def execute(self, slot, subindex, roi, result):
featMatrix=[]
labelsMatrix=[]
for i,labels in enumerate(self.inputs["Labels"]):
if labels.meta.shape is not None:
labels=labels[:].wait()
indexes=numpy.nonzero(labels[...,0].view(numpy.ndarray))
#Maybe later request only part of the region?
image=self.inputs["Images"][i][:].wait()
features=image[indexes]
labels=labels[indexes]
featMatrix.append(features)
labelsMatrix.append(labels)
featMatrix=numpy.concatenate(featMatrix,axis=0)
labelsMatrix=numpy.concatenate(labelsMatrix,axis=0)
# train and store self._forest_count forests in parallel
pool = RequestPool()
for i in range(self._forest_count):
def train_and_store(number):
result[number] = vigra.learning.RandomForest(self._tree_count)
result[number].learnRF(featMatrix.astype(numpy.float32),labelsMatrix.astype(numpy.uint32))
req = pool.request(partial(train_and_store, i))
pool.wait()
return result
def execute(self, slot, subindex, roi, result):
with self._lock:
if self.cache is None:
fullBlockShape = numpy.array([self.blockShape.value for i in self.Input.meta.shape])
fun = self.inputs["Function"].value
#data = self.inputs["Input"][:].wait()
#split up requests into blocks
shape = self.Input.meta.shape
numBlocks = numpy.ceil(shape/(1.0*fullBlockShape)).astype("int")
blockCache = numpy.ndarray(shape = numpy.prod(numBlocks), dtype=self.Output.meta.dtype)
pool = RequestPool()
#blocks holds the different roi keys for each of the blocks
blocks = itertools.product(*[range(i) for i in numBlocks])
blockKeys = []
for b in blocks:
start = b * fullBlockShape
stop = b * fullBlockShape + fullBlockShape
stop = numpy.min(numpy.vstack((stop, shape)), axis=0)
blockKey = roiToSlice(start, stop)
blockKeys.append(blockKey)
def predict_block(i):
data = self.Input[blockKeys[i]].wait()
blockCache[i] = fun(data)
for i,f in enumerate(blockCache):
req = pool.request(partial(predict_block,i))
pool.wait()
pool.clean()
self.cache = [fun(blockCache)]
return self.cache
def execute(self, slot, subindex, roi, result):
t1 = time.perf_counter()
key = roi.toSlice()
nlabels = self.inputs["LabelsCount"].value
traceLogger.debug(
"OpPredictRandomForest: Requesting classifier. roi={}".format(roi))
forests = self.inputs["Classifier"][:].wait()
if any(forest is None for forest in forests):
# Training operator may return 'None' if there was no data to train with
return np.zeros(np.subtract(roi.stop, roi.start),
dtype=np.float32)[...]
traceLogger.debug("OpPredictRandomForest: Got classifier")
#assert RF.labelCount() == nlabels, "ERROR: OpPredictRandomForest, labelCount differs from true labelCount! %r vs. %r" % (RF.labelCount(), nlabels)
newKey = key[:-1]
newKey += (slice(0, self.inputs["Image"].meta.shape[-1], None), )
res = self.inputs["Image"][newKey].wait()
shape = res.shape
prod = np.prod(shape[:-1])
res.shape = (prod, shape[-1])
features = res
predictions = [0] * len(forests)
t2 = time.perf_counter()
pool = RequestPool()
def predict_forest(i):
predictions[i] = forests[i].predict(
np.asarray(features, dtype=np.float32))
predictions[i] = predictions[i].reshape(result.shape[:-1])
for i, f in enumerate(forests):
req = pool.request(partial(predict_forest, i))
pool.wait()
pool.clean()
#predictions[0] = forests[0].predict(np.asarray(features, dtype = np.float32), normalize = False)
#predictions[0] = predictions[0].reshape(result.shape)
prediction = np.dstack(predictions)
result[...] = prediction
# If our LabelsCount is higher than the number of labels in the training set,
# then our results aren't really valid. FIXME !!!
# Duplicate the last label's predictions
#for c in range(result.shape[-1]):
# result[...,c] = prediction[...,min(c+key[-1].start, prediction.shape[-1]-1)]
t3 = time.perf_counter()
logger.debug(
"Predict took %fseconds, actual RF time was %fs, feature time was %fs"
% (t3 - t1, t3 - t2, t2 - t1))
return result
def execute(self, slot, subindex, roi, result):
t1 = time.time()
key = roi.toSlice()
nlabels=self.inputs["LabelsCount"].value
traceLogger.debug("OpPredictRandomForest: Requesting classifier. roi={}".format(roi))
forests=self.inputs["Classifier"][:].wait()
if forests is None or any(x is None for x in forests):
# Training operator may return 'None' if there was no data to train with
return numpy.zeros(numpy.subtract(roi.stop, roi.start), dtype=numpy.float32)[...]
traceLogger.debug("OpPredictRandomForest: Got classifier")
#assert RF.labelCount() == nlabels, "ERROR: OpPredictRandomForest, labelCount differs from true labelCount! %r vs. %r" % (RF.labelCount(), nlabels)
newKey = key[:-1]
newKey += (slice(0,self.inputs["Image"].meta.shape[-1],None),)
res = self.inputs["Image"][newKey].wait()
shape=res.shape
prod = numpy.prod(shape[:-1])
res.shape = (prod, shape[-1])
features=res
predictions = [0]*len(forests)
def predict_forest(number):
predictions[number] = forests[number].predictProbabilities(numpy.asarray(features, dtype=numpy.float32))
t2 = time.time()
# predict the data with all the forests in parallel
pool = RequestPool()
for i,f in enumerate(forests):
req = pool.request(partial(predict_forest, i))
pool.wait()
pool.clean()
prediction=numpy.dstack(predictions)
prediction = numpy.average(prediction, axis=2)
prediction.shape = shape[:-1] + (forests[0].labelCount(),)
#prediction = prediction.reshape(*(shape[:-1] + (forests[0].labelCount(),)))
# If our LabelsCount is higher than the number of labels in the training set,
# then our results aren't really valid. FIXME !!!
# Duplicate the last label's predictions
for c in range(result.shape[-1]):
result[...,c] = prediction[...,min(c+key[-1].start, prediction.shape[-1]-1)]
t3 = time.time()
self.logger.debug("predict roi=%r took %fseconds, actual RF time was %fs, feature time was %fs" % (key, t3-t1, t3-t2, t2-t1))
return result
def execute(self, slot, subindex, roi, result):
with self._lock:
if self.cache is None:
shape = self.Input.meta.shape
# self.blockshape has None in the last dimension to indicate that it should not be
# handled block-wise. None is replaced with the image shape in the respective axis.
fullBlockShape = []
for u, v in zip(self.blockShape.value, shape):
if u is not None:
fullBlockShape.append(u)
else:
fullBlockShape.append(v)
fullBlockShape = numpy.array(fullBlockShape,
dtype=numpy.float64)
# data = self.inputs["Input"][:].wait()
# split up requests into blocks
numBlocks = numpy.ceil(shape / fullBlockShape).astype("int")
blockCache = numpy.ndarray(shape=numpy.prod(numBlocks),
dtype=self.Output.meta.dtype)
pool = RequestPool()
# blocks holds the different roi keys for each of the blocks
blocks = itertools.product(
*[list(range(i)) for i in numBlocks])
blockKeys = []
for b in blocks:
start = b * fullBlockShape
stop = b * fullBlockShape + fullBlockShape
stop = numpy.min(numpy.vstack((stop, shape)), axis=0)
blockKey = roiToSlice(start, stop)
blockKeys.append(blockKey)
fun = self.inputs["Function"].value
def predict_block(i):
data = self.Input[blockKeys[i]].wait()
blockCache[i] = fun(data)
for i, f in enumerate(blockCache):
req = pool.request(partial(predict_block, i))
pool.wait()
pool.clean()
self.cache = [fun(blockCache)]
return self.cache
def execute(self, slot, subindex, roi, result):
with self._lock:
if self.cache is None:
shape = self.Input.meta.shape
# self.blockshape has None in the last dimension to indicate that it should not be
# handled block-wise. None is replaced with the image shape in the respective axis.
fullBlockShape = []
for u, v in zip(self.blockShape.value, shape):
if u is not None:
fullBlockShape.append(u)
else:
fullBlockShape.append(v)
fullBlockShape = numpy.array(fullBlockShape, dtype=numpy.float64)
#data = self.inputs["Input"][:].wait()
#split up requests into blocks
numBlocks = numpy.ceil(shape / fullBlockShape).astype("int")
blockCache = numpy.ndarray(shape = numpy.prod(numBlocks), dtype=self.Output.meta.dtype)
pool = RequestPool()
#blocks holds the different roi keys for each of the blocks
blocks = itertools.product(*[list(range(i)) for i in numBlocks])
blockKeys = []
for b in blocks:
start = b * fullBlockShape
stop = b * fullBlockShape + fullBlockShape
stop = numpy.min(numpy.vstack((stop, shape)), axis=0)
blockKey = roiToSlice(start, stop)
blockKeys.append(blockKey)
fun = self.inputs["Function"].value
def predict_block(i):
data = self.Input[blockKeys[i]].wait()
blockCache[i] = fun(data)
for i,f in enumerate(blockCache):
req = pool.request(partial(predict_block,i))
pool.wait()
pool.clean()
self.cache = [fun(blockCache)]
return self.cache
def execute(self, slot, subindex, roi, result):
with self._lock:
if self.cache is None:
fullBlockShape = numpy.array(
[self.blockShape.value for i in self.Input.meta.shape])
fun = self.inputs["Function"].value
#data = self.inputs["Input"][:].wait()
#split up requests into blocks
shape = self.Input.meta.shape
numBlocks = numpy.ceil(shape /
(1.0 * fullBlockShape)).astype("int")
blockCache = numpy.ndarray(shape=numpy.prod(numBlocks),
dtype=self.Output.meta.dtype)
pool = RequestPool()
#blocks holds the different roi keys for each of the blocks
blocks = itertools.product(*[range(i) for i in numBlocks])
blockKeys = []
for b in blocks:
start = b * fullBlockShape
stop = b * fullBlockShape + fullBlockShape
stop = numpy.min(numpy.vstack((stop, shape)), axis=0)
blockKey = roiToSlice(start, stop)
blockKeys.append(blockKey)
def predict_block(i):
data = self.Input[blockKeys[i]].wait()
blockCache[i] = fun(data)
for i, f in enumerate(blockCache):
req = pool.request(partial(predict_block, i))
pool.wait()
pool.clean()
self.cache = [fun(blockCache)]
return self.cache
def execute(self, slot, subindex, slot_roi, target):
assert slot == self.Features or slot == self.Output
if slot == self.Features:
feature_slice = roiToSlice(slot_roi.start, slot_roi.stop)
index = subindex[0]
feature_slice = list(feature_slice)
# Translate channel slice of this feature to the channel slice of the output slot.
output_channel_offset = self.featureOutputChannels[index][0]
feature_slice[1] = slice(
output_channel_offset + feature_slice[1].start,
output_channel_offset + feature_slice[1].stop)
slot_roi = SubRegion(self.Output, pslice=feature_slice)
# Get output slot region for this channel
return self.execute(self.Output, (), slot_roi, target)
elif slot == self.Output:
# Correlation of variable 'families' representing reference frames:
# ______________________________
# | input/output frame | input/output shape given by slots
# | _________________________ |
# | | smooth frame | | pre-smoothing op needs halo around filter roi
# | | ____________________ | |
# | | |filter frame | | | filter needs halo around target roi
# | | | _______________ | | |
# | | | | target frame | | | | target is given by output_roi
# note: The 'full_' variable prefix refers to the full 5D shape (tczyx), without 'full_' variables mostly
# refer to the 3D space subregion (zyx)
full_output_slice = slot_roi.toSlice()
logger.debug(
f"OpPixelFeaturesPresmoothed: request {slot_roi.pprint()}")
assert (slot_roi.stop <= self.Output.meta.shape).all()
full_output_shape = self.Output.meta.shape
full_output_start, full_output_stop = sliceToRoi(
full_output_slice, full_output_shape)
assert len(full_output_shape) == 5
if all(self.ComputeIn2d.value
): # todo: check for this particular slice
axes2enlarge = (0, 1, 1)
else:
axes2enlarge = (1, 1, 1)
output_shape = full_output_shape[2:]
output_start = full_output_start[2:]
output_stop = full_output_stop[2:]
axistags = self.Output.meta.axistags
target = target.view(vigra.VigraArray)
target.axistags = copy.copy(axistags)
# filter roi in input frame
# sigma = 0.7, because the features receive a pre-smoothed array and don't need much of a neighborhood
input_filter_start, input_filter_stop = roi.enlargeRoiForHalo(
output_start,
output_stop,
output_shape,
0.7,
self.WINDOW_SIZE,
enlarge_axes=axes2enlarge)
# smooth roi in input frame
input_smooth_start, input_smooth_stop = roi.enlargeRoiForHalo(
input_filter_start,
input_filter_stop,
output_shape,
self.max_sigma,
self.WINDOW_SIZE,
enlarge_axes=axes2enlarge,
)
# target roi in filter frame
filter_target_start = roi.TinyVector(output_start -
input_filter_start)
filter_target_stop = roi.TinyVector(output_stop -
input_filter_start)
# filter roi in smooth frame
smooth_filter_start = roi.TinyVector(input_filter_start -
input_smooth_start)
smooth_filter_stop = roi.TinyVector(input_filter_stop -
input_smooth_start)
filter_target_slice = roi.roiToSlice(filter_target_start,
filter_target_stop)
input_smooth_slice = roi.roiToSlice(input_smooth_start,
input_smooth_stop)
# pre-smooth for all requested time slices and all channels
full_input_smooth_slice = (full_output_slice[0], slice(None),
*input_smooth_slice)
req = self.Input[full_input_smooth_slice]
source = req.wait()
req.clean()
req.destination = None
if source.dtype != numpy.float32:
sourceF = source.astype(numpy.float32)
try:
source.resize((1, ), refcheck=False)
except Exception:
pass
del source
source = sourceF
sourceV = source.view(vigra.VigraArray)
sourceV.axistags = copy.copy(self.Input.meta.axistags)
dimCol = len(self.scales)
dimRow = self.matrix.shape[0]
presmoothed_source = [None] * dimCol
source_smooth_shape = tuple(smooth_filter_stop -
smooth_filter_start)
full_source_smooth_shape = (
full_output_stop[0] - full_output_start[0],
self.Input.meta.shape[1],
) + source_smooth_shape
try:
for j in range(dimCol):
for i in range(dimRow):
if self.matrix[i, j]:
# There is at least one filter op with this scale
break
else:
# There is no filter op at this scale
continue
if self.scales[j] > 1.0:
tempSigma = math.sqrt(self.scales[j]**2 - 1.0)
else:
tempSigma = self.scales[j]
presmoothed_source[j] = numpy.ndarray(
full_source_smooth_shape, numpy.float32)
droi = (
(0, *tuple(smooth_filter_start._asint())),
(sourceV.shape[1],
*tuple(smooth_filter_stop._asint())),
)
for i, vsa in enumerate(sourceV.timeIter()):
presmoothed_source[j][
i, ...] = self._computeGaussianSmoothing(
vsa,
tempSigma,
droi,
in2d=self.ComputeIn2d.value[j])
except RuntimeError as e:
if "kernel longer than line" in str(e):
raise RuntimeError(
"Feature computation error:\nYour image is too small to apply a filter with "
f"sigma={self.scales[j]:.1f}. Please select features with smaller sigmas."
)
else:
raise e
del sourceV
try:
source.resize((1, ), refcheck=False)
except ValueError:
# Sometimes this fails, but that's okay.
logger.debug("Failed to free array memory.")
del source
cnt = 0
written = 0
closures = []
# connect individual operators
for i in range(dimRow):
for j in range(dimCol):
if self.matrix[i, j]:
oslot = self.featureOps[i][j].Output
req = None
slices = oslot.meta.shape[1]
if (cnt + slices >= slot_roi.start[1]
and slot_roi.start[1] - cnt < slices
and slot_roi.start[1] + written <
slot_roi.stop[1]):
begin = 0
if cnt < slot_roi.start[1]:
begin = slot_roi.start[1] - cnt
end = slices
if cnt + end > slot_roi.stop[1]:
end = slot_roi.stop[1] - cnt
# feature slice in output frame
feature_slice = (slice(None),
slice(
written, written + end -
begin)) + (slice(None), ) * 3
subtarget = target[feature_slice]
# readjust the roi for the new source array
full_filter_target_slice = [
full_output_slice[0],
slice(begin, end), *filter_target_slice
]
filter_target_roi = SubRegion(
oslot, pslice=full_filter_target_slice)
closure = partial(
oslot.operator.execute,
oslot,
(),
filter_target_roi,
subtarget,
sourceArray=presmoothed_source[j],
)
closures.append(closure)
written += end - begin
cnt += slices
pool = RequestPool()
for c in closures:
pool.request(c)
pool.wait()
pool.clean()
for i in range(len(presmoothed_source)):
if presmoothed_source[i] is not None:
try:
presmoothed_source[i].resize((1, ))
except Exception:
presmoothed_source[i] = None
def execute(self, slot, subindex, slot_roi, target):
assert slot == self.Features or slot == self.Output
if slot == self.Features:
feature_slice = roiToSlice(slot_roi.start, slot_roi.stop)
index = subindex[0]
feature_slice = list(feature_slice)
# Translate channel slice of this feature to the channel slice of the output slot.
output_channel_offset = self.featureOutputChannels[index][0]
feature_slice[1] = slice(
output_channel_offset + feature_slice[1].start, output_channel_offset + feature_slice[1].stop
)
slot_roi = SubRegion(self.Output, pslice=feature_slice)
# Get output slot region for this channel
return self.execute(self.Output, (), slot_roi, target)
elif slot == self.Output:
# Correlation of variable 'families' representing reference frames:
# ______________________________
# | input/output frame | input/output shape given by slots
# | _________________________ |
# | | smooth frame | | pre-smoothing op needs halo around filter roi
# | | ____________________ | |
# | | |filter frame | | | filter needs halo around target roi
# | | | _______________ | | |
# | | | | target frame | | | | target is given by output_roi
# note: The 'full_' variable prefix refers to the full 5D shape (tczyx), without 'full_' variables mostly
# refer to the 3D space subregion (zyx)
full_output_slice = slot_roi.toSlice()
logger.debug(f"OpPixelFeaturesPresmoothed: request {slot_roi.pprint()}")
assert (slot_roi.stop <= self.Output.meta.shape).all()
full_output_shape = self.Output.meta.shape
full_output_start, full_output_stop = sliceToRoi(full_output_slice, full_output_shape)
assert len(full_output_shape) == 5
if all(self.ComputeIn2d.value): # todo: check for this particular slice
axes2enlarge = (0, 1, 1)
else:
axes2enlarge = (1, 1, 1)
output_shape = full_output_shape[2:]
output_start = full_output_start[2:]
output_stop = full_output_stop[2:]
axistags = self.Output.meta.axistags
target = target.view(vigra.VigraArray)
target.axistags = copy.copy(axistags)
# filter roi in input frame
# sigma = 0.7, because the features receive a pre-smoothed array and don't need much of a neighborhood
input_filter_start, input_filter_stop = roi.enlargeRoiForHalo(
output_start, output_stop, output_shape, 0.7, self.WINDOW_SIZE, enlarge_axes=axes2enlarge
)
# smooth roi in input frame
input_smooth_start, input_smooth_stop = roi.enlargeRoiForHalo(
input_filter_start,
input_filter_stop,
output_shape,
self.max_sigma,
self.WINDOW_SIZE,
enlarge_axes=axes2enlarge,
)
# target roi in filter frame
filter_target_start = roi.TinyVector(output_start - input_filter_start)
filter_target_stop = roi.TinyVector(output_stop - input_filter_start)
# filter roi in smooth frame
smooth_filter_start = roi.TinyVector(input_filter_start - input_smooth_start)
smooth_filter_stop = roi.TinyVector(input_filter_stop - input_smooth_start)
filter_target_slice = roi.roiToSlice(filter_target_start, filter_target_stop)
input_smooth_slice = roi.roiToSlice(input_smooth_start, input_smooth_stop)
# pre-smooth for all requested time slices and all channels
full_input_smooth_slice = (full_output_slice[0], slice(None), *input_smooth_slice)
req = self.Input[full_input_smooth_slice]
source = req.wait()
req.clean()
req.destination = None
if source.dtype != numpy.float32:
sourceF = source.astype(numpy.float32)
try:
source.resize((1,), refcheck=False)
except Exception:
pass
del source
source = sourceF
sourceV = source.view(vigra.VigraArray)
sourceV.axistags = copy.copy(self.Input.meta.axistags)
dimCol = len(self.scales)
dimRow = self.matrix.shape[0]
presmoothed_source = [None] * dimCol
source_smooth_shape = tuple(smooth_filter_stop - smooth_filter_start)
full_source_smooth_shape = (
full_output_stop[0] - full_output_start[0],
self.Input.meta.shape[1],
) + source_smooth_shape
try:
for j in range(dimCol):
for i in range(dimRow):
if self.matrix[i, j]:
# There is at least one filter op with this scale
break
else:
# There is no filter op at this scale
continue
if self.scales[j] > 1.0:
tempSigma = math.sqrt(self.scales[j] ** 2 - 1.0)
else:
tempSigma = self.scales[j]
presmoothed_source[j] = numpy.ndarray(full_source_smooth_shape, numpy.float32)
droi = (
(0, *tuple(smooth_filter_start._asint())),
(sourceV.shape[1], *tuple(smooth_filter_stop._asint())),
)
for i, vsa in enumerate(sourceV.timeIter()):
presmoothed_source[j][i, ...] = self._computeGaussianSmoothing(
vsa, tempSigma, droi, in2d=self.ComputeIn2d.value[j]
)
except RuntimeError as e:
if "kernel longer than line" in str(e):
raise RuntimeError(
"Feature computation error:\nYour image is too small to apply a filter with "
f"sigma={self.scales[j]:.1f}. Please select features with smaller sigmas."
)
else:
raise e
del sourceV
try:
source.resize((1,), refcheck=False)
except ValueError:
# Sometimes this fails, but that's okay.
logger.debug("Failed to free array memory.")
del source
cnt = 0
written = 0
closures = []
# connect individual operators
for i in range(dimRow):
for j in range(dimCol):
if self.matrix[i, j]:
oslot = self.featureOps[i][j].Output
req = None
slices = oslot.meta.shape[1]
if (
cnt + slices >= slot_roi.start[1]
and slot_roi.start[1] - cnt < slices
and slot_roi.start[1] + written < slot_roi.stop[1]
):
begin = 0
if cnt < slot_roi.start[1]:
begin = slot_roi.start[1] - cnt
end = slices
if cnt + end > slot_roi.stop[1]:
end = slot_roi.stop[1] - cnt
# feature slice in output frame
feature_slice = (slice(None), slice(written, written + end - begin)) + (slice(None),) * 3
subtarget = target[feature_slice]
# readjust the roi for the new source array
full_filter_target_slice = [full_output_slice[0], slice(begin, end), *filter_target_slice]
filter_target_roi = SubRegion(oslot, pslice=full_filter_target_slice)
closure = partial(
oslot.operator.execute,
oslot,
(),
filter_target_roi,
subtarget,
sourceArray=presmoothed_source[j],
)
closures.append(closure)
written += end - begin
cnt += slices
pool = RequestPool()
for c in closures:
pool.request(c)
pool.wait()
pool.clean()
for i in range(len(presmoothed_source)):
if presmoothed_source[i] is not None:
try:
presmoothed_source[i].resize((1,))
except Exception:
presmoothed_source[i] = None
def execute(self, slot, subindex, roi, result):
progress = 0
numImages = len(self.Images)
self.progressSignal(progress)
featMatrix=[]
labelsMatrix=[]
tagList = []
#result[0] = self._svr
for i,labels in enumerate(self.inputs["ForegroundLabels"]):
if labels.meta.shape is not None:
opGaussian = OpGaussianSmoothing(parent = self, graph = self.graph)
opGaussian.Sigma.setValue(self.Sigma.value)
opGaussian.Input.connect(self.ForegroundLabels[i])
blocks = self.inputs["nonzeroLabelBlocks"][i][0].wait()
reqlistlabels = []
reqlistbg = []
reqlistfeat = []
progress += 10 / numImages
self.progressSignal(progress)
for b in blocks[0]:
request = opGaussian.Output[b]
#request = labels[b]
featurekey = list(b)
featurekey[-1] = slice(None, None, None)
request2 = self.Images[i][featurekey]
request3 = self.inputs["BackgroundLabels"][i][b]
reqlistlabels.append(request)
reqlistfeat.append(request2)
reqlistbg.append(request3)
traceLogger.debug("Requests prepared")
numLabelBlocks = len(reqlistlabels)
progress_outer = [progress]
if numLabelBlocks > 0:
progressInc = (80 - 10)/(numLabelBlocks * numImages)
def progressNotify(req):
progress_outer[0] += progressInc/2
self.progressSignal(progress_outer[0])
for ir, req in enumerate(reqlistfeat):
req.notify_finished(progressNotify)
req.submit()
for ir, req in enumerate(reqlistlabels):
req.notify_finished(progressNotify)
req.submit()
for ir, req in enumerate(reqlistbg):
req.notify_finished(progressNotify)
req.submit()
traceLogger.debug("Requests fired")
#Fixme: Maybe later request only part of the region?
#image=self.inputs["Images"][i][:].wait()
for ir, req in enumerate(reqlistlabels):
labblock = req.wait()
image = reqlistfeat[ir].wait()
labbgblock = reqlistbg[ir].wait()
labblock = labblock.reshape((image.shape[:-1]))
image = image.reshape((-1, image.shape[-1]))
labbgindices = np.where(labbgblock == 2)
labbgindices = np.ravel_multi_index(labbgindices, labbgblock.shape)
newDot, mapping, tags = \
self._svr.prepareDataRefactored(labblock, labbgindices)
#self._svr.prepareData(labblock, smooth = True)
labels = newDot[mapping]
features = image[mapping]
featMatrix.append(features)
labelsMatrix.append(labels)
tagList.append(tags)
progress = progress_outer[0]
traceLogger.debug("Requests processed")
self.progressSignal(80 / numImages)
if len(featMatrix) == 0 or len(labelsMatrix) == 0:
result[:] = None
else:
posTags = [tag[0] for tag in tagList]
negTags = [tag[1] for tag in tagList]
numPosTags = np.sum(posTags)
numTags = np.sum(posTags) + np.sum(negTags)
fullFeatMatrix = np.ndarray((numTags, self.Images[0].meta.shape[-1]), dtype = np.float64)
fullLabelsMatrix = np.ndarray((numTags), dtype = np.float64)
fullFeatMatrix[:] = np.NAN
fullLabelsMatrix[:] = np.NAN
currPosCount = 0
currNegCount = numPosTags
for i, posCount in enumerate(posTags):
fullFeatMatrix[currPosCount:currPosCount + posTags[i],:] = featMatrix[i][:posCount,:]
fullLabelsMatrix[currPosCount:currPosCount + posTags[i]] = labelsMatrix[i][:posCount]
fullFeatMatrix[currNegCount:currNegCount + negTags[i],:] = featMatrix[i][posCount:,:]
fullLabelsMatrix[currNegCount:currNegCount + negTags[i]] = labelsMatrix[i][posCount:]
currPosCount += posTags[i]
currNegCount += negTags[i]
assert(not np.isnan(np.sum(fullFeatMatrix)))
fullTags = [np.sum(posTags), np.sum(negTags)]
#pool = RequestPool()
maxima = np.max(fullFeatMatrix, axis=0)
minima = np.min(fullFeatMatrix, axis=0)
normalizationFactors = (minima,maxima)
boxConstraintList = []
boxConstraints = None
if self.BoxConstraintRois.ready() and self.BoxConstraintValues.ready():
for i, slot in enumerate(zip(self.BoxConstraintRois,self.BoxConstraintValues)):
for constr, val in zip(slot[0].value, slot[1].value):
boxConstraintList.append((i, constr, val))
if len(boxConstraintList) > 0:
boxConstraints = self.constructBoxConstraints(boxConstraintList)
params = self._svr.get_params()
try:
pool = RequestPool()
def train_and_store(i):
result[i] = SVR(minmax = normalizationFactors, **params)
result[i].fitPrepared(fullFeatMatrix, fullLabelsMatrix, tags = fullTags, boxConstraints = boxConstraints, numRegressors
= self.numRegressors, trainAll = False)
for i in range(self.numRegressors):
req = pool.request(partial(train_and_store, i))
pool.wait()
pool.clean()
except:
logger.error("ERROR: could not learn regressor")
logger.error("fullFeatMatrix shape = {}, dtype = {}".format(fullFeatMatrix.shape, fullFeatMatrix.dtype) )
logger.error("fullLabelsMatrix shape = {}, dtype = {}".format(fullLabelsMatrix.shape, fullLabelsMatrix.dtype) )
raise
finally:
self.progressSignal(100)
return result
def execute(self, slot, subindex, roi, result):
progress = 0
self.progressSignal(progress)
numImages = len(self.Images)
key = roi.toSlice()
featMatrix=[]
labelsMatrix=[]
for i,labels in enumerate(self.inputs["Labels"]):
if labels.meta.shape is not None:
#labels=labels[:].wait()
blocks = self.inputs["nonzeroLabelBlocks"][i][0].wait()
progress += 10/numImages
self.progressSignal(progress)
reqlistlabels = []
reqlistfeat = []
traceLogger.debug("Sending requests for {} non-zero blocks (labels and data)".format( len(blocks[0])) )
for b in blocks[0]:
request = labels[b]
featurekey = list(b)
featurekey[-1] = slice(None, None, None)
request2 = self.inputs["Images"][i][featurekey]
reqlistlabels.append(request)
reqlistfeat.append(request2)
traceLogger.debug("Requests prepared")
numLabelBlocks = len(reqlistlabels)
progress_outer = [progress] # Store in list for closure access
if numLabelBlocks > 0:
progressInc = (80-10)/numLabelBlocks/numImages
def progressNotify(req):
# Note: If we wanted perfect progress reporting, we could use lock here
# to protect the progress from being incremented simultaneously.
# But that would slow things down and imperfect reporting is okay for our purposes.
progress_outer[0] += progressInc/2
self.progressSignal(progress_outer[0])
for ir, req in enumerate(reqlistfeat):
image = req.notify_finished(progressNotify)
for ir, req in enumerate(reqlistlabels):
labblock = req.notify_finished(progressNotify)
traceLogger.debug("Requests fired")
for ir, req in enumerate(reqlistlabels):
traceLogger.debug("Waiting for a label block...")
labblock = req.wait()
traceLogger.debug("Waiting for an image block...")
image = reqlistfeat[ir].wait()
indexes=numpy.nonzero(labblock[...,0].view(numpy.ndarray))
features=image[indexes]
labbla=labblock[indexes]
featMatrix.append(features)
labelsMatrix.append(labbla)
progress = progress_outer[0]
traceLogger.debug("Requests processed")
self.progressSignal(80/numImages)
if len(featMatrix) == 0 or len(labelsMatrix) == 0:
# If there was no actual data for the random forest to train with, we return None
result[:] = None
else:
featMatrix=numpy.concatenate(featMatrix,axis=0)
labelsMatrix=numpy.concatenate(labelsMatrix,axis=0)
maxLabel = self.inputs["MaxLabel"].value
labelList = range(1, maxLabel+1) if maxLabel > 0 else list()
try:
logger.debug("Learning with Vigra...")
# train and store self._forest_count forests in parallel
pool = RequestPool()
for i in range(self._forest_count):
def train_and_store(number):
result[number] = vigra.learning.RandomForest(self._tree_count, labels=labelList)
result[number].learnRF( numpy.asarray(featMatrix, dtype=numpy.float32),
numpy.asarray(labelsMatrix, dtype=numpy.uint32))
req = pool.request(partial(train_and_store, i))
pool.wait()
pool.clean()
logger.debug("Vigra finished")
except:
logger.error( "ERROR: could not learn classifier" )
logger.error( "featMatrix shape={}, max={}, dtype={}".format(featMatrix.shape, featMatrix.max(), featMatrix.dtype) )
logger.error( "labelsMatrix shape={}, max={}, dtype={}".format(labelsMatrix.shape, labelsMatrix.max(), labelsMatrix.dtype ) )
raise
finally:
self.progressSignal(100)
return result
def execute(self, slot, subindex, roi, result):
progress = 0
self.progressSignal(progress)
numImages = len(self.Images)
featMatrix=[]
labelsMatrix=[]
tagsMatrix = []
result[0] = SVR(self.UnderMult.value, self.OverMult.value, limitDensity = True, **self.SelectedOption.value)
for i,labels in enumerate(self.inputs["Labels"]):
if labels.meta.shape is not None:
#labels=labels[:].wait()
blocks = self.inputs["nonzeroLabelBlocks"][i][0].wait()
progress += 10/numImages
self.progressSignal(progress)
reqlistlabels = []
reqlistfeat = []
traceLogger.debug("Sending requests for {} non-zero blocks (labels and data)".format( len(blocks[0])) )
for b in blocks[0]:
request = labels[b]
featurekey = list(b)
featurekey[-1] = slice(None, None, None)
request2 = self.inputs["Images"][i][featurekey]
reqlistlabels.append(request)
reqlistfeat.append(request2)
traceLogger.debug("Requests prepared")
numLabelBlocks = len(reqlistlabels)
progress_outer = [progress] # Store in list for closure access
if numLabelBlocks > 0:
progressInc = (80-10)/numLabelBlocks/numImages
def progressNotify(req):
# Note: If we wanted perfect progress reporting, we could use lock here
# to protect the progress from being incremented simultaneously.
# But that would slow things down and imperfect reporting is okay for our purposes.
progress_outer[0] += progressInc/2
self.progressSignal(progress_outer[0])
for ir, req in enumerate(reqlistfeat):
image = req.notify_finished(progressNotify)
for ir, req in enumerate(reqlistlabels):
labblock = req.notify_finished(progressNotify)
traceLogger.debug("Requests fired")
for ir, req in enumerate(reqlistlabels):
traceLogger.debug("Waiting for a label block...")
labblock = req.wait()
traceLogger.debug("Waiting for an image block...")
image = reqlistfeat[ir].wait()
newImg, newDot, mapping, tags = \
result[0].prepareData(image, labblock, sigma = self.Sigma.value, smooth = True, normalize = False)
features = newImg[mapping]
labbla = newDot[mapping]
#indexes=np.nonzero(labblock[...,0].view(np.ndarray))
#features=image[indexes]
#labbla=labblock[indexes]
featMatrix.append(features)
labelsMatrix.append(labbla)
tagsMatrix.append(tags)
progress = progress_outer[0]
traceLogger.debug("Requests processed")
self.progressSignal(80/numImages)
if len(featMatrix) == 0 or len(labelsMatrix) == 0:
# If there was no actual data for the random forest to train with, we return None
result[:] = None
else:
featMatrix=np.concatenate(featMatrix,axis=0)
labelsMatrix=np.concatenate(labelsMatrix,axis=0)
tagsMatrix=np.concatenate(tagsMatrix,axis=0)
try:
logger.debug("Learning with Vigra...")
pool = RequestPool()
#result[0].fitPrepared(featMatrix, labelsMatrix, tagsMatrix, self.Epsilon.value)
req = pool.request(partial(result[0].fitPrepared, featMatrix, labelsMatrix, tagsMatrix, self.Epsilon.value))
pool.wait()
pool.clean()
logger.debug("Vigra finished")
except:
logger.error( "ERROR: could not learn classifier" )
logger.error( "featMatrix shape={}, max={}, dtype={}".format(featMatrix.shape, featMatrix.max(), featMatrix.dtype) )
logger.error( "labelsMatrix shape={}, max={}, dtype={}".format(labelsMatrix.shape, labelsMatrix.max(), labelsMatrix.dtype ) )
raise
finally:
self.progressSignal(100)
return result
def execute(self, slot, subindex, rroi, result):
assert slot == self.Features or slot == self.Output
if slot == self.Features:
key = roiToSlice(rroi.start, rroi.stop)
index = subindex[0]
key = list(key)
channelIndex = self.Input.meta.axistags.index('c')
# Translate channel slice to the correct location for the output slot.
key[channelIndex] = slice(self.featureOutputChannels[index][0] + key[channelIndex].start,
self.featureOutputChannels[index][0] + key[channelIndex].stop)
rroi = SubRegion(self.Output, pslice=key)
# Get output slot region for this channel
return self.execute(self.Output, (), rroi, result)
elif slot == self.outputs["Output"]:
key = rroi.toSlice()
logger.debug("OpPixelFeaturesPresmoothed: request %s" % (rroi.pprint(),))
cnt = 0
written = 0
assert (rroi.stop<=self.outputs["Output"].meta.shape).all()
flag = 'c'
channelAxis=self.inputs["Input"].meta.axistags.index('c')
axisindex = channelAxis
oldkey = list(key)
oldkey.pop(axisindex)
inShape = self.inputs["Input"].meta.shape
hasChannelAxis = (self.Input.meta.axistags.axisTypeCount(vigra.AxisType.Channels) > 0)
#if (self.Input.meta.axistags.axisTypeCount(vigra.AxisType.Channels) == 0):
# noChannels = True
inAxistags = self.inputs["Input"].meta.axistags
shape = self.outputs["Output"].meta.shape
axistags = self.outputs["Output"].meta.axistags
result = result.view(vigra.VigraArray)
result.axistags = copy.copy(axistags)
hasTimeAxis = self.inputs["Input"].meta.axistags.axisTypeCount(vigra.AxisType.Time)
timeAxis=self.inputs["Input"].meta.axistags.index('t')
subkey = popFlagsFromTheKey(key,axistags,'c')
subshape=popFlagsFromTheKey(shape,axistags,'c')
at2 = copy.copy(axistags)
at2.dropChannelAxis()
subshape=popFlagsFromTheKey(subshape,at2,'t')
subkey = popFlagsFromTheKey(subkey,at2,'t')
oldstart, oldstop = roi.sliceToRoi(key, shape)
start, stop = roi.sliceToRoi(subkey,subkey)
maxSigma = max(0.7,self.maxSigma) #we use 0.7 as an approximation of not doing any smoothing
#smoothing was already applied previously
# The region of the smoothed image we need to give to the feature filter (in terms of INPUT coordinates)
# 0.7, because the features receive a pre-smoothed array and don't need much of a neighborhood
vigOpSourceStart, vigOpSourceStop = roi.enlargeRoiForHalo(start, stop, subshape, 0.7, self.WINDOW_SIZE)
# The region of the input that we need to give to the smoothing operator (in terms of INPUT coordinates)
newStart, newStop = roi.enlargeRoiForHalo(vigOpSourceStart, vigOpSourceStop, subshape, maxSigma, self.WINDOW_SIZE)
newStartSmoother = roi.TinyVector(start - vigOpSourceStart)
newStopSmoother = roi.TinyVector(stop - vigOpSourceStart)
roiSmoother = roi.roiToSlice(newStartSmoother, newStopSmoother)
# Translate coordinates (now in terms of smoothed image coordinates)
vigOpSourceStart = roi.TinyVector(vigOpSourceStart - newStart)
vigOpSourceStop = roi.TinyVector(vigOpSourceStop - newStart)
readKey = roi.roiToSlice(newStart, newStop)
writeNewStart = start - newStart
writeNewStop = writeNewStart + stop - start
treadKey=list(readKey)
if hasTimeAxis:
if timeAxis < channelAxis:
treadKey.insert(timeAxis, key[timeAxis])
else:
treadKey.insert(timeAxis-1, key[timeAxis])
if self.inputs["Input"].meta.axistags.axisTypeCount(vigra.AxisType.Channels) == 0:
treadKey = popFlagsFromTheKey(treadKey,axistags,'c')
else:
treadKey.insert(channelAxis, slice(None,None,None))
treadKey=tuple(treadKey)
req = self.inputs["Input"][treadKey]
sourceArray = req.wait()
req.clean()
#req.result = None
req.destination = None
if sourceArray.dtype != numpy.float32:
sourceArrayF = sourceArray.astype(numpy.float32)
try:
sourceArray.resize((1,), refcheck = False)
except:
pass
del sourceArray
sourceArray = sourceArrayF
#if (self.Input.meta.axistags.axisTypeCount(vigra.AxisType.Channels) == 0):
#add a channel dimension to make the code afterwards more uniform
# sourceArray = sourceArray.view(numpy.ndarray)
# sourceArray = sourceArray.reshape(sourceArray.shape+(1,))
sourceArrayV = sourceArray.view(vigra.VigraArray)
sourceArrayV.axistags = copy.copy(inAxistags)
dimCol = len(self.scales)
dimRow = self.matrix.shape[0]
sourceArraysForSigmas = [None]*dimCol
#connect individual operators
try:
for j in range(dimCol):
hasScale = False
for i in range(dimRow):
if self.matrix[i,j]:
hasScale = True
if not hasScale:
continue
destSigma = 1.0
if self.scales[j] > destSigma:
tempSigma = math.sqrt(self.scales[j]**2 - destSigma**2)
else:
destSigma = 0.0
tempSigma = self.scales[j]
vigOpSourceShape = list(vigOpSourceStop - vigOpSourceStart)
if hasTimeAxis:
if timeAxis < channelAxis:
vigOpSourceShape.insert(timeAxis, ( oldstop - oldstart)[timeAxis])
else:
vigOpSourceShape.insert(timeAxis-1, ( oldstop - oldstart)[timeAxis])
vigOpSourceShape.insert(channelAxis, inShape[channelAxis])
sourceArraysForSigmas[j] = numpy.ndarray(tuple(vigOpSourceShape),numpy.float32)
for i,vsa in enumerate(sourceArrayV.timeIter()):
droi = (tuple(vigOpSourceStart._asint()), tuple(vigOpSourceStop._asint()))
tmp_key = getAllExceptAxis(len(sourceArraysForSigmas[j].shape),timeAxis, i)
sourceArraysForSigmas[j][tmp_key] = self._computeGaussianSmoothing(vsa, tempSigma, droi)
else:
droi = (tuple(vigOpSourceStart._asint()), tuple(vigOpSourceStop._asint()))
sourceArraysForSigmas[j] = self._computeGaussianSmoothing(sourceArrayV, tempSigma, droi)
except RuntimeError as e:
if e.message.find('kernel longer than line') > -1:
message = "Feature computation error:\nYour image is too small to apply a filter with sigma=%.1f. Please select features with smaller sigmas." % self.scales[j]
raise RuntimeError(message)
else:
raise e
del sourceArrayV
try:
sourceArray.resize((1,), refcheck = False)
except ValueError:
# Sometimes this fails, but that's okay.
logger.debug("Failed to free array memory.")
del sourceArray
closures = []
#connect individual operators
for i in range(dimRow):
for j in range(dimCol):
val=self.matrix[i,j]
if val:
vop= self.featureOps[i][j]
oslot = vop.outputs["Output"]
req = None
#inTagKeys = [ax.key for ax in oslot.meta.axistags]
#print inTagKeys, flag
if hasChannelAxis:
slices = oslot.meta.shape[axisindex]
if cnt + slices >= rroi.start[axisindex] and rroi.start[axisindex]-cnt<slices and rroi.start[axisindex]+written<rroi.stop[axisindex]:
begin = 0
if cnt < rroi.start[axisindex]:
begin = rroi.start[axisindex] - cnt
end = slices
if cnt + end > rroi.stop[axisindex]:
end -= cnt + end - rroi.stop[axisindex]
key_ = copy.copy(oldkey)
key_.insert(axisindex, slice(begin, end, None))
reskey = [slice(None, None, None) for x in range(len(result.shape))]
reskey[axisindex] = slice(written, written+end-begin, None)
destArea = result[tuple(reskey)]
#readjust the roi for the new source array
roiSmootherList = list(roiSmoother)
roiSmootherList.insert(axisindex, slice(begin, end, None))
if hasTimeAxis:
# The time slice in the ROI doesn't matter:
# The sourceArrayParameter below overrides the input data to be used.
roiSmootherList.insert(timeAxis, 0)
roiSmootherRegion = SubRegion(oslot, pslice=roiSmootherList)
closure = partial(oslot.operator.execute, oslot, (), roiSmootherRegion, destArea, sourceArray = sourceArraysForSigmas[j])
closures.append(closure)
written += end - begin
cnt += slices
else:
if cnt>=rroi.start[axisindex] and rroi.start[axisindex] + written < rroi.stop[axisindex]:
reskey = [slice(None, None, None) for x in range(len(result.shape))]
slices = oslot.meta.shape[axisindex]
reskey[axisindex]=slice(written, written+slices, None)
#print "key: ", key, "reskey: ", reskey, "oldkey: ", oldkey, "resshape:", result.shape
#print "roiSmoother:", roiSmoother
destArea = result[tuple(reskey)]
#print "destination area:", destArea.shape
logger.debug(oldkey, destArea.shape, sourceArraysForSigmas[j].shape)
oldroi = SubRegion(oslot, pslice=oldkey)
#print "passing roi:", oldroi
closure = partial(oslot.operator.execute, oslot, (), oldroi, destArea, sourceArray = sourceArraysForSigmas[j])
closures.append(closure)
written += 1
cnt += 1
pool = RequestPool()
for c in closures:
r = pool.request(c)
pool.wait()
pool.clean()
for i in range(len(sourceArraysForSigmas)):
if sourceArraysForSigmas[i] is not None:
try:
sourceArraysForSigmas[i].resize((1,))
except:
sourceArraysForSigmas[i] = None
