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StyleSegor_Analysis.py
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StyleSegor_Analysis.py
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import os
os.chdir(r'C:\Users\horsepurve\Dropbox\UBR\Analysis\StyleSegor')
import numpy as np
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'] = True
from PIL import Image
#%%
# percentage analysis
import nibabel as nib
train_label_dir = r'C:\Users\horsepurve\Dropbox\UBR\Analysis\TopoSeg\gluon\Axial-cropped\Ground truth'
test_label_dir = r'C:\Users\horsepurve\Dropbox\UBR\Analysis\SemiSegor\submit\ablation_study_ad3_mt'
#%%
sample_no = 0
if sample_no < 10:
gt = r'training_axial_crop_pat'+str(sample_no)+r'-label.nii.gz'
gt_dir = train_label_dir
else:
gt = r'testing_axial_crop_pat'+str(sample_no)+r'-label.nii.gz'
gt_dir = test_label_dir
img_gt = nib.load(os.path.join(gt_dir, gt))
data_gt = img_gt.get_fdata()
print(data_gt.shape)
#%%
def percentage_along(view_index):
"""
label 0: background
label 1: muscle
label 2: blood
"""
if view_index == 0:
x = [i for i in range(data_gt.shape[0])]
area = data_gt.shape[1] * data_gt.shape[2]
y_background = np.zeros(len(x))
y_blood = np.zeros(len(x))
y_muscle = np.zeros(len(x))
y_muscleOblood = np.zeros(len(x))
for j in range(len(x)):
img = data_gt[j,:,:]
y_background[j] = (img==0).flatten().sum() / area
y_muscle[j] = (img==1).flatten().sum() / area
y_blood[j] = (img==2).flatten().sum() / area
if y_blood[j] != 0:
y_muscleOblood[j] = y_muscle[j] / y_blood[j]
if view_index == 1:
x = [i for i in range(data_gt.shape[1])]
area = data_gt.shape[0] * data_gt.shape[2]
y_background = np.zeros(len(x))
y_blood = np.zeros(len(x))
y_muscle = np.zeros(len(x))
y_muscleOblood = np.zeros(len(x))
for j in range(len(x)):
img = data_gt[:,j,:]
y_background[j] = (img==0).flatten().sum() / area
y_muscle[j] = (img==1).flatten().sum() / area
y_blood[j] = (img==2).flatten().sum() / area
if y_blood[j] != 0:
y_muscleOblood[j] = y_muscle[j] / y_blood[j]
if view_index == 2:
x = [i for i in range(data_gt.shape[2])]
area = data_gt.shape[0] * data_gt.shape[1]
y_background = np.zeros(len(x))
y_blood = np.zeros(len(x))
y_muscle = np.zeros(len(x))
y_muscleOblood = np.zeros(len(x))
for j in range(len(x)):
img = data_gt[:,:,j]
y_background[j] = (img==0).flatten().sum() / area
y_muscle[j] = (img==1).flatten().sum() / area
y_blood[j] = (img==2).flatten().sum() / area
if y_blood[j] != 0:
y_muscleOblood[j] = y_muscle[j] / y_blood[j]
return x, y_background, y_blood, y_muscle, y_muscleOblood
#%%
plt.plot(x, y_background, label='background')
plt.plot(x, y_muscle, label='muscle')
plt.plot(x, y_blood, label='blood')
# plt.plot(x, y_muscleOblood, label='muscle over blood')
plt.legend()
plt.title('sample: '+str(sample_no)+', view: '+str(view_index))
#%%
def plot_one_view(view_index, x, y_background, y_muscle, y_blood):
plt.plot(x, y_background, label='background')
plt.plot(x, y_muscle, label='muscle')
plt.plot(x, y_blood, label='blood')
# plt.plot(x, y_muscleOblood, label='muscle over blood')
plt.legend()
plt.title('sample: '+str(sample_no)+', view: '+str(view_index))
def plot_one_sample():
plt.figure(figsize=(10,3))
plt.tight_layout()
x, y_background, y_blood, y_muscle, y_muscleOblood = percentage_along(0)
plt.subplot(1,3,1)
plot_one_view(0, x, y_background, y_muscle, y_blood)
x, y_background, y_blood, y_muscle, y_muscleOblood = percentage_along(1)
plt.subplot(1,3,2)
plot_one_view(1, x, y_background, y_muscle, y_blood)
x, y_background, y_blood, y_muscle, y_muscleOblood = percentage_along(2)
plt.subplot(1,3,3)
plot_one_view(2, x, y_background, y_muscle, y_blood)
#%%
plot_one_sample()
plt.savefig(str(sample_no)+'.png')
#%% ========== ========== ========== ========== ========== ==========
# collect all the data:
id2y_background = {}
id2y_blood = {}
id2y_muscle = {}
for i in range(20):
sample_no = i
if sample_no < 10:
gt = r'training_axial_crop_pat'+str(sample_no)+r'-label.nii.gz'
gt_dir = train_label_dir
else:
gt = r'testing_axial_crop_pat'+str(sample_no)+r'-label.nii.gz'
gt_dir = test_label_dir
img_gt = nib.load(os.path.join(gt_dir, gt))
data_gt = img_gt.get_fdata()
print(data_gt.shape)
id2y_background[sample_no] = {} # 0: view 0 list; 1: view 1 list; 2: view 2 list
for i in range(3): # 3 views
x, y_background, y_blood, y_muscle, y_muscleOblood = percentage_along(i)
id2y_background[sample_no][i] = (1 - y_background) / (1 - y_background).max()
#%%
import pickle
with open('id2y_background.pkl',"wb") as fo:
pickle.dump(id2y_background, fo)
#%%
# get the corresponding location for data augmentation step
sample_name = 'training_axial_crop_pat0_v0_53'
sample_no = int(sample_name.split('_')[3][3:])
view_index = int(sample_name.split('_')[4][1:])
slice_index = int(sample_name.split('_')[-1])
slice_percentage = id2y_background[sample_no][view_index][slice_index]
peak_x_this_sample = np.argmax(id2y_background[sample_no][view_index])
for i in range(10): # for each training slice, we only consider 10 testing style
sample_no = i + 10
fixed_array = id2y_background[sample_no][view_index]
peak_x = np.argmax(fixed_array)
mapped_x_1 = np.argmin(np.abs(fixed_array[:peak_x] - slice_percentage))
mapped_x_2 = np.argmin(np.abs(fixed_array[peak_x:] - slice_percentage)) + peak_x
if slice_index > peak_x_this_sample:
mapped_x = mapped_x_2
else:
mapped_x = mapped_x_1
print(mapped_x)
#%%
# get the corresponding location for test stage
sample_name = 'testing_axial_crop_pat15_v0_63'
sample_no = int(sample_name.split('_')[3][3:])
view_index = int(sample_name.split('_')[4][1:])
slice_index = int(sample_name.split('_')[-1])
slice_percentage = id2y_background[sample_no][view_index][slice_index]
peak_x_this_sample = np.argmax(id2y_background[sample_no][view_index])
for i in range(10): # for each training slice, we only consider 10 testing style
sample_no = i
fixed_array = id2y_background[sample_no][view_index]
peak_x = np.argmax(fixed_array)
mapped_x_1 = np.argmin(np.abs(fixed_array[:peak_x] - slice_percentage))
mapped_x_2 = np.argmin(np.abs(fixed_array[peak_x:] - slice_percentage)) + peak_x
if slice_index > peak_x_this_sample:
mapped_x = mapped_x_2
else:
mapped_x = mapped_x_1
print(mapped_x)
#%% ========== ========== ========== ========== ========== ==========
# prediction diagnosis
import pickle
score_dir = r'D:\Project\UB\Projects\StyleSegor\scoredir_style'
score_file = r'testing_axial_crop_pat16_v0_24_6.pkl'
with open(os.path.join(score_dir, score_file), "rb") as fi:
score_map = pickle.load(fi)
#%% ========== ========== ========== ========== ========== ==========
# size recovering analysis
os.chdir(r'C:\Users\horsepurve\Dropbox\UBR\Analysis\StyleSegor')
test_shape = [(192, 269, 190), # 10
(229, 300, 110), # 11
(172, 257, 205), # 12
(191, 295, 190), # 13
(151, 218, 176), # 14
(260, 228, 215), # 15
(151, 195, 156), # 16
(185, 279, 214), # 17
(214, 279, 169), # 18
(154, 206, 216)] # 19
#%%
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from PIL import Image
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import torchvision.models as models
from utils import *
from models import *
import numpy as np
#%matplotlib inline
#%config InlineBackend.figure_format = 'retina'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#%%
image_dir = r'C:\Users\horsepurve\Desktop\TMP\JPEGImages_test_styled'
output_dir = r'C:\Users\horsepurve\Desktop\TMP\JPEGImages_test_styled_recovered'
image_2 = r'testing_axial_crop_pat10_v0_100_styled_.jpg'
width = (test_shape[0][1],test_shape[0][2])
content_img = read_image(os.path.join(image_dir,image_2), target_width=width).to(device)
#%%
import imageio
img = content_img.cpu().numpy()[0,:,:,:]
img = img.transpose((1, 2, 0))
imageio.imwrite(os.path.join(output_dir,image_2),img)
#%%
# do it all
from os import listdir
from os.path import isfile, join
raw_images = [f for f in listdir(image_dir) if isfile(join(image_dir, f))]
for k in raw_images:
image_2 = k
sample_no = int(k.split('_')[3][3:]) - 10
view_index = int(k.split('_')[4][1:])
if view_index == 0:
width = (test_shape[sample_no][1],test_shape[sample_no][2])
if view_index == 1:
width = (test_shape[sample_no][0],test_shape[sample_no][2])
if view_index == 2:
width = (test_shape[sample_no][0],test_shape[sample_no][1])
content_img = read_image(os.path.join(image_dir,image_2), target_width=width).to(device)
img = content_img.cpu().numpy()[0,:,:,:]
img = img.transpose((1, 2, 0))
imageio.imwrite(os.path.join(output_dir,image_2),img)
print('->', end='')
#%% ========== ========== ========== ========== ========== ==========
# integration analysis
import os
import numpy as np
import nibabel as nib
from nibabel.testing import data_path
import pickle
from PIL import Image
# load raw data
test_index = 14
nii_data = r'C:/Users/horsepurve/Dropbox/UBR/Analysis/TopoSeg/gluon/Axial-cropped/Test dataset/testing_axial_crop_pat'+str(test_index)+'.nii.gz'
img = nib.load(nii_data)
data = img.get_fdata()
print(data.shape)
#%%
# load pkl
print('test sample:', test_index)
pkl_dir = r'D:\Project\UB\Projects\HVSMR\no_style_pkl'
## pkl_dir = r'C:\Users\horsepurve\Desktop\TMP\no_1scale_pkl'
## pkl_dir = r'H:\UB\HVSMR\LCloss\no_1scale_pkl'
'''
[we have:]
no_style_pkl
styled_pkl
no_style_1scale_pkl
styled_1scale_pkl
'''
with open(os.path.join(pkl_dir, str(test_index)+'.pkl'), 'rb') as fi:
score_map0_0 = pickle.load(fi)
score_map0_1 = pickle.load(fi)
score_map0_2 = pickle.load(fi)
score_map1_0 = pickle.load(fi)
score_map1_1 = pickle.load(fi)
score_map1_2 = pickle.load(fi)
score_map2_0 = pickle.load(fi)
score_map2_1 = pickle.load(fi)
score_map2_2 = pickle.load(fi)
score_map_add_0 = np.zeros(score_map0_0.shape)
score_map_add_1 = np.zeros(score_map0_0.shape)
score_map_add_2 = np.zeros(score_map0_0.shape)
score_map_add_0 = score_map0_0 + score_map1_0 + score_map2_0
score_map_add_1 = score_map0_1 + score_map1_1 + score_map2_1
score_map_add_2 = score_map0_2 + score_map1_2 + score_map2_2
#%%
pat_index = test_index
view_index = 1
slice_index = 154
print(data.shape[view_index])
#%%
# show deeplab output
show_3view3label(view_index, slice_index)
# show raw image & dense output
## show_image_dense(pat_index, view_index, slice_index)
#%
# plot to see with different threshold
thresholds = [0,2,4,6,8,10]
blood_threshold = 0
muscle_threshold = 2
show_argmax(view_index,slice_index,thresholds,blood_threshold,muscle_threshold)
show_argmax2(view_index,slice_index,thresholds,blood_threshold,muscle_threshold)
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# now, we want to do new LP analysis
# 1: we need label
print(label.shape)
# 2: we need intensity
print(data.shape)
pat_index = test_index
view_index = 0
slice_index = 92
intensity = get_slice(data, view_index, slice_index)
show(intensity)
predict_all = get_slice(label, view_index, slice_index)
show(predict_all)
# 3: we need score maps
score_map_0 = get_score_map(view_index, slice_index, 0)
score_map_1 = get_score_map(view_index, slice_index, 1)
score_map_2 = get_score_map(view_index, slice_index, 2)
#%%
# 4. crop patch
x0,y0,x_l,y_l = 85,104, 50,50
show_raw_img(intensity, pat_index,view_index,slice_index,(x0,y0,x_l,y_l))
img_crop = intensity[y0:y0+y_l,x0:x0+x_l]
## show(img_crop)
## plt.title('cropped image')
predict_all_crop = predict_all[y0:y0+y_l,x0:x0+x_l]
## show(predict_all_crop)
## plt.title('cropped predict')
score_map_crop_0 = score_map_0[y0:y0+y_l,x0:x0+x_l]
score_map_crop_1 = score_map_1[y0:y0+y_l,x0:x0+x_l]
score_map_crop_2 = score_map_2[y0:y0+y_l,x0:x0+x_l]
score_map_crop_0_norm = norm_slice(score_map_crop_0)
score_map_crop_1_norm = norm_slice(score_map_crop_1)
score_map_crop_2_norm = norm_slice(score_map_crop_2)
#%%
factor = 1
predict_crop = generate_prediction_from_slices_dev(score_map_crop_0,
score_map_crop_1,
score_map_crop_2,
show=True,
factor=factor)
show(predict_crop)
#%%
# so now we have:
# 1. img_crop
# 2. predict_all_crop
# 3. score_map_crop_0, score_map_crop_1, score_map_crop_2
# ========== ========== ========== ========== ========== ==========
# try LP
X = get_X(img_crop, view_index)
labels = np.full(len(X), -1.)
labels[:] = predict_all_crop.flatten()
img_crop_norm = norm_slice(img_crop)
#%
size = img_crop.size
X2 = np.hstack((X,
(img_crop*(1-(1-score_map_crop_0_norm)*(1-score_map_crop_1_norm))).reshape(size,1),
))
#%
# Learn with LabelSpreading
label_spread = label_propagation.LabelSpreading(kernel='rbf', gamma=0.01,
alpha=0.9, # 0.2
max_iter=50, # 30
n_neighbors=5, # 7
tol=0.0001 # 0.001
)
label_spread.fit(X2, labels)
#%
output_labels = label_spread.transduction_
if True:
plt.figure(figsize=(10, 3))
plt.subplot(1,3,1)
plt.imshow(img_crop)
plt.subplot(1,3,2)
plt.imshow(predict_all_crop)
plt.subplot(1,3,3)
plt.imshow(output_labels.reshape(img_crop.shape))
#%%
from scipy import ndimage
from scipy import misc
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#%% ========== ========== ========== ========== ========== ==========
# here we want to try image enhancement
pat_index = test_index
view_index = 1
slice_index = 110
#%%
intensity = get_slice(data, view_index, slice_index)
intensity_norm = image_norm(intensity, inten_cut[pat_index])
score_map_0 = get_score_map(view_index, slice_index, 0) # get_score_map_add
score_map_1 = get_score_map(view_index, slice_index, 1) # get_score_map_add
score_map_2 = get_score_map(view_index, slice_index, 2) # get_score_map_add
score_map_0_norm = norm_slice(score_map_0)
score_map_1_norm = norm_slice(score_map_1)
score_map_2_norm = norm_slice(score_map_2)
show_subplots([intensity,score_map_0,score_map_1,score_map_2])
#%%
factor = 1
prob = 1 - (1-score_map_0_norm)*(1-score_map_1_norm)
prob = prob**factor
intensity_mul = intensity * prob
show(intensity_mul)
#%% ========== ========== ========== ========== ========== ==========
x0,y0,x_l,y_l = 0,0, 700,700
intensity_crop = intensity_norm[y0:y0+y_l,x0:x0+x_l]
prob_c0 = score_map_0[y0:y0+y_l,x0:x0+x_l] # new_map_0
prob_c1 = score_map_1[y0:y0+y_l,x0:x0+x_l] # new_map_1
prob_c2 = score_map_2[y0:y0+y_l,x0:x0+x_l] # new_map_2
'''
prob_c0 = new_map_0[y0:y0+y_l,x0:x0+x_l] # new_map_0
prob_c1 = new_map_1[y0:y0+y_l,x0:x0+x_l] # new_map_1
prob_c2 = new_map_2[y0:y0+y_l,x0:x0+x_l] # new_map_2
s
'''
prob_c0_ = norm_slice(prob_c0)
prob_c1_ = norm_slice(prob_c1)
prob_c2_ = norm_slice(prob_c2)
#%%
show_raw_img(intensity, pat_index,view_index,slice_index,(x0,y0,x_l,y_l))
show_subplots([intensity_crop,prob_c0,prob_c1,prob_c2])
#%%
factor = 0
predict, score_slice = generate_prediction_from_slices_dev_dev(score_map_0,
score_map_1,
score_map_2,
show=True,
factor=factor)
show(predict)
#%%
new_map_0 = score_slice[:,:,0]
new_map_1 = score_slice[:,:,1]
new_map_2 = score_slice[:,:,2]
show_subplots([new_map_0,new_map_1,new_map_2])
#%% naive score cut
mask = np.zeros(new_map_1.shape)
mask[new_map_1>2.5]=1
plt.figure()
plt.imshow(new_map_1)
plt.imshow(mask,alpha=0.3)
#%% ===== ===== ===== ===== ===== =====
a = (Sfun(prob_c1_)+prob_c0_)*intensity_crop
# a = (1 - (1-prob_c0_)*(1-prob_c1_))*intensity_crop
show(a)
# a = intensity_crop
gen_image(a,'run_omen/enhanced.jpg')
#%%
#%% ========== ========== ========== ========== ========== ==========
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# now we want to try image guided smoothing
plot3d(prob_c0,prob_c1,prob_c2)
#%%
center = 24
point_cors = np.vstack((prob_c0.flatten(),prob_c1.flatten(),prob_c2.flatten())).T
center_cors = np.array([[prob_c0.flatten()[center],prob_c1.flatten()[center],prob_c2.flatten()[center]]]*49)
Edist = np.linalg.norm(point_cors-center_cors,axis=1)
rank = np.argsort(Edist)
# plt.plot([i for i in range(49)],Edist[rank])
#%% ========== ========== ========== ========== ========== ==========
kernel = get_kernel(15)
conved = Conv(a, kernel, score_map_0, score_map_1, score_map_2)
show(conved)
gen_image(conved,'run_omen/enhanced.jpg')
#%% ========== ========== ========== ========== ========== ==========
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#%% this is for prediction on score map
a=score_map_0
a = score_map_0+score_map_1*0.2
show(a)
gen_image(a,'run_omen/enhanced.jpg')
#%% ========== ========== ========== ========== ========== ==========
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# here we want to try image synthesis
nii_data_dir = r'C:/Users/horsepurve/Dropbox/UBR/Analysis/TopoSeg/gluon/Axial-cropped/Training dataset/'
gt_dir = r'C:/Users/horsepurve/Dropbox/UBR/Analysis/TopoSeg\gluon/Axial-cropped/Ground truth/'
sample_no = 1
nii_data = r'training_axial_crop_pat'+str(sample_no)+r'.nii.gz'
gt = r'training_axial_crop_pat'+str(sample_no)+r'-label.nii.gz'
img = nib.load(os.path.join(nii_data_dir, nii_data))
data_tr = img.get_fdata()
print(data_tr.shape)
img_gt = nib.load(os.path.join(gt_dir, gt))
data_gt = img_gt.get_fdata()
print(data_gt.shape)
#%%
pat_index = test_index
view_index = 0
slice_index = 55
image = get_slice(data_tr,view_index, slice_index)
label = get_slice(data_gt,view_index, slice_index)
#%%
# we are focusing on label 1 -> muscle and 2 -> blood
inten_muscle = image[label==1]
print('number of muscle pixels:', len(inten_muscle))
inten_blood = image[label==2]
print('number of blood pixels:', len(inten_blood))
#%%
dist_multi([inten_muscle, inten_blood])
#%%
score_map_0_part = score_map_0 / (score_map_1 + score_map_0)
score_map_1_part = score_map_1 / (score_map_1 + score_map_0)
dist_multi([score_map_1_part,score_map_0_part])
syn_image = score_map_0 / 21 * 1117
show(syn_image)
#%%
a = syn_image
gen_image(a,'run_omen/enhanced.jpg')
#%% ========== ========== ========== ========== ========== ==========
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#%% ========== ========== ========== ========== ========== ==========
# integration analysis
import os
import numpy as np
import nibabel as nib
from nibabel.testing import data_path
import pickle
from PIL import Image
# load raw data
test_index = 19
nii_data = r'C:/Users/horsepurve/Dropbox/UBR/Analysis/TopoSeg/gluon/Axial-cropped/Test dataset/testing_axial_crop_pat'+str(test_index)+'.nii.gz'
img = nib.load(nii_data)
data = img.get_fdata()
print(data.shape)
#%%
# load pkl
print('test sample:', test_index)
pkl_dir = r'D:\Project\UB\Projects\HVSMR\no_style_pkl'
## pkl_dir = r'H:\UB\HVSMR\LCloss\no_pkl' # no_pkl no_1scale_pkl with_pkl with_1scale_pkl
'''
[we have:]
no_style_pkl
styled_pkl
no_style_1scale_pkl
styled_1scale_pkl
'''
with open(os.path.join(pkl_dir, str(test_index)+'.pkl'), 'rb') as fi:
score_map0_0 = pickle.load(fi)
score_map0_1 = pickle.load(fi)
score_map0_2 = pickle.load(fi)
score_map1_0 = pickle.load(fi)
score_map1_1 = pickle.load(fi)
score_map1_2 = pickle.load(fi)
score_map2_0 = pickle.load(fi)
score_map2_1 = pickle.load(fi)
score_map2_2 = pickle.load(fi)
score_map_add_0 = np.zeros(score_map0_0.shape)
score_map_add_1 = np.zeros(score_map0_0.shape)
score_map_add_2 = np.zeros(score_map0_0.shape)
score_map_add_0 = score_map0_0 + score_map1_0 + score_map2_0
score_map_add_1 = score_map0_1 + score_map1_1 + score_map2_1
score_map_add_2 = score_map0_2 + score_map1_2 + score_map2_2
#%%
pat_index = test_index
view_index = 2
slice_index = 97
print(data.shape[view_index])
#%%
# show deeplab output
show_3view3label(view_index, slice_index)
# show raw image & dense output
## show_image_dense(pat_index, view_index, slice_index)
#%
# plot to see with different threshold
thresholds = [0,2,4,6,8,10]
blood_threshold = 0
muscle_threshold = 0
show_argmax(view_index,slice_index,thresholds,blood_threshold,muscle_threshold)
show_argmax2(view_index,slice_index,thresholds,blood_threshold,muscle_threshold)
#%%
'''
# generate nii.gz label with gaussian mixture
label = np.zeros(data.shape, dtype=np.int16)
background_threshold = 0
for i in range(data.shape[view_index]):
predict = generate_predict_blood_gaussian_dev(view_index,i)
predict = crop_edge_pixel(predict)
if view_index == 2:
label[:,:,i] = 2 - predict
if view_index == 1:
label[:,i,:] = 2 - predict
if view_index == 0:
label[i,:,:] = 2 - predict
'''
#%% ========== ========== ========== ========== ========== ==========
# ##### generate nii.gz label #####
view_index = 0
label = np.zeros(data.shape, dtype=np.int16)
background_threshold = 0 # 0
muscle_threshold= 0
for i in range(data.shape[view_index]):
predict = generate_predict(view_index,
i,
background_threshold,
blood_threshold,
muscle_threshold=muscle_threshold)
predict = crop_edge_pixel(predict)
if view_index == 2:
label[:,:,i] = 2 - predict
if view_index == 1:
label[:,i,:] = 2 - predict
if view_index == 0:
label[i,:,:] = 2 - predict
#%%
label_out = nib.Nifti1Image(label, np.eye(4))
label_name = 'testing_axial_crop_pat'+str(test_index)+'-label.nii.gz'
nib.save(label_out, label_name)
#%% ========== ========== ========== ========== ========== ==========
'''
view_index = 2
if view_index == 0:
label_all = []
# generate nii.gz label using add mode with gaussian
label = np.zeros(data.shape, dtype=np.int16)
for i in range(data.shape[view_index]):
pred = generate_predict_blood_gaussian_dev(view_index,i)
if view_index == 2:
label[:,:,i] = 2 - pred
if view_index == 1:
label[:,i,:] = 2 - pred
if view_index == 0:
label[i,:,:] = 2 - pred
label_all.append(copy.deepcopy(label))
'''
#%% ========== ========== ========== ========== ========== ==========
view_index = 2
if view_index == 0:
label_all = []
# ##### generate nii.gz label using add mode #####
label = np.zeros(data.shape, dtype=np.int16)
for i in range(data.shape[view_index]):
#% add mode
slice_index = i
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]
#% no normalization
prob_c0_ = prob_c0
prob_c1_ = prob_c1
prob_c2_ = prob_c2
#%
pred, _ = generate_prediction_from_slices_dev_dev(prob_c0_,
prob_c1_,
prob_c2_,
show=False,
factor=0.5,
threshold=0,
muscle_threshold=0)
pred = crop_edge_pixel(pred)
if view_index == 2:
label[:,:,i] = 2 - pred
if view_index == 1:
label[:,i,:] = 2 - pred
if view_index == 0:
label[i,:,:] = 2 - pred
label_all.append(copy.deepcopy(label))
#%% ========== ========== ========== ========== ========== ==========
label = np.zeros(data.shape, dtype=np.int16)
label[:,:,:] = label_voting(label_all)
#%% ========== ========== ========== ========== ========== ==========
label_v0 = label_all[0] # copy.deepcopy(label)
label_v1 = label_all[1] # copy.deepcopy(label)
label_v2 = label_all[2] # copy.deepcopy(label)
#%%
label = np.zeros(data.shape, dtype=np.int16)
union = np.logical_and(label_v0 == label_v1,label_v1 == label_v2)
label[union] = label_v0[union]
#%% ========== ========== ========== ========== ========== ==========
label_out = nib.Nifti1Image(label, np.eye(4))
label_name = 'testing_axial_crop_pat'+str(test_index)+'-label.nii.gz'
nib.save(label_out, label_name)
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
label = np.zeros(data.shape, dtype=np.int16)
label = (label_v0 + label_v1 + label_v2)/3
label = np.round(label)
show(label[:,154,:])
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
# integration analysis
import os
import numpy as np
import nibabel as nib
from nibabel.testing import data_path
import pickle
from PIL import Image
# load raw data
test_index = 10
nii_data = r'C:/Users/horsepurve/Dropbox/UBR/Analysis/TopoSeg/gluon/Axial-cropped/Test dataset/testing_axial_crop_pat'+str(test_index)+'.nii.gz'
img = nib.load(nii_data)
data = img.get_fdata()
print(data.shape)
#%
# load pkl
print('test sample:', test_index)
pkl_dir = r'F:\Project\UB\Projects\HVSMR\no_style_pkl'
## pkl_dir = r'D:\UB\HVSMR\LCloss\no_1scale_pkl' # no_pkl no_1scale_pkl with_pkl with_1scale_pkl
'''
[we have:]
no_style_pkl
styled_pkl
no_style_1scale_pkl
styled_1scale_pkl
'''
with open(os.path.join(pkl_dir, str(test_index)+'.pkl'), 'rb') as fi:
score_map0_0 = pickle.load(fi)
score_map0_1 = pickle.load(fi)
score_map0_2 = pickle.load(fi)
score_map1_0 = pickle.load(fi)
score_map1_1 = pickle.load(fi)
score_map1_2 = pickle.load(fi)
score_map2_0 = pickle.load(fi)
score_map2_1 = pickle.load(fi)
score_map2_2 = pickle.load(fi)
score_map_add_0 = np.zeros(score_map0_0.shape)
score_map_add_1 = np.zeros(score_map0_0.shape)
score_map_add_2 = np.zeros(score_map0_0.shape)
score_map_add_0 = score_map0_0 + score_map1_0 + score_map2_0
score_map_add_1 = score_map0_1 + score_map1_1 + score_map2_1
score_map_add_2 = score_map0_2 + score_map1_2 + score_map2_2
#%% ========== ========== ========== ========== ========== ==========
# large scale ensemble
label_all = []
#%%
mode='single'
background_threshold = 1
muscle_threshold = 2
blood_threshold = -4
factor = 0.2
generate_3_predicted_labels_dev(label_all,mode=mode,
background_threshold=background_threshold,
muscle_threshold=muscle_threshold,
blood_threshold=blood_threshold,factor=factor)
#%%
slice_index = 88
print('ensemble number:', len(label_all))