def _updateHistogram(self, data=None):
"""
data (DataArray): the raw data to use, default to .raw[0]
"""
# Compute histogram and compact version
if data is None:
if isinstance(self.raw, tuple):
data = self._getMergedRawImage(self._das.maxzoom)
elif not self.raw or not isinstance(self.raw, list):
return
data = self.raw[0] if data is None else data
# Depth can change at each image (depends on hardware settings)
self._updateDRange(data)
# Initially, _drange might be None, in which case it will be guessed
hist, edges = img.histogram(data, irange=self._drange)
if hist.size > 256:
chist = img.compactHistogram(hist, 256)
else:
chist = hist
self.histogram._full_hist = hist
self.histogram._edges = edges
# Read-only VA, so we need to go around...
self.histogram._value = chist
self.histogram.notify(chist)
def test_compact(self):
"""
test the compactHistogram()
"""
depth = 4096 # limited depth
size = (1024, 965)
grey_img = numpy.zeros(size, dtype="uint16") + 1500
grey_img[0, 0] = 0
grey_img[0, 1] = depth - 1
hist, edges = img.histogram(grey_img, (0, depth - 1))
# make it compact
chist = img.compactHistogram(hist, 256)
self.assertEqual(len(chist), 256)
self.assertEqual(numpy.sum(chist), numpy.prod(size))
# make it really compact
vchist = img.compactHistogram(hist, 1)
self.assertEqual(vchist[0], numpy.prod(size))
# keep it the same length
nchist = img.compactHistogram(hist, depth)
numpy.testing.assert_array_equal(hist, nchist)
def test_compact(self):
"""
test the compactHistogram()
"""
depth = 4096 # limited depth
size = (1024, 965)
grey_img = numpy.zeros(size, dtype="uint16") + 1500
grey_img[0, 0] = 0
grey_img[0, 1] = depth - 1
hist, edges = img.histogram(grey_img, (0, depth - 1))
# make it compact
chist = img.compactHistogram(hist, 256)
self.assertEqual(len(chist), 256)
self.assertEqual(numpy.sum(chist), numpy.prod(size))
# make it really compact
vchist = img.compactHistogram(hist, 1)
self.assertEqual(vchist[0], numpy.prod(size))
# keep it the same length
nchist = img.compactHistogram(hist, depth)
numpy.testing.assert_array_equal(hist, nchist)
def _updateHistogram(self, data=None):
"""
data (DataArray): the raw data to use, default to .raw[0] - background
(if present).
If will also update the intensityRange if auto_bc is enabled.
"""
# Compute histogram and compact version
if data is None:
if not self.raw:
logging.debug("Not computing histogram as .raw is empty")
return
data = self.raw[0]
if isinstance(data, model.DataArrayShadow):
# Pyramidal => use the smallest version
data = self._getMergedRawImage(data, data.maxzoom)
# We only do background subtraction when automatically selecting raw
bkg = self.background.value
if bkg is not None:
try:
data = img.Subtract(data, bkg)
except Exception as ex:
logging.info(
"Failed to subtract background when computing histogram: %s",
ex)
# Depth can change at each image (depends on hardware settings)
self._updateDRange(data)
# Initially, _drange might be None, in which case it will be guessed
hist, edges = img.histogram(data, irange=self._drange)
if hist.size > 256:
chist = img.compactHistogram(hist, 256)
else:
chist = hist
self.histogram._full_hist = hist
self.histogram._edges = edges
# First update the value, before the intensityRange subscribers are called...
self.histogram._value = chist
if self.auto_bc.value:
self._recomputeIntensityRange()
# Notify last, so intensityRange is correct when subscribers get the new histogram
self.histogram.notify(chist)
def _updateHistogram(self, data=None):
"""
data (DataArray): the raw data to use, default to .raw[0] - background
(if present).
If will also update the intensityRange if auto_bc is enabled.
"""
# Compute histogram and compact version
if data is None:
if not self.raw:
logging.debug("Not computing histogram as .raw is empty")
return
data = self.raw[0]
if isinstance(data, model.DataArrayShadow):
# Pyramidal => use the smallest version
data = self._getMergedRawImage(data, data.maxzoom)
# We only do background subtraction when automatically selecting raw
bkg = self.background.value
if bkg is not None:
try:
data = img.Subtract(data, bkg)
except Exception as ex:
logging.info("Failed to subtract background when computing histogram: %s", ex)
# Depth can change at each image (depends on hardware settings)
self._updateDRange(data)
# Initially, _drange might be None, in which case it will be guessed
hist, edges = img.histogram(data, irange=self._drange)
if hist.size > 256:
chist = img.compactHistogram(hist, 256)
else:
chist = hist
self.histogram._full_hist = hist
self.histogram._edges = edges
# First update the value, before the intensityRange subscribers are called...
self.histogram._value = chist
if self.auto_bc.value:
self._recomputeIntensityRange()
# Notify last, so intensityRange is correct when subscribers get the new histogram
self.histogram.notify(chist)
def _updateHistogram(self, data=None):
"""
data (DataArray): the raw data to use, default to .raw[0]
"""
# Compute histogram and compact version
if not self.raw and data is None:
return
data = self.raw[0] if data is None else data
# Initially, _drange might be None, in which case it will be guessed
hist, edges = img.histogram(data, irange=self._drange)
if hist.size > 256:
chist = img.compactHistogram(hist, 256)
else:
chist = hist
self.histogram._full_hist = hist
self.histogram._edges = edges
# Read-only VA, so we need to go around...
self.histogram._value = chist
self.histogram.notify(chist)
def _updateHistogram(self, data=None):
"""
data (DataArray): the raw data to use, default to .raw[0]
"""
# Compute histogram and compact version
if not self.raw and data is None:
return
data = self.raw[0] if data is None else data
# Initially, _drange might be None, in which case it will be guessed
hist, edges = img.histogram(data, irange=self._drange)
if hist.size > 256:
chist = img.compactHistogram(hist, 256)
else:
chist = hist
self.histogram._full_hist = hist
self.histogram._edges = edges
# Read-only VA, so we need to go around...
self.histogram._value = chist
self.histogram.notify(chist)
