy(image),
crs=WEB_MERCATOR_CRS,
affine=base_affine).save(source)
convert_to_cog(source, dest)
assert (os.path.exists(dest))
assert not os.path.exists("%s.msk" % dest)
with rasterio.open(dest) as raster:
data = raster.read()
assert (image.data == data).all()
mask = raster.read_masks()
expected_mask = _mask_from_masked_array(image)
assert (expected_mask == mask).all()
# to confirtm that creating a cog remove namespace tags
assert raster.tags(ns='rio_overview').get('resampling',
'empty') == 'empty'
for i in raster.indexes:
assert raster.overviews(i) == _calc_overviews_factors(raster)
assert _has_internal_perdataset_mask(raster)
def make_test_raster(value=0, band_names=[], height=3, width=4, dtype=np.uint16,
crs=WEB_MERCATOR_CRS, affine=None):
if affine is None:
affine = Affine.translation(10, 12) * Affine.scale(1, -1)
shape = [len(band_names), height, width]
array = np.full(shape, value, dtype=dtype)
mask = np.full(shape, False, dtype=np.bool)
image = np.ma.array(data=array, mask=mask)
raster = tl.GeoRaster2(image=image, affine=affine, crs=crs, band_names=band_names)
return raster
def test_cog_simple_file():
with TemporaryDirectory() as dir_name:
file_name = os.path.join(dir_name, 'temp.tif')
image = sample_raster_image()
tl.GeoRaster2(deepcopy(image),
crs=WEB_MERCATOR_CRS,
affine=base_affine).save(file_name)
with rasterio.open(file_name) as raster:
data = raster.read()
assert (image.data == data).all()
mask = raster.read_masks()
expected_mask = _mask_from_masked_array(image)
assert (expected_mask == mask).all()
assert _has_internal_perdataset_mask(raster)
def test_cog_move_telluric_tags_to_general_tags_space():
with TemporaryDirectory() as dir_name:
source = os.path.join(dir_name, 'source.tif')
dest = os.path.join(dir_name, 'dest.tif')
image = sample_raster_image(height=800, width=900)
band_names = ['red', 'green', 'blue']
tl.GeoRaster2(deepcopy(image),
crs=WEB_MERCATOR_CRS,
affine=base_affine,
band_names=band_names).save(source)
convert_to_cog(source, dest)
tags = tl.GeoRaster2.tags(dest)
assert (json.loads(tags['telluric_band_names']) == band_names)
raster = tl.GeoRaster2.open(dest)
assert raster.band_names == band_names
def test_colorize_jet_with_range():
raster = tl.GeoRaster2(image=np.array([i / 256 for i in range(256)],
dtype=np.float16).reshape(
(1, 16, 16)),
band_names=['red'],
crs=WGS84_CRS,
affine=affine.Affine(2, 0, 0, 0, 1, 0))
heatmap = raster.colorize('jet', vmin=-1, vmax=1)
assert (np.array_equal(heatmap.band_names, ['red', 'green', 'blue']))
assert (np.array_equal(heatmap.image.data[:, 0, 0],
[0, 0, 0])) # nodata remains nodata
assert (np.array_equal(heatmap.image.mask[:, 0, 0],
[True, True, True])) # nodata remains nodata
mask = heatmap.image.mask
assert (len(mask[mask]) == 3)
def test_colorize_jet():
raster = tl.GeoRaster2(image=np.array([i / 256 for i in range(256)],
dtype=np.float16).reshape(
(1, 16, 16)),
band_names=['red'],
crs=WGS84_CRS,
affine=affine.Affine(2, 0, 0, 0, 1, 0))
heatmap = raster.colorize('jet')
assert (np.array_equal(heatmap.band_names, ['red', 'green', 'blue']))
assert (np.array_equal(heatmap.image.data[:, 0, 0],
[0, 0, 0])) # nodata remains nodata
assert (np.array_equal(heatmap.image.mask[:, 0, 0],
[True, True, True])) # nodata remains nodata
assert (np.array_equal(heatmap.image.data[:, 0, 1], [0, 0, 127])) # blue
assert (np.array_equal(heatmap.image.mask[:, 0, 1],
[False, False, False])) # blue
assert (np.array_equal(
heatmap.image.data[:, heatmap.height - 1, heatmap.width - 1],
[127, 0, 0])) # red
assert (np.array_equal(
heatmap.image.mask[:, heatmap.height - 1, heatmap.width - 1],
[False, False, False])) # red
def test_cog_calc_overviews_factors(height, factors):
image = sample_raster_image(height=height, width=900)
raster = tl.GeoRaster2(image, crs=WEB_MERCATOR_CRS, affine=base_affine)
assert (_calc_overviews_factors(raster) == factors)
def test_raster_with_no_url():
raster = tl.GeoRaster2(np.random.uniform(0, 256, (3, 391, 370)),
affine=Affine(1.0000252884112817, 0.0, 2653750.345511198,
0.0, -1.0000599330133702, 4594461.485763356),
crs=CRS({'init': 'epsg:3857'}))
return raster