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Made python3 compatible #6

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28 changes: 14 additions & 14 deletions Estimate_W.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,9 +37,9 @@ def Wfast(img,nstains,lamb,num_patches,patchsize,level,background_correction=Fal
I_percentile=90

if ydim*xdim>max_size:
print "Finding patches for W estimation:"
print("Finding patches for W estimation:")
for j in range(20):
#print "Patch Sampling Attempt:",i+1
#print("Patch Sampling Attempt:",i+1
initBias=int(math.ceil(patchsize/2)+1)
xx=np.array(range(initBias,xdim-initBias,patchsize))
yy=np.array(range(initBias,ydim-initBias,patchsize))
Expand All @@ -63,20 +63,20 @@ def Wfast(img,nstains,lamb,num_patches,patchsize,level,background_correction=Fal
break
patchsize=int(patchsize*0.95)
valid_inp=np.array(valid_inp)
print "Number of patches sampled for W estimation:", len(valid_inp)
print("Number of patches sampled for W estimation:", len(valid_inp))
else:
patch=np.asarray(img.read_region((0,0),level,(xdim,ydim)))
patch=patch[:,:,:3]
valid_inp=[]
valid_inp.append(patch)
white_pixels= patch[np.sum((patch>white_cutoff),axis=2)==3]
print "Image small enough...W estimation done using whole image"
print("Image small enough...W estimation done using whole image")

if background_correction:
print "Number of white pixels sampled",len(white_pixels)
print("Number of white pixels sampled",len(white_pixels))
if len(white_pixels)<min_num_white:
i0=np.array([255.0,255.0,255.0])
print "Not enough white pixels found, default background intensity assumed"
print("Not enough white pixels found, default background intensity assumed")
elif len(white_pixels)>0:
i0 = np.percentile(white_pixels,I_percentile,axis=0)[:3]
else:
Expand All @@ -101,7 +101,7 @@ def Wfast(img,nstains,lamb,num_patches,patchsize,level,background_correction=Fal
if WS.shape[0]==1:
Wsource=WS[0,:3,:]
else:
print "Median color basis of",len(WS),"patches used as actual color basis"
print("Median color basis of",len(WS),"patches used as actual color basis")
Wsource=np.zeros((3,nstains))
for k in range(nstains):
Wsource[:,k]=[np.median(WS[:,0,k]),np.median(WS[:,1,k]),np.median(WS[:,2,k])]
Expand All @@ -110,15 +110,15 @@ def Wfast(img,nstains,lamb,num_patches,patchsize,level,background_correction=Fal

if Wsource.sum()==0:
if patchsize*0.95<100:
print "No suitable patches found for learning W. Please relax constraints"
print("No suitable patches found for learning W. Please relax constraints")
return None #to prevent infinite recursion
else:
print "W estimation failed, matrix of all zeros found. Trying again..."
print("W estimation failed, matrix of all zeros found. Trying again...")
return Wfast(img,nstains,lamb,min(100,num_patches*1.5),int(patchsize_original*0.95),level)
else:
return Wsource,i0
else:
print "No suitable patches found for learning W. Please relax constraints"
print("No suitable patches found for learning W. Please relax constraints")
return None,None

def patch_Valid(patch,threshold):
Expand All @@ -131,15 +131,15 @@ def patch_Valid(patch,threshold):
temp = tempr*tempg*tempb
r,c = np.shape((temp))
prob= float(np.sum(temp))/float((r*c))
#print prob
#print(prob)
if prob>threshold:
return False
else:
return True

def W_sort(W):
# All sorting done such that first column is H, second column is E
# print W
# print(W)

method = 3

Expand All @@ -153,15 +153,15 @@ def W_sort(W):
# 3. Using r/b ratios of the vectors. H must have a larger value of r/b.
r_b_1 = W[0][0]/W[2][0]
r_b_2 = W[0][1]/W[2][1]
# print r_b_1, r_b_2
# print(r_b_1, r_b_2)
if r_b_1<r_b_2: #else no need to switch
W[:,[0, 1]] = W[:,[1, 0]]
elif method==4:
# 4. Using r-b values of the vectors. H must have a larger value of r-b.
# This is equivalent to comparing the ratios of e^(-r)/e^(-b)
r_b_1 = W[0][0]-W[2][0]
r_b_2 = W[0][1]-W[2][1]
# print r_b_1, r_b_2
# print(r_b_1, r_b_2)
if r_b_1<r_b_2: #else no need to switch
Wsource[:,[0, 1]] = Wsource[:,[1, 0]]

Expand Down
96 changes: 48 additions & 48 deletions Run_ColorNorm.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,7 @@ def run_batch_colornorm(filenames,nstains,lamb,output_direc,img_level,background


file_no=0
print "To be normalized:",filenames[1:],"using",filenames[0]
print("To be normalized:",filenames[1:],"using",filenames[0]
for filename in filenames:

display_separator()
Expand All @@ -101,22 +101,22 @@ def run_batch_colornorm(filenames,nstains,lamb,output_direc,img_level,background


tic=time.time()
print

print()
I = openslide.open_slide(filename)
if img_level>=I.level_count:
print "Level",img_level,"unavailable for image, proceeding with level 0"
print("Level",img_level,"unavailable for image, proceeding with level 0")
level=0
else:
level=img_level
xdim,ydim=I.level_dimensions[level]
ds=I.level_downsamples[level]

if file_no==0:
print "Target Stain Separation in progress:",filename,str(xdim)+str("x")+str(ydim)
print("Target Stain Separation in progress:",filename,str(xdim)+str("x")+str(ydim))
else:
print "Source Stain Separation in progress:",filename,str(xdim)+str("x")+str(ydim)
print "\t \t \t \t \t \t \t \t \t \t Time: 0"
print("Source Stain Separation in progress:",filename,str(xdim)+str("x")+str(ydim))
print("\t \t \t \t \t \t \t \t \t \t Time: 0")


#parameters for W estimation
Expand All @@ -127,40 +127,40 @@ def run_batch_colornorm(filenames,nstains,lamb,output_direc,img_level,background

Wi,i0 = Wfast(I,nstains,lamb,num_patches,patchsize,level,background_correction)
if i0 is None:
print "No white background detected"
print("No white background detected")
i0=i0_default

if not background_correction:
print "Background correction disabled, default background intensity assumed"
print("Background correction disabled, default background intensity assumed")
i0=i0_default

if Wi is None:
print "Color Basis Matrix Estimation failed...image normalization skipped"
print("Color Basis Matrix Estimation failed...image normalization skipped")
continue
print "W estimated",
print "\t \t \t \t \t \t Time since processing started:",round(time.time()-tic,3)
print("W estimated"),
print("\t \t \t \t \t \t Time since processing started:",round(time.time()-tic,3))
Wi=Wi.astype(np.float32)

if file_no==0:
print "Target Color Basis Matrix:"
print Wi
print "Target Color Basis Matrix Size:"
print Wi.shape
print("Target Color Basis Matrix:")
print(Wi)
print("Target Color Basis Matrix Size:")
print(Wi.shape)

Wi_target=np.transpose(Wi)
tar_i0=i0
print "Target Image Background white intensity:",i0
print("Target Image Background white intensity:",i0)
else:
print "Source Color Basis Matrix:"
print Wi
print("Source Color Basis Matrix:")
print(Wi)

print "Source Image Background white intensity:",i0
print("Source Image Background white intensity:",i0)

_max=2000
#raise valueError()
print
print()
if (xdim*ydim)<=(_max*_max):
print "Small image processing..."
print("Small image processing...")
img=np.asarray(I.read_region((0,0),level,(xdim,ydim)),dtype=np.float32)[:,:,:3]

Hiv=session1.run(Hiv1,feed_dict={Img1:img, Wis1: Wi,src_i_0:i0})
Expand All @@ -173,44 +173,44 @@ def run_batch_colornorm(filenames,nstains,lamb,output_direc,img_level,background
if file_no==0:
file_no+=1
Hta_Rmax = np.copy(H_Rmax)
print "Target H calculated",
print "\t \t \t \t \t \t \t Total Time:",round(time.time()-tic,3)
print("Target H calculated"),
print("\t \t \t \t \t \t \t Total Time:",round(time.time()-tic,3))
display_separator()
continue

print "Color Normalization in progress..."
print("Color Normalization in progress...")

norm_fac = np.divide(Hta_Rmax,H_Rmax).astype(np.float32)
session1.run(fwrite,feed_dict={shape:np.array(img.shape),Wit1: Wi_target,Hiv2:Hiv,sav_name:s,tar_i_0:tar_i0,normfac:norm_fac})

print "File written to:",s
print "\t \t \t \t \t \t \t \t \t \t Total Time:",round(time.time()-tic,3)
print("File written to:"+s)
print("\t \t \t \t \t \t \t \t \t \t Total Time:",round(time.time()-tic,3))
display_separator()

else:
_maxtf=2550#changed from initial 3000
x_max=xdim
y_max=min(max(int(_maxtf*_maxtf/x_max),1),ydim)
print "Large image processing..."
print("Large image processing...")
if file_no==0:
Hivt=np.memmap('H_target', dtype='float32', mode='w+', shape=(xdim*ydim,2))
else:
Hivs=np.memmap('H_source', dtype='float32', mode='w+', shape=(xdim*ydim,2))
sourcenorm=np.memmap('wsi', dtype='uint8', mode='w+', shape=(ydim,xdim,3))
x_tl = range(0,xdim,x_max)
y_tl = range(0,ydim,y_max)
print "WSI divided into",str(len(x_tl))+"x"+str(len(y_tl))
print("WSI divided into",str(len(x_tl))+"x"+str(len(y_tl)))
count=0
print "Patch-wise H calculation in progress..."
print("Patch-wise H calculation in progress...")
ind=0
perc=[]
for x in x_tl:
for y in y_tl:
count+=1
xx=min(x_max,xdim-x)
yy=min(y_max,ydim-y)
print "Processing:",count," patch size",str(xx)+"x"+str(yy),
print "\t \t Time since processing started:",round(time.time()-tic,3)
print("Processing:",count," patch size",str(xx)+"x"+str(yy)),
print("\t \t Time since processing started:",round(time.time()-tic,3))
img=np.asarray(I.read_region((int(ds*x),int(ds*y)),level,(xx,yy)),dtype=np.float32)[:,:,:3]

Hiv = session1.run(Hiv1,feed_dict={Img1:img, Wis1: Wi,src_i_0:i0})
Expand All @@ -233,23 +233,23 @@ def run_batch_colornorm(filenames,nstains,lamb,output_direc,img_level,background
ind+=len(Hiv)

if file_no==0:
print "Target H calculated",
print("Target H calculated"),
Hta_Rmax = np.percentile(np.array(perc),50,axis=0)
file_no+=1
del Hivt
print "\t \t \t \t \t Time since processing started:",round(time.time()-tic,3)
print("\t \t \t \t \t Time since processing started:",round(time.time()-tic,3))
ind=0
continue

print "Source H calculated",
print "\t \t \t \t \t Time since processing started:",round(time.time()-tic,3)
print("Source H calculated"),
print("\t \t \t \t \t Time since processing started:",round(time.time()-tic,3))
Hso_Rmax = np.percentile(np.array(perc),50,axis=0)
print "H Percentile calculated",
print "\t \t \t \t Time since processing started:",round(time.time()-tic,3)
print("H Percentile calculated"),
print("\t \t \t \t Time since processing started:",round(time.time()-tic,3))

_normfac=np.divide(Hta_Rmax,Hso_Rmax).astype(np.float32)

print "Color Normalization in progress..."
print("Color Normalization in progress...")
count=0
ind=0
np_max=1000
Expand All @@ -258,7 +258,7 @@ def run_batch_colornorm(filenames,nstains,lamb,output_direc,img_level,background
y_max=min(max(int(np_max*np_max/x_max),1),ydim)
x_tl = range(0,xdim,x_max)
y_tl = range(0,ydim,y_max)
print "Patch-wise color normalization in progress..."
print("Patch-wise color normalization in progress...")
total=len(x_tl)*len(y_tl)

prev_progress=0
Expand All @@ -277,21 +277,21 @@ def run_batch_colornorm(filenames,nstains,lamb,output_direc,img_level,background
ind+=pix
percent=5*int(count*20/total) #nearest 5 percent
if percent>prev_progress and percent<100:
print str(percent)+" percent complete...",
print "\t \t \t \t \t Time since processing started:",round(time.time()-tic,3)
print(str(percent)+" percent complete..."),
print("\t \t \t \t \t Time since processing started:",round(time.time()-tic,3))
prev_progress=percent
print "Color Normalization complete!",
print "\t \t \t \t Time since processing started:",round(time.time()-tic,3)
print("Color Normalization complete!"),
print("\t \t \t \t Time since processing started:",round(time.time()-tic,3))

p = time.time()-tic
s = output_direc+base_s.replace(".", "_")+" (using "+base_t.replace(".", "_")+" "+correc+").tif"
print "Saving normalized image..."
print("Saving normalized image...")
#cv2.imwrite(s,cv2.cvtColor(sourcenorm, cv2.COLOR_RGB2BGR))
pyimg = numpy2vips(sourcenorm)
pyimg.tiffsave(s, tile=True, compression='lzw',xres=5000,yres=5000, bigtiff=True,pyramid=True,Q=100)#xres and yres should be controlled to produce finer or coarser tif
del sourcenorm
print "File written to:",s
print "\t \t \t \t \t \t \t \t \t Total Time:",round(time.time()-tic,3)
print("File written to:"+s)
print("\t \t \t \t \t \t \t \t \t Total Time:",round(time.time()-tic,3))
display_separator()

file_no+=1
Expand All @@ -313,5 +313,5 @@ def run_colornorm(source_filename,target_filename,nstains,lamb,output_direc,leve


def display_separator():
print "________________________________________________________________________________________________"
print
print("________________________________________________________________________________________________")
print()
16 changes: 8 additions & 8 deletions Run_StainSep.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,21 +12,21 @@

def run_stainsep(filename,nstains,lamb,output_direc="",background_correction=True):

print
print "Running stain separation on:",filename
print()
print("Running stain separation on:",filename)

level=0

I = openslide.open_slide(filename)
xdim,ydim=I.level_dimensions[level]
img=np.asarray(I.read_region((0,0),level,(xdim,ydim)))[:,:,:3]

print "Fast stain separation is running...."
print("Fast stain separation is running....")
Wi,Hi,Hiv,stains=Faststainsep(I,img,nstains,lamb,level,background_correction)

#print "\t \t \t \t \t \t Time taken:",elapsed
#print("\t \t \t \t \t \t Time taken:",elapsed

print "Color Basis Matrix:\n",Wi
print("Color Basis Matrix:\n",Wi)

fname=os.path.splitext(os.path.basename(filename))[0]
cv2.imwrite(output_direc+fname+"-0_original.png",cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
Expand All @@ -38,7 +38,7 @@ def Faststainsep(I_obj,I,nstains,lamb,level,background_correction):
s=I.shape
ndimsI = len(s)
if ndimsI!=3:
print "Input Image I should be 3-dimensional!"
print("Input Image I should be 3-dimensional!")
sys.exit(0)
rows = s[0]
cols = s[1]
Expand All @@ -51,10 +51,10 @@ def Faststainsep(I_obj,I,nstains,lamb,level,background_correction):


if background_correction:
print "Background intensity:",i0
print("Background intensity:",i0)
else:
i0 = np.array([255.,255.,255.])
print "Background correction disabled, default background intensity assumed"
print("Background correction disabled, default background intensity assumed")

#Beer-Lambert tranformation
V,VforW=BLtrans(I,i0) #V=WH see in paper
Expand Down
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