def analyseData(dd):
#ss = dd.sum(0)
#dnp.plot.image(ss)
##ss is the flat field
print "Calculating median for the dataset ..."
med = dnp.median(dd, 0)
print "Median calculated"
if plotData: dnp.plot.image(med)
Sleep.sleep(2000)
#flatFieldCorrectedSum = ss/med
#dnp.plot.image(flatFieldCorrectedSum)
xVals = []
yVals = []
##
print "Calculating centroid ..."
centroids = []
for i in range(18):
#dnp.plot.image(dd[i, :, :] / med)
im = dd[i, :, :] / med
im = javaImage.medianFilter(im, [3, 3])
im = dnp.array(im)
threshold = 0.85
if plotData: dnp.plot.image(im)
if plotData: Sleep.sleep(1000)
if plotData: dnp.plot.image(im < threshold)
if plotData: Sleep.sleep(1000)
cent = dnp.centroid(im < threshold)
yVals.append(cent[0])
xVals.append(cent[1])
centroids.append(cent)
print "y:" + `yVals`
print('')
print "x:" + `xVals`
#help(dnp.fit.ellipsefit)
if plotData: dnp.plot.line(dnp.array(xVals), dnp.array(yVals))
ellipseFitValues = dnp.fit.ellipsefit(xVals, yVals)
print "major: " + `ellipseFitValues[0]`
print "minor semi-axis: " + `ellipseFitValues[1]`
print "major axis angle: " + `ellipseFitValues[2]`
print "centre co-ord 1: " + `ellipseFitValues[3]`
print "centre co-ord 2: " + `ellipseFitValues[4]`
print "centroids:" + `centroids`
xOffset = getXOffset(ellipseFitValues[1], ellipseFitValues[0])
zOffset = getZOffset(ellipseFitValues[2])
return xOffset, zOffset, centroids, ellipseFitValues
def get_image_data(
previewmean=False,
preview_frames=True,
visit_directory="/dls/i12/data/2014/cm4963-2/",
flatfile=37384,
projectionfile=37385,
threshold=0.5,
max_tries=15,
prompt=False,
request_input_command=request_DAWN_Input,
):
working_directory = "%s/rawdata" % visit_directory
flatfilename = "%s/%i.nxs" % (working_directory, flatfile)
projectionfilename = "%s/%i.nxs" % (working_directory, projectionfile)
# TODO put a unique time stamp on the otuput
# TODO make a folder for the outputs
print ("FlatFilename = %s" % flatfilename)
print ("projectionfilename = %s" % projectionfilename)
print "Loading flat field image %s\n\t to data processing pipeline" % flatfilename
ff = None
try_number = 0
while ff == None and try_number < max_tries:
try_number += 1
try:
print ("Trying to open the file %s" % (flatfilename))
data = dnp.io.load(flatfilename)
ff = data["entry1"]["pco4000_dio_hdf"]["data"][...].mean(0)
except:
print ("ff is not null is it %s" % ff)
print ("ff is not null is of type %s" % type(ff))
print ("Failed to load %i, will try again in 5 seconds" % (try_number))
time.sleep(5)
ff = dnp.array(javaImage.medianFilter(ff._jdataset(), [3, 3]))
dnp.plot.image(ff)
time.sleep(5)
print "Loading projections %s into data processing pipeline" % projectionfilename
data = dnp.io.load(projectionfilename)
try:
dd = data["entry1"]["pco4000_dio_hdf"]["data"]
except:
print "Couldn not find the data in %s" % projectionfilename
raise
print "Processing data"
# apply any filtering
# exclude spheres which are not completely in the field of view
if previewmean:
print "slow look at all the data"
preview = dd[:, :, :].mean(0) * 1.0 / ff
dnp.plot.image(preview[10:-1, :])
time.sleep(2)
print "Generating centroid data"
thresholdOK = False
xs = []
ys = []
while not thresholdOK:
xs = []
ys = []
for i in range(dd.shape[0]):
cor = dd[i, :, :] * 1.0 / ff
# print "Applying median filter"
cor = dnp.array(javaImage.medianFilter(cor._jdataset(), [3, 3]))
cor = cor[10:-1, :] < threshold
if preview_frames:
dnp.plot.clear()
time.sleep(0.01)
dnp.plot.image(cor)
time.sleep(0.2)
y, x = dnp.centroid(cor)
xs.append(x)
ys.append(y)
if prompt == False:
thresholdOK = True
else:
response = request_input_command(
"Current threshold is %f, y for ok, otherwise enter a new threshold value " % (threshold)
)
if response == "y":
thresholdOK = True
else:
try:
threshold = float(response)
except:
print "could not interpret %s as a float" % response
return (xs, ys)
def get_image_data(previewmean=False, preview_frames=True,visit_directory='/dls/i12/data/2014/cm4963-2/',flatfile=37384, projectionfile=37385, threshold=0.5, max_tries=15,prompt=False, request_input_command=request_DAWN_Input):
working_directory="%s/rawdata"%visit_directory
flatfilename="%s/%i.nxs"%(working_directory,flatfile)
projectionfilename="%s/%i.nxs"%(working_directory,projectionfile)
#TODO put a unique time stamp on the otuput
#TODO make a folder for the outputs
print("FlatFilename = %s"% flatfilename)
print("projectionfilename = %s"% projectionfilename)
print "Loading flat field image %s\n\t to data processing pipeline" % flatfilename
ff = None
try_number = 0
while (ff == None and try_number < max_tries):
try_number += 1
try:
print("Trying to open the file %s" % (flatfilename))
data = dnp.io.load(flatfilename)
ff = data['entry1']['pco4000_dio_hdf']['data'][...].mean(0)
except:
print("ff is not null is it %s" % ff)
print("ff is not null is of type %s" % type(ff))
print("Failed to load %i, will try again in 5 seconds" % (try_number))
time.sleep(5)
ff = dnp.array(javaImage.medianFilter(ff._jdataset(), [3, 3]))
dnp.plot.image(ff)
time.sleep(5)
print "Loading projections %s into data processing pipeline"% projectionfilename
data = dnp.io.load(projectionfilename)
try:
dd = data['entry1']['pco4000_dio_hdf']['data']
except:
print"Couldn not find the data in %s"%projectionfilename
raise
print "Processing data"
# apply any filtering
# exclude spheres which are not completely in the field of view
if (previewmean):
print "slow look at all the data"
preview = dd[:,:,:].mean(0)*1.0/ff
dnp.plot.image(preview[10:-1,:])
time.sleep(2)
print "Generating centroid data"
thresholdOK = False
xs = []
ys = []
while not thresholdOK:
xs = []
ys = []
for i in range(dd.shape[0]):
cor = dd[i,:,:]*1.0/ff
#print "Applying median filter"
cor = dnp.array(javaImage.medianFilter(cor._jdataset(), [3, 3]))
cor = cor[10:-1,:]<threshold
if (preview_frames):
dnp.plot.clear()
time.sleep(0.01)
dnp.plot.image(cor)
time.sleep(.2)
y,x = dnp.centroid(cor)
xs.append(x)
ys.append(y)
if (prompt==False):
thresholdOK=True
else:
response = request_input_command("Current threshold is %f, y for ok, otherwise enter a new threshold value " % (threshold))
if response == 'y' :
thresholdOK = True
else :
try :
threshold = float(response)
except :
print "could not interpret %s as a float" % response
return(xs,ys)
def findshift(a, b, rect=None):
'''Find translation vector from b to a using rectangular ROI rect'''
return _image.findTranslation2D(a, b, rect)
def findshift(a, b, rect=None):
"""Find translation vector from b to a using rectangular ROI rect"""
return _image.findTranslation2D(a, b, rect._jroi())
