def testBasic(self):
data = numpy.zeros((10, 100, 100), numpy.uint8)
data[4, 30:40, 50:60] = 1
data[7, 45:55, 30:35] = 255
bb_roi = nonzero_bounding_box(data)
assert isinstance(bb_roi, numpy.ndarray)
assert (bb_roi == [[4, 30, 30], [8, 55, 60]]).all()
def testBasic(self):
data = numpy.zeros( (10,100,100), numpy.uint8 )
data[4, 30:40, 50:60] = 1
data[7, 45:55, 30:35] = 255
bb_roi = nonzero_bounding_box(data)
assert isinstance(bb_roi, numpy.ndarray)
assert (bb_roi == [[4,30,30], [8,55,60]]).all()
def execute(self, slot, subindex, roi, result):
classifier_factory = self.ClassifierFactory.value
assert issubclass(type(classifier_factory), LazyflowPixelwiseClassifierFactoryABC), \
"Factory is of type {}, which does not satisfy the LazyflowPixelwiseClassifierFactoryABC interface."\
"".format( type(classifier_factory) )
# Accumulate all non-zero blocks of each image into lists
label_data_blocks = []
image_data_blocks = []
for image_slot, label_slot, nonzero_block_slot in zip(
self.Images, self.Labels, self.nonzeroLabelBlocks):
block_slicings = nonzero_block_slot.value
for block_slicing in block_slicings:
# Get labels
block_label_roi = sliceToRoi(block_slicing,
label_slot.meta.shape)
block_label_data = label_slot(*block_label_roi).wait()
# Shrink roi to bounding box of actual label pixels
bb_roi_within_block = nonzero_bounding_box(block_label_data)
block_label_bb_roi = bb_roi_within_block + block_label_roi[0]
# Double-check that there is at least 1 non-zero label in the block.
if (block_label_bb_roi[1] > block_label_bb_roi[0]).all():
# Ask for the halo needed by the classifier
axiskeys = image_slot.meta.getAxisKeys()
halo_shape = classifier_factory.get_halo_shape(axiskeys)
assert len(halo_shape) == len(block_label_roi[0])
assert halo_shape[
-1] == 0, "Didn't expect a non-zero halo for channel dimension."
# Expand block by halo, but keep clipped to image bounds
padded_label_roi, bb_roi_within_padded = enlargeRoiForHalo(
*block_label_bb_roi,
shape=label_slot.meta.shape,
sigma=halo_shape,
window=1,
return_result_roi=True)
# Copy labels to new array, which has size == bounding-box + halo
padded_label_data = numpy.zeros(
padded_label_roi[1] - padded_label_roi[0],
label_slot.meta.dtype)
padded_label_data[roiToSlice(
*bb_roi_within_padded)] = block_label_data[roiToSlice(
*bb_roi_within_block)]
padded_image_roi = numpy.array(padded_label_roi)
assert (padded_image_roi[:, -1] == [0, 1]).all()
num_channels = image_slot.meta.shape[-1]
padded_image_roi[:, -1] = [0, num_channels]
# Ensure the results are plain ndarray, not VigraArray,
# which some classifiers might have trouble with.
padded_image_data = numpy.asarray(
image_slot(*padded_image_roi).wait())
label_data_blocks.append(padded_label_data)
image_data_blocks.append(padded_image_data)
if len(image_data_blocks) == 0:
result[0] = None
else:
channel_names = self.Images[0].meta.channel_names
axistags = self.Images[0].meta.axistags
logger.debug("Training new pixelwise classifier: {}".format(
classifier_factory.description))
classifier = classifier_factory.create_and_train_pixelwise(
image_data_blocks, label_data_blocks, axistags, channel_names)
result[0] = classifier
if classifier is not None:
assert issubclass(type(classifier), LazyflowPixelwiseClassifierABC), \
"Classifier is of type {}, which does not satisfy the LazyflowPixelwiseClassifierABC interface."\
"".format( type(classifier) )
def execute(self, slot, subindex, roi, result):
classifier_factory = self.ClassifierFactory.value
assert issubclass(type(classifier_factory), LazyflowPixelwiseClassifierFactoryABC), \
"Factory is of type {}, which does not satisfy the LazyflowPixelwiseClassifierFactoryABC interface."\
"".format( type(classifier_factory) )
# Accumulate all non-zero blocks of each image into lists
label_data_blocks = []
image_data_blocks = []
for image_slot, label_slot, nonzero_block_slot in zip(self.Images, self.Labels, self.nonzeroLabelBlocks):
block_slicings = nonzero_block_slot.value
for block_slicing in block_slicings:
# Get labels
block_label_roi = sliceToRoi( block_slicing, label_slot.meta.shape )
block_label_data = label_slot(*block_label_roi).wait()
# Shrink roi to bounding box of actual label pixels
bb_roi_within_block = nonzero_bounding_box(block_label_data)
block_label_bb_roi = bb_roi_within_block + block_label_roi[0]
# Double-check that there is at least 1 non-zero label in the block.
if (block_label_bb_roi[1] > block_label_bb_roi[0]).all():
# Ask for the halo needed by the classifier
axiskeys = image_slot.meta.getAxisKeys()
halo_shape = classifier_factory.get_halo_shape(axiskeys)
assert len(halo_shape) == len( block_label_roi[0] )
assert halo_shape[-1] == 0, "Didn't expect a non-zero halo for channel dimension."
# Expand block by halo, but keep clipped to image bounds
padded_label_roi, bb_roi_within_padded = enlargeRoiForHalo( *block_label_bb_roi,
shape=label_slot.meta.shape,
sigma=halo_shape,
window=1,
return_result_roi=True )
# Copy labels to new array, which has size == bounding-box + halo
padded_label_data = numpy.zeros( padded_label_roi[1] - padded_label_roi[0], label_slot.meta.dtype )
padded_label_data[roiToSlice(*bb_roi_within_padded)] = block_label_data[roiToSlice(*bb_roi_within_block)]
padded_image_roi = numpy.array( padded_label_roi )
assert (padded_image_roi[:, -1] == [0,1]).all()
num_channels = image_slot.meta.shape[-1]
padded_image_roi[:, -1] = [0, num_channels]
# Ensure the results are plain ndarray, not VigraArray,
# which some classifiers might have trouble with.
padded_image_data = numpy.asarray( image_slot(*padded_image_roi).wait() )
label_data_blocks.append( padded_label_data )
image_data_blocks.append( padded_image_data )
if len(image_data_blocks) == 0:
result[0] = None
else:
channel_names = self.Images[0].meta.channel_names
axistags = self.Images[0].meta.axistags
logger.debug("Training new pixelwise classifier: {}".format( classifier_factory.description ))
classifier = classifier_factory.create_and_train_pixelwise( image_data_blocks, label_data_blocks, axistags, channel_names )
result[0] = classifier
if classifier is not None:
assert issubclass(type(classifier), LazyflowPixelwiseClassifierABC), \
"Classifier is of type {}, which does not satisfy the LazyflowPixelwiseClassifierABC interface."\
"".format( type(classifier) )
def test_empty_data(self):
data = numpy.zeros((10, 100, 100), numpy.uint8)
bb_roi = nonzero_bounding_box(data)
assert isinstance(bb_roi, numpy.ndarray)
assert (bb_roi == [[0, 0, 0], [0, 0, 0]]).all()
def test_empty_data(self):
data = numpy.zeros( (10,100,100), numpy.uint8 )
bb_roi = nonzero_bounding_box(data)
assert isinstance(bb_roi, numpy.ndarray)
assert (bb_roi == [[0,0,0], [0,0,0]]).all()
