def fastSkinDetection(rgb, threshold=70, color=[255,20,147]):
res = rgb.copy()
lab = skimage.color.rgb2lab(rgb)
mask = threshold< lab[:,:,0]; mask=ndimage.binary_erosion(mask,iterations=5); mask=ndimage.binary_dilation(mask,iterations=10); mask=invert(mask)
res[mask] = array(color).reshape(1,-1).repeat(mask.sum(),axis=0)
return res
def fastSkinDetection(rgb, threshold=70, color=[255, 20, 147]):
res = rgb.copy()
lab = skimage.color.rgb2lab(rgb)
mask = threshold < lab[:, :, 0]
mask = ndimage.binary_erosion(mask, iterations=5)
mask = ndimage.binary_dilation(mask, iterations=10)
mask = invert(mask)
res[mask] = array(color).reshape(1, -1).repeat(mask.sum(), axis=0)
return res
def gmask(cutimage, xcntr, ycntr, NXPTS, NYPTS, line_s):
imagefile = c.imagefile
sex_cata = c.sex_cata
threshold = c.threshold
thresh_area = c.thresh_area
mask_reg = c.mask_reg
x = n.reshape(n.arange(NXPTS * NYPTS),(NXPTS, NYPTS)) / NYPTS
x = x.astype(n.float32)
y = n.reshape(n.arange(NXPTS * NYPTS),(NXPTS, NYPTS)) % NYPTS
y = y.astype(n.float32)
values = line_s.split()
mask_file = 'M_' + str(cutimage)[:-5] + '.fits'
xcntr_o = float(values[1]) #x center of the object
ycntr_o = float(values[2]) #y center of the object
mag = float(values[7]) #Magnitude
radius = float(values[9]) #Half light radius
mag_zero = c.mag_zero #magnitude zero point
sky = float(values[10]) #sky
pos_ang = float(values[11]) #position angle
axis_rat = 1.0 / float(values[12]) #axis ration b/a
area_o = float(values[13]) # object area
major_axis = float(values[14]) #major axis of the object
eg = 1.0 - axis_rat
one_minus_eg_sq = (1.0 - eg)**2.0
si = n.sin(pos_ang * n.pi / 180.0)
co = n.cos(pos_ang * n.pi / 180.0)
tx = (x - xcntr + 1.0) * co + (y - ycntr + 1.0) * si
ty = (xcntr - 1.0 -x) * si + (y - ycntr + 1.0) * co
R = n.sqrt(tx**2.0 + ty**2.0 / one_minus_eg_sq)
tmp_mask = n.zeros((NXPTS, NYPTS))
f = pyfits.open(c.datadir + cutimage)
galaxy = f[0].data
f.close()
galaxy = n.swapaxes(galaxy, 0, 1)
startR = 3.0
while startR < max(NXPTS / 2.0, NYPTS / 2.0):
galaxymax = galaxy[n.where(R < startR)].max()
tmp_mask[n.where(galaxy > galaxymax)] = 1
galaxy[n.where(tmp_mask == 1)] = 0.0
galaxy[n.where(R < startR)] = 0.0
startR += 1.0
tmp_mask = im.binary_fill_holes(tmp_mask)
tmp_mask = im.binary_erosion(tmp_mask)
f = pyfits.open('TmpElliMask1.fits')
ellip_mask = f[0].data
f.close()
ellip_mask = n.swapaxes(ellip_mask, 0, 1)
tmp_mask[n.where(ellip_mask == 1)] = 0
z = n.zeros((NXPTS, NYPTS))
for line_j in open(sex_cata,'r'):
try:
values = line_j.split()
xcntr_n = float(values[1]) #x center of the neighbour
ycntr_n = float(values[2]) #y center of the neighbour
mag = float(values[7]) #Magnitude
radius = float(values[9]) #Half light radius
sky = float(values[10]) #sky
pos_ang = float(values[11]) #position angle
axis_rat = 1.0/float(values[12]) #axis ration b/a
si = n.sin(pos_ang * n.pi / 180.0)
co = n.cos(pos_ang * n.pi / 180.0)
area_n = float(values[13]) #neighbour area
maj_axis = float(values[14])#major axis of neighbour
eg = 1.0 - axis_rat
one_minus_eg_sq = (1.0-eg)**2.0
if(abs(xcntr_n - xcntr_o) < NXPTS / 2.0 + 30.0 and \
abs(ycntr_n - ycntr_o) < NYPTS / 2.0 + 30.0 and \
xcntr_n != xcntr_o and ycntr_n != ycntr_o):
if(abs(xcntr_n - xcntr_o) > threshold * (major_axis + \
maj_axis) or abs(ycntr_n - ycntr_o) > threshold * \
(major_axis + maj_axis) or area_n < thresh_area * area_o):
if((xcntr_o - xcntr_n) < 0):
xn = xcntr + abs(xcntr_n - xcntr_o)
if((ycntr_o - ycntr_n) < 0):
yn = ycntr + abs(ycntr_n - ycntr_o)
if((xcntr_o - xcntr_n) > 0):
xn = xcntr - (xcntr_o - xcntr_n)
if((ycntr_o - ycntr_n) > 0):
yn = ycntr - (ycntr_o - ycntr_n)
tx = x - xn + 1.0
ty = y - yn + 1.0
R = n.sqrt(tx**2.0 + ty**2.0)
# tx = (x - xn + 0.5) * co + (y - yn + 0.5) * si
# ty = (xn - 0.5 -x) * si + (y - yn + 0.5) * co
# R = n.sqrt(tx**2.0 + ty**2.0 / one_minus_eg_sq)
z[n.where(R<=mask_reg*maj_axis)] = 1
except:
pass
if c.NoMask:
z[n.where(z > 0)] = 0
elif c.NormMask:
pass
else:
z = z + tmp_mask
z[n.where(z > 0)] = 1
z = im.binary_dilation(z, iterations=2)
z = im.binary_fill_holes(z)
hdu = pyfits.PrimaryHDU(n.swapaxes(z, 0, 1).astype(n.float32))
hdu.writeto(mask_file)
try:
os.remove('TmpElliMask.fits')
except:
pass
def gmask(cutimage, xcntr, ycntr, NXPTS, NYPTS, line_s):
imagefile = c.imagefile
sex_cata = c.sex_cata
threshold = c.threshold
thresh_area = c.thresh_area
avoidme = c.avoidme
mask_reg = c.mask_reg
x = n.reshape(n.arange(NXPTS * NYPTS), (NXPTS, NYPTS)) / NYPTS
x = x.astype(n.float32)
y = n.reshape(n.arange(NXPTS * NYPTS), (NXPTS, NYPTS)) % NYPTS
y = y.astype(n.float32)
target = SEx_obj(NXPTS, NYPTS, line_s)
R = target.calc_rad(x, y)
mask_file = 'M_' + c.fstring + '.fits'
tmp_mask = n.zeros((NXPTS, NYPTS))
f = pyfits.open(os.path.join(c.datadir, cutimage))
galaxy = f[0].data
f.close()
galaxy = n.swapaxes(galaxy, 0, 1)
# search for and mask unusually bright pixels, assuming a smooth profile
startR = 3.0 # the pixel radius that we start at
while startR < 4 * target.radius: #max(NXPTS / 2.0, NYPTS / 2.0):
galaxymax = galaxy[n.where(R < startR)].max()
tmp_mask[n.where(galaxy > galaxymax)] = 1
galaxy[n.where(tmp_mask == 1)] = 0.0
galaxy[n.where(R < startR)] = 0.0
startR += 1.0 # step through radii in single pixel annuli
tmp_mask = im.binary_fill_holes(tmp_mask)
tmp_mask = im.binary_erosion(tmp_mask)
f = pyfits.open('TmpElliMask1.fits')
ellip_mask = f[0].data
f.close()
ellip_mask = n.swapaxes(ellip_mask, 0, 1)
tmp_mask[n.where(ellip_mask == 1)] = 0
z = n.zeros((NXPTS, NYPTS))
for line_j in open(sex_cata, 'r'):
if line_j[0] != '#': #line is not a comment
neighbor = SEx_obj(NXPTS, NYPTS, line_j)
if target.mask_or_fit(neighbor, threshold, thresh_area,
avoidme) == 1:
#neighbor.set_axis_rat(1.0) #make masks circular
# recenter in chip coordinates
#print 'masking ', line_j
xn = xcntr - target.xcntr + neighbor.xcntr
yn = ycntr - target.ycntr + neighbor.ycntr
#print neighbor.xcntr, neighbor.ycntr
#print xn, yn
neighbor.set_center(xn, yn)
R = neighbor.calc_rad(x, y)
z[n.where(R <= mask_reg * neighbor.maj_axis)] = 1
#elif target.mask_or_fit(neighbor,threshold,thresh_area,avoidme)==0:
#print "source ", line_j, " should be fit"
if c.NoMask:
z[n.where(z > 0)] = 0
elif c.NormMask:
pass
else:
z = z + tmp_mask
z[n.where(z > 0)] = 1
z = im.binary_dilation(z, iterations=2)
z = im.binary_fill_holes(z)
hdu = pyfits.PrimaryHDU(n.swapaxes(z, 0, 1).astype(n.float32))
hdu.writeto(mask_file)
try:
os.remove('TmpElliMask.fits')
except:
pass
def gmask(cutimage, xcntr, ycntr, NXPTS, NYPTS, line_s):
imagefile = c.imagefile
sex_cata = c.sex_cata
threshold = c.threshold
thresh_area = c.thresh_area
avoidme = c.avoidme
mask_reg = c.mask_reg
x = n.reshape(n.arange(NXPTS * NYPTS),(NXPTS, NYPTS)) / NYPTS
x = x.astype(n.float32)
y = n.reshape(n.arange(NXPTS * NYPTS),(NXPTS, NYPTS)) % NYPTS
y = y.astype(n.float32)
target = SEx_obj(NXPTS, NYPTS, line_s)
R = target.calc_rad(x,y)
mask_file = 'M_' + c.fstring + '.fits'
tmp_mask = n.zeros((NXPTS, NYPTS))
f = pyfits.open(c.datadir + cutimage)
galaxy = f[0].data
f.close()
galaxy = n.swapaxes(galaxy, 0, 1)
# search for and mask unusually bright pixels, assuming a smooth profile
startR = 3.0 # the pixel radius that we start at
while startR < 4*target.radius: #max(NXPTS / 2.0, NYPTS / 2.0):
galaxymax = galaxy[n.where(R < startR)].max()
tmp_mask[n.where(galaxy > galaxymax)] = 1
galaxy[n.where(tmp_mask == 1)] = 0.0
galaxy[n.where(R < startR)] = 0.0
startR += 1.0 # step through radii in single pixel annuli
tmp_mask = im.binary_fill_holes(tmp_mask)
tmp_mask = im.binary_erosion(tmp_mask)
f = pyfits.open('TmpElliMask1.fits')
ellip_mask = f[0].data
f.close()
ellip_mask = n.swapaxes(ellip_mask, 0, 1)
tmp_mask[n.where(ellip_mask == 1)] = 0
z = n.zeros((NXPTS, NYPTS))
for line_j in open(sex_cata,'r'):
if line_j[0] != '#': #line is not a comment
neighbor = SEx_obj(NXPTS, NYPTS, line_j)
if target.mask_or_fit(neighbor,threshold,thresh_area,avoidme)==1:
#neighbor.set_axis_rat(1.0) #make masks circular
# recenter in chip coordinates
#print 'masking ', line_j
xn = xcntr - target.xcntr + neighbor.xcntr
yn = ycntr - target.ycntr + neighbor.ycntr
#print neighbor.xcntr, neighbor.ycntr
#print xn, yn
neighbor.set_center(xn, yn)
R = neighbor.calc_rad(x,y)
z[n.where(R<=mask_reg*neighbor.maj_axis)] = 1
#elif target.mask_or_fit(neighbor,threshold,thresh_area,avoidme)==0:
#print "source ", line_j, " should be fit"
if c.NoMask:
z[n.where(z > 0)] = 0
elif c.NormMask:
pass
else:
z = z + tmp_mask
z[n.where(z > 0)] = 1
z = im.binary_dilation(z, iterations=2)
z = im.binary_fill_holes(z)
hdu = pyfits.PrimaryHDU(n.swapaxes(z, 0, 1).astype(n.float32))
hdu.writeto(mask_file)
try:
os.remove('TmpElliMask.fits')
except:
pass
def emask(cutimage, xcntr, ycntr, NXPTS, NYPTS, line_s, galflag):
imagefile = c.imagefile
sex_cata = c.sex_cata
clus_cata = c.out_cata
threshold = c.threshold
thresh_area = c.thresh_area
mask_reg = c.mask_reg
avoidme = c.avoidme
mag_zero = c.mag_zero #magnitude zero point
x = n.reshape(n.arange(NXPTS * NYPTS),(NXPTS, NYPTS)) / NYPTS
x = x.astype(n.float32)
y = n.reshape(n.arange(NXPTS * NYPTS),(NXPTS, NYPTS)) % NYPTS
y = y.astype(n.float32)
target = SEx_obj(NXPTS, NYPTS, line_s)
R =target.calc_rad(x,y)
mask_file = 'EM_' + c.fstring + '.fits'
tmp_mask = n.zeros((NXPTS, NYPTS))
f = pyfits.open(c.datadir + cutimage)
galaxy = f[0].data
f.close()
galaxy = n.swapaxes(galaxy, 0, 1)
startR = 3.0
while startR < 4*target.radius: #max(NXPTS / 2.0, NYPTS / 2.0):
galaxymax = galaxy[n.where(R < startR)].max()
tmp_mask[n.where(galaxy > galaxymax)] = 1
galaxy[n.where(tmp_mask == 1)] = 0.0
galaxy[n.where(R < startR)] = 0.0
startR += 1.0
tmp_mask = im.binary_fill_holes(tmp_mask)
tmp_mask = im.binary_erosion(tmp_mask)
z = n.zeros((NXPTS, NYPTS))
z1 = n.zeros((NXPTS, NYPTS))
for line_j in open(sex_cata,'r'):
if line_j[0] != '#': #line is not a comment
neighbor = SEx_obj(NXPTS, NYPTS, line_j)
if target.mask_or_fit(neighbor,threshold,thresh_area,avoidme)==1:
# neighbor.set_axis_rat(1.0) #make masks circular
# recenter in chip coordinates
xn = xcntr - target.xcntr + neighbor.xcntr
yn = ycntr - target.ycntr + neighbor.ycntr
neighbor.set_center(xn, yn)
R = neighbor.calc_rad(x,y)
if galflag ==1:
z[n.where(R<=mask_reg*neighbor.maj_axis)] = 1
z1[n.where(R <= 2 * mask_reg * neighbor.maj_axis)] = 1
elif galflag == 0:
z[n.where(R <= neighbor.maj_axis * 2.5)] = 1
if(galflag):
if exists("TmpElliMask1.fits"):
os.remove("TmpElliMask1.fits")
hdu = pyfits.PrimaryHDU(n.swapaxes(z1, 0, 1).astype(n.float32))
hdu.writeto("TmpElliMask1.fits")
if c.NoMask:
z[n.where(z > 0)] = 0
elif c.NormMask:
pass
else:
z = z + tmp_mask
z[n.where(z > 0)] = 1
if exists("TmpElliMask.fits"):
os.remove("TmpElliMask.fits")
hdu = pyfits.PrimaryHDU(n.swapaxes(z, 0, 1).astype(n.float32))
hdu.writeto("TmpElliMask.fits")
hdu = pyfits.PrimaryHDU(n.swapaxes(z, 0, 1).astype(n.float32))
hdu.writeto(mask_file)
else:
try:
os.remove("BMask.fits")
except:
pass
if c.NoMask:
z[n.where(z > 0)] = 0
elif c.NormMask:
pass
else:
z = z + tmp_mask
z[n.where(z > 0)] = 1
z = im.binary_dilation(z, iterations=15)
hdu = pyfits.PrimaryHDU(n.swapaxes(z, 0, 1).astype(n.float32))
hdu.writeto("BMask.fits")
os.system("cp BMask.fits B.fits")
def mask(cutimage, size, line_s, galflag):
imagefile = c.imagefile
sex_cata = c.sex_cata
clus_cata = c.out_cata
threshold = c.threshold
thresh_area = c.thresh_area
mask_reg = c.mask_reg
x = n.reshape(n.arange(size*size),(size,size)) % size
x = x.astype(n.float32)
y = n.reshape(n.arange(size*size),(size,size)) / size
y = y.astype(n.float32)
values = line_s.split()
mask_file = 'EM_' + str(cutimage)[:-5] + '.fits'
xcntr_o = float(values[1]) #x center of the object
ycntr_o = float(values[2]) #y center of the object
if c.galcut:
xcntr = xcntr_o
ycntr = ycntr_o
else:
xcntr = size / 2.0 + 1.0 + xcntr_o - int(xcntr_o)
ycntr = size / 2.0 + 1.0 + ycntr_o - int(ycntr_o)
mag = float(values[7]) #Magnitude
radius = float(values[9]) #Half light radius
mag_zero = c.mag_zero #magnitude zero point
sky = float(values[10]) #sky
pos_ang = float(values[11]) #position angle
axis_rat = 1.0 / float(values[12]) #axis ration b/a
area_o = float(values[13]) # object area
major_axis = float(values[14]) #major axis of the object
eg = 1.0 - axis_rat
one_minus_eg_sq = (1.0 - eg)**2.0
si = n.sin(pos_ang * n.pi / 180.0)
co = n.cos(pos_ang * n.pi / 180.0)
tx = (x - xcntr + 0.5) * co + (y - ycntr + 0.5) * si
ty = (xcntr - 0.5 -x) * si + (y - ycntr + 0.5) * co
R = n.sqrt(tx**2.0 + ty**2.0 / one_minus_eg_sq)
tmp_mask = n.zeros((size, size))
f = pyfits.open(cutimage)
galaxy = f[0].data
f.close()
startR = 3.0
while startR < size/2.0:
galaxymax = galaxy[n.where(R < startR)].max()
tmp_mask[n.where(galaxy > galaxymax)] = 1
galaxy[n.where(tmp_mask == 1)] = 0.0
galaxy[n.where(R < startR)] = 0.0
startR += 1.0
tmp_mask = im.binary_fill_holes(tmp_mask)
tmp_mask = im.binary_erosion(tmp_mask)
z = n.zeros((size,size))
z1 = n.zeros((size,size))
# print z
for line_j in open(sex_cata,'r'):
try:
values = line_j.split()
xcntr_n = float(values[1]) #x center of the neighbour
ycntr_n = float(values[2]) #y center of the neighbour
mag = float(values[7]) #Magnitude
radius = float(values[9]) #Half light radius
sky = float(values[10]) #sky
pos_ang = float(values[11]) #position angle
axis_rat = 1.0/float(values[12]) #axis ration b/a
si = n.sin(pos_ang * n.pi / 180.0)
co = n.cos(pos_ang * n.pi / 180.0)
area = float(values[13])
maj_axis = float(values[14])#major axis of neighbour
eg = 1.0 - axis_rat
one_minus_eg_sq = (1.0-eg)**2.0
if(abs(xcntr_n - xcntr_o) < size/2.0 and \
abs(ycntr_n - ycntr_o) < size/2.0 and \
xcntr_n != xcntr_o and ycntr_n != ycntr_o and galflag == 1):
if((xcntr_o - xcntr_n) < 0):
xn = xcntr + abs(xcntr_n - xcntr_o)
if((ycntr_o - ycntr_n) < 0):
yn = ycntr + abs(ycntr_n - ycntr_o)
if((xcntr_o - xcntr_n) > 0):
xn = xcntr - (xcntr_o -xcntr_n)
if((ycntr_o - ycntr_n) > 0):
yn = ycntr - (ycntr_o -ycntr_n)
tx = (x - xn + 0.5) * co + (y - yn + 0.5) * si
ty = (xn - 0.5 -x) * si + (y - yn + 0.5) * co
R = n.sqrt(tx**2.0 + ty**2.0 / one_minus_eg_sq)
z[n.where(R<=mask_reg*maj_axis)] = 1
z1[n.where(R<=2*mask_reg*maj_axis)] = 1
if(abs(xcntr_n - xcntr_o) < size/2.0 and \
abs(ycntr_n - ycntr_o) < size/2.0 and galflag == 0):
if((xcntr_o - xntr_n) < 0):
xn = xcntr + abs(xcntr_n - xcntr_o)
if((ycntr_o - ycntr_n) < 0):
yn = ycntr + abs(ycntr_n - ycntr_o)
if((xcntr_o - xcntr_n) > 0):
xn = xcntr - (xcntr_o -xcntr_n)
if((ycntr_o - ycntr_n) > 0):
yn = ycntr - (ycntr_o -ycntr_n)
if(xcntr_n == xcntr_o and ycntr_n == ycntr_o):
xn = xcntr
yn = ycntr
tx = (x - xn + 0.5) * co + (y - yn + 0.5) * si
ty = (xn - 0.5 -x) * si + (y - yn + 0.5) * co
R = n.sqrt(tx**2.0 + ty**2.0 / one_minus_eg_sq)
z[n.where(R<=mask_reg*maj_axis*2.0)] = 1
z1[n.where(R<=2*mask_reg*maj_axis)] = 1
except:
i=1
if(galflag):
if exists("TmpElliMask1.fits"):
os.remove("TmpElliMask1.fits")
else:
pass
hdu = pyfits.PrimaryHDU(z1.astype(n.float32))
hdu.writeto("TmpElliMask1.fits")
hdu = pyfits.PrimaryHDU(z.astype(n.float32))
hdu.writeto("TmpElliMask.fits")
z = z + tmp_mask
z[n.where(z > 0)] = 1
hdu = pyfits.PrimaryHDU(z.astype(n.float32))
hdu.writeto(mask_file)
else:
try:
os.remove("BMask.fits")
except:
pass
hdu = pyfits.PrimaryHDU(z.astype(n.float32))
hdu.writeto("BMask.fits")