def insertImage(self, input, target=None, outtext=None):
if not self.needImage(input):
return
## differnce in binning requires scaling
## assume equal x and y binning
binscale = float(input.binx) / self.binx
binmatrix = binscale * numpy.identity(2)
## affine transform matrix is input matrix
atmatrix = numpy.dot(binmatrix, input.trans.matrix)
atmatrix = numpy.dot(self.outputmatrix, atmatrix)
## affine_transform function requires the inverse matrix
atmatrix = numpy.linalg.inv(atmatrix)
## calculate offset position
if target is None:
instage = input.stage
else:
instage = input.trans.transform(target, input.stage, input.bin)
pixels = self.trans.itransform(instage, self.stage, self.bin, True)
shift = [pixels['row'],pixels['col']]
offset = affine.affine_transform_offset(input.shape, self.shape, atmatrix, shift)
if outtext:
tile={}
path = input.fileref.path
fname = input.fileref.filename
fullname = os.path.join(path, fname)
args = tuple(atmatrix.flat) + tuple(shift)
tile['fullname'] = fullname
tile['name'] = fname
tile['height'] = input.shape[0]
tile['width'] = input.shape[1]
tile['matrix'] = args
return tile
if target is None:
## affine transform into temporary output
output = numpy.zeros(self.shape, numpy.float32)
inputarray = memmapMRC(input.fileref)
#inputarray = splines.filter(input.fileref.filename, inputarray)
scipy.ndimage.affine_transform(inputarray, atmatrix, offset=offset, output=output, output_shape=self.shape, mode='constant', cval=0.0, order=splineorder, prefilter=False)
if self.image is None:
self.image = numpy.zeros(self.shape, numpy.float32)
## copy temporary into final
numpy.putmask(self.image, output, output)
self.inserted += 1
else:
pix = self.shape[0]/2.0+shift[0],self.shape[1]/2.0+shift[1]
self.targets.append(pix)
def correct_tilt(self, imagedata):
'''
takes imagedata and calculates a corrected image
'''
## from imagedata
im = imagedata['image']
alpha = imagedata['scope']['stage position']['a']
if abs(alpha) < self.alpha_threshold:
return False
beamtilt = imagedata['scope']['beam tilt']
ht = imagedata['scope']['high tension']
mag = imagedata['scope']['magnification']
tem = imagedata['scope']['tem']
cam = imagedata['camera']['ccdcamera']
## from DB
tiltcenter = self.getRotationCenter(tem, ht, mag)
# if no tilt center, then cannot do this
if tiltcenter is None:
self.node.logger.info(
'not correcting tilted images, no rotation center found')
return False
tx = beamtilt['x'] - tiltcenter['x']
ty = beamtilt['y'] - tiltcenter['y']
bt = (tx, ty)
if max(abs(bt[0]), abs(bt[1])) < self.bt_threshold:
# don't transform if beam tilt is small enough
return False
alphadeg = alpha * 180 / 3.14159
self.node.logger.info('correcting tilts, stage: %s deg beam: %s,%s' %
(alphadeg, tx, ty))
defocusmatrix = self.getBeamTiltMatrix(tem, cam, ht, mag)
stagematrix = self.getStageMatrix(tem, cam, ht, mag)
mat = self.affine_transform_matrix(defocusmatrix, stagematrix, bt,
alpha)
scope = imagedata['scope']
camera = imagedata['camera']
## calculate pixel shift to get to image shift 0,0
imageshift = dict(scope['image shift'])
#imageshift['x'] *= -1
#imageshift['y'] *= -1
pixelshift = self.itransform(imageshift, scope, camera)
pixelshift = (pixelshift['row'], pixelshift['col'])
offset = affine.affine_transform_offset(im.shape, im.shape, mat,
pixelshift)
mean = self.edge_mean(im)
im2 = scipy.ndimage.affine_transform(im,
mat,
offset=offset,
mode='constant',
cval=mean)
#im2 = self.filter.convolve(im2)
imagedata['image'] = im2
return True
def correct_tilt(self, imagedata):
'''
takes imagedata and calculates a corrected image
'''
## from imagedata
im = imagedata['image']
alpha = imagedata['scope']['stage position']['a']
if abs(alpha) < self.alpha_threshold:
return False
beamtilt = imagedata['scope']['beam tilt']
ht = imagedata['scope']['high tension']
mag = imagedata['scope']['magnification']
tem = imagedata['scope']['tem']
cam = imagedata['camera']['ccdcamera']
## from DB
tiltcenter = self.getRotationCenter(tem, ht, mag)
# if no tilt center, then cannot do this
if tiltcenter is None:
self.node.logger.info('not correcting tilted images, no rotation center found')
return False
tx = beamtilt['x'] - tiltcenter['x']
ty = beamtilt['y'] - tiltcenter['y']
bt = (tx,ty)
if max(abs(bt[0]),abs(bt[1])) < self.bt_threshold:
# don't transform if beam tilt is small enough
return False
alphadeg = alpha * 180 / 3.14159
self.node.logger.info('correcting tilts, stage: %s deg beam: %s,%s' % (alphadeg, tx, ty))
defocusmatrix = self.getBeamTiltMatrix(tem, cam, ht, mag)
stagematrix = self.getStageMatrix(tem, cam, ht, mag)
mat = self.affine_transform_matrix(defocusmatrix, stagematrix, bt, alpha)
scope = imagedata['scope']
camera = imagedata['camera']
## calculate pixel shift to get to image shift 0,0
imageshift = dict(scope['image shift'])
#imageshift['x'] *= -1
#imageshift['y'] *= -1
pixelshift = self.itransform(imageshift, scope, camera)
pixelshift = (pixelshift['row'], pixelshift['col'])
offset = affine.affine_transform_offset(im.shape, im.shape, mat, pixelshift)
mean=self.edge_mean(im)
im2 = scipy.ndimage.affine_transform(im, mat, offset=offset, mode='constant', cval=mean)
#im2 = self.filter.convolve(im2)
imagedata['image'] = im2
return True
def correct_tilt(self, imagedata):
"""
takes imagedata and calculates a corrected image
"""
## from imagedata
im = imagedata["image"]
alpha = imagedata["scope"]["stage position"]["a"]
if abs(alpha) < self.alpha_threshold:
return False
beamtilt = imagedata["scope"]["beam tilt"]
ht = imagedata["scope"]["high tension"]
mag = imagedata["scope"]["magnification"]
tem = imagedata["scope"]["tem"]
cam = imagedata["camera"]["ccdcamera"]
## from DB
tiltcenter = self.getRotationCenter(tem, ht, mag)
# if no tilt center, then cannot do this
if tiltcenter is None:
self.node.logger.info("not correcting tilted images, no rotation center found")
return False
tx = beamtilt["x"] - tiltcenter["x"]
ty = beamtilt["y"] - tiltcenter["y"]
bt = (tx, ty)
if max(abs(bt[0]), abs(bt[1])) < self.bt_threshold:
# don't transform if beam tilt is small enough
return False
alphadeg = alpha * 180 / 3.14159
self.node.logger.info("correcting tilts, stage: %s deg beam: %s,%s" % (alphadeg, tx, ty))
defocusmatrix = self.getBeamTiltMatrix(tem, cam, ht, mag)
stagematrix = self.getStageMatrix(tem, cam, ht, mag)
mat = self.affine_transform_matrix(defocusmatrix, stagematrix, bt, alpha)
scope = imagedata["scope"]
camera = imagedata["camera"]
## calculate pixel shift to get to image shift 0,0
imageshift = dict(scope["image shift"])
# imageshift['x'] *= -1
# imageshift['y'] *= -1
pixelshift = self.itransform(imageshift, scope, camera)
pixelshift = (pixelshift["row"], pixelshift["col"])
offset = affine.affine_transform_offset(im.shape, im.shape, mat, pixelshift)
mean = self.edge_mean(im)
im2 = scipy.ndimage.affine_transform(im, mat, offset=offset, mode="constant", cval=mean)
# im2 = self.filter.convolve(im2)
imagedata["image"] = im2
return True
def insertImage(self, input, target=None, outtext=None):
if not self.needImage(input):
return
## differnce in binning requires scaling
## assume equal x and y binning
binscale = float(input.binx) / self.binx
binmatrix = binscale * numpy.identity(2)
## affine transform matrix is input matrix
atmatrix = numpy.dot(binmatrix, input.trans.matrix)
atmatrix = numpy.dot(self.outputmatrix, atmatrix)
## affine_transform function requires the inverse matrix
atmatrix = numpy.linalg.inv(atmatrix)
## calculate offset position
if target is None:
instage = input.stage
else:
instage = input.trans.transform(target, input.stage, input.bin)
pixels = self.trans.itransform(instage, self.stage, self.bin, True)
shift = [pixels['row'], pixels['col']]
offset = affine.affine_transform_offset(input.shape, self.shape,
atmatrix, shift)
if outtext:
tile = {}
path = input.fileref.path
fname = input.fileref.filename
fullname = os.path.join(path, fname)
args = tuple(atmatrix.flat) + tuple(shift)
tile['fullname'] = fullname
tile['name'] = fname
tile['height'] = input.shape[0]
tile['width'] = input.shape[1]
tile['matrix'] = args
return tile
if target is None:
## affine transform into temporary output
output = numpy.zeros(self.shape, numpy.float32)
inputarray = memmapMRC(input.fileref)
#inputarray = splines.filter(input.fileref.filename, inputarray)
scipy.ndimage.affine_transform(inputarray,
atmatrix,
offset=offset,
output=output,
output_shape=self.shape,
mode='constant',
cval=0.0,
order=splineorder,
prefilter=False)
if self.image is None:
self.image = numpy.zeros(self.shape, numpy.float32)
## copy temporary into final
numpy.putmask(self.image, output, output)
self.inserted += 1
else:
pix = self.shape[0] / 2.0 + shift[0], self.shape[1] / 2.0 + shift[1]
self.targets.append(pix)
