def radon(image, stepsize=2, maskrad=None): from multiprocessing import Queue, Process t0 = time.time() ### prepare mask if maskrad is None: maskrad = image.shape[0]/2 blackcircle = imagefun.filled_circle(image.shape, maskrad) mask = 1 - blackcircle nsteps = int(math.ceil(180/stepsize)) queuelist = [] for row in range(nsteps): angle = -row*stepsize queue = Queue() queuelist.append(queue) #below is equivalent to "project(image, row, angle, mask, queue)" proc = Process(target=project, args=(image, row, angle, mask, queue)) proc.start() proc.join() ### assemble radon image radonimage = numpy.zeros( (nsteps, image.shape[0]) ) for queue in queuelist: row, line = queue.get() radonimage[row, :] = line #radonlr = numpy.fliplr(radonimage) #radonimage = numpy.vstack((radonimage, radonlr)) imagefile.arrayToJpeg(radonimage, "radonimage.jpg", msg=False) print "Multi radon completed in %s"%(apDisplay.timeString(time.time() - t0)) return radonimage
def edgeNorm(imgarray): """ normalize an image with mean = 0 and stddev = 1.0 in the outer ring of a particle """ edgeMask = imagefun.filled_circle(imgarray.shape) return normStdevMask(imgarray, edgeMask)
def radon(image, stepsize=2, maskrad=None):
from multiprocessing import Queue, Process
t0 = time.time()
### prepare mask
if maskrad is None:
maskrad = image.shape[0] / 2
blackcircle = imagefun.filled_circle(image.shape, maskrad)
mask = 1 - blackcircle
nsteps = int(math.ceil(180 / stepsize))
queuelist = []
for row in range(nsteps):
angle = -row * stepsize
queue = Queue()
queuelist.append(queue)
#below is equivalent to "project(image, row, angle, mask, queue)"
proc = Process(target=project, args=(image, row, angle, mask, queue))
proc.start()
proc.join()
### assemble radon image
radonimage = numpy.zeros((nsteps, image.shape[0]))
for queue in queuelist:
row, line = queue.get()
radonimage[row, :] = line
#radonlr = numpy.fliplr(radonimage)
#radonimage = numpy.vstack((radonimage, radonlr))
imagefile.arrayToJpeg(radonimage, "radonimage.jpg", msg=False)
print "Multi radon completed in %s" % (apDisplay.timeString(time.time() -
t0))
return radonimage
def preLoopFunctions(self): super(Makestack2Loop, self).preLoopFunctions() ### create an edge map for edge statistics box = self.boxsize self.boxedpartdatas = None ### use a radius one pixel less than the boxsize self.edgemap = imagefun.filled_circle((box, box), box/2.0-1.0) self.summedParticles = numpy.zeros((box, box))
def radonlist(imagelist, stepsize=2, maskrad=None, msg=None):
"""
computes Radon transform of image list
"""
if msg is None and len(imagelist) > 50:
msg = True
elif msg is None:
msg = False
### Note: multiprocessing version not compatible with python 2.4
from multiprocessing import Queue, Process
t0 = time.time()
### prepare mask
shape = imagelist[0].shape
if maskrad is None:
maskrad = shape[0] / 2
blackcircle = imagefun.filled_circle(shape, maskrad)
mask = 1 - blackcircle
### preform radon transform for each image
queuelist = []
if msg is True:
apDisplay.printMsg("Performing Radon transforms with multiprocessor")
for imageid in range(len(imagelist)):
if msg is True and imageid % 50 == 0:
### FUTURE: add time estimate
sys.stderr.write(".")
image = imagelist[imageid]
queue = Queue()
queuelist.append(queue)
#below is equivalent to "radonImage(image, imageid, stepsize, mask, queue)"
proc = Process(target=radonImage,
args=(image, imageid, stepsize, mask, queue))
proc.start()
proc.join()
### assemble radon image list
radonimagelist = range(len(imagelist))
for queue in queuelist:
imageid, radonimage = queue.get()
radonimagelist[imageid] = radonimage
if msg is True:
sys.stderr.write("\n")
print "Multi Radon images complete in %s" % (
apDisplay.timeString(time.time() - t0))
return radonimagelist
def radonlist(imagelist, stepsize=2, maskrad=None, msg=None):
"""
computes Radon transform of image list
"""
if msg is None and len(imagelist) > 50:
msg = True
elif msg is None:
msg = False
### Note: multiprocessing version not compatible with python 2.4
from multiprocessing import Queue, Process
t0 = time.time()
### prepare mask
shape = imagelist[0].shape
if maskrad is None:
maskrad = shape[0]/2
blackcircle = imagefun.filled_circle(shape, maskrad)
mask = 1 - blackcircle
### preform radon transform for each image
queuelist = []
if msg is True:
apDisplay.printMsg("Performing Radon transforms with multiprocessor")
for imageid in range(len(imagelist)):
if msg is True and imageid % 50 == 0:
### FUTURE: add time estimate
sys.stderr.write(".")
image = imagelist[imageid]
queue = Queue()
queuelist.append(queue)
#below is equivalent to "radonImage(image, imageid, stepsize, mask, queue)"
proc = Process(target=radonImage, args=(image, imageid, stepsize, mask, queue))
proc.start()
proc.join()
### assemble radon image list
radonimagelist = range(len(imagelist))
for queue in queuelist:
imageid, radonimage = queue.get()
radonimagelist[imageid] = radonimage
if msg is True:
sys.stderr.write("\n")
print "Multi Radon images complete in %s"%(apDisplay.timeString(time.time()-t0))
return radonimagelist
def classicradon(image, stepsize=2): """ computes Radon transform of image """ radonlist = [] nsteps = int(math.ceil(180/stepsize)) blackcircle = imagefun.filled_circle(image.shape, image.shape[0]/2*0.75) mask = 1 - blackcircle maskline = mask.sum(axis=0) + 1 for i in range(nsteps): rotated = ndimage.rotate(image, -i*stepsize, reshape=False, order=1) rotated = mask*rotated line = rotated.sum(axis=0) radonlist.append(line/maskline) radon = numpy.array(radonlist) radon = radon/radon.std() #radonlr = numpy.fliplr(radon) #radon = numpy.vstack((radon, radonlr)) return radon
def project(image, row, angle, mask, queue): #print "%d, angle=%.3f"%(row, angle) ### prepare mask if mask is None: maskrad = image.shape[0]/2 blackcircle = imagefun.filled_circle(image.shape, maskrad) mask = 1 - blackcircle maskline = mask.sum(axis=0) + 1 ### rotate and project image rotated = ndimage.rotate(image, angle, reshape=False, order=1) rotated = mask*rotated #imagefile.arrayToJpeg(rotated, "rotated%02d.jpg"%(row)) line = rotated.sum(axis=0) ### insert into radon array #print "insert %d, %.3f"%(row, line.mean()) line = line/maskline queue.put([row, line]) return
def project(image, row, angle, mask, queue):
#print "%d, angle=%.3f"%(row, angle)
### prepare mask
if mask is None:
maskrad = image.shape[0] / 2
blackcircle = imagefun.filled_circle(image.shape, maskrad)
mask = 1 - blackcircle
maskline = mask.sum(axis=0) + 1
### rotate and project image
rotated = ndimage.rotate(image, angle, reshape=False, order=1)
rotated = mask * rotated
#imagefile.arrayToJpeg(rotated, "rotated%02d.jpg"%(row))
line = rotated.sum(axis=0)
### insert into radon array
#print "insert %d, %.3f"%(row, line.mean())
line = line / maskline
queue.put([row, line])
return
def classicradon(image, stepsize=2):
"""
computes Radon transform of image
"""
radonlist = []
nsteps = int(math.ceil(180 / stepsize))
blackcircle = imagefun.filled_circle(image.shape,
image.shape[0] / 2 * 0.75)
mask = 1 - blackcircle
maskline = mask.sum(axis=0) + 1
for i in range(nsteps):
rotated = ndimage.rotate(image, -i * stepsize, reshape=False, order=1)
rotated = mask * rotated
line = rotated.sum(axis=0)
radonlist.append(line / maskline)
radon = numpy.array(radonlist)
radon = radon / radon.std()
#radonlr = numpy.fliplr(radon)
#radon = numpy.vstack((radon, radonlr))
return radon
def radonTransform(image, stepsize=2, maskrad=None):
"""
computes Radon transform of image
"""
radonlist = []
if maskrad is None:
maskrad = image.shape[0]/2
blackcircle = imagefun.filled_circle(image.shape, maskrad)
mask = 1 - blackcircle
maskline = mask.sum(axis=0) + 1
nsteps = int(math.ceil(180/stepsize))
for i in range(nsteps):
rotated = ndimage.rotate(image, -i*stepsize, reshape=False, order=1)
rotated = mask*rotated
line = rotated.sum(axis=0)
radonlist.append(line/maskline)
radon = numpy.array(radonlist)
#radonlr = numpy.fliplr(radon)
#radon = numpy.vstack((radon, radonlr))
return radon
def radonTransform(image, stepsize=2, maskrad=None): """ computes Radon transform of image """ radonlist = [] if maskrad is None: maskrad = image.shape[0]/2 blackcircle = imagefun.filled_circle(image.shape, maskrad) mask = 1 - blackcircle maskline = mask.sum(axis=0) + 1 nsteps = int(math.ceil(180/stepsize)) for i in range(nsteps): rotated = ndimage.rotate(image, -i*stepsize, reshape=False, order=1) rotated = mask*rotated line = rotated.sum(axis=0) radonlist.append(line/maskline) radon = numpy.array(radonlist) #radonlr = numpy.fliplr(radon) #radon = numpy.vstack((radon, radonlr)) return radon
