ASCEE/pyqtgraph
5
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

ImageItem.getHistogram is more clever about constructing histograms:

Browse Source 
- 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'
This commit is contained in:
Luke Campagnola 2014-01-14 22:22:50 -05:00
parent 12149b1dc4
commit e8dd3e6e57
1 changed files with 33 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
pyqtgraph/graphicsItems/ImageItem.py
View File

@ -289,23 +289,45 @@ class ImageItem(GraphicsObject):
self.render()
self.qimage.save(fileName, *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, dtype=np.int)
else:
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):