def getCentroids(fnames):
'''True
From a list of filenames, load the filenames, clean up the images, find stars
in the images, and return a list of centroids and the number of stars found in
each image.
'''
n = len(fnames)
centroids = []
numstars = []
for count,fname in enumerate(fnames):
# Display status:
print 'Loading and centroiding filename ' + str(count+1) + ' of ' + str(n) + '.'
# Load the image:
image = imgutil.loadimg(fname,from_database=True)
# Clean up image, if it hasn't been already:
if not fname.find('_norm.tif'):
image = flatfield.ImgNormalize(image, Method="mean")
# Find stars in image:
#centers = centroid.findstars(image)
# Nighttime:
centers = centroid.findstars(image,zreject=4, zthresh=3.2, zpeakthresh=5, min_pix_per_star=6, max_pix_per_star=60, oblongness=2,debug=False)
# Daytime:
#centers = centroid.findstars(image,zreject=3, zthresh=3.0, zpeakthresh=4, min_pix_per_star=6, max_pix_per_star=60, oblongness=2,debug=False)
# Get centroids:
centroids.append(centroid.imgcentroid(image,centers))
# store number of stars centroided per frame
numstars.append(len(centroids[count]))
return centroids,numstars
def main():
# Enable import from other dirs in DataAnalysis
sys.path.append("../")
# Addtl imports for main script
from util import imgutil as imgutil
from util import submethods as subm
# Load the image:
image = imgutil.loadimg('/home/sticky/Daystar/img_1348368011_459492_00146_00000_1.dat')
# Get a good estimation for the background level and variance:
(mean,std) = frobomad(image)
# Do column subtraction:
image = subm.colmeansub(image)
# Find star centroids:
centroids = findstars(image,std=std,debug=True)
# Refine centroids using two methods
iwc = imgcentroid(image, centroids, "iwc")
gauss = imgcentroid(image, centroids, "gauss")
print "---"
for each in centroids:
print each
print "---"
for each in iwc:
print each
print "---"
for each in gauss:
print each
# Display image:
#imgutil.dispimg(image,5)
# Display image with stars circled:
#imgutil.circstars(image,iwc + gauss,1)
return 0
def main():
# Enable import from other dirs in DataAnalysis
sys.path.append("../")
# Addtl imports for main script
from util import imgutil as imgutil
from util import submethods as subm
# Load the image:
image = imgutil.loadimg(
'/home/sticky/Daystar/img_1348368011_459492_00146_00000_1.dat')
# Get a good estimation for the background level and variance:
(mean, std) = frobomad(image)
# Do column subtraction:
image = subm.colmeansub(image)
# Find star centroids:
centroids = findstars(image, std=std, debug=True)
# Refine centroids using two methods
iwc = imgcentroid(image, centroids, "iwc")
gauss = imgcentroid(image, centroids, "gauss")
print "---"
for each in centroids:
print each
print "---"
for each in iwc:
print each
print "---"
for each in gauss:
print each
# Display image:
#imgutil.dispimg(image,5)
# Display image with stars circled:
#imgutil.circstars(image,iwc + gauss,1)
return 0
def main():
print 'Loading filenames from database.'
db = database.Connect()
data = db.select("select id,raw_fn from rawdata where(norm_fn='0') limit 2000")
fnames=data.raw_fn.tolist()
ids=data.id.tolist()
n = len(fnames)
for count,fname in enumerate(fnames):
path=os.environ.get('RawBaseLoc')
dir_path="/".join((path + fname).split("/")[:-1])+"/norm/"
if not os.path.isdir(dir_path):
os.mkdir(dir_path)
# norm_fn= str.replace(fname,'.dat','_norm.tif')
full_path=(path+fname).split('/')
full_path[-1]=str.replace("norm/"+full_path[-1],'.dat','_norm.tif')
norm_fn='/'.join(full_path[-3:])
full_path='/'.join(full_path)
if not os.path.isfile(full_path):
print "Normalizng " + fname + " image # %s of %s" % (count,n)
# Load the image:
image = imgutil.loadimg(fname,from_database=True,load_full=True)
# Normalize up image:
image = flatfield.ImgNormalize(image, Method="mean")
print "Saving normalized image as " + norm_fn
# Save the image with _norm.tif appended
imgutil.saveimg(image,full_path)
del image # Remove from memory to make go faster?
print "Adding normalized image to database in 'norm_fn': " + full_path
query="UPDATE rawdata SET norm_fn='"+ norm_fn + "' WHERE(id="+str(ids[count])+")"
db.execute_statement(query)
from util import submethods as subm
from db import RawData as database
from analysis import flatfield
import time
###################################################################################
# Main
###################################################################################
# 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,
# Jed Diller # Script to load and save a tiff and dat file # 10/8/12 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)
# Get the directory above the directory this file is in
sys.path.append(os.path.join(os.path.dirname(__file__), '../'))
# More imports
import numpy as np
import copy as cp
import time
# Addtl imports for main script
from util import imgutil as imgutil
from analysis import submethods as sm
from analysis import centroid as centroid
from analysis import flatfield as flat
# Load the image:
image = imgutil.loadimg('/home/sticky/Daystar/day1.dat')
#image = imgutil.loadimg('/home/sticky/Daystar/img_1348355543_717188_00015_00000_0.dat',
# load_full=True)
#image = imgutil.loadimg('/home/sticky/Daystar/img_1348370070_656182_00172_00000_1.dat')
# Apply flatfield
img1 = flat.ImgNormalize(image, Method="mean", source="image")
#img1 = flat.ImgNormalize(img1, Method="mean", source="image")
img2 = sm.colmeansub(image)
# Find stars
centers = centroid.findstars(img1, zreject=3, zthresh=3.0, zpeakthresh=4, min_pix_per_star=6, max_pix_per_star=60, oblongness=5,debug=False)
C = centroid.imgcentroid(image,centers)
import script_setup from util import imgutil as imgutil from analysis import submethods as submethods import numpy as np # 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...'
###################################################################################
# Import modules from analysis (the correct way to do it):
###################################################################################
from analysis import centroid as centroid
from db import RawData as database
from analysis import submethods as sm
from util import imgutil as imgutil
import pylab as pl
# ----------------- Plot Different Centroid Methods on Same Star-------------------------------
print 'saving plots of centroid methods on same star'
db = database.Connect()
name = db.select('select raw_fn from rawdata where burst_num = 172 limit 1'
).raw_fn.tolist()[0]
img = imgutil.loadimg(name, from_database=True)
# find good star
centers = centroid.findstars(img)
print centers
goodstar = []
goodstar.append(centers[10])
#goodstar = list((tuple(centers[10])) # good centroid at about 1417,275
(x, y) = goodstar[0][0]
(w, h) = goodstar[0][1]
centroid_iwc = centroid.imgcentroid(img, goodstar, method="iwc")
centroid_cog = centroid.imgcentroid(img, goodstar, method="cog")
centroid_gauss = centroid.imgcentroid(img, goodstar, method="gauss")
(frame, (xframe, yframe), frame_centroid) = sm.windowsub(img, (x, y), (w, h),
import script_setup from util import imgutil as imgutil from analysis import submethods as submethods import numpy as np # 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
import script_setup
###################################################################################
# Import modules from analysis (the correct way to do it):
###################################################################################
from analysis import centroid as centroid
from db import RawData as database
from analysis import submethods as sm
from util import imgutil as imgutil
import pylab as pl
# ----------------- Plot Different Centroid Methods on Same Star-------------------------------
print 'saving plots of centroid methods on same star'
db = database.Connect()
name = db.select('select raw_fn from rawdata where burst_num = 172 limit 1').raw_fn.tolist()[0]
img = imgutil.loadimg(name,from_database=True)
# find good star
centers = centroid.findstars(img)
print centers
goodstar = []
goodstar.append(centers[10])
#goodstar = list((tuple(centers[10])) # good centroid at about 1417,275
(x,y) = goodstar[0][0]
(w,h) = goodstar[0][1]
centroid_iwc = centroid.imgcentroid(img, goodstar, method="iwc")
centroid_cog = centroid.imgcentroid(img, goodstar, method="cog")
centroid_gauss = centroid.imgcentroid(img, goodstar, method="gauss")
(frame, (xframe,yframe), frame_centroid) = sm.windowsub(img, (x,y), (w,h), neg=True, scale=1)
from util import imgutil as imgutil
from util import submethods as subm
from db import RawData as database
from analysis import flatfield
import time
###################################################################################
# Main
###################################################################################
# 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
# Jed Diller # Script to load and save a tiff and dat file # 10/8/12 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)
print "Computing quaternions for each image"
quats,matched_centroids,nummatchstars = getQuaternions(centroids)
# update quaternions into the database
print"Inserting quaterninos, number of matched stars, and matched stars into the database"
for count,q in enumerate(quats):
db.insert_quat(q,id[count])
db.insert_num_matched_centroids(nummatchstars[count],id[count])
db.insert_matched_centroids([matched_centroids[count]],id[count])
print 'Find yaw, pitch, and roll rms.'
# Get the roll, pitch, yaw variances:
delta_t = 0.1 # s
motion_frequency = 3.5 # Hz
var_y,var_p,var_r,a,b,c = tracking.FindVariance(quats,delta_t=delta_t,motion_frequency=motion_frequency,plot=plot)
toc = time.clock()
print 'YPR rms: ',np.sqrt(var_y),np.sqrt(var_p),np.sqrt(var_r)
print 'Total time: ' + str(toc - tic) + ' s'
#############################################################
# Plots:
#############################################################
if plot:
image0 = imgutil.loadimg(fnames[0],from_database=True)
plots.starnum(numstars)
plots.starnum(nummatchstars)
plots.starpaths(image0,centroids)
plots.starpaths(image0,matched_centroids)
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
