def select_method(Method):
'''
Selects a method that returns the column averages for an array A.
'''
if Method.lower() == "mean":
func = lambda A: np.average(A, axis=0)
elif Method.lower() == "median":
func = lambda A: np.median(A, axis=0)
elif Method.lower() == "mode":
func = lambda A: mode(A, axis=0)[0]
elif Method.lower() == "frobustmean":
func = lambda A: centroid.frobomad(A, axis=0)[0]
elif Method.lower() == "robustmean":
func = lambda A: chzphot.robomad(A)[0]
elif Method.lower() == "g******g":
func = lambda A: np.average(np.vstack(
(np.median(A, axis=0), centroid.frobomad(A, axis=0)[0],
mode(A, axis=0)[0], np.average(A, axis=0))),
axis=0)
else: # Use Kevin's Frobomad
print "ERROR!!! Invalid normalization method input. Please try using"
print "mean, median, mode, robustmean, or g******g."
print "Just using the Robust Mean By Default"
print ""
func = lambda A: centroid.frobomad(A, axis=0)[0]
return func
def FindNormFactor(target,imgArray,Method="Mean",Scalar=False):
"""
Purpose: Find the normilization vector to apply to the image
"""
rows,cols = imgArray.shape
norm_factor = []
for col in range(0,cols):
if Method.lower() == "mean":
norm_factor.append(target/np.mean(imgArray[:,col]))
elif Method.lower() == "median":
norm_factor.append(target/np.median(imgArray[:,col]))
elif Method.lower() == "mode":
norm_factor.append(target/mode(imgArray[:,col])[0])
elif Method.lower() == "robustmean":
norm_factor.append(target/centroid.frobomad(imgArray[:,col])[0])
elif Method.lower() == "g******g":
norm_factor.append(target/np.mean([np.median(imgArray[:,col]),centroid.frobomad(imgArray[:,col])[0],np.mean(imgArray[:,col]),mode(imgArray[:,col])[0]]))
else: # Use Kevin's Frobomad
print "ERROR!!! Invalid normalization method input. Please try using"
print "mean"
print "median"
print "mode"
print "robustmean"
print "g******g"
print "Just using the Robust Mean By Default"
print ""
norm_factor.append(target/centroid.frobomad(imgArray[:,col])[0])
return norm_factor
def FindNormFactor(target, imgArray, Method="Mean", Scalar=False):
"""
Purpose: Find the normilization vector to apply to the image
"""
rows, cols = imgArray.shape
norm_factor = []
for col in range(0, cols):
if Method.lower() == "mean":
norm_factor.append(target / np.mean(imgArray[:, col]))
elif Method.lower() == "median":
norm_factor.append(target / np.median(imgArray[:, col]))
elif Method.lower() == "mode":
norm_factor.append(target / mode(imgArray[:, col])[0])
elif Method.lower() == "robustmean":
norm_factor.append(target / centroid.frobomad(imgArray[:, col])[0])
elif Method.lower() == "g******g":
norm_factor.append(target / np.mean([
np.median(imgArray[:, col]),
centroid.frobomad(imgArray[:, col])[0],
np.mean(imgArray[:, col]),
mode(imgArray[:, col])[0]
]))
else: # Use Kevin's Frobomad
print "ERROR!!! Invalid normalization method input. Please try using"
print "mean"
print "median"
print "mode"
print "robustmean"
print "g******g"
print "Just using the Robust Mean By Default"
print ""
norm_factor.append(target / centroid.frobomad(imgArray[:, col])[0])
return norm_factor
def PlotComparison(old_img, new_img, title="Title"):
"""
Purpose: Generate a 2x2 plot showing image statistics to compare before/after
gain normalization.
Inputs: -old_img {numpy array}- Original pre-normalized image
-old_img {numpy array}- Original pre-normalized image
"""
rows, cols = new_img.shape
oldstd = []
newstd = []
oldmed = []
newmed = []
for col in range(0, cols):
oldstd.append(np.std(old_img[:, col]))
newstd.append(np.std(new_img[:, col]))
oldmed.append(centroid.frobomad(old_img[:, col])[0])
newmed.append(centroid.frobomad(new_img[:, col])[0])
randcol = pylab.randint(0, cols, 500)
randrow = pylab.randint(0, rows, 500)
pylab.figure(num=None,
figsize=(13, 7),
dpi=80,
facecolor='w',
edgecolor='k')
pylab.title(title)
#Standard Deviation Comparison
pylab.subplot(2, 2, 1)
pylab.plot(oldstd)
pylab.plot(newstd)
pylab.legend(['Before Normalization \sigma', 'After Normalization \sigma'])
pylab.xlabel('Column')
pylab.ylabel('USELESS Standard Deviation')
pylab.subplot(2, 2, 3)
pylab.plot(oldmed)
pylab.plot(newmed)
pylab.legend(['Before Normalization Rmean', 'After Normalization Rmean'])
pylab.xlabel('Column')
pylab.ylabel('Robust Mean')
#fourrier signal
pylab.subplot(2, 2, 2)
pylab.hist(old_img[randrow, randcol], bins=75)
pylab.xlabel('Hist')
pylab.ylabel('Old Rand Selection Intensity')
pylab.subplot(2, 2, 4)
pylab.hist(new_img[randrow, randcol], bins=75)
pylab.xlabel('Hist')
pylab.ylabel('New Rand Selection Intensity')
def PlotComparison(old_img,new_img,title="Title"):
"""
Purpose: Generate a 2x2 plot showing image statistics to compare before/after
gain normalization.
Inputs: -old_img {numpy array}- Original pre-normalized image
-old_img {numpy array}- Original pre-normalized image
"""
rows,cols=new_img.shape
oldstd=[]
newstd=[]
oldmed=[]
newmed=[]
for col in range(0,cols):
oldstd.append(np.std(old_img[:,col]))
newstd.append(np.std(new_img[:,col]))
oldmed.append(centroid.frobomad(old_img[:,col])[0])
newmed.append(centroid.frobomad(new_img[:,col])[0])
randcol=pylab.randint(0,cols,500)
randrow=pylab.randint(0,rows,500)
pylab.figure(num=None, figsize=(13, 7), dpi=80, facecolor='w', edgecolor='k')
pylab.title(title)
#Standard Deviation Comparison
pylab.subplot(2,2,1)
pylab.plot(oldstd)
pylab.plot(newstd)
pylab.legend(['Before Normalization \sigma','After Normalization \sigma'])
pylab.xlabel('Column')
pylab.ylabel('USELESS Standard Deviation')
pylab.subplot(2,2,3)
pylab.plot(oldmed)
pylab.plot(newmed)
pylab.legend(['Before Normalization Rmean','After Normalization Rmean'])
pylab.xlabel('Column')
pylab.ylabel('Robust Mean')
#fourrier signal
pylab.subplot(2,2,2)
pylab.hist(old_img[randrow,randcol],bins=75)
pylab.xlabel('Hist')
pylab.ylabel('Old Rand Selection Intensity')
pylab.subplot(2,2,4)
pylab.hist(new_img[randrow,randcol],bins=75)
pylab.xlabel('Hist')
pylab.ylabel('New Rand Selection Intensity')
def select_method(Method):
'''
Selects a method that returns the column averages for an array A.
'''
if Method.lower() == "mean":
func = lambda A: np.average(A, axis=0)
elif Method.lower() == "median":
func = lambda A: np.median(A, axis=0)
elif Method.lower() == "mode":
func = lambda A: mode(A, axis=0)[0]
elif Method.lower() == "frobustmean":
func = lambda A: centroid.frobomad(A, axis=0)[0]
elif Method.lower() == "robustmean":
func = lambda A: chzphot.robomad(A)[0]
elif Method.lower() == "g******g":
func = lambda A: np.average(np.vstack(( np.median(A, axis=0),
centroid.frobomad(A, axis=0)[0],
mode(A, axis=0)[0],
np.average(A, axis=0) )),
axis=0)
else: # Use Kevin's Frobomad
print "ERROR!!! Invalid normalization method input. Please try using"
print "mean, median, mode, robustmean, or g******g."
print "Just using the Robust Mean By Default"
print ""
func = lambda A: centroid.frobomad(A, axis=0)[0]
return func
def DarkColNormalize(imgArray,top=0,target=None,Plot=0,Method="Mean"):
"""
Purpose: Normalize image array based on dark columns.
Inputs: imgArray -numpy array- image array
top -bool {optional}- Indicates if this is a "top" oriented image array. For top sensor half,
dark rows come after the information image array. Otherwise, this assumes the dark rows
come first.
"""
rows,cols = imgArray.shape
# print "rows",rows,"cols",cols
if top == 0:
darkrows = imgArray[0:16,:]
else:
darkrows = imgArray[rows-16:rows,:]
# Get target normalization (which is median of the top or bottom half including dark rows))
if target is None:
target = centroid.frobomad(imgArray)[0]
# Get normalization factor
norm_factor=FindNormFactor(target,darkrows,Method=Method)
# Apply Normalization Factor
new_imgArray = imgArray*norm_factor
# IF assuming BIAS, not GAIN
# new_imgArray = imgArray.copy()
# for col in range(0,cols):
# new_imgArray[:,col] = np.subtract(imgArray[:,col],np.mean(darkrows[:,col]))
if Plot:
if top:
PlotComparison(imgArray,new_imgArray,title="Top Sensor Half")
else:
PlotComparison(imgArray,new_imgArray,title="Bottom Sensor Half")
# Cut off dark rows and columns and return
if top:
final_image = new_imgArray[0:1080][:,16:2560+16]
else:
final_image = new_imgArray[16:1080+16][:,16:2560+16]
return final_image
def DarkColNormalize(imgArray, top=0, target=None, Plot=0, Method="Mean"):
"""
Purpose: Normalize image array based on dark columns.
Inputs: imgArray -numpy array- image array
top -bool {optional}- Indicates if this is a "top" oriented image array. For top sensor half,
dark rows come after the information image array. Otherwise, this assumes the dark rows
come first.
"""
rows, cols = imgArray.shape
# print "rows",rows,"cols",cols
if top == 0:
darkrows = imgArray[0:16, :]
else:
darkrows = imgArray[rows - 16:rows, :]
# Get target normalization (which is median of the top or bottom half including dark rows))
if target is None:
target = centroid.frobomad(imgArray)[0]
# Get normalization factor
norm_factor = FindNormFactor(target, darkrows, Method=Method)
# Apply Normalization Factor
new_imgArray = imgArray * norm_factor
# IF assuming BIAS, not GAIN
# new_imgArray = imgArray.copy()
# for col in range(0,cols):
# new_imgArray[:,col] = np.subtract(imgArray[:,col],np.mean(darkrows[:,col]))
if Plot:
if top:
PlotComparison(imgArray, new_imgArray, title="Top Sensor Half")
else:
PlotComparison(imgArray, new_imgArray, title="Bottom Sensor Half")
# Cut off dark rows and columns and return
if top:
final_image = new_imgArray[0:1080][:, 16:2560 + 16]
else:
final_image = new_imgArray[16:1080 + 16][:, 16:2560 + 16]
return final_image
def ImgColNormalize(imgArray,target=None, Rows=0,Method="mean"):
"THIS WONT WORK UNLESS LOOKING AT SOMETHING FLAT"
"""
Purpose: Normalize image array based on dark columns.
Inputs: imgArray -numpy array- image array
target -desired norm level {optional}- Set this to the gain you wished the image normalized to. Otherwise
the overall image median is used by default.
"""
rows,cols = imgArray.shape
# Get target normalization
if target is None:
target = centroid.frobomad(imgArray[800:1200,700:1300])[0]
# Get normalization factor
norm_factor=FindNormFactor(target,imgArray,Method=Method)
# Apply Normalization Factor
new_imgArray = imgArray*norm_factor
return new_imgArray
def ImgColNormalize(imgArray, target=None, Rows=0, Method="mean"):
"THIS WONT WORK UNLESS LOOKING AT SOMETHING FLAT"
"""
Purpose: Normalize image array based on dark columns.
Inputs: imgArray -numpy array- image array
target -desired norm level {optional}- Set this to the gain you wished the image normalized to. Otherwise
the overall image median is used by default.
"""
rows, cols = imgArray.shape
# Get target normalization
if target is None:
target = centroid.frobomad(imgArray[800:1200, 700:1300])[0]
# Get normalization factor
norm_factor = FindNormFactor(target, imgArray, Method=Method)
# Apply Normalization Factor
new_imgArray = imgArray * norm_factor
return new_imgArray
def ImgNormalize(imgArray, bg=None, Method="mean", source="image"):
"""
Purpose: Normalize an image based on column averages. "bg" is the image
background value, and is computed by frobomad if left unspecified. The
default method is "mean." Image values are used by default, unless "source"
is set to "dark."
Timing: For one image, **just** finding the column values takes:
mean = 0.05 sec
median = 0.25 sec
frobomad = 1.6 sec
mode = 15 sec
g******g = 15 sec
"""
# Deepcopy
img = np.int16( cp.deepcopy(imgArray) )
# Get size of the image
[ysize, xsize] = img.shape
middle = int(ysize/2)
# Get column value method
func = select_method(Method)
# Separate dark rows and image
dark = None
if (ysize, xsize) == (2192,2592):
# Delete dark columns on the left/right sides of the image (16 cols)
img = img[:, 16:xsize-16]
# Separate the dark rows from the image and update middle
dark = img[middle-16:middle+16]
img = img[np.r_[0:middle-16, middle+16:ysize] ]
middle -= 16;
# Use dark rows for column data
if source == "dark":
if dark is None:
raise RuntimeError('Image must be uncropped to use source="dark"')
else:
topCol = func(dark[:16])
botCol = func(dark[16:])
elif source == "image":
topCol = func(img[:middle])
botCol = func(img[middle:])
else:
raise RuntimeError('Source must be image or dark')
# Find top and bottom bg
if bg is None:
bg = centroid.frobomad(img[:middle])[0]
# Apply column averages to image
# top= img[:middle]*bg[0]/np.tile(topCol, (middle,1))
# bottom=img[middle:]* bg[1]/np.tile(botCol, (middle,1))
# img=np.append(top,bottom,0)
img[:middle] = img[:middle]*bg/np.tile(topCol, (middle,1))
img[middle:] = img[middle:]*bg/np.tile(botCol, (middle,1))
# return subtracted image
return img
def ImgNormalize(imgArray, bg=None, Method="mean", source="image"):
"""
Purpose: Normalize an image based on column averages. "bg" is the image
background value, and is computed by frobomad if left unspecified. The
default method is "mean." Image values are used by default, unless "source"
is set to "dark."
Timing: For one image, **just** finding the column values takes:
mean = 0.05 sec
median = 0.25 sec
frobomad = 1.6 sec
mode = 15 sec
g******g = 15 sec
"""
# Deepcopy
img = np.int16(cp.deepcopy(imgArray))
# Get size of the image
[ysize, xsize] = img.shape
middle = int(ysize / 2)
# Get column value method
func = select_method(Method)
# Separate dark rows and image
dark = None
if (ysize, xsize) == (2192, 2592):
# Delete dark columns on the left/right sides of the image (16 cols)
img = img[:, 16:xsize - 16]
# Separate the dark rows from the image and update middle
dark = img[middle - 16:middle + 16]
img = img[np.r_[0:middle - 16, middle + 16:ysize]]
middle -= 16
# Use dark rows for column data
if source == "dark":
if dark is None:
raise RuntimeError('Image must be uncropped to use source="dark"')
else:
topCol = func(dark[:16])
botCol = func(dark[16:])
elif source == "image":
topCol = func(img[:middle])
botCol = func(img[middle:])
else:
raise RuntimeError('Source must be image or dark')
# Find top and bottom bg
if bg is None:
bg = centroid.frobomad(img[:middle])[0]
# Apply column averages to image
# top= img[:middle]*bg[0]/np.tile(topCol, (middle,1))
# bottom=img[middle:]* bg[1]/np.tile(botCol, (middle,1))
# img=np.append(top,bottom,0)
img[:middle] = img[:middle] * bg / np.tile(topCol, (middle, 1))
img[middle:] = img[middle:] * bg / np.tile(botCol, (middle, 1))
# return subtracted image
return img
