def test_slice(self):
img = trios.Imageset([('a', 'b', None), ('c', 'b', None),
('d', 'b', None), ('e', 'b', None)])
img2 = img[:2]
self.assertEqual(type(img2), trios.Imageset)
self.assertEqual(img2.input_preffix, img.input_preffix)
self.assertEqual(img2.output_preffix, img.output_preffix)
self.assertEqual(img2.mask_preffix, img.mask_preffix)
def test_write_read(self):
img = trios.Imageset([('a', 'b', None), ('c', 'b', 'er')])
img.write('test-imageset.txt')
img2 = trios.Imageset.read('test-imageset.txt')
self.assertEqual(len(img), len(img2))
self.assertEqual(img[0], img2[0])
self.assertEqual(img[1], img2[1])
def test_append(self):
img = trios.Imageset()
with self.assertRaises(TypeError) as context:
img.append('image_name')
with self.assertRaises(TypeError) as context:
img.append(('image_name', ))
with self.assertRaises(TypeError) as context:
img.append(('image_name', 'out_name'))
img.append(('image_name', 'out_name', 'mask_name'))
self.assertEqual(img.image_list[-1],
('image_name', 'out_name', 'mask_name'))
self.assertEqual(img[-1], ('image_name', 'out_name', 'mask_name'))
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))
def test_creation_empty(self):
img = trios.Imageset()
self.assertEqual(img.image_list, [])
self.assertEqual(img.input_preffix, '')
self.assertEqual(img.output_preffix, '')
self.assertEqual(img.mask_preffix, '')
def test_len(self):
imgset = trios.Imageset()
self.assertEqual(len(imgset), 0)
imgset.append(('asdl', '1sdf', 'ldkasl'))
self.assertEqual(len(imgset), 1)
def test_indexing(self):
img = trios.Imageset([('a', 'b', None), ('c', 'b', None),
('d', 'b', None), ('e', 'b', None)])
self.assertEqual(img[0], ('a', 'b', None))
self.assertEqual(img[-1], ('e', 'b', None))
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')
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)