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 _data10nat():
# Sparse annotations
train_data = get_10lamb_old(5)
from datasets.examples import get_10lamb
from data.conversion_tools import annotations2y
train_data.y_tr = annotations2y(get_10lamb().get("annot_tflearning"))
return train_data
def class_imbalance():
y = annotations2y(imread('/home/lameeus/data/ghent_altar/input/hierarchy/10_lamb/annotations/kfold/annot_comb.png'))
n = np.sum(y, axis=(0, 1))
print(n)
print('f:', n/np.sum(n))
return
def set_img_x(self):
if self.set_nr == 13:
train_data = get_13(self.mod)
from data.datatools import imread
from data.conversion_tools import annotations2y
train_data.y_te = np.copy(train_data.y_tr)
train_data.y_tr = annotations2y(imread(
'/home/lameeus/data/ghent_altar/input/hierarchy/13_small/clean_annot_practical.png'
),
thresh=.9)
img_x, img_y, _, img_y_te = get_training_data(train_data)
# Normalise the input!
img_x = rescale0to1(img_x)
self.img_x = img_x
self.img_y_tr = img_y
self.img_y_te = img_y_te
train_data_10 = get_10lamb_6patches(self.mod).get_train_data_all()
img_x_10, img_y_10, _, _ = get_training_data(train_data_10)
# Normalise the input!
img_x_10 = rescale0to1(img_x_10)
self.flow_tr_set = get_flow(self.img_x,
self.img_y_tr,
w_patch=self.w_patch,
w_ext_in=self.w_ext_in_ti)
self.flow_tr_10 = get_flow(img_x_10,
img_y_10,
w_patch=self.w_patch,
w_ext_in=self.w_ext_in_ti)
n_multiply = 10
self.flow_tr_set_10 = get_flow([self.img_x] * n_multiply + [img_x_10],
[self.img_y_tr] * n_multiply +
[img_y_10],
w_patch=self.w_patch,
w_ext_in=self.w_ext_in_ti)
self.flow_ae_tr = get_flow(
self.img_x,
self.img_x,
w_patch=self.w_patch,
w_ext_in=self.w_ext_in_ae,
)
def data_lamb():
img_x, _ = xy_from_df(get_10lamb(), 5)
if 0:
img_y_val = annotations2y(get_10lamb_kfold().get("annot_clean_comb"))
else:
# "Improved" evaluation set
from datasets.default_trainingsets import get_10lamb_6patches
kFoldTrainData = get_10lamb_6patches(5)
img_y_val_lst = []
for i in [1, 2, 3, 5]:
y_i = kFoldTrainData.k_split_i(i).get_y_test()
img_y_val_lst.append(y_i)
img_y_val = np.sum(img_y_val_lst, axis=0)
return img_x, img_y_val
def lamb():
folder_in = '/net/gaimfs/research/lameeus/data/Nina/'
im_clean = imread(os.path.join(folder_in, 'clean.png'))
im_detection = imread(os.path.join(folder_in, 'paintloss_tiunet_enc0.png'))
extra1 = imread(
'/scratch/lameeus/data/ghent_altar/input/hierarchy/10lamb/mask_pred_comb.png'
)
b1 = np.equal(extra1, 255)
im_update = np.copy(im_clean)
extra2 = imread('/home/lameeus/Desktop/extra_annot.png')
cyan = [0, 255, 255]
im_update[im_detection.astype(bool), :] = cyan
im_update[b1, :] = cyan
y_extra2 = annotations2y(extra2)
y_extra2_0 = y_extra2[..., 0].astype(bool)
y_extra2_1 = y_extra2[..., 1].astype(bool)
im_update[y_extra2_0, :] = im_clean[y_extra2_0, :]
im_update[y_extra2_1, :] = cyan
plt.imshow(im_update)
from data.conversion_tools import detect_colour
paintloss_updated = detect_colour(im_update, 'cyan')
imsave('/net/gaimfs/research/lameeus/data/Nina/detection_updated.png',
paintloss_updated)
return im_update
def crop_left_bot_script():
panel_nr = 19 # 13, 19
modality = 'annot'
modality = 'src'
folder = f'/home/lameeus/data/ghent_altar/input/hierachy/{panel_nr}_small'
if modality == 'annot':
path_mod = os.path.join(folder, 'annot' + '.tif')
elif modality == 'src':
folder_src = f'/scratch/lameeus/data/ghent_altar/output/hierarchy/{panel_nr}_small/fancy'
path_mod = os.path.join(folder_src, f'{panel_nr}_3_src-kf.jpg')
path_clean = os.path.join(folder, 'clean.tif')
img_clean = get_image(path_clean)[..., :3]
img_mod = get_image(path_mod)
img_clean = get_crop(img_clean)
if modality == 'annot':
img_mod = get_crop(img_mod)
elif modality == 'src':
img_mod = resize(img_mod, (300, 300))
y_mod = annotations2y(img_mod, thresh=.7)
if modality == 'src':
plt.imshow(y_mod[..., 0])
cyan = [0, 255, 255]
green = [0, 127, 0]
lime = [50, 205, 50]
gray = [127, 127, 127]
white = [255, 255, 255]
black = [0, 0, 0]
b1 = y_mod[..., 1].astype(bool)
img_clean_y = np.copy(img_clean)
img_clean_y[b1, :] = cyan
# background
def grayer(img, img_clean, b1):
frac = .5
img[~b1, :] = (frac * img_clean[~b1, :] +
(1 - frac) * np.asarray(gray)).astype(img.dtype)
def darker(img, img_clean, b1):
frac = .5
img[~b1, :] = (frac * img_clean[~b1, :] +
(1 - frac) * np.asarray(black)).astype(img.dtype)
grayer(img_clean_y, img_clean, b1)
# darker(img, img_clean, b1)
plt.imshow(img_clean_y)
plt.show()
if 0:
path_save = f'/scratch/lameeus/data/ghent_altar/output/hierarchy/{panel_nr}_small/fancy/shuang_{modality}.png'
if os.path.exists(path_save) == False:
save_img(path_save, img_clean_y)
else:
print(f"didn't save: {path_save}")
return img_clean_y
def xy_from_df(df, mod):
img_x = x_from_df(df, mod)
img_y = annotations2y(df.get('annot'))
return img_x, img_y
def combine_annot(b_save=True):
ext = 100
red = [255, 0, 0]
blue = [0, 0, 255]
folder_annots = '/home/lameeus/data/ghent_altar/input/hierachy/10_lamb/annotations/lameeus'
folder_save = '/home/lameeus/data/ghent_altar/input/hierachy/10_lamb/annotations/kfold'
# Clean image
im_clean = imread(
'/home/lameeus/data/ghent_altar/input/hierachy/10_lamb/clean.png')
im_white = 255 * np.ones(shape=im_clean.shape, dtype=np.uint8)
im_annot = np.copy(im_white)
im_annot_clean = np.copy(im_clean)
def apply_redblue(im, h0, h1, w0, w1, b0, b1):
# red to red
# rest to blue
im[h0:h1, w0:w1, :][~b1, :] = blue
im[h0:h1, w0:w1, :][b1, :] = red
for i in range(6):
im = imread(os.path.join(folder_annots,
f'crop{i+1}_annotation_lm.png'))
w0, w1, h0, h1 = lst_get[i]()
im_crop = im[ext:-ext, ext:-ext, :]
y_crop = annotations2y(im_crop, thresh=.9)
b1 = y_crop[..., 1].astype(bool)
if 0:
plt.figure()
plt.imshow(im_crop)
plt.show()
if b_save:
im_annot_i = np.copy(im_white)
im_annot_clean_i = np.copy(im_clean)
apply_redblue(im_annot_i, h0, h1, w0, w1, ~b1, b1)
apply_redblue(im_annot_clean_i, h0, h1, w0, w1, ~b1, b1)
imsave(os.path.join(folder_save, f'annot_{i+1}.png'), im_annot_i)
imsave(os.path.join(folder_save, f'annot_clean_{i+1}.png'),
im_annot_clean_i)
apply_redblue(im_annot, h0, h1, w0, w1, ~b1, b1)
apply_redblue(im_annot_clean, h0, h1, w0, w1, ~b1, b1)
plt.figure()
plt.imshow(im_annot)
plt.show()
if b_save:
imsave(os.path.join(folder_save, 'annot_comb.png'), im_annot)
imsave(os.path.join(folder_save, 'annot_clean_comb.png'),
im_annot_clean)
return
