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helper.py
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import os
import numpy as np
import nibabel as nib
from nibabel.testing import data_path
import pickle
from PIL import Image
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-white')
import matplotlib as mpl
mpl.rcParams['image.cmap'] = 'viridis'
mpl.rcParams['axes.grid'] = False
#%%
def get_slice(data_3d, view_index, slice_index):
if view_index == 0:
return copy.deepcopy(data_3d[slice_index,:,:])
if view_index == 1:
return copy.deepcopy(data_3d[:,slice_index,:])
if view_index == 2:
return copy.deepcopy(data_3d[:,:,slice_index])
def show(image):
plt.figure()
plt.imshow(image)
plt.colorbar()
def get_X(data, view_index):
"""
get coordinates on 2D plane
"""
if len(data.shape) == 2:
ny, nx = (data.shape[0], data.shape[1])
x = np.linspace(0, 1, nx)
y = np.linspace(0, 1, ny)
xv, yv = np.meshgrid(x, y)
X = np.vstack((xv.flatten(), yv.flatten())).T
return X
if view_index == 0:
ny, nx = (data.shape[1], data.shape[2])
if view_index == 1:
ny, nx = (data.shape[0], data.shape[2])
if view_index == 2:
ny, nx = (data.shape[0], data.shape[1])
x = np.linspace(0, 1, nx)
y = np.linspace(0, 1, ny)
xv, yv = np.meshgrid(x, y)
X = np.vstack((xv.flatten(), yv.flatten())).T
return X
def plot_cut2(data):
line_dense = sns.distplot(data.flatten(),
hist=False,
kde_kws={"color": 'k',"alpha":0.8}).get_lines()[-1].get_data()
density_percentage = 0.01
density_cut = density_percentage*max(line_dense[1])
inten_cut = max(line_dense[0][line_dense[1]>density_cut])
return inten_cut
def get_data_normed(data, inten_cut):
print('>>> min/max before norm: ',data.min(),data.max())
print('>>> intensity cut:', inten_cut)
data[data>inten_cut] = inten_cut
## data = (data-data.mean()) / data.std() # do we need this?
print('>>> min/max after norm: ',data.min(),data.max())
data = (data-data.min()) / (data.max()-data.min())
return data
def norm_slice(image):
return (image-image.min()) / (image.max()-image.min())
#%%
import copy
def get_score_map(view_index, slice_index, class_index):
if view_index == 0:
if class_index == 0:
return copy.deepcopy(score_map0_0[slice_index,:,:])
if class_index == 1:
return copy.deepcopy(score_map0_1[slice_index,:,:])
if class_index == 2:
return copy.deepcopy(score_map0_2[slice_index,:,:])
if view_index == 1:
if class_index == 0:
return copy.deepcopy(score_map1_0[:,slice_index,:])
if class_index == 1:
return copy.deepcopy(score_map1_1[:,slice_index,:])
if class_index == 2:
return copy.deepcopy(score_map1_2[:,slice_index,:])
if view_index == 2:
if class_index == 0:
return copy.deepcopy(score_map2_0[:,:,slice_index])
if class_index == 1:
return copy.deepcopy(score_map2_1[:,:,slice_index])
if class_index == 2:
return copy.deepcopy(score_map2_2[:,:,slice_index])
def get_score_map_add(view_index, slice_index, class_index):
"""
add mode
"""
if view_index == 0:
if class_index == 0:
return copy.deepcopy(score_map_add_0[slice_index,:,:])
if class_index == 1:
return copy.deepcopy(score_map_add_1[slice_index,:,:])
if class_index == 2:
return copy.deepcopy(score_map_add_2[slice_index,:,:])
if view_index == 1:
if class_index == 0:
return copy.deepcopy(score_map_add_0[:,slice_index,:])
if class_index == 1:
return copy.deepcopy(score_map_add_1[:,slice_index,:])
if class_index == 2:
return copy.deepcopy(score_map_add_2[:,slice_index,:])
if view_index == 2:
if class_index == 0:
return copy.deepcopy(score_map_add_0[:,:,slice_index])
if class_index == 1:
return copy.deepcopy(score_map_add_1[:,:,slice_index])
if class_index == 2:
return copy.deepcopy(score_map_add_2[:,:,slice_index])
#%%
from matplotlib.patches import Rectangle
import matplotlib.patches as patches
def show_raw_img(intensity, pat_index,view_index,slice_index,corr=None,show=True):
# show raw image with patch box
img = intensity
if show:
fig,ax = plt.subplots(1)
ax.imshow(img)
if corr is not None:
rect = patches.Rectangle((corr[0],corr[1]),corr[-2],corr[-1],
linewidth=1,edgecolor='r',facecolor='none')
ax.add_patch(rect)
plt.show()
#%%
def show_subplots(sub_list):
"""
show several subplots
"""
L = len(sub_list)
fig = plt.figure(figsize=(4*L, 4))
for i in range(L):
plt.subplot(1,L,i+1)
plt.imshow(sub_list[i])
plt.colorbar()
#%%
def Sfun(array):
y = (1 / (1 + np.exp(-array*6)) - 0.5) * 2
return y
def Sfun_backup(array):
"""
this might be right
"""
return (1 / (1 + np.exp(-array*6)) - 0.5) * 2
def show_Sfun():
x = np.linspace(0, 1, num=50)
plt.figure()
plt.plot(x, Sfun(x))
plt.grid()
plt.title('S function')
plt.xlabel('x')
plt.ylabel('y')
#%%
inten_cut = {
0: 2121.4978945974694,
1: 1577.0722181546216,
2: 2179.357768170086,
3: 1144.9356572412464,
4: 1642.8813878339804,
5: 1978.4297697927975,
6: 1954.6497703789728,
7: 1995.6486857980428,
8: 1409.5082685065245,
9: 2108.6989414349923,
10: 975.421704067435,
11: 1890.8242649674153,
12: 1333.9869781783125,
13: 1502.899109327496,
14: 1537.8815429635738,
15: 1454.23099043816,
16: 1240.4368168986562,
17: 361.41670132471705,
18: 478.9789280065819,
19: 531.4438227653775
}
#%%
def show_3view3label(view_index, slice_index):
fig = plt.figure(figsize=(13, 9))
# view 0: ---------- ---------- ----------
plt.subplot(4,3,1)
if view_index == 2:
plt.imshow(score_map0_0[:,:,slice_index])
if view_index == 1:
plt.imshow(score_map0_0[:,slice_index,:])
if view_index == 0:
plt.imshow(score_map0_0[slice_index,:,:])
plt.title('view 0: blood')
plt.subplot(4,3,2)
if view_index == 2:
plt.imshow(score_map0_1[:,:,slice_index])
if view_index == 1:
plt.imshow(score_map0_1[:,slice_index,:])
if view_index == 0:
plt.imshow(score_map0_1[slice_index,:,:])
plt.title('view 0: muscle')
plt.subplot(4,3,3)
if view_index == 2:
plt.imshow(score_map0_2[:,:,slice_index])
if view_index == 1:
plt.imshow(score_map0_2[:,slice_index,:])
if view_index == 0:
plt.imshow(score_map0_2[slice_index,:,:])
plt.title('view 0: background')
# view 1: ---------- ---------- ----------
plt.subplot(4,3,4)
if view_index == 2:
plt.imshow(score_map1_0[:,:,slice_index])
if view_index == 1:
plt.imshow(score_map1_0[:,slice_index,:])
if view_index == 0:
plt.imshow(score_map1_0[slice_index,:,:])
plt.title('view 1: blood')
plt.subplot(4,3,5)
if view_index == 2:
plt.imshow(score_map1_1[:,:,slice_index])
if view_index == 1:
plt.imshow(score_map1_1[:,slice_index,:])
if view_index == 0:
plt.imshow(score_map1_1[slice_index,:,:])
plt.title('view 1: muscle')
plt.subplot(4,3,6)
if view_index == 2:
plt.imshow(score_map1_2[:,:,slice_index])
if view_index == 1:
plt.imshow(score_map1_2[:,slice_index,:])
if view_index == 0:
plt.imshow(score_map1_2[slice_index,:,:])
plt.title('view 1: background')
# view 2: ---------- ---------- ----------
plt.subplot(4,3,7)
if view_index == 2:
plt.imshow(score_map2_0[:,:,slice_index])
if view_index == 1:
plt.imshow(score_map2_0[:,slice_index,:])
if view_index == 0:
plt.imshow(score_map2_0[slice_index,:,:])
plt.title('view 2: blood')
plt.subplot(4,3,8)
if view_index == 2:
plt.imshow(score_map2_1[:,:,slice_index])
if view_index == 1:
plt.imshow(score_map2_1[:,slice_index,:])
if view_index == 0:
plt.imshow(score_map2_1[slice_index,:,:])
plt.title('view 2: muscle')
plt.subplot(4,3,9)
if view_index == 2:
plt.imshow(score_map2_2[:,:,slice_index])
if view_index == 1:
plt.imshow(score_map2_2[:,slice_index,:])
if view_index == 0:
plt.imshow(score_map2_2[slice_index,:,:])
plt.title('view 2: background')
# view add: ---------- ---------- ----------
if view_index == 2:
score_map_add_0 = score_map0_0[:,:,slice_index] + \
score_map1_0[:,:,slice_index] + \
score_map2_0[:,:,slice_index]
score_map_add_1 = score_map0_1[:,:,slice_index] + \
score_map1_1[:,:,slice_index] + \
score_map2_1[:,:,slice_index]
score_map_add_2 = score_map0_2[:,:,slice_index] + \
score_map1_2[:,:,slice_index] + \
score_map2_2[:,:,slice_index]
if view_index == 1:
score_map_add_0 = score_map0_0[:,slice_index,:] + \
score_map1_0[:,slice_index,:] + \
score_map2_0[:,slice_index,:]
score_map_add_1 = score_map0_1[:,slice_index,:] + \
score_map1_1[:,slice_index,:] + \
score_map2_1[:,slice_index,:]
score_map_add_2 = score_map0_2[:,slice_index,:] + \
score_map1_2[:,slice_index,:] + \
score_map2_2[:,slice_index,:]
if view_index == 0:
score_map_add_0 = score_map0_0[slice_index,:,:] + \
score_map1_0[slice_index,:,:] + \
score_map2_0[slice_index,:,:]
score_map_add_1 = score_map0_1[slice_index,:,:] + \
score_map1_1[slice_index,:,:] + \
score_map2_1[slice_index,:,:]
score_map_add_2 = score_map0_2[slice_index,:,:] + \
score_map1_2[slice_index,:,:] + \
score_map2_2[slice_index,:,:]
plt.subplot(4,3,10)
plt.imshow(score_map_add_0)
plt.title('view add: blood')
plt.subplot(4,3,11)
plt.imshow(score_map_add_1)
plt.title('view add: muscle')
plt.subplot(4,3,12)
plt.imshow(score_map_add_2)
plt.title('view add: background')
plt.suptitle('this view: '+str(view_index)+', slice: '+str(slice_index),fontsize=11, fontweight='bold')
#%%
def generate_predict(view_index, slice_index, threshold=0, blood_threshold=0, muscle_threshold=0):
'''
Note: we only consider this view
'''
if view_index == 2:
score_slice = np.zeros((data.shape[0],data.shape[1],3))
score_slice[:,:,0] = score_map2_0[:,:,slice_index]-blood_threshold
score_slice[:,:,1] = score_map2_1[:,:,slice_index]+muscle_threshold
score_slice[:,:,2] = score_map2_2[:,:,slice_index]-threshold
predict = np.argmax(score_slice, axis=2)
if view_index == 1:
score_slice = np.zeros((data.shape[0],data.shape[2],3))
score_slice[:,:,0] = score_map1_0[:,slice_index,:]-blood_threshold
score_slice[:,:,1] = score_map1_1[:,slice_index,:]+muscle_threshold
score_slice[:,:,2] = score_map1_2[:,slice_index,:]-threshold
predict = np.argmax(score_slice, axis=2)
if view_index == 0:
score_slice = np.zeros((data.shape[1],data.shape[2],3))
score_slice[:,:,0] = score_map0_0[slice_index,:,:]-blood_threshold
score_slice[:,:,1] = score_map0_1[slice_index,:,:]+muscle_threshold
score_slice[:,:,2] = score_map0_2[slice_index,:,:]-threshold
predict = np.argmax(score_slice, axis=2)
return predict
#%%
def generate_predict2(view_index, slice_index, threshold=0, blood_threshold=0, muscle_threshold=0):
'''
Note: we consider the added view
'''
if view_index == 2:
score_slice = np.zeros((data.shape[0],data.shape[1],3))
score_slice[:,:,0] = score_map_add_0[:,:,slice_index]-blood_threshold
score_slice[:,:,1] = score_map_add_1[:,:,slice_index]+muscle_threshold
score_slice[:,:,2] = score_map_add_2[:,:,slice_index]-threshold
predict = np.argmax(score_slice, axis=2)
if view_index == 1:
score_slice = np.zeros((data.shape[0],data.shape[2],3))
score_slice[:,:,0] = score_map_add_0[:,slice_index,:]-blood_threshold
score_slice[:,:,1] = score_map_add_1[:,slice_index,:]+muscle_threshold
score_slice[:,:,2] = score_map_add_2[:,slice_index,:]-threshold
predict = np.argmax(score_slice, axis=2)
if view_index == 0:
score_slice = np.zeros((data.shape[1],data.shape[2],3))
score_slice[:,:,0] = score_map_add_0[slice_index,:,:]-blood_threshold
score_slice[:,:,1] = score_map_add_1[slice_index,:,:]+muscle_threshold
score_slice[:,:,2] = score_map_add_2[slice_index,:,:]-threshold
predict = np.argmax(score_slice, axis=2)
return predict
#%%
import copy
def show_mul_prob(prob_list, class_name="", figure=False, factor=1):
"""
here we use this function by different orders
that is, we always fix the first element in the list
"""
if figure:
fig = plt.figure(figsize=(12, 4))
prob_c0_norm = norm_slice(prob_list[0])
prob_c1_norm = norm_slice(prob_list[1])
prob_c2_norm = norm_slice(prob_list[2])
prob_0 = (1-prob_c0_norm)**factor
prob_1 = (1-prob_c1_norm)**factor
prob_2 = (1-prob_c2_norm)**factor
if figure:
plt.subplot(1,4,1)
plt.imshow(prob_list[0])
plt.subplot(1,4,2)
plt.imshow(prob_list[0]*prob_1)
plt.subplot(1,4,3)
plt.imshow(prob_list[0]*prob_2)
plt.subplot(1,4,4)
plt.imshow(prob_list[0]*prob_1*prob_2)
plt.suptitle(class_name)
'''
# default
return copy.deepcopy(prob_list[0]*prob_1*prob_2), \
copy.deepcopy(prob_list[1]*prob_0*prob_2), \
copy.deepcopy(prob_list[2]*prob_1*prob_0)
'''
return copy.deepcopy(prob_list[0]*prob_1*prob_2), \
copy.deepcopy(prob_list[1]*prob_0*prob_2), \
copy.deepcopy(prob_list[2]*prob_1*prob_0)
def generate_predict_mul(view_index,
slice_index,
threshold=0,
blood_threshold=0,
muscle_threshold=0,
factor=1):
'''
Note: in this function, we use adjusted score maps
Note: we only consider this view
'''
if view_index == 2:
score_slice = np.zeros((data.shape[0],data.shape[1],3))
score_slice2 = np.zeros((data.shape[0],data.shape[1],3))
score_slice[:,:,0] = score_map2_0[:,:,slice_index]-blood_threshold
score_slice[:,:,1] = score_map2_1[:,:,slice_index]+muscle_threshold
score_slice[:,:,2] = score_map2_2[:,:,slice_index]-threshold
score_slice2[:,:,0],score_slice2[:,:,1],score_slice2[:,:,2]=show_mul_prob([score_slice[:,:,0], \
score_slice[:,:,1], \
score_slice[:,:,2]],
factor=factor)
predict = np.argmax(score_slice2, axis=2)
if view_index == 1:
score_slice = np.zeros((data.shape[0],data.shape[2],3))
score_slice2 = np.zeros((data.shape[0],data.shape[2],3))
score_slice[:,:,0] = score_map1_0[:,slice_index,:]-blood_threshold
score_slice[:,:,1] = score_map1_1[:,slice_index,:]+muscle_threshold
score_slice[:,:,2] = score_map1_2[:,slice_index,:]-threshold
score_slice2[:,:,0],score_slice2[:,:,1],score_slice2[:,:,2]=show_mul_prob([score_slice[:,:,0], \
score_slice[:,:,1], \
score_slice[:,:,2]],
factor=factor)
predict = np.argmax(score_slice2, axis=2)
if view_index == 0:
score_slice = np.zeros((data.shape[1],data.shape[2],3))
score_slice2 = np.zeros((data.shape[1],data.shape[2],3))
score_slice[:,:,0] = score_map0_0[slice_index,:,:]-blood_threshold
score_slice[:,:,1] = score_map0_1[slice_index,:,:]+muscle_threshold
score_slice[:,:,2] = score_map0_2[slice_index,:,:]-threshold
score_slice2[:,:,0],score_slice2[:,:,1],score_slice2[:,:,2]=show_mul_prob([score_slice[:,:,0], \
score_slice[:,:,1], \
score_slice[:,:,2]],
factor=factor)
predict = np.argmax(score_slice2, axis=2)
return predict
#%%
def show_argmax(view_index, slice_index, thresholds, blood_threshold=0,muscle_threshold=0):
'''
eg:
thresholds = [0,4,8]
'''
fig = plt.figure(figsize=(15, 4))
for i in range(len(thresholds)):
plt.subplot(1,len(thresholds),i+1)
predict = generate_predict(view_index,slice_index,thresholds[i],blood_threshold,muscle_threshold)
plt.imshow(predict)
plt.title('threshold: '+str(thresholds[i]))
plt.suptitle('show_argmax: view: '+str(view_index)+', slice:'+str(slice_index))
def show_argmax2(view_index, slice_index, thresholds, blood_threshold=0,muscle_threshold=0):
'''
eg:
thresholds = [0,4,8]
'''
fig = plt.figure(figsize=(15, 4))
for i in range(len(thresholds)):
plt.subplot(1,len(thresholds),i+1)
predict = generate_predict2(view_index,slice_index,thresholds[i],blood_threshold,muscle_threshold)
plt.imshow(predict)
plt.title('threshold: '+str(thresholds[i]))
plt.suptitle('show_argmax2: view: '+str(view_index)+', slice:'+str(slice_index))
#%%
def crop_edge_pixel(predict):
"""
minor refine
"""
pix_num = 2
predict[:,-pix_num:] = 2
predict[-pix_num:,:] = 2
return predict
#%%
def dist(array, label=""):
"""
show dist plot
"""
if len(array.shape) != 1:
flatten = array.flatten()
else:
flatten = array
plt.figure()
sns.distplot(flatten, hist=False, color="r", label=label)
#%%
def dist_multi(array):
"""
show dist plot
"""
plt.figure()
num = len(array)
for i in range(num):
if len(array[i].shape) != 1:
flatten = array[i].flatten()
else:
flatten = array[i]
sns.distplot(flatten, hist=False, label=str(i)) # , color="r"
#%%
def generate_prediction_from_slices(slice_0, slice_1, slice_2):
"""
here we use three slices to generate label
"""
score_slice = np.zeros((slice_0.shape[0], slice_0.shape[1],3))
score_slice[:,:,0] = slice_0
score_slice[:,:,1] = slice_1
score_slice[:,:,2] = slice_2
predict = np.argmax(score_slice, axis=2)
return predict
def generate_prediction_from_slices_dev(slice_0, slice_1, slice_2,show=False,factor=1):
"""
here we use three slices to generate label
"""
if show:
plt.figure(figsize=(15, 8))
plt.subplot(2,3,1)
plt.imshow(slice_0)
plt.subplot(2,3,2)
plt.imshow(slice_1)
plt.subplot(2,3,3)
plt.imshow(slice_2)
slice_0_ = copy.deepcopy(slice_0)
slice_1_ = copy.deepcopy(slice_1)
slice_2_ = copy.deepcopy(slice_2)
slice_0_ = norm_slice(slice_0_)
slice_1_ = norm_slice(slice_1_)
slice_2_ = norm_slice(slice_2_)
slice_0_ = (1-slice_0_)**factor
slice_1_ = (1-slice_1_)**factor
slice_2_ = (1-slice_2_)**factor
score_slice = np.zeros((slice_0.shape[0], slice_0.shape[1],3))
score_slice[:,:,0] = slice_0*slice_1_*slice_2_
score_slice[:,:,1] = slice_1*slice_0_*slice_2_
score_slice[:,:,2] = slice_2*slice_0_*slice_1_
predict = np.argmax(score_slice, axis=2)
if show:
plt.subplot(2,3,4)
plt.imshow(score_slice[:,:,0])
plt.subplot(2,3,5)
plt.imshow(score_slice[:,:,1])
plt.subplot(2,3,6)
plt.imshow(score_slice[:,:,2])
return predict
#%%
def generate_prediction_from_slices_dev_dev(slice_0,
slice_1,
slice_2,
show=False,
factor=1,
threshold=0,
muscle_threshold=0,
blood_threshold=0):
"""
here we use three slices to generate label
"""
if show:
plt.figure(figsize=(15, 8))
plt.subplot(2,3,1)
plt.imshow(slice_0)
plt.subplot(2,3,2)
plt.imshow(slice_1)
plt.subplot(2,3,3)
plt.imshow(slice_2)
slice_0_ = copy.deepcopy(slice_0)
slice_1_ = copy.deepcopy(slice_1)
slice_2_ = copy.deepcopy(slice_2)
slice_0_ = norm_slice(slice_0_)
slice_1_ = norm_slice(slice_1_)
slice_2_ = norm_slice(slice_2_)
'''
min_value = np.min((score_map_0,score_map_1,score_map_2))
slice_add_ = slice_0_ + slice_1_ + slice_2_
slice_0_ = (slice_0_-min_value)/slice_add_
slice_1_ = (slice_1_-min_value)/slice_add_
slice_2_ = (slice_2_-min_value)/slice_add_
'''
slice_0__ = (1-slice_0_)**factor
slice_1__ = (1-slice_1_)**factor
slice_2__ = (1-slice_2_)**factor
score_slice = np.zeros((slice_0.shape[0], slice_0.shape[1],3))
score_slice[:,:,0] = slice_1__*slice_2__*(slice_0-blood_threshold)
score_slice[:,:,1] = slice_0__*slice_2__*(slice_1+muscle_threshold)
score_slice[:,:,2] = slice_0__*slice_1__*(slice_2-threshold)
predict = np.argmax(score_slice, axis=2)
if show:
plt.subplot(2,3,4)
plt.imshow(score_slice[:,:,0])
plt.subplot(2,3,5)
plt.imshow(score_slice[:,:,1])
plt.subplot(2,3,6)
plt.imshow(score_slice[:,:,2])
return predict, score_slice
#%%
from matplotlib import cm
import imageio
def gen_image(image,path,inten_cut=0):
"""
generate one image
"""
a = copy.deepcopy(image)
if inten_cut != 0:
a[a>inten_cut] = inten_cut
a = (a-a.mean()) / a.std()
a = (a-a.min()) / (a.max()-a.min())
c = cm.viridis(a,bytes=True)[:,:,0:3]
imageio.imwrite(path, c)
def image_norm(image,inten_cut):
a = copy.deepcopy(image)
if inten_cut != 0:
a[a>inten_cut] = inten_cut
a = (a-a.mean()) / a.std()
a = (a-a.min()) / (a.max()-a.min())
return a
#%%
def gaussian2(x, y, mu=0, sig=1):
"""
gaussian kernel
"""
return (1 / (sig * sig * 2 * np.pi)) * np.exp(-(x*x + y*y) / (2 * np.power(sig, 2.)))
def get_kernel(length):
"""
get gaussian kernel
"""
kernel = np.zeros((length,length))
for j in range(length):
kernel[j,:] = [gaussian2(i-(length-1)/2,j-(length-1)/2) for i in range(length)]
return kernel
#%%
def get_point(prob_c0,prob_c1,prob_c2,center):
"""
given an image patch and the center point, get the point of interest
"""
point_cors = np.vstack((prob_c0.flatten(),
prob_c1.flatten(),
prob_c2.flatten())).T
num_points = 2*center + 1
center_cors = np.array([[prob_c0.flatten()[center],
prob_c1.flatten()[center],
prob_c2.flatten()[center]]]*num_points)
Edist = np.linalg.norm(point_cors-center_cors,axis=1)
rank = np.argsort(Edist) # eg. [24, 23, 28, 25, 32, 39, 45, ...]
num_cut = -1 # we use 8 points
return rank[:num_cut]
def Conv(intensity, kernel, score_map_0, score_map_1, score_map_2):
"""
perform simple convolution
"""
select_point = False
conved = np.zeros(intensity.shape)
center = int(kernel.size / 2) # eg.: 7*7 -> 49 -> 24
L = int((kernel.shape[0] - 1) / 2)
length = kernel.shape[0]
intensity_pad = np.pad(intensity, ((L, L), (L, L)), 'constant', constant_values=((0, 0), (0, 0)))
score_pad_value = -5
score_map_0_pad = np.pad(score_map_0, ((L, L), (L, L)), 'constant', constant_values=((score_pad_value, score_pad_value), (score_pad_value, score_pad_value)))
score_map_1_pad = np.pad(score_map_1, ((L, L), (L, L)), 'constant', constant_values=((score_pad_value, score_pad_value), (score_pad_value, score_pad_value)))
score_map_2_pad = np.pad(score_map_2, ((L, L), (L, L)), 'constant', constant_values=((score_pad_value, score_pad_value), (score_pad_value, score_pad_value)))
for i in range(conved.shape[0]):
for j in range(conved.shape[1]):
intensity_crop = intensity_pad[i:i+length,j:j+length]
prob_c0 = score_map_0_pad[i:i+length,j:j+length]
prob_c1 = score_map_1_pad[i:i+length,j:j+length]
prob_c2 = score_map_2_pad[i:i+length,j:j+length]
raw_conv = intensity_crop * kernel
if select_point:
points_list = get_point(prob_c0,prob_c1,prob_c2,center)
conv = raw_conv.flatten()[points_list]
conv = conv.mean()
conved[i,j] = conv
else:
conved[i,j] = raw_conv.flatten().mean()
return conved
#%%
from mpl_toolkits.mplot3d import Axes3D
def plot3d(arr1, arr2, arr3):
"""
3D scatter plot
"""
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = arr1.flatten()
y = arr2.flatten()
z = arr3.flatten()
ax.scatter(x, y, z, c='r', marker='o')
center = int(len(x) / 2)
print('center point:', center, x[center], y[center], z[center])
ax.scatter(x[center], y[center], z[center], c='b', marker='o')
ax.set_xlabel('arr1')
ax.set_ylabel('arr2')
ax.set_zlabel('arr3')
plt.show()
#%%
# this is for label propagation
from sklearn.semi_supervised import label_propagation
#%%
from sklearn import mixture
def generate_predict_blood_gaussian(view_index, slice_index, threshold=0, blood_threshold=0):
'''
Note: we consider the added view
And: only see the blood, with gaussian mixture
'''
if view_index == 2:
score_slice = np.zeros((data.shape[0],data.shape[1]))
score_slice[:,:] = score_map_add_0[:,:,slice_index] # 0
if view_index == 1:
score_slice = np.zeros((data.shape[0],data.shape[2]))
score_slice[:,:] = score_map_add_0[:,slice_index,:] # 0
if view_index == 0:
score_slice = np.zeros((data.shape[1],data.shape[2]))
score_slice[:,:] = score_map_add_0[slice_index,:,:] # 0
# gaussian mixture
flattened = score_slice.flatten()
data_ = np.array(flattened).reshape(-1,1)
clf = mixture.GaussianMixture(n_components=2,
covariance_type='full')
clf.fit(data_)
# threshold to intensity
smaller_mean = clf.means_.min()
threshold_inten = smaller_mean*2 + 0
print(slice_index, threshold_inten)
predict = np.zeros(score_slice.shape)
predict[:,:] = 2 # all background
predict[score_slice>threshold_inten]=0 # 0
return predict
#%%
def generate_predict_blood_gaussian_dev(view_index, slice_index, threshold=0, blood_threshold=0):
'''
Note: we consider the added view
And: only see the blood, with gaussian mixture
'''
if view_index == 2:
score_slice = np.zeros((data.shape[0],data.shape[1]))
score_slice[:,:] = score_map2_1[:,:,slice_index] # 0
if view_index == 1:
score_slice = np.zeros((data.shape[0],data.shape[2]))
score_slice[:,:] = score_map1_1[:,slice_index,:] # 0
if view_index == 0:
score_slice = np.zeros((data.shape[1],data.shape[2]))
score_slice[:,:] = score_map0_1[slice_index,:,:] # 0
# gaussian mixture
flattened = score_slice.flatten()
data_ = np.array(flattened).reshape(-1,1)
clf = mixture.GaussianMixture(n_components=2,
covariance_type='full')
clf.fit(data_)
# threshold to intensity
smaller_mean = clf.means_.min()
threshold_inten = smaller_mean*2 - 3
print(slice_index, threshold_inten)
predict = np.zeros(score_slice.shape)
predict[:,:] = 2 # all background
predict[score_slice>threshold_inten]=1 # 0
# ----- ----- ----- ----- -----
if view_index == 2:
score_slice = np.zeros((data.shape[0],data.shape[1]))
score_slice[:,:] = score_map2_0[:,:,slice_index] # 0
if view_index == 1:
score_slice = np.zeros((data.shape[0],data.shape[2]))
score_slice[:,:] = score_map1_0[:,slice_index,:] # 0
if view_index == 0:
score_slice = np.zeros((data.shape[1],data.shape[2]))
score_slice[:,:] = score_map0_0[slice_index,:,:] # 0
# gaussian mixture
flattened = score_slice.flatten()
data_ = np.array(flattened).reshape(-1,1)
clf = mixture.GaussianMixture(n_components=2,
covariance_type='full')
clf.fit(data_)
# threshold to intensity
threshold_inten = smaller_mean*2 - 1.5
print(slice_index, threshold_inten)
predict[score_slice>threshold_inten]=0 # 0
return predict
#%%
def change_label(label,from_indicator,to_indicator):
"""
label: 3d label
indicator: [1,2,0] etc.
"""
label_ = np.zeros(label.shape)
for i in range(3):
label_[label==from_indicator[i]] = to_indicator[i]
return label_
def generate_1_predicted_labels(mode='single',
view_index=0,
background_threshold=0,
muscle_threshold=0,
blood_threshold=0,
factor=0):
label = np.zeros(data.shape, dtype=np.int16)
for i in range(data.shape[view_index]):
#% add mode
slice_index = i
if mode == 'single':
prob_c0 = get_score_map(view_index, slice_index, 0)#[y0:y0+y_l,x0:x0+x_l]
prob_c1 = get_score_map(view_index, slice_index, 1)#[y0:y0+y_l,x0:x0+x_l]
prob_c2 = get_score_map(view_index, slice_index, 2)#[y0:y0+y_l,x0:x0+x_l]
elif mode == 'add':
prob_c0 = get_score_map_add(view_index, slice_index, 0)#[y0:y0+y_l,x0:x0+x_l]
prob_c1 = get_score_map_add(view_index, slice_index, 1)#[y0:y0+y_l,x0:x0+x_l]
prob_c2 = get_score_map_add(view_index, slice_index, 2)#[y0:y0+y_l,x0:x0+x_l]
else:
print('Error mode')
return 0
#%
pred, _ = generate_prediction_from_slices_dev_dev(prob_c0,
prob_c1,
prob_c2,
show=False,
factor=factor,
threshold=background_threshold,
muscle_threshold=muscle_threshold,
blood_threshold=blood_threshold)
pred = crop_edge_pixel(pred)
if view_index == 2:
label[:,:,i] = pred # 2 - pred
if view_index == 1:
label[:,i,:] = pred # 2 - pred
if view_index == 0:
label[i,:,:] = pred # 2 - pred
return label
#%%
def generate_3_predicted_labels(label_all,mode='single', # single view or add view
background_threshold=0,
muscle_threshold=0,
blood_threshold=0,
factor=0):
"""
append 3 predicted labels to current label list
we treat 3 view equally
"""
for i in range(3):
label = generate_1_predicted_labels(mode=mode,
view_index=i,
background_threshold=background_threshold,
muscle_threshold=muscle_threshold,
blood_threshold=blood_threshold,
factor=factor)
# label = change_label(label,[2,1,0],[2,0,1])
label_all.append(copy.deepcopy(label))
print('current ensemble number is:', len(label_all))
#%%
import cv2
def label_smoothing(label,view_index=0):
"""
label is a 3D ndarray
"""
k_size = 5
label_ = np.zeros(label.shape)
label__ = label.astype(np.uint8)
if view_index == 0:
for i in range(label.shape[0]):
label_[i,:,:] = cv2.medianBlur(label__[i,:,:], k_size)
if view_index == 1:
for i in range(label.shape[1]):
label_[:,i,:] = cv2.medianBlur(label__[:,i,:], k_size)
if view_index == 2:
for i in range(label.shape[2]):
label_[:,:,i] = cv2.medianBlur(label__[:,:,i], k_size)
return label_
#%%
def label_voting(label_all):
"""
simple counting based voting
"""
print('voting number', len(label_all))
shape = label_all[0].shape
score_label = np.zeros((3,shape[0],shape[1],shape[2]))
label_all_ = np.array(label_all)
score_label[2,:,:,:] = np.sum(label_all_==2,axis=0)
score_label[1,:,:,:] = np.sum(label_all_==1,axis=0)
score_label[0,:,:,:] = np.sum(label_all_==0,axis=0)
predict = np.argmax(score_label, axis=0)
return predict
#%%
from scipy.ndimage import measurements # label
#%%
def get_score_map_dev(view_index, slice_index, class_index):
if view_index == 0:
slice_max = score_map0_0.shape[0]-1
if class_index == 0:
if slice_index == 0:
current_map = score_map0_0[slice_index,:,:]+score_map0_0[slice_index+1,:,:]
current_map = current_map/2
elif slice_index == slice_max:
current_map = score_map0_0[slice_index,:,:]+score_map0_0[slice_index-1,:,:]
current_map = current_map/2
else:
current_map = score_map0_0[slice_index,:,:]+score_map0_0[slice_index-1,:,:]+score_map0_0[slice_index+1,:,:]
current_map = current_map/3
return copy.deepcopy(current_map)
if class_index == 1:
if slice_index == 0:
current_map = score_map0_1[slice_index,:,:]+score_map0_1[slice_index+1,:,:]