Commit e8dd3e6e authored by Luke Campagnola's avatar Luke Campagnola
Browse files

ImageItem.getHistogram is more clever about constructing histograms:

  - integer dtype images now have integer-aligned bins, with bin number
    determined by a target value
  - step size is automatically chosen based on a target image size
  - bins and step arguments have default values 'auto'
parent 12149b1d
......@@ -289,23 +289,45 @@ class ImageItem(GraphicsObject):
self.render(), *args)
def getHistogram(self, bins=500, step=3):
def getHistogram(self, bins='auto', step='auto', targetImageSize=200, targetHistogramSize=500, **kwds):
"""Returns x and y arrays containing the histogram values for the current image.
The step argument causes pixels to be skipped when computing the histogram to save time.
For an explanation of the return format, see numpy.histogram().
The *step* argument causes pixels to be skipped when computing the histogram to save time.
If *step* is 'auto', then a step is chosen such that the analyzed data has
dimensions roughly *targetImageSize* for each axis.
The *bins* argument and any extra keyword arguments are passed to
np.histogram(). If *bins* is 'auto', then a bin number is automatically
chosen based on the image characteristics:
* Integer images will have approximately *targetHistogramSize* bins,
with each bin having an integer width.
* All other types will have *targetHistogramSize* bins.
This method is also used when automatically computing levels.
if self.image is None:
return None,None
stepData = self.image[::step, ::step]
if not np.iterable(bins):
mn = stepData.min()
mx = stepData.max()
if stepData.dtype.kind in "ui": # unsigned or signed int
# we want max - min to be a multiple of nbins
range = mn, mn + np.ceil((mx - mn) / bins) * bins
if step == 'auto':
step = (np.ceil(self.image.shape[0] / targetSize),
np.ceil(self.image.shape[1] / targetSize))
if np.isscalar(step):
step = (step, step)
stepData = self.image[::step[0], ::step[1]]
if bins == 'auto':
if stepData.dtype.kind in "ui":
mn = stepData.min()
mx = stepData.max()
step = np.ceil((mx-mn) / 500.)
bins = np.arange(mn, mx+1.01*step, step,
range = mn, mx
hist = np.histogram(stepData, bins=bins, range=range)
bins = 500
kwds['bins'] = bins
hist = np.histogram(stepData, **kwds)
return hist[1][:-1], hist[0]
def setPxMode(self, b):
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