def checkMoveError(self):
#maxerror = self.settings['max error']
#limit = (int(maxerror*2), int(maxerror*2))
oldshape = self.oldimagedata['image'].shape
limit = oldshape
location = oldshape[0] / 2.0 - 0.5 + self.origmove[
0], oldshape[1] / 2.0 - 0.5 + self.origmove[1]
im1 = imagefun.crop_at(self.origimagedata['image'],
location,
limit,
mode='constant',
cval=0.0)
im2 = imagefun.crop_at(self.newimagedata['image'], 'center', limit)
pc = correlator.phase_correlate(im2, im1, zero=False)
pc = scipy.ndimage.gaussian_filter(pc, 1)
subpixelpeak = self.peakfinder.subpixelPeak(newimage=pc,
guess=(0.5, 0.5),
limit=limit)
res = self.peakfinder.getResults()
unsignedpixelpeak = res['unsigned pixel peak']
peaktargets = [(unsignedpixelpeak[1], unsignedpixelpeak[0])]
r_error = subpixelpeak[0]
c_error = subpixelpeak[1]
self.setImage(pc, 'Correlation')
peaktargets = [(unsignedpixelpeak[1], unsignedpixelpeak[0])]
self.setTargets(peaktargets, 'Peak')
## calculate error distance
scope = self.newimagedata['scope']
camera = self.newimagedata['camera']
mag = scope['magnification']
tem = scope['tem']
ccdcamera = camera['ccdcamera']
pixelsize = self.pcal.retrievePixelSize(tem, ccdcamera, mag)
cbin = camera['binning']['x']
rbin = camera['binning']['y']
rpix = r_error * rbin
cpix = c_error * cbin
pixdist = math.hypot(rpix, cpix)
distance = pixdist * pixelsize
return r_error, c_error, distance
def makeTemplateA(self, current=None):
newimagedata = self.currentimagedata
if current is not None:
tempinfo = {
'image': self.currentimagedata,
'center_row': current[0],
'center_column': current[1]
}
else:
# find most recent template info from this session
qtempinfo = leginondata.DynamicTemplateData(session=self.session)
try:
tempinfo = qtempinfo.query(results=1)[0]
except:
self.images['templateA'] = None
self.logger.info('No template found for this session.')
return None
self.oldpeakinfo = {}
for key in ('minsum', 'snr'):
self.oldpeakinfo[key] = tempinfo[key]
oldimagedata = tempinfo['image']
if oldimagedata['correction channel'] == newimagedata[
'correction channel']:
self.logger.info('reversing template corrector channel')
try:
oldimagedata = self.reverseCorrectorChannel(oldimagedata)
except:
self.logger.warning(
'failed to reverse template corrector channel')
oldimage = oldimagedata['image']
newimage = newimagedata['image']
# use template info to crop out region of interest from old image
center_r = tempinfo['center_row']
center_c = tempinfo['center_column']
tempsize = self.settings['template size']
if tempsize == 100:
imcenter_r = oldimage.shape[0] / 2
imcenter_c = oldimage.shape[1] / 2
shift_r = imcenter_r - center_r
shift_c = imcenter_c - center_c
templateA = scipy.ndimage.shift(oldimage, (shift_r, shift_c),
mode='wrap')
else:
tempsize = int(tempsize * oldimage.shape[0] / 100.0)
tempshape = tempsize, tempsize
templateA = imagefun.crop_at(oldimage, (center_r, center_c),
tempshape,
mode='wrap')
self.images['templateA'] = templateA
self.setImage(templateA, 'templateA')
return templateA
def checkMoveError(self): #maxerror = self.settings['max error'] #limit = (int(maxerror*2), int(maxerror*2)) oldshape = self.oldimagedata['image'].shape limit = oldshape location = oldshape[0]/2.0-0.5+self.origmove[0], oldshape[1]/2.0-0.5+self.origmove[1] im1 = imagefun.crop_at(self.origimagedata['image'], location, limit, mode='constant', cval=0.0) im2 = imagefun.crop_at(self.newimagedata['image'], 'center', limit) pc = correlator.phase_correlate(im2, im1, zero=False) pc = scipy.ndimage.gaussian_filter(pc,1) subpixelpeak = self.peakfinder.subpixelPeak(newimage=pc, guess=(0.5,0.5), limit=limit) res = self.peakfinder.getResults() unsignedpixelpeak = res['unsigned pixel peak'] peaktargets = [(unsignedpixelpeak[1], unsignedpixelpeak[0])] r_error = subpixelpeak[0] c_error = subpixelpeak[1] self.setImage(pc, 'Correlation') peaktargets = [(unsignedpixelpeak[1], unsignedpixelpeak[0])] self.setTargets(peaktargets, 'Peak') ## calculate error distance scope = self.newimagedata['scope'] camera = self.newimagedata['camera'] mag = scope['magnification'] tem = scope['tem'] ccdcamera = camera['ccdcamera'] pixelsize = self.pcal.retrievePixelSize(tem, ccdcamera, mag) cbin = camera['binning']['x'] rbin = camera['binning']['y'] rpix = r_error * rbin cpix = c_error * cbin pixdist = math.hypot(rpix,cpix) distance = pixdist * pixelsize return r_error, c_error, distance
def calculatePowerImage(self, imagedata):
imarray = imagedata["image"]
imageshape = imarray.shape
if imageshape[0] != imageshape[1]:
new_dim = min(imageshape)
imarray = imagefun.crop_at(imarray, (imageshape[0] / 2, imageshape[1] / 2), (new_dim, new_dim))
imageshape = imarray.shape
if self.settings["reduced"]:
size = max(imageshape)
if size > 1024:
imarray = scipy.ndimage.zoom(imarray, 1024.0 / imageshape[0])
self.logger.info("Calculating power spectrum for image")
pow = imagefun.power(imarray, self.settings["mask radius"])
self.setImage(numpy.asarray(pow, numpy.float32), "Power")
self.imageshape = imageshape
return pow
def makeTemplateA(self, current=None):
newimagedata = self.currentimagedata
if current is not None:
tempinfo = {"image": self.currentimagedata, "center_row": current[0], "center_column": current[1]}
else:
# find most recent template info from this session
qtempinfo = leginondata.DynamicTemplateData(session=self.session)
try:
tempinfo = qtempinfo.query(results=1)[0]
except:
self.images["templateA"] = None
self.logger.info("No template found for this session.")
return None
self.oldpeakinfo = {}
for key in ("minsum", "snr"):
self.oldpeakinfo[key] = tempinfo[key]
oldimagedata = tempinfo["image"]
if oldimagedata["correction channel"] == newimagedata["correction channel"]:
self.logger.info("reversing template corrector channel")
try:
oldimagedata = self.reverseCorrectorChannel(oldimagedata)
except:
self.logger.warning("failed to reverse template corrector channel")
oldimage = oldimagedata["image"]
newimage = newimagedata["image"]
# use template info to crop out region of interest from old image
center_r = tempinfo["center_row"]
center_c = tempinfo["center_column"]
tempsize = self.settings["template size"]
if tempsize == 100:
imcenter_r = oldimage.shape[0] / 2
imcenter_c = oldimage.shape[1] / 2
shift_r = imcenter_r - center_r
shift_c = imcenter_c - center_c
templateA = scipy.ndimage.shift(oldimage, (shift_r, shift_c), mode="wrap")
else:
tempsize = int(tempsize * oldimage.shape[0] / 100.0)
tempshape = tempsize, tempsize
templateA = imagefun.crop_at(oldimage, (center_r, center_c), tempshape, mode="wrap")
self.images["templateA"] = templateA
self.setImage(templateA, "templateA")
return templateA
def makeTemplateA(self, current=None):
newimagedata = self.currentimagedata
if current is not None:
tempinfo = {'image': self.currentimagedata, 'center_row': current[0], 'center_column': current[1]}
else:
# find most recent template info from this session
qtempinfo = leginondata.DynamicTemplateData(session=self.session)
try:
tempinfo = qtempinfo.query(results=1)[0]
except:
self.images['templateA'] = None
self.logger.info('No template found for this session.')
return None
self.oldpeakinfo = {}
for key in ('minsum', 'snr'):
self.oldpeakinfo[key] = tempinfo[key]
oldimagedata = tempinfo['image']
if oldimagedata['correction channel'] == newimagedata['correction channel']:
self.logger.info('reversing template corrector channel')
try:
oldimagedata = self.reverseCorrectorChannel(oldimagedata)
except:
self.logger.warning('failed to reverse template corrector channel')
oldimage = oldimagedata['image']
newimage = newimagedata['image']
# use template info to crop out region of interest from old image
center_r = tempinfo['center_row']
center_c = tempinfo['center_column']
tempsize = self.settings['template size']
if tempsize == 100:
imcenter_r = oldimage.shape[0] / 2
imcenter_c = oldimage.shape[1] / 2
shift_r = imcenter_r - center_r
shift_c = imcenter_c - center_c
templateA = scipy.ndimage.shift(oldimage, (shift_r, shift_c), mode='wrap')
else:
tempsize = int(tempsize * oldimage.shape[0] / 100.0)
tempshape = tempsize,tempsize
templateA = imagefun.crop_at(oldimage, (center_r, center_c), tempshape, mode='wrap')
self.images['templateA'] = templateA
self.setImage(templateA, 'templateA')
return templateA
def calculatePowerImage(self, imagedata):
imarray = imagedata['image']
imageshape = imarray.shape
if imageshape[0] != imageshape[1]:
new_dim = min(imageshape)
imarray = imagefun.crop_at(imarray,
(imageshape[0] / 2, imageshape[1] / 2),
(new_dim, new_dim))
imageshape = imarray.shape
if self.settings['reduced']:
size = max(imageshape)
if size > 1024:
imarray = scipy.ndimage.zoom(imarray, 1024.0 / imageshape[0])
self.logger.info('Calculating power spectrum for image')
pow = imagefun.power(imarray, self.settings['mask radius'])
self.setImage(numpy.asarray(pow, numpy.float32), 'Power')
self.imageshape = imageshape
return pow
