def test_export2tiff_data_band_tuple_time_tuple(self):
data = np.arange(10 * 3 * 2 * 6, dtype=float).reshape(10, 3, 2, 6)
bands = (1, 4)
times = (2, 8)
bands_selection = np.arange(bands[0], bands[1] + 1)
times_selection = np.arange(times[0], times[1] + 1)
subset = data[times_selection][..., bands_selection].squeeze()
eop = EOPatch.load(self.PATCH_FILENAME)
eop.data['data'] = data
with tempfile.TemporaryDirectory() as tmp_dir_name:
tmp_file_name = 'temp_file.tiff'
task = ExportToTiff((FeatureType.DATA, 'data'),
folder=tmp_dir_name,
band_indices=bands,
date_indices=times,
image_dtype=data.dtype)
task.execute(eop, filename=tmp_file_name)
# split times and bands in raster and mimic the initial shape
raster = read_data(os.path.join(tmp_dir_name, tmp_file_name))
raster = raster.reshape(raster.shape[0], raster.shape[1],
len(times_selection), len(bands_selection))
raster = np.moveaxis(raster, -2, 0)
self.assertTrue(np.all(subset == raster))
def test_export2tiff_order(self):
data = np.arange(10 * 3 * 2 * 6, dtype=float).reshape(10, 3, 2, 6)
bands = [2, 3, 0]
times = [1, 7]
# create ordered subset
ordered_subset = []
for t in times:
for b in bands:
ordered_subset.append(data[t][..., b])
ordered_subset = np.array(ordered_subset)
ordered_subset = np.moveaxis(ordered_subset, 0, -1)
eop = EOPatch.load(self.PATCH_FILENAME)
eop.data['data'] = data
with tempfile.TemporaryDirectory() as tmp_dir_name:
tmp_file_name = 'temp_file.tiff'
task = ExportToTiff((FeatureType.DATA, 'data'),
folder=tmp_dir_name,
band_indices=bands,
date_indices=times,
image_dtype=data.dtype)
task.execute(eop, filename=tmp_file_name)
raster = read_data(os.path.join(tmp_dir_name, tmp_file_name))
self.assertTrue(np.all(ordered_subset == raster))
def setUpClass(cls):
geojson = read_data(os.path.join(cls.INPUT_FOLDER,
'cies_islands.json'))
cls.area = shape(geojson)
cls.test_cases = [
cls.SplitterTestCase('BBoxSplitter',
BBoxSplitter([cls.area],
CRS.WGS84,
5,
reduce_bbox_sizes=True),
bbox_len=19),
cls.SplitterTestCase('OsmSplitter',
OsmSplitter([cls.area],
CRS.WGS84,
15,
reduce_bbox_sizes=True),
bbox_len=24),
cls.SplitterTestCase('TileSplitter',
TileSplitter(
[cls.area],
CRS.WGS84, ('2017-10-01', '2018-03-01'),
tile_split_shape=40,
data_source=DataSource.SENTINEL2_L1C,
instance_id=cls.INSTANCE_ID,
reduce_bbox_sizes=True),
bbox_len=13)
]
def test_export2tiff_mask_tuple_string(self):
eop = EOPatch.load(self.PATCH_FILENAME)
dates = np.array(eop.timestamp)
mask = np.arange(len(dates) * 3 * 2 * 1).reshape(len(dates), 3, 2, 1)
eop.mask['mask'] = mask
indices = [2, 4]
# day time gets floored
times = (datetime_to_iso(dates[indices[0]]),
datetime_to_iso(dates[indices[1]]))
selection = np.nonzero(
np.where((dates >= iso_to_datetime(times[0])) &
(dates <= iso_to_datetime(times[1])), dates, 0))
subset = mask[selection].squeeze()
with tempfile.TemporaryDirectory() as tmp_dir_name:
tmp_file_name = 'temp_file.tiff'
task = ExportToTiff((FeatureType.MASK, 'mask'),
folder=tmp_dir_name,
date_indices=times)
task.execute(eop, filename=tmp_file_name)
# rasterio saves `bands` to the last dimension, move it up front
raster = read_data(os.path.join(tmp_dir_name, tmp_file_name))
raster = np.moveaxis(raster, -1, 0)
self.assertTrue(np.all(subset == raster))
def test_img_read(self):
W, H = 2048, 2048
formats = [('tif', 13577.49), ('jpg', 52.41), ('png', 52.34), ('jp2', 47.09)]
for ext, exp_mean in formats:
with self.subTest(msg=ext):
filename = os.path.join(self.INPUT_FOLDER, 'ml.{}'.format(ext))
img = read_data(filename)
self.assertEqual(np.shape(img), (W, H, 3) if ext != 'jp2' else (343, 343, 3),
"{} image dimension mismatch".format(ext))
self.assertAlmostEqual(np.mean(img), exp_mean, delta=1e-1,
msg="{} image has incorrect values".format(ext))
new_filename = os.path.join(self.OUTPUT_FOLDER, os.path.split(filename)[-1])
write_data(new_filename, img)
new_img = read_data(new_filename)
if ext != 'jpg':
self.assertTrue(np.array_equal(img, new_img), msg="Original and new image are not the same")
def test_img_read(self):
W, H = 2048, 2048
exts = frozenset(['tif', 'jpg', 'png'])
for ext in exts:
with self.subTest(msg=ext):
filepath = '{}/ml.{}'.format(self.INPUT_FOLDER, ext)
img = read_data(filepath)
w, h, d = np.shape(img)
self.assertEqual(w, W, "Width mismatch")
self.assertEqual(h, H, "Height mismatch")
self.assertEqual(d, 3, "Expected RGB")
def test_export2tiff_mask_timeless(self):
mask_timeless = np.arange(3 * 3 * 1).reshape(3, 3, 1)
subset = mask_timeless.squeeze()
eop = EOPatch.load(self.PATCH_FILENAME)
eop.mask_timeless['mask_timeless'] = mask_timeless
with tempfile.TemporaryDirectory() as tmp_dir_name:
tmp_file_name = 'temp_file.tiff'
task = ExportToTiff((FeatureType.MASK_TIMELESS, 'mask_timeless'),
folder=tmp_dir_name)
task.execute(eop, filename=tmp_file_name)
raster = read_data(os.path.join(tmp_dir_name, tmp_file_name))
self.assertTrue(np.all(subset == raster))
def test_img_read(self):
W, H = 2048, 2048
formats = [('tif', 13577.49), ('jpg', 52.41), ('png', 52.34),
('jp2', 47.09)]
for ext, exp_mean in formats:
with self.subTest(msg=ext):
filepath = '{}/ml.{}'.format(self.INPUT_FOLDER, ext)
img = read_data(filepath)
self.assertEqual(np.shape(img), (W, H, 3) if ext != 'jp2' else
(343, 343, 3),
"{} image dimension mismatch".format(ext))
self.assertAlmostEqual(
np.mean(img),
exp_mean,
delta=1e-1,
msg="{} image has incorrect values".format(ext))
def test_export2tiff_mask_single(self):
mask = np.arange(5 * 3 * 3 * 1).reshape(5, 3, 3, 1)
times = [4]
subset = mask[times].squeeze()
eop = EOPatch.load(self.PATCH_FILENAME)
eop.mask['mask'] = mask
with tempfile.TemporaryDirectory() as tmp_dir_name:
tmp_file_name = 'temp_file.tiff'
task = ExportToTiff((FeatureType.MASK, 'mask'),
folder=tmp_dir_name,
date_indices=times)
task.execute(eop, filename=tmp_file_name)
raster = read_data(os.path.join(tmp_dir_name, tmp_file_name))
self.assertTrue(np.all(subset == raster))
def test_export2tiff_scalar_timeless_list(self):
scalar_timeless = np.arange(5)
bands = [3, 0, 2]
subset = scalar_timeless[bands]
eop = EOPatch.load(self.PATCH_FILENAME)
eop.scalar_timeless['scalar_timeless'] = scalar_timeless
with tempfile.TemporaryDirectory() as tmp_dir_name:
tmp_file_name = 'temp_file.tiff'
task = ExportToTiff(
(FeatureType.SCALAR_TIMELESS, 'scalar_timeless'),
folder=tmp_dir_name,
band_indices=bands)
task.execute(eop, filename=tmp_file_name)
raster = read_data(os.path.join(tmp_dir_name, tmp_file_name))
self.assertTrue(np.all(subset == raster))
def test_export2tiff_data_timeless_band_list(self):
data_timeless = np.arange(3 * 2 * 5, dtype=float).reshape(3, 2, 5)
bands = [2, 4, 1, 0]
subset = data_timeless[..., bands].squeeze()
eop = EOPatch.load(self.PATCH_FILENAME)
eop.data_timeless['data_timeless'] = data_timeless
with tempfile.TemporaryDirectory() as tmp_dir_name:
tmp_file_name = 'temp_file.tiff'
task = ExportToTiff((FeatureType.DATA_TIMELESS, 'data_timeless'),
folder=tmp_dir_name,
band_indices=bands,
image_dtype=data_timeless.dtype)
task.execute(eop, filename=tmp_file_name)
raster = read_data(os.path.join(tmp_dir_name, tmp_file_name))
self.assertTrue(np.all(subset == raster))
def test_export2tiff_mask_list(self):
mask = np.arange(5 * 3 * 2 * 1).reshape(5, 3, 2, 1)
times = [4, 2]
subset = mask[times].squeeze()
eop = EOPatch.load(self.PATCH_FILENAME)
eop.mask['mask'] = mask
with tempfile.TemporaryDirectory() as tmp_dir_name:
tmp_file_name = 'temp_file.tiff'
task = ExportToTiff((FeatureType.MASK, 'mask'),
folder=tmp_dir_name,
date_indices=times)
task.execute(eop, filename=tmp_file_name)
# rasterio saves `bands` to the last dimension, move it up front
raster = read_data(os.path.join(tmp_dir_name, tmp_file_name))
raster = np.moveaxis(raster, -1, 0)
self.assertTrue(np.all(subset == raster))
def test_export2tiff_scalar_band_single_time_single(self):
scalar = np.arange(10 * 6, dtype=float).reshape(10, 6)
bands = [3]
times = [7]
subset = scalar[times][..., bands].squeeze()
eop = EOPatch.load(self.PATCH_FILENAME)
eop.scalar['scalar'] = scalar
with tempfile.TemporaryDirectory() as tmp_dir_name:
tmp_file_name = 'temp_file.tiff'
task = ExportToTiff((FeatureType.SCALAR, 'scalar'),
folder=tmp_dir_name,
band_indices=bands,
date_indices=times,
image_dtype=scalar.dtype)
task.execute(eop, filename=tmp_file_name)
raster = read_data(os.path.join(tmp_dir_name, tmp_file_name))
self.assertTrue(np.all(subset == raster))
def load_truth(self):
return [tuple(item) for item in read_data(self.get_filename())]
