print 'Opening: ' + fname
image = imgutil.loadimg(fname, from_database=True)
tic = time.clock()
# Clean-up the image:
#image = flatfield.NormalizeColumnGains(image,Plot=0,Wiener=0)
image = flatfield.ImgNormalize(image, Method="mean")
toc = time.clock()
# Find star centroids:
centroids = []
(centers) = centroid.findstars(image,
zreject=3,
zthresh=3.0,
zpeakthresh=4,
min_pix_per_star=6,
max_pix_per_star=60,
oblongness=2,
debug=True)
centroids = centroid.imgcentroid(image, centers)
# Display image with stars circled:
vf = 3
if centroids:
imgutil.circstars(image, centroids, 20, viewfactor=vf)
else:
print 'Oopsies... we found ZERO centroids. Now how do you feel?'
imgutil.dispimg(image, viewfactor=vf)
print 'Total time: ', toc - tic, ' s'
#DRCMoutfile = '../testimgdir/DRCMsubdimg.tif' # dark row column average subtraction #CMoutfile = '../testimgdir/CM10xsubdimg.tif' # column average subtraction #CSMoutfile = '../testimgdir/CM10xsubdimg.tif' # column sigma range average subtraction # load print 'loading image...' img = imgutil.loadimg(datpic) #fullimg = imgutil.loadimg(datpic,'full') # do subtractions print 'doing subtraction...' #DRCMimg = submethods.darkcolsub(fullimg) #CMimg = submethods.colmeansub(img) #CSMimg = submethods.colsigsub(img) ix,iy = 1080,1080-10 iw,ih = 4,5 window,(x,y),(cx,cy) = submethods.windowsub(img,(ix,iy),(iw,ih)) # display images #imgutil.dispimg(DRCMimg) print 'displaying image...' #imgutil.dispimg(CMimg,10) # displayed with an optional view factor of 10 imgutil.dispimg(window) #imgutil.dispimg(img[100:100+int(5*2.5)+1][:,100:100+int(4*2.5)+1]) # save images #imgutil.saveimg(DRCMimg,DRCMoutfile) #imgutil.saveimg(CMimg,CMoutfile,10) # saved with an optional view factor of 10
import script_setup from util import imgutil as imgutil # from util import * # image in paths tifpicIN = '../testimgdir/img_1348355543_717188_00015_00000_0_gray.tif' datpicIN = '../testimgdir/img_1348355543_717188_00015_00000_0.dat' #image out path tifpicOUT = '../testimgdir/tifpicsaved.tif' datpicOUT = '../testimgdir/datpicsaved.tif' loadedtif = imgutil.loadimg(tifpicIN) loadeddat = imgutil.loadimg(datpicIN) # display loaded dat imgutil.dispimg(loadedtif) imgutil.dispimg(loadeddat) # try out new centroids display #cents = [(0,0),(100.0,100.0),(500.5,500.5),(700,1700),(1200,1200),(1500.5,1500.5),(2000,2000)] #loadeddat[700][1700] = 60000 #imgutil.circstars(loadeddat,cents) # test new load with optional cropping #fulldat = imgutil.loadimg(datpicIN,'full') #imgutil.dispimg(fulldat) # save loaded pictures #imgutil.saveimg(loadedtif,tifpicOUT) #imgutil.saveimg(loadeddat,datpicOUT)
# image path setup datpic = '../testimgdir/img_1348355543_717188_00015_00000_0.dat' #DRCMoutfile = '../testimgdir/DRCMsubdimg.tif' # dark row column average subtraction #CMoutfile = '../testimgdir/CM10xsubdimg.tif' # column average subtraction #CSMoutfile = '../testimgdir/CM10xsubdimg.tif' # column sigma range average subtraction # load print 'loading image...' img = imgutil.loadimg(datpic) #fullimg = imgutil.loadimg(datpic,'full') # do subtractions print 'doing subtraction...' #DRCMimg = submethods.darkcolsub(fullimg) #CMimg = submethods.colmeansub(img) #CSMimg = submethods.colsigsub(img) ix, iy = 1080, 1080 - 10 iw, ih = 4, 5 window, (x, y), (cx, cy) = submethods.windowsub(img, (ix, iy), (iw, ih)) # display images #imgutil.dispimg(DRCMimg) print 'displaying image...' #imgutil.dispimg(CMimg,10) # displayed with an optional view factor of 10 imgutil.dispimg(window) #imgutil.dispimg(img[100:100+int(5*2.5)+1][:,100:100+int(4*2.5)+1]) # save images #imgutil.saveimg(DRCMimg,DRCMoutfile) #imgutil.saveimg(CMimg,CMoutfile,10) # saved with an optional view factor of 10
###################################################################################
# Load the image:
db = database.Connect()
fnames = db.select('select raw_fn from rawdata where burst_num = 185 limit 2').raw_fn.tolist()
fnames.sort()
fname = fnames[0]
print 'Opening: ' + fname
image = imgutil.loadimg(fname,from_database=True)
tic = time.clock()
# Clean-up the image:
#image = flatfield.NormalizeColumnGains(image,Plot=0,Wiener=0)
image = flatfield.ImgNormalize(image, Method="mean")
toc = time.clock()
# Find star centroids:
centroids = []
(centers) = centroid.findstars(image,zreject=3, zthresh=3.0, zpeakthresh=4, min_pix_per_star=6, max_pix_per_star=60, oblongness=2,debug=True)
centroids = centroid.imgcentroid(image,centers)
# Display image with stars circled:
vf = 3
if centroids:
imgutil.circstars(image,centroids,20,viewfactor=vf)
else:
print 'Oopsies... we found ZERO centroids. Now how do you feel?'
imgutil.dispimg(image,viewfactor=vf)
print 'Total time: ',toc - tic,' s'
import script_setup from util import imgutil as imgutil # from util import * # image in paths tifpicIN = '../testimgdir/img_1348355543_717188_00015_00000_0_gray.tif' datpicIN = '../testimgdir/img_1348355543_717188_00015_00000_0.dat' #image out path tifpicOUT = '../testimgdir/tifpicsaved.tif' datpicOUT = '../testimgdir/datpicsaved.tif' loadedtif = imgutil.loadimg(tifpicIN) loadeddat = imgutil.loadimg(datpicIN) # display loaded dat imgutil.dispimg(loadedtif) imgutil.dispimg(loadeddat) # try out new centroids display #cents = [(0,0),(100.0,100.0),(500.5,500.5),(700,1700),(1200,1200),(1500.5,1500.5),(2000,2000)] #loadeddat[700][1700] = 60000 #imgutil.circstars(loadeddat,cents) # test new load with optional cropping #fulldat = imgutil.loadimg(datpicIN,'full') #imgutil.dispimg(fulldat) # save loaded pictures #imgutil.saveimg(loadedtif,tifpicOUT) #imgutil.saveimg(loadeddat,datpicOUT)
def main():
'''
Use this to test the procedural differences in image normalization"
'''
# Method
Method = "mean"
# Load image
# fn = "/Users/zachdischner/Desktop/StarTest_9_9_2012/Gray/img_1347267746_089087_00006_00017_0_gray.tif"
# fn = "/Users/zachdischner/Desktop/img_1353551010_808501_00000_00039_1.dat"
fn = "/Users/zachdischner/Desktop/img.dat"
img=imgutil.loadimg(fn,load_full=1)
# Print means and standard deviations
print "Using " + Method + " Method for normalization"
print ""
print "Original image mean and standard deviation: %s and %s " % (np.mean(img),np.std(img))
i2 = NormalizeColumnGains(img,Plot=1,JustDark=1,Method=Method)
pylab.title('Just Dark Row Normalization Using ' + Method )
print "Just using Dark rows, image size is: "
print i2.shape
print "Dark Row based image mean and standard deviation: %s and %s " % (np.mean(i2),np.std(i2))
i3 = NormalizeColumnGains(img,Plot=1,Method=Method)
pylab.title('Using Dark row and Whole Image Using ' + Method )
print "Now using the entire image, image size is: "
print i3.shape
print "DR + Image Column mean and standard deviation: %s and %s " % (np.mean(i3),np.std(i3))
print "Adding a Wiener Filter"
iwiener=NormalizeColumnGains(img,Plot=1,Method=Method,Wiener=1)
print "DR + Image Column + Wiener mean and standard deviation: %s and %s " % (np.mean(iwiener),np.std(iwiener))
# Plotting
pylab.figure(num=None, figsize=(13, 7), dpi=80, facecolor='w', edgecolor='k')
pylab.subplot(2,2,1)
pylab.imshow(np.multiply(img,4), cmap=None, norm=None, aspect=None, interpolation='nearest', vmin=0, vmax=2048, origin='upper')
pylab.title('Original Image')
pylab.subplot(2,2,2)
pylab.imshow(np.multiply(i2,4), cmap=None, norm=None, aspect=None, interpolation='nearest', vmin=0, vmax=2048, origin='upper')
pylab.title("Dark Row normalization Using " + Method )
pylab.subplot(2,2,3)
pylab.imshow(np.multiply(i3,4), cmap=None, norm=None, aspect=None, interpolation='nearest', vmin=0, vmax=2048, origin='upper')
pylab.title('DR and Img Col Using ' + Method)
pylab.subplot(2,2,4)
# pylab.imshow(np.multiply(iwiener,4), cmap=None, norm=None, aspect=None, interpolation='nearest', vmin=0, vmax=2048, origin='upper')
# pylab.title('Adding Wiener Filter to ' + Method)
# oldmean=centroid.frobomad(img, axis=0)[0]
# newmean=centroid.frobomad(i3, axis=0)[0]
oldmean=np.mean(img,axis=0)
newmean=np.mean(i3,axis=0)
pylab.plot(oldmean)
pylab.plot(newmean)
pylab.legend(['Before Normalization Col RMean','After Normalization Col RMean'])
pylab.xlabel('Column')
pylab.ylabel('Mean')
imgutil.dispimg(i3,viewfactor=4.)
pylab.title('DR + IMGnorm')
imgutil.dispimg(iwiener,viewfactor=4.)
pylab.title('DR + IMGnorm + Wiener')
return i3
def main():
'''
Use this to test the procedural differences in image normalization"
'''
# Method
Method = "mean"
# Load image
# fn = "/Users/zachdischner/Desktop/StarTest_9_9_2012/Gray/img_1347267746_089087_00006_00017_0_gray.tif"
# fn = "/Users/zachdischner/Desktop/img_1353551010_808501_00000_00039_1.dat"
fn = "/Users/zachdischner/Desktop/img.dat"
img = imgutil.loadimg(fn, load_full=1)
# Print means and standard deviations
print "Using " + Method + " Method for normalization"
print ""
print "Original image mean and standard deviation: %s and %s " % (
np.mean(img), np.std(img))
i2 = NormalizeColumnGains(img, Plot=1, JustDark=1, Method=Method)
pylab.title('Just Dark Row Normalization Using ' + Method)
print "Just using Dark rows, image size is: "
print i2.shape
print "Dark Row based image mean and standard deviation: %s and %s " % (
np.mean(i2), np.std(i2))
i3 = NormalizeColumnGains(img, Plot=1, Method=Method)
pylab.title('Using Dark row and Whole Image Using ' + Method)
print "Now using the entire image, image size is: "
print i3.shape
print "DR + Image Column mean and standard deviation: %s and %s " % (
np.mean(i3), np.std(i3))
print "Adding a Wiener Filter"
iwiener = NormalizeColumnGains(img, Plot=1, Method=Method, Wiener=1)
print "DR + Image Column + Wiener mean and standard deviation: %s and %s " % (
np.mean(iwiener), np.std(iwiener))
# Plotting
pylab.figure(num=None,
figsize=(13, 7),
dpi=80,
facecolor='w',
edgecolor='k')
pylab.subplot(2, 2, 1)
pylab.imshow(np.multiply(img, 4),
cmap=None,
norm=None,
aspect=None,
interpolation='nearest',
vmin=0,
vmax=2048,
origin='upper')
pylab.title('Original Image')
pylab.subplot(2, 2, 2)
pylab.imshow(np.multiply(i2, 4),
cmap=None,
norm=None,
aspect=None,
interpolation='nearest',
vmin=0,
vmax=2048,
origin='upper')
pylab.title("Dark Row normalization Using " + Method)
pylab.subplot(2, 2, 3)
pylab.imshow(np.multiply(i3, 4),
cmap=None,
norm=None,
aspect=None,
interpolation='nearest',
vmin=0,
vmax=2048,
origin='upper')
pylab.title('DR and Img Col Using ' + Method)
pylab.subplot(2, 2, 4)
# pylab.imshow(np.multiply(iwiener,4), cmap=None, norm=None, aspect=None, interpolation='nearest', vmin=0, vmax=2048, origin='upper')
# pylab.title('Adding Wiener Filter to ' + Method)
# oldmean=centroid.frobomad(img, axis=0)[0]
# newmean=centroid.frobomad(i3, axis=0)[0]
oldmean = np.mean(img, axis=0)
newmean = np.mean(i3, axis=0)
pylab.plot(oldmean)
pylab.plot(newmean)
pylab.legend(
['Before Normalization Col RMean', 'After Normalization Col RMean'])
pylab.xlabel('Column')
pylab.ylabel('Mean')
imgutil.dispimg(i3, viewfactor=4.)
pylab.title('DR + IMGnorm')
imgutil.dispimg(iwiener, viewfactor=4.)
pylab.title('DR + IMGnorm + Wiener')
return i3
