def testDeleteLabel2(self):
"""
Another test to check behavior after deleting an entire label class from the sparse array.
This one ensures that different blocks have different max label values before the delete occurs.
"""
op = self.op
slicing = self.slicing
data = self.data
# assert op.maxLabel.value == 2
# Choose slicings that do NOT intersect with any of the previous data or with each other
# The goal is to make sure that the data for each slice ends up in a separate block
slicing1 = numpy.s_[0:1, 60:65, 0:10, 3:7, 0:1]
slicing2 = numpy.s_[0:1, 90:95, 0:90, 3:7, 0:1]
expectedData = self.data[...]
labels1 = numpy.ndarray(slicing2shape(slicing1), dtype=numpy.uint8)
labels1[...] = 1
op.Input[slicing1] = labels1
expectedData[slicing1] = labels1
labels2 = numpy.ndarray(slicing2shape(slicing2), dtype=numpy.uint8)
labels2[...] = 2
op.Input[slicing2] = labels2
expectedData[slicing2] = labels2
# Sanity check:
# Does the data contain our new labels?
assert (op.Output[...].wait() == expectedData).all()
assert expectedData.max() == 2
# assert op.maxLabel.value == 2
# Delete label 1
op.deleteLabel.setValue(1)
outputData = op.Output[...].wait()
# Expected: All 1s removed, all 2s converted to 1s
expectedData = numpy.where(expectedData == 1, 0, expectedData)
expectedData = numpy.where(expectedData == 2, 1, expectedData)
expectedData = numpy.ma.masked_array(expectedData,
mask=self.data.mask,
fill_value=self.data.fill_value,
shrink=False)
assert numpy.all(outputData == expectedData)
assert numpy.all(outputData.mask == expectedData.mask)
assert numpy.all(outputData.fill_value == expectedData.fill_value)
def testDeleteLabel2(self):
"""
Another test to check behavior after deleting an entire label class from the sparse array.
This one ensures that different blocks have different max label values before the delete occurs.
"""
op = self.op
slicing = self.slicing
data = self.data
#assert op.maxLabel.value == 2
# Choose slicings that do NOT intersect with any of the previous data or with each other
# The goal is to make sure that the data for each slice ends up in a separate block
slicing1 = sl[0:1, 60:65, 0:10, 3:7, 0:1]
slicing2 = sl[0:1, 90:95, 0:90, 3:7, 0:1]
expectedData = self.data[...]
labels1 = numpy.ndarray(slicing2shape(slicing1), dtype=numpy.uint8)
labels1[...] = 1
op.Input[slicing1] = labels1
expectedData[slicing1] = labels1
labels2 = numpy.ndarray(slicing2shape(slicing2), dtype=numpy.uint8)
labels2[...] = 2
op.Input[slicing2] = labels2
expectedData[slicing2] = labels2
# Sanity check:
# Does the data contain our new labels?
assert (op.Output[...].wait() == expectedData).all()
assert expectedData.max() == 2
#assert op.maxLabel.value == 2
# Delete label 1
op.deleteLabel.setValue(1)
outputData = op.Output[...].wait()
# Expected: All 1s removed, all 2s converted to 1s
expectedData = numpy.where(expectedData == 1, 0, expectedData)
expectedData = numpy.where(expectedData == 2, 1, expectedData)
expectedData = numpy.ma.masked_array(expectedData,
mask=self.data.mask,
fill_value=self.data.fill_value,
shrink=False)
assert numpy.all(outputData == expectedData)
assert numpy.all(outputData.mask == expectedData.mask)
assert numpy.all(outputData.fill_value == expectedData.fill_value)
def setup(self):
graph = Graph()
op = OpCompressedUserLabelArray(graph=graph)
arrayshape = (1, 100, 100, 10, 1)
op.inputs["shape"].setValue(arrayshape)
blockshape = (1, 10, 10, 10, 1
) # Why doesn't this work if blockshape is an ndarray?
op.inputs["blockShape"].setValue(blockshape)
op.eraser.setValue(100)
dummyData = vigra.VigraArray(arrayshape,
axistags=vigra.defaultAxistags("txyzc"),
dtype=numpy.uint8)
op.Input.setValue(dummyData)
slicing = numpy.s_[0:1, 1:15, 2:36, 3:7, 0:1]
inDataShape = slicing2shape(slicing)
inputData = (3 * numpy.random.random(inDataShape)).astype(numpy.uint8)
op.Input[slicing] = inputData
data = numpy.zeros(arrayshape, dtype=numpy.uint8)
data[slicing] = inputData
# Sanity check...
assert (op.Output[:].wait()[slicing] == data[slicing]).all()
self.op = op
self.slicing = slicing
self.inData = inputData
self.data = data
def setup(self):
graph = Graph()
op = OpCompressedUserLabelArray(graph=graph)
arrayshape = (1,100,100,10,1)
op.inputs["shape"].setValue( arrayshape )
blockshape = (1,10,10,10,1) # Why doesn't this work if blockshape is an ndarray?
op.inputs["blockShape"].setValue( blockshape )
op.eraser.setValue(100)
dummyData = vigra.VigraArray(arrayshape, axistags=vigra.defaultAxistags('txyzc'))
op.Input.setValue( dummyData )
slicing = sl[0:1, 1:15, 2:36, 3:7, 0:1]
inDataShape = slicing2shape(slicing)
inputData = ( 3*numpy.random.random(inDataShape) ).astype(numpy.uint8)
op.Input[slicing] = inputData
data = numpy.zeros(arrayshape, dtype=numpy.uint8)
data[slicing] = inputData
# Sanity check...
assert (op.Output[:].wait()[slicing] == data[slicing]).all()
self.op = op
self.slicing = slicing
self.inData = inputData
self.data = data
def testSetInSlot(self):
logger.info("Generating sample data...")
sampleData = numpy.indices((100, 200, 150), dtype=numpy.float32).sum(0)
sampleData = sampleData.view(vigra.VigraArray)
sampleData.axistags = vigra.defaultAxistags('xyz')
graph = Graph()
opData = OpArrayPiper(graph=graph)
opData.Input.setValue(sampleData)
op = OpCompressedCache(parent=None, graph=graph)
#logger.debug("Setting block shape...")
op.BlockShape.setValue([100, 75, 50])
op.Input.connect(opData.Output)
assert op.Output.ready()
slicing = numpy.s_[0:100, 0:75, 0:50]
expectedData = numpy.ones(slicing2shape(slicing), dtype=int)
# This is what we're testing.
#logger.debug("Forcing external data...")
op.Input[slicing] = expectedData
#logger.debug("Requesting data...")
readData = op.Output[slicing].wait()
#logger.debug("Checking data...")
assert (readData == expectedData).all(), "Incorrect output!"
def testEraseBlock(self):
"""
If we use the eraser to remove all labels from a block,
it should be removed from the CleanBlocks slot.
"""
op = self.op
slicing = self.slicing
inData = self.inData
data = self.data
# BEFORE (convert to tuple)
clean_blocks_before = [ (tuple(a),tuple(b)) for (a,b) in op.CleanBlocks.value ]
block_slicing = sl[0:1, 10:20, 10:20, 0:10, 0:1]
block_roi = ((0,10,10,0,0), (1,20,20,10,1))
eraser_data = 100 * numpy.ones( slicing2shape(block_slicing), dtype=numpy.uint8 )
op.Input[block_slicing] = eraser_data
expected_data = data.copy()
expected_data[block_slicing] = 0
# quick sanity check: the data was actually cleared by the eraser
assert (op.Output[:].wait() == expected_data).all()
# AFTER (convert to tuple)
clean_blocks_after = [ (tuple(a),tuple(b)) for (a,b) in op.CleanBlocks.value ]
before_set = set(map(tuple, clean_blocks_before))
after_set = set(map(tuple, clean_blocks_after))
assert before_set - set([block_roi]) == after_set
def testSetInSlot(self):
logger.info("Generating sample data...")
sampleData = numpy.indices((100, 200, 150), dtype=numpy.float32).sum(0)
sampleData = sampleData.view( vigra.VigraArray )
sampleData.axistags = vigra.defaultAxistags('xyz')
graph = Graph()
opData = OpArrayPiper( graph=graph )
opData.Input.setValue( sampleData )
op = OpCompressedCache( parent=None, graph=graph )
#logger.debug("Setting block shape...")
op.BlockShape.setValue( [100, 75, 50] )
op.Input.connect( opData.Output )
assert op.Output.ready()
slicing = numpy.s_[ 0:100, 0:75, 0:50 ]
expectedData = numpy.ones( slicing2shape(slicing), dtype=int )
# This is what we're testing.
#logger.debug("Forcing external data...")
op.Input[slicing] = expectedData
#logger.debug("Requesting data...")
readData = op.Output[slicing].wait()
#logger.debug("Checking data...")
assert (readData == expectedData).all(), "Incorrect output!"
def testOutputCrash(self):
"""
Bare-minimum test: Just make sure we can request the output without crashing.
"""
slicing = sl[0:1, 0:5, 6:20, 30:40, 0:1]
result = self.op.Output[slicing].wait()
assert result.shape == slicing2shape(slicing)
def testEraseBlock(self):
"""
If we use the eraser to remove all labels from a block,
it should be removed from the CleanBlocks slot.
"""
op = self.op
slicing = self.slicing
inData = self.inData
data = self.data
# BEFORE (convert to tuple)
clean_blocks_before = [(tuple(a), tuple(b))
for (a, b) in op.CleanBlocks.value]
block_slicing = numpy.s_[0:1, 10:20, 10:20, 0:10, 0:1]
block_roi = ((0, 10, 10, 0, 0), (1, 20, 20, 10, 1))
eraser_data = 100 * numpy.ones(slicing2shape(block_slicing),
dtype=numpy.uint8)
op.Input[block_slicing] = eraser_data
expected_data = data.copy()
expected_data[block_slicing] = 0
# quick sanity check: the data was actually cleared by the eraser
assert (op.Output[:].wait() == expected_data).all()
# AFTER (convert to tuple)
clean_blocks_after = [(tuple(a), tuple(b))
for (a, b) in op.CleanBlocks.value]
before_set = set(map(tuple, clean_blocks_before))
after_set = set(map(tuple, clean_blocks_after))
assert before_set - set([block_roi]) == after_set
def setup(self):
graph = Graph()
try:
import blist
except ImportError:
raise unittest.SkipTest
from lazyflow.operators.opBlockedSparseLabelArray import OpBlockedSparseLabelArray
op = OpBlockedSparseLabelArray(graph=graph)
arrayshape = (1,100,100,10,1)
op.inputs["shape"].setValue( arrayshape )
blockshape = (1,10,10,10,1) # Why doesn't this work if blockshape is an ndarray?
op.inputs["blockShape"].setValue( blockshape )
op.eraser.setValue(100)
dummyData = vigra.VigraArray(arrayshape, axistags=vigra.defaultAxistags('txyzc'))
op.Input.setValue( dummyData )
slicing = sl[0:1, 1:15, 2:36, 3:7, 0:1]
inDataShape = slicing2shape(slicing)
inputData = ( 3*numpy.random.random(inDataShape) ).astype(numpy.uint8)
op.Input[slicing] = inputData
data = numpy.zeros(arrayshape, dtype=numpy.uint8)
data[slicing] = inputData
self.op = op
self.slicing = slicing
self.inData = inputData
self.data = data
def setup(self):
graph = Graph()
op = OpCompressedUserLabelArray(graph=graph)
arrayshape = (1,100,100,10,1)
op.inputs["shape"].setValue( arrayshape )
blockshape = (1,10,10,10,1) # Why doesn't this work if blockshape is an ndarray?
op.inputs["blockShape"].setValue( blockshape )
op.eraser.setValue(100)
op.Input.meta.axistags = vigra.defaultAxistags('txyzc')
op.Input.meta.has_mask = True
dummyData = numpy.zeros(arrayshape, dtype=numpy.uint8)
dummyData = numpy.ma.masked_array(dummyData, mask=numpy.ma.getmaskarray(dummyData), fill_value=numpy.uint8(0), shrink=False)
op.Input.setValue( dummyData )
slicing = sl[0:1, 1:15, 2:36, 3:7, 0:1]
inDataShape = slicing2shape(slicing)
inputData = ( 3*numpy.random.random(inDataShape) ).astype(numpy.uint8)
inputData = numpy.ma.masked_array(inputData, mask=numpy.ma.getmaskarray(inputData), fill_value=numpy.uint8(0), shrink=False)
inputData[:, 0] = numpy.ma.masked
op.Input[slicing] = inputData
data = numpy.ma.zeros(arrayshape, dtype=numpy.uint8, fill_value=numpy.uint8(0))
data[slicing] = inputData
self.op = op
self.slicing = slicing
self.inData = inputData
self.data = data
def testOutputCrash(self):
"""
Bare-minimum test: Just make sure we can request the output without crashing.
"""
slicing = sl[0:1, 0:5, 6:20, 30:40, 0:1]
result = self.op.Output[slicing].wait()
assert result.shape == slicing2shape(slicing)
def testEraseAll(self):
"""
Test behavior when all labels of a particular class are erased.
Note that this is not the same as deleting a label class, but should have the same effect on the output slots.
"""
op = self.op
slicing = self.slicing
data = self.data
# assert op.maxLabel.value == 2
newSlicing = list(slicing)
newSlicing[1] = slice(1, 2)
# Add some new labels for a class that hasn't been seen yet (3)
threeData = numpy.ndarray(slicing2shape(newSlicing), dtype=numpy.uint8)
threeData[...] = 3
op.Input[newSlicing] = threeData
expectedData = data[...]
expectedData[newSlicing] = 3
# Sanity check: Are the new labels in the data?
outputData = op.Output[...].wait()
assert numpy.all(outputData == expectedData)
assert numpy.all(outputData.mask == expectedData.mask)
assert numpy.all(outputData.fill_value == expectedData.fill_value)
assert expectedData.max() == 3
# assert op.maxLabel.value == 3
# Now erase all the 3s
eraserData = numpy.ones(slicing2shape(newSlicing),
dtype=numpy.uint8) * 100
op.Input[newSlicing] = eraserData
expectedData = data[...]
expectedData[newSlicing] = 0
# The data we erased should be zeros
outputData = op.Output[...].wait()
assert numpy.all(outputData == expectedData)
assert numpy.all(outputData.mask == expectedData.mask)
assert numpy.all(outputData.fill_value == expectedData.fill_value)
# The maximum label should be reduced, because all the 3s were removed.
assert expectedData.max() == 2
def testEraseAll(self):
"""
Test behavior when all labels of a particular class are erased.
Note that this is not the same as deleting a label class, but should have the same effect on the output slots.
"""
op = self.op
slicing = self.slicing
data = self.data
#assert op.maxLabel.value == 2
newSlicing = list(slicing)
newSlicing[1] = slice(1,2)
# Add some new labels for a class that hasn't been seen yet (3)
threeData = numpy.ndarray(slicing2shape(newSlicing), dtype=numpy.uint8)
threeData[...] = 3
op.Input[newSlicing] = threeData
expectedData = data[...]
expectedData[newSlicing] = 3
# Sanity check: Are the new labels in the data?
outputData = op.Output[...].wait()
assert numpy.all(outputData == expectedData)
assert numpy.all(outputData.mask == expectedData.mask)
assert numpy.all(outputData.fill_value == expectedData.fill_value)
assert expectedData.max() == 3
#assert op.maxLabel.value == 3
# Now erase all the 3s
eraserData = numpy.ones(slicing2shape(newSlicing), dtype=numpy.uint8) * 100
op.Input[newSlicing] = eraserData
expectedData = data[...]
expectedData[newSlicing] = 0
# The data we erased should be zeros
outputData = op.Output[...].wait()
assert numpy.all(outputData == expectedData)
assert numpy.all(outputData.mask == expectedData.mask)
assert numpy.all(outputData.fill_value == expectedData.fill_value)
# The maximum label should be reduced, because all the 3s were removed.
assert expectedData.max() == 2
def testWithSeeds(self):
"""
Bare-minimum test: Just make sure we can request the output without crashing.
"""
seeds = numpy.zeros(self.inputData.shape, dtype=numpy.uint32)
self.op.SeedImage.setValue(seeds)
slicing = sl[0:1, 0:5, 6:20, 30:40, 0:1]
result = self.op.Output[slicing].wait()
assert result.shape == slicing2shape(slicing)
def testWithSeeds(self):
"""
Bare-minimum test: Just make sure we can request the output without crashing.
"""
seeds = numpy.zeros( self.inputData.shape, dtype=numpy.uint32 )
self.op.SeedImage.setValue( seeds )
slicing = sl[0:1, 0:5, 6:20, 30:40, 0:1]
result = self.op.Output[slicing].wait()
assert result.shape == slicing2shape(slicing)
def testWithFullSeeds(self):
"""
If every pixel is seeded, there's nothing for the watershed to do.
Output should be a copy of the seeds
"""
# Random seed data
seeds = 4 * numpy.random.random(self.inputData.shape)
seeds = seeds.astype(numpy.uint32)
seeds += 1
self.op.SeedImage.setValue(seeds)
slicing = sl[0:1, 0:5, 6:20, 30:40, 0:1]
result = self.op.Output[slicing].wait()
assert result.shape == slicing2shape(slicing)
assert (result == seeds[slicing]).all()
def testWithFullSeeds(self):
"""
If every pixel is seeded, there's nothing for the watershed to do.
Output should be a copy of the seeds
"""
# Random seed data
seeds = 4 * numpy.random.random( self.inputData.shape )
seeds = seeds.astype(numpy.uint32)
seeds += 1
self.op.SeedImage.setValue( seeds )
slicing = sl[0:1, 0:5, 6:20, 30:40, 0:1]
result = self.op.Output[slicing].wait()
assert result.shape == slicing2shape(slicing)
assert (result == seeds[slicing]).all()
def setup(self):
graph = Graph()
op = OpSparseLabelArray(graph=graph)
arrayshape = numpy.array([1, 10, 10, 10, 1])
op.inputs["shape"].setValue(tuple(arrayshape))
op.eraser.setValue(100)
slicing = sl[0:1, 1:5, 2:6, 3:7, 0:1]
inDataShape = slicing2shape(slicing)
inputData = (3 * numpy.random.random(inDataShape)).astype(numpy.uint8)
op.Input[slicing] = inputData
data = numpy.zeros(arrayshape, dtype=numpy.uint8)
data[slicing] = inputData
self.op = op
self.slicing = slicing
self.inData = inputData
self.data = data
def setup(self):
graph = Graph()
op = OpSparseLabelArray(graph=graph)
arrayshape = numpy.array([1,10,10,10,1])
op.inputs["shape"].setValue( tuple(arrayshape) )
op.eraser.setValue(100)
slicing = sl[0:1, 1:5, 2:6, 3:7, 0:1]
inDataShape = slicing2shape(slicing)
inputData = ( 3*numpy.random.random(inDataShape) ).astype(numpy.uint8)
op.Input[slicing] = inputData
data = numpy.zeros(arrayshape, dtype=numpy.uint8)
data[slicing] = inputData
self.op = op
self.slicing = slicing
self.inData = inputData
self.data = data
def testSetInSlot_masked(self):
logger.info("Generating sample data...")
sampleData = numpy.indices((100, 200, 150), dtype=numpy.float32).sum(0)
sampleData = sampleData.view(numpy.ma.masked_array)
sampleData.set_fill_value(numpy.float32(numpy.nan))
sampleData[0] = numpy.ma.masked
graph = Graph()
opData = OpArrayPiper(graph=graph)
opData.Input.meta.has_mask = True
opData.Input.meta.axistags = vigra.defaultAxistags("xyz")
opData.Input.setValue(sampleData)
op = OpCompressedCache(parent=None, graph=graph)
# logger.debug("Setting block shape...")
op.BlockShape.setValue([100, 75, 50])
op.Input.connect(opData.Output)
assert op.Output.ready()
slicing = numpy.s_[0:100, 0:75, 0:50]
expectedData = numpy.ma.ones(slicing2shape(slicing), dtype=int)
sampleData.set_fill_value(numpy.float32(numpy.nan))
expectedData[0] = numpy.ma.masked
# This is what we're testing.
# logger.debug("Forcing external data...")
op.Input[slicing] = expectedData
# logger.debug("Requesting data...")
readData = op.Output[slicing].wait()
# logger.debug("Checking data...")
assert (
(readData == expectedData).all()
and (readData.mask == expectedData.mask).all()
and (
(readData.fill_value == expectedData.fill_value)
| (numpy.isnan(readData.fill_value) & numpy.isnan(expectedData.fill_value))
).all()
), "Incorrect output!"
def setup(self):
graph = Graph()
op = OpDenseLabelArray(graph=graph)
arrayshape = (1,100,100,10,1)
op.EraserLabelValue.setValue(100)
dummyData = vigra.VigraArray(arrayshape, axistags=vigra.defaultAxistags('txyzc'))
op.MetaInput.setValue( dummyData )
slicing = sl[0:1, 1:15, 2:36, 3:7, 0:1]
inDataShape = slicing2shape(slicing)
inputData = ( 3*numpy.random.random(inDataShape) ).astype(numpy.uint8)
op.LabelSinkInput[slicing] = inputData
data = numpy.zeros(arrayshape, dtype=numpy.uint8)
data[slicing] = inputData
self.op = op
self.slicing = slicing
self.inData = inputData
self.data = data
def testSetInSlot_masked(self):
logger.info("Generating sample data...")
sampleData = numpy.indices((100, 200, 150), dtype=numpy.float32).sum(0)
sampleData = sampleData.view(numpy.ma.masked_array)
sampleData.set_fill_value(numpy.float32(numpy.nan))
sampleData[0] = numpy.ma.masked
graph = Graph()
opData = OpArrayPiper(graph=graph)
opData.Input.meta.has_mask = True
opData.Input.meta.axistags = vigra.defaultAxistags('xyz')
opData.Input.setValue(sampleData)
op = OpCompressedCache(parent=None, graph=graph)
#logger.debug("Setting block shape...")
op.BlockShape.setValue([100, 75, 50])
op.Input.connect(opData.Output)
assert op.Output.ready()
slicing = numpy.s_[0:100, 0:75, 0:50]
expectedData = numpy.ma.ones(slicing2shape(slicing), dtype=int)
sampleData.set_fill_value(numpy.float32(numpy.nan))
expectedData[0] = numpy.ma.masked
# This is what we're testing.
#logger.debug("Forcing external data...")
op.Input[slicing] = expectedData
#logger.debug("Requesting data...")
readData = op.Output[slicing].wait()
#logger.debug("Checking data...")
assert (readData == expectedData).all() and \
(readData.mask == expectedData.mask).all() and \
((readData.fill_value == expectedData.fill_value) |
(numpy.isnan(readData.fill_value) & numpy.isnan(expectedData.fill_value))).all(),\
"Incorrect output!"
def setup(self):
graph = Graph()
op = OpDenseLabelArray(graph=graph)
arrayshape = (1, 100, 100, 10, 1)
op.EraserLabelValue.setValue(100)
dummyData = vigra.VigraArray(arrayshape,
axistags=vigra.defaultAxistags("txyzc"))
op.MetaInput.setValue(dummyData)
slicing = numpy.s_[0:1, 1:15, 2:36, 3:7, 0:1]
inDataShape = slicing2shape(slicing)
inputData = (3 * numpy.random.random(inDataShape)).astype(numpy.uint8)
op.LabelSinkInput[slicing] = inputData
data = numpy.zeros(arrayshape, dtype=numpy.uint8)
data[slicing] = inputData
self.op = op
self.slicing = slicing
self.inData = inputData
self.data = data
def setup(self):
try:
import blist
except ImportError:
raise unittest.SkipTest
from lazyflow.operators.opSparseLabelArray import OpSparseLabelArray
graph = Graph()
op = OpSparseLabelArray(graph=graph)
arrayshape = numpy.array([1,10,10,10,1])
op.inputs["shape"].setValue( tuple(arrayshape) )
op.eraser.setValue(100)
slicing = sl[0:1, 1:5, 2:6, 3:7, 0:1]
inDataShape = slicing2shape(slicing)
inputData = ( 3*numpy.random.random(inDataShape) ).astype(numpy.uint8)
op.Input[slicing] = inputData
data = numpy.zeros(arrayshape, dtype=numpy.uint8)
data[slicing] = inputData
self.op = op
self.slicing = slicing
self.inData = inputData
self.data = data
def setup(self):
try:
import blist
except ImportError:
raise unittest.SkipTest
from lazyflow.operators.opSparseLabelArray import OpSparseLabelArray
graph = Graph()
op = OpSparseLabelArray(graph=graph)
arrayshape = numpy.array([1, 10, 10, 10, 1])
op.inputs["shape"].setValue(tuple(arrayshape))
op.eraser.setValue(100)
slicing = sl[0:1, 1:5, 2:6, 3:7, 0:1]
inDataShape = slicing2shape(slicing)
inputData = (3 * numpy.random.random(inDataShape)).astype(numpy.uint8)
op.Input[slicing] = inputData
data = numpy.zeros(arrayshape, dtype=numpy.uint8)
data[slicing] = inputData
self.op = op
self.slicing = slicing
self.inData = inputData
self.data = data
def __getitem__(self, slicing):
sl3d = (slicing[1], slicing[2], slicing[3])
ret = np.zeros(slicing2shape(slicing), dtype=self.dtype)
ret[0, :, :, :, 0] = self.a[tuple(sl3d)]
return ret
def __getitem__(self, slicing):
sl3d = (slicing[1], slicing[2], slicing[3])
ret = np.zeros(slicing2shape(slicing), dtype=self.dtype)
ret[0, :, :, :, 0] = self.a[tuple(sl3d)]
return ret