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.make_mask.py
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#%%
"""original script "ms_data.py"
makes binary label with HE/IHC input image
"""
import numpy as np
from numpy import *
import cv2 as cv
import os, glob
import matplotlib.pyplot as plt
from skimage.color import rgb2hed, hed2rgb,separate_stains, combine_stains
from skimage.exposure import rescale_intensity
from matplotlib.colors import LinearSegmentedColormap
from skimage import data
from mpl_toolkits.mplot3d.axes3d import Axes3D #--- For 3D plot
from skimage.exposure import rescale_intensity
from cv2 import ximgproc
from pathlib import Path
size = 256
zoom = "1.0"
dpl = {"chips": "*HE*%d*%s/"%(size, zoom),
"ihcs": "*IHC*%d*%s/"%(size, zoom)}
# %%
H_DAB = array([
[0.65,0.70,0.29],
[0.07, 0.99, 0.11],
[0.27,0.57,0.78]
])
H_he = H_DAB.copy()
H_he[2,:] = np.cross(H_DAB[0,:], H_DAB[1,:])
H_ki67 = H_DAB.copy()
H_ki67[1,:] = np.cross(H_DAB[0,:], H_DAB[2,:])
cmap_hema = LinearSegmentedColormap.from_list('mycmap', ['white', 'navy'])
cmap_eosin = LinearSegmentedColormap.from_list('mycmap', ['white','darkviolet'])
cmap_dab = LinearSegmentedColormap.from_list('mycmap', ['white','saddlebrown'])
print("Trans. H.E", H_he)
print("Trans. Ki67", H_ki67)
#%%
def reject_outliers(data, m=3):
dt = data.copy()
dt[(abs(data - np.mean(data)) > m * np.std(data))] = np.mean(data)
return dt
def in_range(d):
return (0, np.max(cv.GaussianBlur(d.copy(), (3,3), 0)))
def flip8(imname):
imSrc = cv.imread(imname)
for kr in range(4):
for pr in range(2):
if kr+pr==0: continue
im = imSrc
for k in range(kr):
im = cv.rotate(im, cv.ROTATE_90_CLOCKWISE)
if pr != 0:
im = cv.flip(im, flipCode=1)
# plt.imshow(im)
# plt.title("r%dp%d"%(kr*90, pr))
# plt.show()
cv.imwrite(imname[:-4] +"[r%dp%d].tif"%(kr*90, pr), im)
return 0
def norm_by_row(M):
for k in range(M.shape[1]):
M[k,:] /= np.sqrt(np.sum(np.power(M[k,:],2)))
return M
def showbychan(im_ihc):
for k in range(3):
plt.figure()
plt.imshow(im_ihc[:, :, k], cmap="gray")
def rgbdeconv(rgb, conv_matrix, C=0):
rgb = rgb.copy().astype(float)
rgb += C
stains = np.reshape(-np.log10(rgb), (-1, 3)) @ conv_matrix
return np.reshape(stains, rgb.shape)
def hecconv(stains, conv_matrix, C=0):
# from skimage.exposure import rescale_intensity
stains = stains.astype(float)
logrgb2 = -np.reshape(stains, (-1, 3)) @ conv_matrix
rgb2 = np.power(10, logrgb2)
return np.reshape(rgb2 - C, stains.shape)
def surf(matIn, name="fig", div = (50, 50), SIZE = (8, 6)):
x = np.arange(0, matIn.shape[0])
y = np.arange(0, matIn.shape[1])
x, y = np.meshgrid(y, x)
fig = plt.figure(figsize = SIZE)
ax = Axes3D(fig)
ax.plot_surface(x, y, matIn, rstride=div[0], cstride=div[1], cmap='jet')
plt.title(name)
plt.show()
for dp in dpl.keys():
for imname in glob.glob(os.path.join(dpl[dp], '*.tif')):
if dp == "chips":
continue
im_hex = cv.imread(imname, cv.CV_32F)
H = H_he
Hinv = linalg.inv(norm_by_row(H))
img = cv.cvtColor(im_hex, cv.COLOR_BGR2RGB)/255.
img[img==0] = 1E-6
im_sepa_hex=abs(rgbdeconv(img, Hinv))
h = im_sepa_hex[:,:,0];e = im_sepa_hex[:,:,1];d_r = im_sepa_hex[:,:,2];
# fig = plt.figure(figsize=(20,20));
# plt.subplot(221);plt.imshow(img);plt.title("Input")
# axis=plt.subplot(222);plt.imshow(rescale_intensity(h, in_range=in_range(h)), cmap=cmap_hema);plt.title("Hema."),axis.axis('off')
# plt.subplot(223);plt.imshow(rescale_intensity(e, in_range=in_range(e)), cmap=cmap_eosin);plt.title("Eosin")
# plt.subplot(224);plt.imshow(rescale_intensity(d_r, in_range=in_range(d_r)), cmap=cmap_dab);plt.title("Residual")
# fig.tight_layout()
# # print(im_sepa_hex)
# surf(d_r, "residual", div=(50,50))
h1 = rescale_intensity(h, in_range=in_range(reject_outliers(h)))
e1 = rescale_intensity(h, in_range=in_range(reject_outliers(e)))
# plt.figure(figsize=(10,10));plt.imshow(h1, cmap=cmap_hema)
# surf(h1, "Rescaled Hematoxylin")
# plt.figure(figsize=(10,10));plt.imshow(h1>0.6, cmap="gray")
# imci=abs(hecconv(im_sepa_hex, H))
# plt.figure(figsize = (10,10));
# plt.imshow(imci)
# surf(hmod, "Pseudo DAB surf.", div=(50,50))
lbl = np.dstack([h1, e1])
surf(h1, "H1")
surf(e1, "e1")
lbl_toshow = np.dstack([lbl, zeros_like(h1)])
# plt.figure(figsize=(10,10));plt.imshow(lbl_toshow)
# plt.show()
# plt.figure();plt.hist(h.reshape(-1,), 50)
# plt.figure();plt.hist(e.reshape(-1,), 50)
plt.imsave("lb.png", lbl_toshow)
elif dp =="ihcs":
# ## 实验：ki67染色解组及重组
H = H_ki67
Hinv = linalg.inv(norm_by_row(H))
im_ki67 = cv.imread(imname, cv.CV_32F)
img = cv.cvtColor(im_ki67, cv.COLOR_BGR2RGB)/255
img[img==0] = 1E-6
im_sepa_ki67=abs(rgbdeconv(img, Hinv))
h = im_sepa_ki67[:,:,0];e_r = im_sepa_ki67[:,:,1];d = im_sepa_ki67[:,:,2];
# fig = plt.figure(figsize=(20,20));
# plt.subplot(221);plt.imshow(img);plt.title("Input")
# plt.subplot(222);plt.imshow(rescale_intensity(h, in_range=in_range(h)), cmap=cmap_hema);plt.title("Hema.")
# plt.subplot(223);plt.imshow(rescale_intensity(e_r, in_range=in_range(e_r)), cmap=cmap_eosin);plt.title("Residual (Eosin)")
# plt.subplot(224);plt.imshow(rescale_intensity(d, in_range=in_range(d)), cmap=cmap_dab);plt.title("DAB")
# fig.tight_layout()
# surf(e_r, "Residual (Eosin)")
# surf(d, "DAB")
# plt.imshow(rescale_intensity(d, in_range=in_range(d)), cmap="gray")
# h1 = rescale_intensity(h, in_range=in_range(reject_outliers(h)))
# d1 = rescale_intensity(d, in_range=in_range(reject_outliers(d)))
h1= h
d1 =d
d2 = (d1*255).astype(uint8)
guide = d2
geps = 1000; grad=10
guidedDAB = ximgproc.guidedFilter(guide=guide, src=d2, radius=grad, eps=geps, dDepth=-1)
gd = ximgproc.guidedFilter(guide=guide, src=d2, radius=grad, eps=geps, dDepth=-1)
# gd = rescale_intensity(gd, in_range=in_range(gd))
guidedDAB = guidedDAB * (guidedDAB > 255*0.2)
_,dmask = cv.threshold(guidedDAB,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)
guidedDAB = ((dmask) > 0).astype(uint8)
# plt.figure(figsize=(20,10));plt.subplot(121);plt.imshow(gd, cmap="gray");plt.axis('off')
MOP_SIZE, MCL_SIZE = 5, 5
guidedDAB = cv.morphologyEx(guidedDAB, op=cv.MORPH_OPEN, kernel=np.ones((MOP_SIZE,MOP_SIZE), uint8))
guidedDAB = cv.morphologyEx(guidedDAB, op=cv.MORPH_CLOSE, kernel=np.ones((MCL_SIZE, MCL_SIZE), uint8))
# plt.subplot(122);plt.imshow(guidedDAB, cmap = "gray");plt.axis('off');plt.tight_layout()
# h1 = rescale_intensity(h,
# in_range=in_range(reject_outliers(h)))
# d1 = rescale_intensity(d, in_range(reject_outliers(d)))
# %%
# plt.figure(figsize=(10,10));
lbl = np.dstack([h1, guidedDAB*d1])
lbl_toshow = np.dstack([lbl, zeros_like(h1)])
# plt.imshow(lbl_toshow)
# plt.figure();plt.hist(h.reshape(-1,), 50)
# plt.figure();plt.hist(d.reshape(-1,), 50)
plt.show()
bs = Path(imname).stem
if len(np.unique(gd.reshape(-1))) < 5: # quantitize noise
gd *= 0
guidedDAB *= 0
plt.imsave("Label_%d/"%size + bs + ".png", gd, cmap="gray")
plt.imsave("Mask_%d/"%size + bs + ".png", guidedDAB, cmap="gray")