def train_combination(windows, classifiers, extractors, training1, procs=None):
'''
Build a combination of operators using the provided feature extractor and classifiers.
The `procs` parameter can be used to tune the number of parallel training jobs.
'''
nwins = len(windows)
ops = [
trios.WOperator(windows[i], classifiers[i], extractors[i])
for i in range(nwins)
]
args = zip(ops, itertools.repeat(training1))
pool = mp.Pool(procs)
trained_ops = pool.map(__train_parallel, args)
pool.close()
pool.join()
del pool
return CombinationPattern(*trained_ops)
def test_pickle_woperator_tree(self):
win = np.ones((9, 3), np.uint8)
ims = trios.Imageset.read("images/level1.set")
wop = trios.WOperator(win, SKClassifier(DecisionTreeClassifier()),
RAWFeatureExtractor)
wop.train(ims)
temp = tempfile.NamedTemporaryFile(delete=False)
name = temp.name
temp.close()
gz = gzip.GzipFile(name, mode='w+b')
pickle.dump(wop, gz, -1)
gz.close()
gz = gzip.GzipFile(name, mode='rb')
wop2 = pickle.load(gz)
gz.close()
compare_window(wop.window, wop2.window)
compare_raw_fe(wop.extractor, wop2.extractor)
self.assertEqual(wop.trained, wop2.trained)
self.assertEqual(type(wop.classifier.cls), type(wop2.classifier.cls))
import numpy as np
import trios.shortcuts.persistence as p
import trios.shortcuts.window as w
if __name__ == '__main__':
np.random.seed(10)
images = trios.Imageset.read('images/level1.set')
images2 = trios.Imageset.read('images/level2.set')
test = trios.Imageset.read('images/test.set')
domain = np.ones((7, 7), np.uint8)
ops = []
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')
from trios.contrib.kern_approx import NystromFeatures
from trios.classifiers import SKClassifier
from trios.feature_extractors import Aperture
import trios
import numpy as np
from sklearn.svm import LinearSVC
from aperture import training, testset
if __name__ == '__main__':
np.random.seed(10)
domain = np.ones((11, 11), np.uint8)
ap = Aperture(domain, k=10, mul=0.5)
ka_features = NystromFeatures(ap,
training,
n_components=200,
kernel='gaussian',
gamma=0.01,
batch_size=20000)
svm = SKClassifier(LinearSVC(), partial=True)
op = trios.WOperator(domain, svm, ka_features)
op.train(training)
print('Accuracy:', 1 - op.eval(testset[:2]))
from trios.classifiers import SKClassifier
from sklearn.tree import DecisionTreeClassifier
from trios.contrib.features.sobel import SobelExtractor
import trios
import numpy as np
import trios.shortcuts.persistence as p
drive_location = 'datasets/drive'
training = trios.Imageset([
('%s/training/images/%2d_training.tif'%(drive_location, i),
'%s/training/1st_manual/%2d_manual1.gif'%(drive_location, i),
'%s/training/mask/%2d_training_mask.gif'%(drive_location, i))
for i in range(21, 41)])
testset = trios.Imageset([
('%s/test/images/%02d_test.tif'%(drive_location, i),
'%s/test/1st_manual/%02d_manual1.gif'%(drive_location, i),
'%s/test/mask/%02d_test_mask.gif'%(drive_location, i))
for i in range(1, 21)])
if __name__ == '__main__':
win = np.ones((9,9), np.uint8)
sobel = SobelExtractor(window=win)
op = trios.WOperator(win, SKClassifier(DecisionTreeClassifier()), sobel)
print('Training')
op.train(training)
print('Evaluating')
print('Accuracy:', 1 - op.eval(testset))
from trios.classifiers.isi import ISI
from trios.feature_extractors import RAWBitFeatureExtractor
import trios
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(7, 7)
# ISI requires features coded in the bits of an int32
op = trios.WOperator(win, ISI, RAWBitFeatureExtractor)
op.train(training_set)
print('Error: ', op.eval(test))
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))
if __name__ == '__main__':
np.random.seed(10)
images = trios.Imageset.read('../jung-images/level1.set')
images2 = trios.Imageset.read('../jung-images/level2.set')
test = trios.Imageset.read('../jung-images/test.set')
domain = np.ones((9, 7), np.uint8)
# Train all 50 transforms before starting NILC and apply them to images2
# This takes the fist 50 elements from the iterator and returns them.
operators_50 = list(
itertools.islice(operator_with_random_window(images, domain), 50))
comb = CombinationPattern(*operators_50)
Z, y = comb.extract_dataset(images2, True)
# nilc_precomputed requires the target vector coded in 0-1 labels
y = y / 255
which_ops, cls2nd, progress = nilc_precomputed(Z, y, lamb=4.2, max_age=10)
selected_transforms = [
op for i, op in enumerate(operators_50) if i in which_ops
]
comb_final = CombinationPattern(*selected_transforms)
op2 = trios.WOperator(domain, cls2nd, comb_final)
print('Final error:', op2.eval(test))
print('Number of transforms:', len(op2.extractor))
plot_progress(progress)
from sklearn.tree import DecisionTreeClassifier
from trios.contrib.features.fourier import FourierExtractor
import trios
import numpy as np
import trios.shortcuts.persistence as p
drive_location = 'datasets/drive'
training = trios.Imageset([
('%s/training/images/%2d_training.tif' % (drive_location, i),
'%s/training/1st_manual/%2d_manual1.gif' % (drive_location, i),
'%s/training/mask/%2d_training_mask.gif' % (drive_location, i))
for i in range(21, 41)
])
testset = trios.Imageset([
('%s/test/images/%02d_test.tif' % (drive_location, i),
'%s/test/1st_manual/%02d_manual1.gif' % (drive_location, i),
'%s/test/mask/%02d_test_mask.gif' % (drive_location, i))
for i in range(1, 21)
])
if __name__ == '__main__':
win = np.ones((9, 9), np.uint8)
op = trios.WOperator(win, SKClassifier(DecisionTreeClassifier()),
FourierExtractor)
print('Training')
op.train(training)
print('Evaluating')
print('Accuracy:', 1 - op.eval(testset))
from sklearn.tree import DecisionTreeClassifier
from trios.contrib.features.moments import MomentsExtractor
import trios
import numpy as np
import trios.shortcuts.persistence as p
drive_location = 'datasets/'
training = trios.Imageset([
('%s/training/images/%2d_training.tif' % (drive_location, i),
'%s/training/1st_manual/%2d_manual1.gif' % (drive_location, i),
'%s/training/mask/%2d_training_mask.gif' % (drive_location, i))
for i in range(21, 41)
])
testset = trios.Imageset([
('%s/test/images/%02d_test.tif' % (drive_location, i),
'%s/test/1st_manual/%02d_manual1.gif' % (drive_location, i),
'%s/test/mask/%02d_test_mask.gif' % (drive_location, i))
for i in range(1, 21)
])
if __name__ == '__main__':
win = np.ones((9, 9), np.uint8)
moments = MomentsExtractor(win, order=4)
op = trios.WOperator(win, SKClassifier(DecisionTreeClassifier()), moments)
print('Training')
op.train(training)
print('Evaluating')
print('Accuracy:', 1 - op.eval(testset))
from sklearn.tree import DecisionTreeClassifier
from trios.feature_extractors import RAWFeatureExtractor
import trios
import numpy as np
import trios.shortcuts.persistence as p
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')
from trios.classifiers import SKClassifier
from sklearn import svm
from sklearn.tree import DecisionTreeClassifier
from trios.contrib.features.lbp import LBPExtractor
from trios.contrib.features.featurecombination import FeatureCombinationExtractor
from trios.feature_extractors import RAWFeatureExtractor
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)
lbp = LBPExtractor(window=win, batch_size=1000)
raw = RAWFeatureExtractor(window=win, batch_size=1000)
feats = []
feats.append(lbp)
feats.append(raw)
combination = FeatureCombinationExtractor(*feats)
op = trios.WOperator(
win, SKClassifier(svm.LinearSVC(class_weight='balanced'),
partial=True), combination)
op.train(images)
test = trios.Imageset.read('images/test.set')
print('Accuracy', op.eval(test, procs=1))
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))
from sklearn.tree import DecisionTreeClassifier
from trios.feature_extractors import RAWFeatureExtractor, CombinationPattern
import trios
import numpy as np
import trios.shortcuts.persistence as p
import trios.shortcuts.window as w
if __name__ == '__main__':
np.random.seed(10) # set this to select the same window everytime
images = trios.Imageset.read('../jung-images/level1.set')
images2 = trios.Imageset.read('../jung-images/level2.set')
test = trios.Imageset.read('../jung-images/test.set')
domain = np.ones((9, 7), np.uint8)
windows = [w.random_win(domain, 40, True) for i in range(5)]
ops = []
for i in range(5):
op = trios.WOperator(windows[i], SKClassifier(DecisionTreeClassifier()), RAWFeatureExtractor)
print('Training...', i)
op.train(images)
ops.append(op)
comb = CombinationPattern(*ops)
wop2 = trios.WOperator(comb.window, SKClassifier(DecisionTreeClassifier()), comb)
print('Training 2nd level')
wop2.train(images2)
print('Error', wop2.eval(test))
