def apply_batch(operator, testset, folder_prefix, procs=None):
'''
Applies an operator in all images of a given testset. The
images are saved in folder_prefix with the same name as the
groundtruth images.
'''
for (i, o, m) in testset:
inp = p.load_image(i)
msk = p.load_mask_image(m, inp.shape, operator.window)
o_name = os.path.split(o)[1]
res = operator.apply(inp, msk)
p.save_image(res, '%s/%s' % (folder_prefix, o_name))
for i in range(5):
win = w.random_win(domain, 40, True)
op = trios.WOperator(win, SKClassifier(DecisionTreeClassifier()),
RAWFeatureExtractor)
print('Training...', i)
op.train(images)
ops.append(op)
comb = CombinationPattern(*ops)
wop2 = trios.WOperator(comb.window,
SKClassifier(DecisionTreeClassifier(),
ordered=True),
comb,
batch=True)
print('Training 2nd level')
wop2.train(images2)
# save trained operator
p.save_gzip(wop2, 'dt-tl-jung.op')
# and load it later
wop2 = p.load_gzip('dt-tl-jung.op')
# load image and apply operator. Second argument is application mask.
img = p.load_image('images/jung-1a.png')
print('Applying to image jung-1a.png')
out = wop2.apply(img, img)
p.save_image(out, 'out-isi-jung-1a.png')
print('Accuracy', wop2.eval(test, procs=1))
print(wop2.cite_me())
from trios.classifiers import SKClassifier
from sklearn.tree import DecisionTreeClassifier
from trios.feature_extractors import Aperture
import trios
import numpy as np
import trios.shortcuts.persistence as p
if __name__ == '__main__':
images = trios.Imageset([('images_gl/einstein-noise.png',
'images_gl/einstein-original.png', None)])
win = np.ones((7, 7), np.uint8)
# use Decision Tree Classifier and raw pixels as features.
ap = Aperture(win, 10, mul=0.5)
op = trios.WOperator(win, SKClassifier(DecisionTreeClassifier()), ap)
print('Training...')
op.train(images)
# save trained operator
p.save_gzip(op, 'ap-einstein.op')
# and load it later
op2 = p.load_gzip('ap-einstein.op')
# load image and apply operator. Second argument is application mask.
img = p.load_image('images_gl/einstein-noise.png')
print('Applying to image images_gl/einstein-noise.png')
out = op.apply(img, img)
p.save_image(out, 'out-ap-einstein.png')
images = trios.Imageset.read('images/level1.set')
win = np.ones((5, 5), np.uint8)
# use Decision Tree Classifier and raw pixels as features.
op = trios.WOperator(win, SKClassifier(DecisionTreeClassifier()),
RAWFeatureExtractor)
print('Training...')
op.train(images)
# save trained operator
p.save_gzip(op, 'dt-jung.op')
# and load it later
op2 = p.load_gzip('dt-jung.op')
# load image and apply operator. Second argument is application mask.
img = p.load_image('images/jung-1a.png')
print('Applying to image jung-1a.png')
out = op.apply(img, img)
p.save_image(out, 'out-dt-jung-1a.png')
test = trios.Imageset.read('images/test.set')
print('Accuracy', op.eval(test, procs=1, per_image=True))
print('Accuracy', op.eval(test, procs=2, per_image=True))
print('Accuracy', op.eval(test, procs=8, per_image=True))
op.extractor.batch_size = 100
out_5000 = op.apply(img, img)
assert np.sum(out_5000 != out) == 0
p.save_image(out, 'out-dt-jung-1a-2.png')
#print(op2.cite_me())
import trios.shortcuts.persistence as p
import trios
import numpy as np
if __name__ == '__main__':
jung_op = p.load_gzip('trained-jung.op.gz')
input13 = p.load_image('jung-images/jung-15b.png')
# The jung operator uses the input image as mask. Thus, it erases
# all pixels that do not belong to an s.
result = jung_op.apply(input13, input13)
p.save_image(result, 'jung13-output.png')
testset = trios.Imageset.read('jung-images/test.set')
print('Error:', jung_op.eval(testset))
from trios.classifiers import SKClassifier
from sklearn.tree import DecisionTreeClassifier
from trios.contrib.features.lbp import LBPExtractor
import trios
import numpy as np
import trios.shortcuts.persistence as p
if __name__ == '__main__':
images = trios.Imageset.read('images/training.set')
win = np.ones((5, 5), np.uint8)
op = trios.WOperator(win, SKClassifier(DecisionTreeClassifier()),
LBPExtractor)
op.train(images)
img = p.load_image('images/jung-1a.png')
lbp = op.apply(img, img)
p.save_image(lbp, 'lbp-out.png')
test = trios.Imageset.read('images/test.set')
print('Accuracy', op.eval(test, procs=7))
import pyximport
pyximport.install()
from trios.classifiers import SKClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import svm
from trios.contrib.features.sobel import SobelExtractor
import trios
import numpy as np
import trios.shortcuts.persistence as p
if __name__ == '__main__':
images = trios.Imageset.read('images/training.set')
win = np.ones((5, 5), np.uint8)
sobel = SobelExtractor(window=win, batch_size=1000)
op = trios.WOperator(win, SKClassifier(svm.LinearSVC()), sobel)
op.train(images)
img= p.load_image('images/jung-1a.png')
lbp = op.apply(img, img)
p.save_image(lbp, 'sobel-out.png')
test = trios.Imageset.read('images/test.set')
print('Accuracy', op.eval(test, procs=7))