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 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 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 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 __init__(self, folder):
self._imgs = {}
# TODO only read image on the spot (less memory intensive)
for file in os.listdir(folder):
root, ext = os.path.splitext(file)
if ext.lower() in ['.png', '.tif', '.jpg']:
path = os.path.join(folder, file)
img = imread(path)
self._imgs[root] = img
super().__init__()
def get_19SE_shuang_crack(mod, n_per_class=80, debug=False, n_outputs=2):
"""
John the evangelist see Journal 2020 S. Huang
:param mod:
:param n_per_class:
:return:
"""
img_x, img_y_full = xy_from_df(get_19hand(), mod)
_, img_y = panel19withoutRightBot(img_y_full)
img_y_tr, img_y_te = _split_train_n_per_class(img_y, n_per_class)
from data.datatools import imread
img_y_tr_crack300 = imread(
"/home/lameeus/data/ghent_altar/input/hierarchy/19_small/grd_crack.png"
)
# Init
img_y_tr_crack = np.zeros(img_y.shape)
# Watch out, both classes are now marked as one
img_y_tr_crack[1083:1083 + 300,
1740:1740 + 300, :][img_y_tr_crack300 > 0, :] = 1
img_y_tr_crack, _ = _split_train_n_per_class(img_y_tr_crack, n_per_class)
img_y_tr_crack[..., 1] = 0 # Fix from above!
if n_outputs == 2:
img_y_tr_dual = np.logical_or(img_y_tr, img_y_tr_crack)
elif n_outputs == 3:
# Add a third class: Cracks
img_y_tr_dual = np.concatenate([img_y_tr, img_y_tr_crack[..., :1]],
axis=-1)
else:
raise ValueError(n_per_class)
assert 2 * n_per_class <= np.sum(img_y_tr_dual) <= 3 * n_per_class
if debug:
import matplotlib.pyplot as plt
plt.imshow(np.sum(img_y_tr, axis=-1))
plt.show()
train_data = TrainData(img_x, img_y_tr_dual, img_y_te)
return train_data
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,
)
# Replace paint loss in original with inpaint
def inpaint_replacer(im_orig, b_mask, im_inpaint):
assert im_orig.shape[:2] == b_mask.shape == im_inpaint.shape[:2]
im_new = im_orig.copy()
im_new[b_mask, :] = im_inpaint[b_mask, :]
return im_new
if __name__ == '__main__':
f = "C:/Users/admin/Downloads"
# Read original
im_orig = imread(
"C:/Users/admin/OneDrive - ugentbe/data/10_lamb/clean.png")
# Read paint loss detection
b_mask = detect_colour(
imread(
"C:/Users/admin/OneDrive - ugentbe/data/images_paper/1319_10nat_V3.png"
), "cyan")
if 0:
plt.imshow(b_mask)
# Read inpaint
im_inpaint = imread(os.path.join(f, "inpainting_comb.jpg"))
im_inpainted_new = inpaint_replacer(im_orig, b_mask, im_inpaint)
plt.imshow(im_inpainted_new)
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
# TODO double check performance!
import os
folder = 'C:/Users/admin/OneDrive - ugentbe/Documents/2019journal paper/2020_04_06/images/results_compare'
from data.datatools import imread, imsave
# Annot
if '13' in data:
data_nr = 13
elif '19' in data:
data_nr = 19
else:
raise ValueError(data)
im_clean = imread(os.path.join(folder, f'{data_nr}_3_clean.jpg'))
im_annot = imread(os.path.join(folder, f'{data_nr}_3_annot.png'))
im_sh = imread(os.path.join(folder, f'{data_nr}_3_src.png'))
# TODO get binary
from data.conversion_tools import detect_colour
b_annot = detect_colour(im_annot, 'cyan', thresh=.9)
b_sh = detect_colour(im_sh, 'cyan', thresh=.9)
# Performance measures: