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!")
sys.exit(0)
rows = s[0]
cols = s[1]
num_patches=20
patchsize=100
#Estimate stain color bases + acceleration
Wi,i0=Wfast(I_obj,nstains,lamb,num_patches,patchsize,level)
if background_correction:
print ("Background intensity:",i0)
else:
i0 = np.array([255.,255.,255.])
print ("Background correction disabled, default background intensity assumed")
#Beer-Lambert tranformation
V,VforW=BLtrans(I,i0) #V=WH see in paper
Hiv=np.transpose(np.dot(np.linalg.pinv(Wi),np.transpose(V))) #Pseudo-inverse
Hiv[Hiv<0]=0
Hi=np.reshape(Hiv,(rows,cols,nstains))
#calculate the color image for each stain
sepstains = []
for i in range(nstains):
vdAS = np.reshape(Hiv[:,i],(rows*cols,1))*np.reshape(Wi[:,i],(1,3))
sepstains.append(np.uint8(i0*np.reshape(np.exp(-vdAS), (rows, cols, 3))))
return Wi,Hi,Hiv,sepstains
def run_batch_colornorm(filenames,
nstains,
lamb,
output_direc,
img_level,
background_correction=True,
config=None):
if config is None:
config = tf.ConfigProto(log_device_placement=False)
g_1 = tf.Graph()
with g_1.as_default():
Wis1 = tf.placeholder(tf.float32)
Img1 = tf.placeholder(tf.float32, shape=(None, None, 3))
src_i_0 = tf.placeholder(tf.float32)
s = tf.shape(Img1)
Img_vecd = tf.reshape(tf.minimum(Img1, src_i_0), [s[0] * s[1], s[2]])
V = tf.log(src_i_0 + 1.0) - tf.log(Img_vecd + 1.0)
Wi_inv = tf.transpose(tf.py_func(np.linalg.pinv, [Wis1], tf.float32))
Hiv1 = tf.nn.relu(tf.matmul(V, Wi_inv))
Wit1 = tf.placeholder(tf.float32)
Hiv2 = tf.placeholder(tf.float32)
sav_name = tf.placeholder(tf.string)
tar_i_0 = tf.placeholder(tf.float32)
normfac = tf.placeholder(tf.float32)
shape = tf.placeholder(tf.int32)
Hsonorm = Hiv2 * normfac
source_norm = tf.cast(
tar_i_0 * tf.exp(
(-1) * tf.reshape(tf.matmul(Hsonorm, Wit1), shape)), tf.uint8)
enc = tf.image.encode_png(source_norm)
fwrite = tf.write_file(sav_name, enc)
session1 = tf.Session(graph=g_1, config=config)
file_no = 0
print "To be normalized:", filenames[1:], "using", filenames[0]
for filename in filenames:
display_separator()
if background_correction:
correc = "back-correc"
else:
correc = "no-back-correc"
base_t = os.path.basename(filenames[0]) #target.svs
fname_t = os.path.splitext(base_t)[0] #target
base_s = os.path.basename(filename) #source.svs
fname_s = os.path.splitext(base_s)[0] #source
f_form = os.path.splitext(base_s)[1] #.svs
s = output_direc + base_s.replace(
".", "_") + " (using " + base_t.replace(
".", "_") + " " + correc + ").png"
# s=output_direc+base_s.replace(".", "_")+" (no-norm using "+base_t.replace(".", "_")+").png"
#s=output_direc+fname_s+"_normalized.png"
tic = time.time()
print
I = openslide.open_slide(filename)
if img_level >= I.level_count:
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)
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"
#parameters for W estimation
num_patches = 20
patchsize = 1000 #length of side of square
i0_default = np.array([255., 255., 255.], dtype=np.float32)
Wi, i0 = Wfast(I, nstains, lamb, num_patches, patchsize, level,
background_correction)
if i0 is None:
print "No white background detected"
i0 = i0_default
if not background_correction:
print "Background correction disabled, default background intensity assumed"
i0 = i0_default
if Wi is None:
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)
Wi = Wi.astype(np.float32)
if file_no == 0:
print "Target Color Basis Matrix:"
print Wi
Wi_target = np.transpose(Wi)
tar_i0 = i0
print "Target Image Background white intensity:", i0
else:
print "Source Color Basis Matrix:"
print Wi
print "Source Image Background white intensity:", i0
_max = 2000
print
if (xdim * ydim) <= (_max * _max):
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
})
# Hta_Rmax = np.percentile(Hiv,q=99.,axis=0)
H_Rmax = np.ones((nstains, ), dtype=np.float32)
for i in range(nstains):
t = Hiv[:, i]
H_Rmax[i] = np.percentile(t[t > 0], q=99., axis=0)
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)
display_separator()
continue
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)
display_separator()
else:
_maxtf = 3000
x_max = xdim
y_max = min(max(int(_maxtf * _maxtf / x_max), 1), ydim)
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))
count = 0
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)
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
})
if file_no == 0:
Hivt[ind:ind + len(Hiv), :] = Hiv
_Hta_Rmax = np.ones((nstains, ), dtype=np.float32)
for i in range(nstains):
t = Hiv[:, i]
_Hta_Rmax[i] = np.percentile(t[t > 0],
q=99.,
axis=0)
perc.append([_Hta_Rmax[0], _Hta_Rmax[1]])
ind += len(Hiv)
continue
else:
Hivs[ind:ind + len(Hiv), :] = Hiv
_Hso_Rmax = np.ones((nstains, ), dtype=np.float32)
for i in range(nstains):
t = Hiv[:, i]
_Hso_Rmax[i] = np.percentile(t[t > 0],
q=99.,
axis=0)
perc.append([_Hso_Rmax[0], _Hso_Rmax[1]])
ind += len(Hiv)
if file_no == 0:
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)
ind = 0
continue
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)
_normfac = np.divide(Hta_Rmax, Hso_Rmax).astype(np.float32)
print "Color Normalization in progress..."
count = 0
ind = 0
np_max = 1000
x_max = xdim
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..."
total = len(x_tl) * len(y_tl)
prev_progress = 0
for x in x_tl:
for y in y_tl:
count += 1
xx = min(x_max, xdim - x)
yy = min(y_max, ydim - y)
pix = xx * yy
sh = np.array([yy, xx, 3])
#Back projection into spatial intensity space (Inverse Beer-Lambert space)
sourcenorm[y:y + yy, x:x + xx, :3] = session1.run(
source_norm,
feed_dict={
Hiv2: np.array(Hivs[ind:ind + pix, :]),
Wit1: Wi_target,
normfac: _normfac,
shape: sh,
tar_i_0: tar_i0
})
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)
prev_progress = percent
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 + ").png"
print "Saving normalized image..."
cv2.imwrite(s, cv2.cvtColor(sourcenorm, cv2.COLOR_RGB2BGR))
del sourcenorm
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
if os.path.exists("H_target"):
os.remove("H_target")
if os.path.exists("H_source"):
os.remove("H_source")
if os.path.exists("wsi"):
os.remove("wsi")
session1.close()