from trios.classifiers.isi import ISI
from trios.feature_extractors import RAWBitFeatureExtractor
import trios
import numpy as np
import trios.shortcuts.persistence as p
if __name__ == '__main__':
images = trios.Imageset.read('images/level1.set')
win = np.ones((5, 5), np.uint8)
# ISI requires features coded in the bits of an int32
op = trios.WOperator(win, ISI, RAWBitFeatureExtractor)
print('Training...')
op.train(images)
# save trained operator
p.save_gzip(op, 'isi-jung.op')
# and load it later
op2 = p.load_gzip('isi-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-isi-jung-1a.png')
test = trios.Imageset.read('images/test.set')
print('Accuracy', op.eval(test, procs=7))
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 RAWFeatureExtractor
import trios
import numpy as np
import trios.shortcuts.persistence as p
import trios.shortcuts.window as trios_win
if __name__ == '__main__':
training_set = trios.Imageset.read('jung-images/training.set')
test = trios.Imageset.read('jung-images/test.set')
win = trios_win.rectangle(9, 7)
classifier = SKClassifier(DecisionTreeClassifier())
fext = RAWFeatureExtractor(win)
op = trios.WOperator(win, classifier, fext)
op.train(training_set)
p.save_gzip(op, 'basic-jung.op.gz')
print('Error: ', op.eval(test))
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')
trios.show_eval_progress = False
if __name__ == '__main__':
np.random.seed(10)
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
import trios
import trios.shortcuts.persistence as p
if __name__ == '__main__':
# Imageset can be defined directly in code, like below
images = trios.Imageset([('input.png', 'output.png', None),
('input2.png', 'output2.png', 'mask.png')])
# Definitions in code can be put in a module and imported like regular
# Python code.
# It can also be saved to a gziped file using the persistence
p.save_gzip(images, 'imageset.gz')
# And loaded back
images2 = p.load_gzip('imageset.gz')
assert images[0] == images2[0]
assert images[1] == images2[1]
assert len(images) == len(images2)
# Or saved to a text-only format using the read and write methods.
images.write('imageset-text.txt')
images3 = trios.Imageset.read('imageset-text.txt')
assert images[0] == images3[0]
assert images[1] == images3[1]
assert len(images) == len(images3)
import sys
from trios.classifiers import sk_classifier
from trios import woperator
import trios.shortcuts.persistence as p
if __name__ == '__main__':
sys.modules['trios.WOperator'] = woperator
sys.modules['trios.classifiers.SKClassifier'] = sk_classifier
obj = p.load_gzip(sys.argv[1])
p.save_gzip(obj, sys.argv[2])