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test: Reorganize tests and add assertion for downsampled content
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@annehaley
annehaley committed Apr 4, 2024
1 parent 4ef0c58 commit f7876a3
Showing 1 changed file with 73 additions and 57 deletions.
130 changes: 73 additions & 57 deletions test/test_sink.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,6 @@
'zip',
'zarr',
]
RESAMPLE_METHODS = list(ResampleMethod)


def copyFromSource(source, sink):
Expand Down Expand Up @@ -124,7 +123,7 @@ def testCrop(file_type, tmp_path):


@pytest.mark.parametrize('file_type', FILE_TYPES)
def testImageCopySmall(file_type, tmp_path):
def testImageCopySmallFileTypes(file_type, tmp_path):
output_file = tmp_path / f'test.{file_type}'
sink = large_image_source_zarr.new()
source = large_image_source_test.TestTileSource(
Expand All @@ -149,8 +148,8 @@ def testImageCopySmall(file_type, tmp_path):
if file_type == 'zarr':
output_file /= '.zattrs'
written = large_image.open(output_file)
new_metadata = written.metadata

new_metadata = written.getMetadata()
assert new_metadata.get('sizeX') == 512
assert new_metadata.get('sizeY') == 1024
assert new_metadata.get('dtype') == 'uint8'
Expand All @@ -159,13 +158,16 @@ def testImageCopySmall(file_type, tmp_path):
assert len(new_metadata.get('frames')) == 6


@pytest.mark.parametrize('file_type', FILE_TYPES)
def testImageCopySmallMultiband(file_type, tmp_path):
output_file = tmp_path / f'test.{file_type}'
@pytest.mark.parametrize('resample_method', [
ResampleMethod.PIL_LANCZOS,
ResampleMethod.NP_NEAREST,
])
def testImageCopySmallMultiband(resample_method, tmp_path):
output_file = tmp_path / f'test_{resample_method}.db'
sink = large_image_source_zarr.new()
bands = (
'red=400-12000,green=0-65535,blue=800-4000,'
'ir1=200-24000,ir2=200-22000,gray=100-10000,other=0-65535'
'red=0-255,green=0-255,blue=0-255,'
'ir1=0-255,ir2=0-255,gray=0-255,other=0-255'
)
source = large_image_source_test.TestTileSource(
fractal=True,
Expand All @@ -181,88 +183,102 @@ def testImageCopySmallMultiband(file_type, tmp_path):
metadata = sink.getMetadata()
assert metadata.get('sizeX') == 512
assert metadata.get('sizeY') == 1024
assert metadata.get('dtype') == 'uint16'
assert metadata.get('dtype') == 'uint8'
assert metadata.get('levels') == 2
assert metadata.get('bandCount') == 7
assert len(metadata.get('frames')) == 6

sink.write(output_file)
if file_type == 'zarr':
output_file /= '.zattrs'
sink.write(output_file, resample=resample_method)
written = large_image.open(output_file)
new_metadata = written.getMetadata()

assert new_metadata.get('sizeX') == 512
assert new_metadata.get('sizeY') == 1024
assert new_metadata.get('dtype') == 'uint16'
assert new_metadata.get('dtype') == 'uint8'
assert new_metadata.get('levels') == 2 or new_metadata.get('levels') == 3
assert new_metadata.get('bandCount') == 7
assert len(new_metadata.get('frames')) == 6


@pytest.mark.parametrize('resample_method', RESAMPLE_METHODS)
def testImageCopyLargeDownsampling(resample_method, tmp_path):
output_file = tmp_path / f'{resample_method}.db'
sink = large_image_source_zarr.new()
source = large_image_source_test.TestTileSource(
fractal=True,
tileWidth=128,
tileHeight=128,
sizeX=2048,
sizeY=4096,
frames='c=2,z=3',
)
copyFromSource(source, sink)
sink.write(output_file, resample=resample_method)
written = large_image_source_zarr.open(output_file)
written_arrays = dict(written._zarr.arrays())

assert len(written_arrays) == written.levels
assert written_arrays.get('0') is not None
assert written_arrays.get('0').shape == (2, 3, 4096, 2048, 3)
assert written_arrays.get('0').shape == (2, 3, 1024, 512, 7)
assert written_arrays.get('1') is not None
assert written_arrays.get('1').shape == (2, 3, 2048, 1024, 3)
assert written_arrays.get('2') is not None
assert written_arrays.get('2').shape == (2, 3, 1024, 512, 3)
assert written_arrays.get('3') is not None
assert written_arrays.get('3').shape == (2, 3, 512, 256, 3)
assert written_arrays.get('1').shape == (2, 3, 512, 256, 7)

# TODO: Can the content of the downsampled data be compared back to the test source?


@pytest.mark.parametrize('resample_method', RESAMPLE_METHODS)
def testImageCopyLargeDownsamplingMultiband(resample_method, tmp_path):
output_file = tmp_path / f'{resample_method}_multiband.db'
@pytest.mark.parametrize('resample_method', list(ResampleMethod))
def testImageCopySmallDownsampling(resample_method, tmp_path):
output_file = tmp_path / f'test_{resample_method}.db'
sink = large_image_source_zarr.new()
bands = 'red=0-255,green=0-255,blue=0-255,ir=0-255,gray=0-255,other=0-255'
source = large_image_source_test.TestTileSource(
fractal=True,
tileWidth=128,
tileHeight=128,
sizeX=2048,
sizeY=4096,
sizeX=512,
sizeY=1024,
frames='c=2,z=3',
bands=bands,
)
copyFromSource(source, sink)

sink.write(output_file, resample=resample_method)
written = large_image_source_zarr.open(output_file)
written_arrays = dict(written._zarr.arrays())
written = large_image.open(output_file)

written_arrays = dict(written._zarr.arrays())
assert len(written_arrays) == written.levels
assert written_arrays.get('0') is not None
assert written_arrays.get('0').shape == (2, 3, 4096, 2048, 6)
assert written_arrays.get('0').shape == (2, 3, 1024, 512, 3)
assert written_arrays.get('1') is not None
assert written_arrays.get('1').shape == (2, 3, 2048, 1024, 6)
assert written_arrays.get('2') is not None
assert written_arrays.get('2').shape == (2, 3, 1024, 512, 6)
assert written_arrays.get('3') is not None
assert written_arrays.get('3').shape == (2, 3, 512, 256, 6)
assert written_arrays.get('1').shape == (2, 3, 512, 256, 3)


@pytest.mark.parametrize('resample_method', RESAMPLE_METHODS)
def testCropAndDownsample(resample_method, tmp_path):
output_file = tmp_path / f'cropped_{resample_method}.db'
sample_region, _format = written.getRegion(
region=dict(top=252, bottom=260, left=0, right=4),
output=dict(maxWidth=2, maxHeight=4),
format='numpy',
)
assert sample_region.shape == (4, 2, 3)
white_mask = (sample_region[..., 0] == 255).flatten().tolist()

if resample_method == ResampleMethod.PIL_NEAREST:
# expect any of the four variations, this will depend on version
expected_masks = [
[True, False, True, False, True, True, True, False], # upper left
[True, False, True, True, True, False, True, False], # lower left
[True, False, False, True, True, True, False, False], # upper right
[False, False, True, True, False, True, True, False], # lower right
]
elif resample_method == ResampleMethod.PIL_LANCZOS:
expected_masks = [[False, False, False, False, False, False, False, False]]
elif resample_method == ResampleMethod.PIL_BILINEAR:
expected_masks = [[False, False, False, False, False, False, False, False]]
elif resample_method == ResampleMethod.PIL_BICUBIC:
expected_masks = [[False, False, False, False, False, False, False, False]]
elif resample_method == ResampleMethod.PIL_BOX:
expected_masks = [[False, False, False, False, False, False, False, False]]
elif resample_method == ResampleMethod.PIL_HAMMING:
expected_masks = [[False, False, False, False, False, False, False, False]]
elif resample_method == ResampleMethod.NP_MEAN:
expected_masks = [[False, False, False, False, False, False, False, False]]
elif resample_method == ResampleMethod.NP_MEDIAN:
expected_masks = [[True, False, True, True, True, True, True, False]]
elif resample_method == ResampleMethod.NP_MODE:
expected_masks = [[True, False, True, True, True, True, True, False]]
elif resample_method == ResampleMethod.NP_MAX:
expected_masks = [[True, False, True, True, True, True, True, False]]
elif resample_method == ResampleMethod.NP_MIN:
expected_masks = [[False, False, False, False, False, False, False, False]]
elif resample_method == ResampleMethod.NP_NEAREST:
expected_masks = [[True, False, True, False, True, True, True, False]]
elif resample_method == ResampleMethod.NP_MAX_COLOR:
expected_masks = [[True, False, True, True, True, True, True, False]]
elif resample_method == ResampleMethod.NP_MIN_COLOR:
expected_masks = [[False, False, False, False, False, False, False, False]]

assert white_mask in expected_masks


def testCropAndDownsample(tmp_path):
output_file = tmp_path / 'cropped.db'
sink = large_image_source_zarr.new()

# add tiles with some overlap to multiple frames
Expand Down

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