def getImageStats(self, image):
if image is None:
return {'mean': None, 'stdev': None, 'min': None, 'max': None}
stats = arraystats.all(image)
stats['stdev'] = stats['std']
return stats
def setNumericImage(self, numericimage):
'''
Set the numeric image, update bitmap, update buffer, set viewport size,
scroll, and refresh the screen.
'''
if not isinstance(numericimage, numpy.ndarray):
raise TypeError('image must be numpy.ndarray')
center = self.getScrolledCenter()
self.imagedata = numericimage
stats = arraystats.all(self.imagedata)
self.statspanel.set(stats)
self.sizer.SetItemMinSize(self.statspanel, self.statspanel.GetSize())
dflt_std = 5
## use these...
dflt_min = stats['mean'] - dflt_std * stats['std']
dflt_max = stats['mean'] + dflt_std * stats['std']
## unless they go beyond min and max of image
dflt_min = max(dflt_min, stats['min'])
dflt_max = min(dflt_max, stats['max'])
value = (dflt_min, dflt_max)
self.contrasttool.setRange((stats['min'], stats['max']), value)
self.setBitmap()
self.setVirtualSize()
self.setBuffer()
self.setScrolledCenter(center)
self.UpdateDrawing()
self.sizer.Layout()
def setNumericImage(self, numericimage):
'''
Set the numeric image, update bitmap, update buffer, set viewport size,
scroll, and refresh the screen.
'''
if not isinstance(numericimage, numpy.ndarray):
raise TypeError('image must be numpy.ndarray')
center = self.getScrolledCenter()
self.imagedata = numericimage
stats = arraystats.all(self.imagedata)
self.statspanel.set(stats)
self.sizer.SetItemMinSize(self.statspanel, self.statspanel.GetSize())
dflt_std = 5
## use these...
dflt_min = stats['mean'] - dflt_std * stats['std']
dflt_max = stats['mean'] + dflt_std * stats['std']
## unless they go beyond min and max of image
dflt_min = max(dflt_min, stats['min'])
dflt_max = min(dflt_max, stats['max'])
value = (dflt_min, dflt_max)
self.contrasttool.setRange((stats['min'], stats['max']), value)
self.setBitmap()
self.setVirtualSize()
self.setBuffer()
self.setScrolledCenter(center)
self.UpdateDrawing()
self.sizer.Layout()
def getImageStats(self, image):
if image is None:
return {'mean': None, 'stdev': None, 'min': None, 'max': None}
stats = arraystats.all(image)
stats['stdev'] = stats['std']
return stats
def matchMags(self, mags):
# acquire first image at current state
oldimagedata = self.acquireImage()
self.findRegions(oldimagedata['image'])
mrc.write(oldimagedata['image'], 'imref.mrc')
stats = arraystats.all(oldimagedata['image'])
shape = oldimagedata['image'].shape
# determine limits to adjust exposure of other mags
limitmax = 1.5 * stats['mean']
limitmin = 0.5 * stats['mean']
self.logger.info('image1 mean: %f, limits: %f-%f' % (stats['mean'], limitmin, limitmax))
## iterate through mags
runningresult = numpy.identity(3)
for i,mag in enumerate(mags):
self.instrument.tem.Magnification = mag
self.pause()
newimagedata = self.acquireWithinRange(limitmin, limitmax)
self.findRegions(newimagedata['image'])
mrc.write(newimagedata['image'], 'im%02d.mrc' % (i,))
minsize = self.settings['minsize']
maxsize = self.settings['maxsize']
self.logger.info('matchimages')
result = self.matchImages(oldimagedata['image'], newimagedata['image'], minsize, maxsize)
runningresult = numpy.dot(result, runningresult)
self.logger.info('transforms')
final_step = affine.transform(newimagedata['image'], result, shape)
final_all = affine.transform(newimagedata['image'], runningresult, shape)
self.logger.info('writing result mrcs')
mrc.write(final_step, 'trans%02d.mrc' % (i,))
mrc.write(final_all, 'transall%02d.mrc' % (i,))
oldimagedata = newimagedata
def getImageStats(self, image):
if image is None:
return {"mean": None, "stdev": None, "min": None, "max": None}
stats = arraystats.all(image)
stats["stdev"] = stats["std"]
return stats
def read_mrc(filename):
a = mrc.read(filename)
info = {}
info['array'] = a
stats = arraystats.all(a)
info['min'] = stats['min']
info['max'] = stats['max']
return info
def read_mrc(filename):
a = mrc.read(filename)
info = {}
info['array'] = a
stats = arraystats.all(a)
info['min'] = stats['min']
info['max'] = stats['max']
return info
def read_mrc(filename):
a = mrc.read(filename)
info = {}
info["array"] = a
stats = arraystats.all(a)
info["min"] = stats["min"]
info["max"] = stats["max"]
return info
def brightestStats(self, im, percent):
# look only at the brightest 1% of the pixels
sortedpixels = numpy.sort(im, axis=None)
npixels = len(sortedpixels)
nbrightest = int(percent / 100.0 * npixels)
brightest = sortedpixels[-nbrightest:]
stats = arraystats.all(brightest)
self.logger.info('Top %.1f%% stats: mean: %.1f, std: %.1f, min: %.1f, max: %.1f' % (percent, stats['mean'],stats['std'],stats['min'],stats['max']))
return stats
def brightestStats(self, im, percent):
# look only at the brightest 1% of the pixels
sortedpixels = numpy.sort(im, axis=None)
npixels = len(sortedpixels)
nbrightest = int(percent / 100.0 * npixels)
brightest = sortedpixels[-nbrightest:]
stats = arraystats.all(brightest)
self.logger.info(
'Top %.1f%% stats: mean: %.1f, std: %.1f, min: %.1f, max: %.1f' %
(percent, stats['mean'], stats['std'], stats['min'], stats['max']))
return stats
def array2jpg(pictpath,im,imin=None,imax=None,size=512):
jpgpath = pictpath+'.jpg'
imshape = im.shape
scale = float(size)/imshape[1]
im = apImage.scaleImage(im,scale)
stats = arraystats.all(im)
if imin is not None and imax is not None:
range = (imin,imax)
else:
range = stats['mean']-3*stats['std'],stats['mean']+3*stats['std']
numpil.write(im,jpgpath, format = 'JPEG', limits=range)
def array2jpg(pictpath, im, imin=None, imax=None, size=512):
jpgpath = pictpath + '.jpg'
imshape = im.shape
scale = float(size) / imshape[1]
im = apImage.scaleImage(im, scale)
stats = arraystats.all(im)
if imin is not None and imax is not None:
range = (imin, imax)
else:
range = stats['mean'] - 3 * stats['std'], stats[
'mean'] + 3 * stats['std']
numpil.write(im, jpgpath, format='JPEG', limits=range)
def publishStats(self, imagedata): im = imagedata['image'] if im is None: return allstats = arraystats.all(im) statsdata = leginondata.AcquisitionImageStatsData() statsdata['session'] = self.session statsdata['min'] = allstats['min'] statsdata['max'] = allstats['max'] statsdata['mean'] = allstats['mean'] statsdata['stdev'] = allstats['std'] statsdata['image'] = imagedata self.publish(statsdata, database=True)
def publishStats(self, imagedata): im = imagedata['image'] if im is None: return allstats = arraystats.all(im) statsdata = leginondata.AcquisitionImageStatsData() statsdata['session'] = self.session statsdata['min'] = allstats['min'] statsdata['max'] = allstats['max'] statsdata['mean'] = allstats['mean'] statsdata['stdev'] = allstats['std'] statsdata['image'] = imagedata self.publish(statsdata, database=True)
def setImage(self, imagedata):
if isinstance(imagedata, numpy.ndarray):
self.setNumericImage(imagedata)
elif isinstance(imagedata, Image.Image):
self.setPILImage(imagedata)
stats = arraystats.all(imagedata)
self.statspanel.set(stats)
self.sizer.SetItemMinSize(self.statspanel, self.statspanel.GetSize())
self.sizer.Layout()
elif imagedata is None:
self.clearImage()
self.statspanel.set({})
self.sizer.SetItemMinSize(self.statspanel, self.statspanel.GetSize())
self.sizer.Layout()
else:
raise TypeError('Invalid image data type for setting image')
def publishStats(self, imagedata):
im = imagedata['image']
stats = arraystats.all(im)
mn = stats['min']
mx = stats['max']
mean = stats['mean']
std = stats['std']
statsdata = leginondata.AcquisitionImageStatsData()
statsdata['min'] = mn
statsdata['max'] = mx
statsdata['mean'] = mean
statsdata['stdev'] = std
statsdata['image'] = imagedata
self.publish(statsdata, database=True)
return 'min: %s, max: %s, mean: %s, stdev: %s' % (mn, mx, mean, std)
def publishStats(self, imagedata):
im = imagedata['image']
stats = arraystats.all(im)
mn = stats['min']
mx = stats['max']
mean = stats['mean']
std = stats['std']
statsdata = leginondata.AcquisitionImageStatsData()
statsdata['min'] = mn
statsdata['max'] = mx
statsdata['mean'] = mean
statsdata['stdev'] = std
statsdata['image'] = imagedata
self.publish(statsdata, database=True)
return 'min: %s, max: %s, mean: %s, stdev: %s' % (mn,mx,mean,std)
def setImage(self, imagedata):
if isinstance(imagedata, numpy.ndarray):
self.setNumericImage(imagedata)
elif isinstance(imagedata, Image.Image):
self.setPILImage(imagedata)
stats = arraystats.all(imagedata)
self.statspanel.set(stats)
self.sizer.SetItemMinSize(self.statspanel,
self.statspanel.GetSize())
self.sizer.Layout()
elif imagedata is None:
self.clearImage()
self.statspanel.set({})
self.sizer.SetItemMinSize(self.statspanel,
self.statspanel.GetSize())
self.sizer.Layout()
else:
raise TypeError('Invalid image data type for setting image')
def matchMags(self, mags):
# acquire first image at current state
oldimagedata = self.acquireImage()
self.findRegions(oldimagedata['image'])
mrc.write(oldimagedata['image'], 'imref.mrc')
stats = arraystats.all(oldimagedata['image'])
shape = oldimagedata['image'].shape
# determine limits to adjust exposure of other mags
limitmax = 1.5 * stats['mean']
limitmin = 0.5 * stats['mean']
self.logger.info('image1 mean: %f, limits: %f-%f' %
(stats['mean'], limitmin, limitmax))
## iterate through mags
runningresult = numpy.identity(3)
for i, mag in enumerate(mags):
self.instrument.tem.Magnification = mag
self.pause()
newimagedata = self.acquireWithinRange(limitmin, limitmax)
self.findRegions(newimagedata['image'])
mrc.write(newimagedata['image'], 'im%02d.mrc' % (i, ))
minsize = self.settings['minsize']
maxsize = self.settings['maxsize']
self.logger.info('matchimages')
result = self.matchImages(oldimagedata['image'],
newimagedata['image'], minsize, maxsize)
runningresult = numpy.dot(result, runningresult)
self.logger.info('transforms')
final_step = affine.transform(newimagedata['image'], result, shape)
final_all = affine.transform(newimagedata['image'], runningresult,
shape)
self.logger.info('writing result mrcs')
mrc.write(final_step, 'trans%02d.mrc' % (i, ))
mrc.write(final_all, 'transall%02d.mrc' % (i, ))
oldimagedata = newimagedata
