def predict_average(self):
y_lst = []
for i_fold in range(6):
for k in range(18, 19 + 1):
for epoch in range(99, 100 + 1):
y = self.predict(i_fold=i_fold, k=k, epoch=epoch)
y_lst.append(y)
y_avg = np.mean(y_lst, axis=0)
y_avg_bin = self.get_bin(y_avg)
img_clean = self.img_x[..., :3]
y_avg_bin_overlay = semi_transparant(img_clean, y_avg_bin)
concurrent([y_avg[..., 1], y_avg_bin, y_avg_bin_overlay, img_clean])
if 0:
path_save = f'/scratch/lameeus/data/ghent_altar/output/hierarchy/10_lamb/' \
f'paintloss_tiunet_enc{self.fixed_enc}.png'
imsave(path_save, y_avg_bin)
if 1:
path_save = f'/scratch/lameeus/data/ghent_altar/output/hierarchy/10_lamb/' \
f'paintloss_overlay_tiunet_enc{self.fixed_enc}.png'
imsave(path_save, y_avg_bin_overlay)
return 1
def folds_annot():
train_data = get_10lamb_old(5)
img_x, _, _, _ = get_training_data(train_data)
img_clean = img_x[..., :3]
lst_get = [get_borders1, get_borders2, get_borders3, get_borders4, get_borders5, get_borders6]
for i_fold in range(6):
img_annot = imread(f'/home/lameeus/data/ghent_altar/input/hierachy/10_lamb/annotations/kfold/annot_{i_fold+1}.png')
y1 = annotations2y(img_annot, thresh=.8)[..., 1]
a = semi_transparant(img_clean, y1.astype(bool))
w0, w1, h0, h1 = lst_get[i_fold]()
clean_annot_crop = a[h0:h1, w0:w1, :]
img_clean_crop = img_clean[h0:h1, w0:w1, :]
if 0: concurrent([img_clean_crop, clean_annot_crop])
folder_save = '/scratch/lameeus/data/ghent_altar/input/hierarchy/10lamb/ifolds'
imsave(os.path.join(folder_save, f'clean_crop_ifold{i_fold}.png'), img_clean_crop)
imsave(os.path.join(folder_save, f'clean_annot_crop_ifold{i_fold}.png'), clean_annot_crop)
pass
def continues_learning():
folder = '/home/lameeus/data/ghent_altar/input/hierarchy/13_small'
im_clean = imread(os.path.join(folder, 'clean.png'))[..., :3]
im_annot0 = imread(os.path.join(folder, 'annot.tif'))
im_annot1 = imread(os.path.join(folder, 'clean_annot_practical.png'))
y_true = annotations2y(im_annot0)
y_true_extra = annotations2y(im_annot1, thresh=.9)
folder = '/home/lameeus/data/ghent_altar/output/hierarchy/13_small/practical_annotations'
y_pred0 = imread(os.path.join(folder, 'pred_transfer_kfoldenc2_ifold0_avg.png'))
folder = '/home/lameeus/data/ghent_altar/output/hierarchy/13_small'
y_pred1 = imread(os.path.join(folder, 'pred_transfer_kfoldenc2_ifold0_avg_epoch50_J0427.png'))
from performance.testing import optimal_test_thresh_equal_distribution
from scripts.scripts_performance.main_performance import foo_performance
from figures_paper.overlay import semi_transparant
def get_bin(y_pred):
assert len(y_pred.shape) == 2
y_pred01 = np.stack([1-y_pred, y_pred], axis=-1)
thresh = optimal_test_thresh_equal_distribution(y_true, y_pred01)
print(foo_performance(y_true, y_pred01, thresh))
y_pred_bin = y_pred >= thresh
return y_pred_bin
y_pred0_bin = get_bin(y_pred0)
y_pred1_bin = get_bin(y_pred1)
y_pred0_bin_fancy = semi_transparant(im_clean, y_pred0_bin)
y_pred1_bin_fancy = semi_transparant(im_clean, y_pred1_bin)
concurrent([im_clean, y_true[..., 0], y_true_extra[..., 0], y_pred0, y_pred1, y_pred0_bin, y_pred1_bin,
y_pred0_bin_fancy,
y_pred1_bin_fancy])
folder_save = '/home/lameeus/data/ghent_altar/output/hierarchy/13_small/fancy'
imsave(os.path.join(folder_save, 'overalytrain10.png'), y_pred0_bin_fancy)
imsave(os.path.join(folder_save, 'overalytrain10train13.png'), y_pred1_bin_fancy)
return
def predict_compare_regular(self):
img_y_all = self.k_fold_train_data.get_train_data_all().get_y_train()
lst_get = [
get_borders1, get_borders2, get_borders3, get_borders4,
get_borders5, get_borders6
]
img_clean = self.img_x[..., :3]
for i_fold in range(6):
y_lst = []
# Average prediction
print(f'i_fold = {i_fold}')
for k in [17, 18, 19]:
print(f'k = {k}')
for epoch in [36, 37, 38, 39, 40]:
print(f'epoch = {epoch}')
y = self.predict_regular(i_fold=i_fold, k=k, epoch=epoch)
y_lst.append(y)
y_avg = np.mean(y_lst, axis=0)
y_avg_bin = self.get_bin(y_avg)
# Performance
img_y_te = self.k_fold_train_data.k_split_i(i_fold).get_y_test()
thresh = optimal_test_thresh_equal_distribution(img_y_all, y_avg)
perf = foo_performance(img_y_te, y_avg, thresh)
# CROP
w0, w1, h0, h1 = lst_get[i_fold]()
y_avg_bin_crop = y_avg_bin[h0:h1, w0:w1]
clean_crop = img_clean[h0:h1, w0:w1, :]
y_avg_bin_transparent_crop = semi_transparant(
clean_crop, y_avg_bin_crop)
if 0:
concurrent(
[clean_crop, y_avg_bin_crop, y_avg_bin_transparent_crop])
folder_save = '/scratch/lameeus/data/ghent_altar/output/hierarchy/10_lamb/ifolds_regular_tiunet'
filename = f'_tiunet_ifold{i_fold}_jacc{perf["jaccard"]:.3f}.png'
# Save y_bin
imsave(os.path.join(folder_save, 'binpred' + filename),
y_avg_bin_crop,
b_check_duplicate=False)
# Save overlay
imsave(os.path.join(folder_save, 'overlay' + filename),
y_avg_bin_transparent_crop,
b_check_duplicate=False)
def combine_stitches():
folder = '/home/lameeus/data/ghent_altar/output/hierarchy/10_lamb/inpainting'
im_base = imread(os.path.join(folder, 'inpainting_stitch_f2_g3_fixed_v0_linear_c256.png'))
im_leftbot = imread(os.path.join(folder, 'inpainting_stitch_f2_g3_fixed_v1_linear_c256_manual.png'))
im_base[3*256:, :(3)*256, :] = im_leftbot[3*256:, :(3)*256, :]
if 0:
plt.imshow(im_base)
imsave(os.path.join(folder, 'inpainting_comb.png'), im_base)
from figures_paper.overlay import semi_transparant
im_clean = imread('/home/lameeus/data/ghent_altar/input/hierarchy/10_lamb/clean.png')
im_paintloss = imread('/home/lameeus/data/ghent_altar/output/hierarchy/10_lamb/detection_updated.png')
im_overlay = semi_transparant(im_clean, im_paintloss)
if 1:
plt.imshow(im_overlay)
folder = '/home/lameeus/data/ghent_altar/output/hierarchy/10_lamb/fancy'
imsave(os.path.join(folder, 'det_overlay.png'), im_overlay)
def foo(n_segm, b=0):
y_pred = n_segm.predict(self.img_x)
thresh_single = optimal_test_thresh_equal_distribution(
self.img_y_te, y_pred)
# data_single_i = {'k': self.k,
# 'i_fold': i_fold,
# 'epoch': epoch}
print(foo_performance(self.img_y_te, y_pred, thresh_single))
img_clean = self.img_x[..., :3]
concurrent([
img_clean, y_pred[..., 1], y_pred[..., 1] >= thresh_single,
semi_transparant(img_clean, y_pred[..., 1] >= thresh_single)
])
if b:
from data.datatools import imsave
folder = '/home/lameeus/data/ghent_altar/output/hierarchy/'
info_epoch = f'_epoch{n_segm.epoch}' if n_segm.epoch > 0 else ''
filename = folder + f'13_small/pred_transfer_kfoldenc{self.fixed_enc}_ifold{self.i_fold}_avg{info_epoch}.png'
imsave(filename, y_pred[..., 1])
def predict_compare(self):
img_y_all = self.k_fold_train_data.get_train_data_all().get_y_train()
lst_get = [
get_borders1, get_borders2, get_borders3, get_borders4,
get_borders5, get_borders6
]
img_clean = self.img_x[..., :3]
for i_fold in range(6):
for i_fixed_enc in range(3):
self.fixed_enc = i_fixed_enc
y_lst = []
# Average prediction
for k in [17, 18, 19]:
for epoch in [96, 97, 98, 99, 100]:
y = self.predict(i_fold=i_fold, k=k, epoch=epoch)
y_lst.append(y)
y_avg = np.mean(y_lst, axis=0)
y_avg_bin = self.get_bin(y_avg)
# Performance
img_y_te = self.k_fold_train_data.k_split_i(
i_fold).get_y_test()
thresh = optimal_test_thresh_equal_distribution(
img_y_all, y_avg)
perf = foo_performance(img_y_te, y_avg, thresh)
# CROP
w0, w1, h0, h1 = lst_get[i_fold]()
y_avg_bin_crop = y_avg_bin[h0:h1, w0:w1]
clean_crop = img_clean[h0:h1, w0:w1, :]
y_avg_bin_transparent_crop = semi_transparant(
clean_crop, y_avg_bin_crop)
if 0:
concurrent([
clean_crop, y_avg_bin_crop, y_avg_bin_transparent_crop
])
# Save
if self.fixed_enc == 0: info_enc = 'Train'
elif self.fixed_enc == 1: info_enc = 'Fixed'
elif self.fixed_enc == 2: info_enc = 'FixedTrain'
folder_save = '/scratch/lameeus/data/ghent_altar/output/hierarchy/10_lamb/ifolds'
filename = f'_enc{info_enc}_ifold{i_fold}_jacc{perf["jaccard"]:.3f}.png'
# Save y_bin
imsave(os.path.join(folder_save, 'binpred' + filename),
y_avg_bin_crop,
b_check_duplicate=False)
# Save overlay
imsave(os.path.join(folder_save, 'overlay' + filename),
y_avg_bin_transparent_crop,
b_check_duplicate=False)
def main():
b_encoder_fixed = False
info_enc_fixed = '_enc_fixed'
folder_weights = '/scratch/lameeus/data/ghent_altar/net_weight/10lamb_kfold_pretrained'
folder_save = '/home/lameeus/data/ghent_altar/dataframes'
filename_single = f'pretrained_unet_10lamb_kfold_single'
filename_avg_pred = f'pretrained_unet_10lamb_kfold_avgpred'
folder_weights += info_enc_fixed if b_encoder_fixed else ''
filename_single += info_enc_fixed if b_encoder_fixed else ''
filename_avg_pred += info_enc_fixed if b_encoder_fixed else ''
fold_range = range(6)
# fold_range = [0, 1]
k = 10
epoch_range = range(1, 40 + 1)
w_ext_in = 28
k_fold_train_data = get_10lamb_6patches(5) # 5 is the number of modalities
train_data_all = k_fold_train_data.get_train_data_all()
img_x = train_data_all.get_x_train()
img_x = rescale0to1(img_x)
img_clean = img_x[..., :3]
img_y_all = train_data_all.get_y_train()
b_plot = False
for i_fold in fold_range:
print(i_fold)
img_y_te = k_fold_train_data.k_split_i(i_fold).get_y_test()
# Init for range epochs
lst_data_single = []
lst_data_avg_pred = []
list_y_pred = []
model = None
for epoch in np.sort(epoch_range)[::-1]:
filepath_model = os.path.join(
folder_weights, f'unet_enc_k{k}_ifold{i_fold}/w_{epoch}.h5')
model = load_model_quick(filepath_model, model=model)
n = NeuralNet(model, w_ext=w_ext_in)
y_pred = n.predict(img_x)
"""
Average out predictions
"""
list_y_pred.append(y_pred)
y_avg_pred = np.mean(list_y_pred, axis=0)
thresh_single = optimal_test_thresh_equal_distribution(
img_y_all, y_pred)
thresh_avg_pred = optimal_test_thresh_equal_distribution(
img_y_all, y_avg_pred)
y_pred_bin = np.greater_equal(y_pred[..., 1], thresh_single)
dict_perf = foo_performance(img_y_te, y_pred, thresh_single)
print(dict_perf)
if b_plot:
concurrent([
y_pred_bin, img_clean,
semi_transparant(img_clean, y_pred_bin),
semi_transparant(img_clean, img_y_te[..., 1].astype(bool))
])
data_single_i = {'k': k, 'i_fold': i_fold, 'epoch': epoch}
data_avg_pred_i = {
'k': k,
'i_fold': i_fold,
'epoch_start': epoch,
'epoch_end': max(epoch_range)
}
data_single_i.update(dict_perf)
data_avg_pred_i.update(
foo_performance(img_y_te, y_avg_pred, thresh_avg_pred))
lst_data_single.append(data_single_i)
lst_data_avg_pred.append(data_avg_pred_i)
df_single = pd.DataFrame(lst_data_single)
df_avg_pred = pd.DataFrame(lst_data_avg_pred)
path_single = os.path.join(folder_save, filename_single + '.csv')
path_avg_pred = os.path.join(folder_save, filename_avg_pred + '.csv')
pandas_save(path_single, df_single, append=True)
pandas_save(path_avg_pred, df_avg_pred, append=True)
return
def transfer_learning(
epoch=25, # Could check a few
b_plot=False):
d = 2 # 1, 2
img_x, img_y_val = data_lamb()
k = 10
model_name = 'ti-unet'
w_ext = 10 if d == 1 else 26
# train_data:
y_pred_lst = []
n = ['clean']
# train_data_lst = ['1319_10', '10', '1319', '1319_101319']
train_data_lst = ['10nat', '1319_10nat', '1319_10nat1319', '1319']
data_i_lst = {}
for train_data in train_data_lst:
print(train_data)
epoch_start = 50
epoch_corr = epoch + epoch_start if train_data[:5] == '1319_' else epoch
if train_data == '1319':
epoch_corr = 50
path = f'C:/Users/admin/Data/ghent_altar/net_weight/{train_data}/{model_name}_d{d}_k{k}/w_{epoch_corr}.h5'
try:
model = load_model_quick(path)
except Exception as e:
print(e)
continue
neural_net = NeuralNet(model, w_ext=w_ext, norm_x=True)
y_pred = neural_net.predict(img_x)
# baseline
data_i = _eval_func_single(img_y_val, y_pred, metric='kappa')
print(data_i)
if 0:
"""
Checking which
baseline ~ .22
i = 0: .268, Remove huge improvement ( a lot of "green" background annotated as paint loss)
i = 1: .228 Keep!
i = 2: .179 keep! Drop (keep!!
i = 3: .159 keep! Even more important
i = 4: .252 Remove (huge problem right top)
i = 5: .233 Keep, quit relevant
"""
from datasets.default_trainingsets import get_10lamb_6patches
kFoldTrainData = get_10lamb_6patches(5)
_eval_func_single(
kFoldTrainData.k_split_i(0).get_y_train(),
y_pred,
metric='kappa') # Check what is influence without!
data_i_lst[train_data] = data_i
data_i = _eval_func_single(img_y_val, y_pred, metric='jaccard')
print(data_i)
y_pred_lst.append(y_pred)
n.append(train_data)
# plt.imshow(neural_net.predict(img_x[::2,::2,:])[..., 1])
if b_plot:
concurrent([img_x[..., :3]] + [a[..., 1] for a in y_pred_lst], n)
if 0:
from figures_paper.overlay import semi_transparant
from data.datatools import imread, imsave
t = [data_i_lst[n_i]['thresh'] for n_i in train_data_lst]
p = []
for i, train_data in enumerate(train_data_lst):
b = np.greater_equal(y_pred_lst[i][..., 1], t[i])
k = semi_transparant(img_x[..., :3], b, 0)
p.append(k)
imsave(
os.path.join(
"C:/Users/admin/OneDrive - ugentbe/data/images_paper",
train_data + '.png'), k)
concurrent(p)
return data_i_lst
print(data_i)
# Show before saving all
from figures_paper.overlay import semi_transparant
l = [im_clean]
for i, b in enumerate([b_annot, b_sh, b_pred, b_pred_unet]):
if i == 0:
# Probably not really interesting to change the colour
l.append(
semi_transparant(im_clean,
b.astype(bool),
color1='cyan',
color2='grey',
transparency=0,
transparancy2=.5))
else:
l.append(
semi_transparant(im_clean,
b.astype(bool),
color2='grey',
transparency=0,
transparancy2=.5))
concurrent(l)
folder_out = 'C:/Users/admin/OneDrive - ugentbe/data/images_paper'
imsave(os.path.join(folder_out, f'{data}_clean.png'), im_clean)
imsave(os.path.join(folder_out, f'{data}_annot.png'), l[1])