def test_ImageWriter():
samples = [('nut_color', 1), ('nut_grayscale', 2)]
inpath = 'tests/data/img_formats/*.bmp'
img_samples = samples >> ReadImage(0, inpath) >> Collect()
imagepath = 'tests/data/test_*.bmp'
names = samples >> Get(0) >> Collect()
img_samples >> WriteImage(0, imagepath, names) >> Consume()
for sample, name in zip(img_samples, names):
filepath = 'tests/data/test_{}.bmp'.format(name)
arr = load_image(filepath)
assert np.array_equal(arr, sample[0])
os.remove(filepath)
pathfunc = lambda sample, name: 'tests/data/test_{}.jpg'.format(name)
img_samples >> WriteImage(0, pathfunc) >> Consume()
for i, sample in enumerate(img_samples):
filepath = 'tests/data/test_{}.jpg'.format(i)
os.path.exists(filepath)
os.remove(filepath)
with pytest.raises(ValueError) as ex:
img_samples >> WriteImage(0, ()) >> Consume()
assert str(ex.value).startswith('Expect path or function')
def test_CheckNaN():
assert [1, 2] >> CheckNaN() >> Collect() == [1, 2]
with pytest.raises(RuntimeError) as ex:
[1, np.NaN, 3] >> CheckNaN() >> Consume()
assert str(ex.value).startswith('NaN encountered')
with pytest.raises(RuntimeError) as ex:
[(1, np.NaN), (2, 4)] >> CheckNaN() >> Consume()
assert str(ex.value).startswith('NaN encountered')
def test_BuildBatch_verbose():
with Redirect() as out:
samples = [[1], [2], [3]]
build_batch = (nb.BuildBatch(2,
verbose=True).input(0, 'number', 'uint8'))
samples >> build_batch >> Consume()
assert out.getvalue() == '[2:uint8]\n[1:uint8]\n'
with Redirect() as out:
samples = [(np.array([1, 2, 3]), 0), (np.array([4, 5, 6]), 1),
(np.array([7, 8, 9]), 1)]
build_batch = (nb.BuildBatch(2, verbose=True).input(
0, 'vector', 'float32').output(1, 'one_hot', 'uint8', 2))
samples >> build_batch >> Consume()
expected = '[[2x3:float32], [2x2:uint8]]\n[[1x3:float32], [1x2:uint8]]\n'
assert out.getvalue() == expected
"""
.. module:: read_images
:synopsis: Example for reading and viewing of image data
"""
from nutsflow import Consume, Print
from nutsml import ReadLabelDirs, ReadImage, ViewImageAnnotation, PrintColType
if __name__ == "__main__":
show_image = ViewImageAnnotation(0, 1, pause=1, figsize=(3, 3))
(ReadLabelDirs('images', '*.png') >> Print() >> ReadImage(0) >>
PrintColType() >> show_image >> Consume())
"""
from __future__ import print_function
from glob import glob
from nutsflow import Collect, Consume, Get, Zip, Map, ArgMax, Print
from nutsml import (TransformImage, BuildBatch, ReadImage, ViewImageAnnotation,
ConvertLabel)
BATCH_SIZE = 128
if __name__ == "__main__":
from cnn_train import create_network, load_names
convert_label = ConvertLabel(None, load_names())
rerange = TransformImage(0).by('rerange', 0, 255, 0, 1, 'float32')
show_image = ViewImageAnnotation(0, 1, pause=1, figsize=(3, 3),
interpolation='spline36')
pred_batch = BuildBatch(BATCH_SIZE).input(0, 'image', 'float32')
print('loading network...')
network = create_network()
network.load_weights()
print('predicting...')
samples = glob('images/*.png') >> Print() >> ReadImage(None) >> Collect()
predictions = (samples >> rerange >> pred_batch >>
network.predict() >> convert_label)
samples >> Get(0) >> Zip(predictions) >> show_image >> Consume()
"""
.. module:: view_augmented_images
:synopsis: Example nuts-ml pipeline for viewing augmented image data
"""
from nutsflow import Take, Consume
from nutsml import ViewImageAnnotation, AugmentImage
if __name__ == "__main__":
from cnn_train import load_samples
train_samples, _ = load_samples()
p = 0.5
augment = (AugmentImage(0)
.by('identical', 1.0)
.by('brightness', p, [0.7, 1.3])
.by('rotate', p, [-10, 10])
.by('fliplr', p))
show_image = ViewImageAnnotation(0, 1, pause=1, figsize=(2, 2),
interpolation='spline36')
(train_samples >> Take(10) >> augment >> show_image >> Consume())
"""
.. module:: read_images
:synopsis: Example nuts-ml pipeline for reading and viewing image data
"""
from glob import glob
from nutsflow import Consume
from nutsml import ReadImage, ViewImage, PrintColType
if __name__ == "__main__":
show_image = ViewImage(0, pause=1, figsize=(2, 2), interpolation='spline36')
paths = glob('images/*.png')
paths >> ReadImage(None) >> PrintColType() >> show_image >> Consume()
"""
.. module:: view_train_images
:synopsis: Example nuts-ml pipeline reading and viewing image data
"""
from nutsflow import Take, Consume, MapCol
from nutsml import ViewImageAnnotation, PrintColType
if __name__ == "__main__":
from cnn_train import load_samples, load_names
train_samples, _ = load_samples()
names = load_names()
id2name = MapCol(1, lambda i: names[i])
show_image = ViewImageAnnotation(0, 1, pause=1, figsize=(2, 2),
interpolation='spline36')
(train_samples >> Take(10) >> id2name >> PrintColType() >>
show_image >> Consume())
"""
from __future__ import print_function
from nutsflow import Collect, Consume, Get, Zip, Map, Format, ArgMax
from nutsml import (TransformImage, BuildBatch, ReadImage, ReadLabelDirs,
ViewImageAnnotation)
BATCH_SIZE = 128
if __name__ == "__main__":
from mlp_train import create_network
TransformImage.register('flatten', lambda img: img.flatten())
transform = (TransformImage(0).by('rerange', 0, 255, 0, 1,
'float32').by('flatten'))
show_image = ViewImageAnnotation(0, (1, 2), pause=1, figsize=(4, 4))
pred_batch = BuildBatch(BATCH_SIZE).by(0, 'vector', 'float32')
print('loading network...')
network = create_network()
network.load_weights()
print('predicting...')
samples = ReadLabelDirs('images', '*.png') >> ReadImage(0) >> Collect()
truelabels = samples >> Get(1) >> Format('true: {}')
predictions = (samples >> transform >> pred_batch >> network.predict() >>
Map(ArgMax()) >> Format('pred: {}'))
samples >> Get(0) >> Zip(predictions,
truelabels) >> show_image >> Consume()
"""
.. module:: write_images
:synopsis: Example nuts-ml pipeline for writing of images
"""
from nutsflow import (Take, Print, Consume, Enumerate, Zip, Format, MapCol,
Get)
from nutsml import WriteImage, ConvertLabel
if __name__ == "__main__":
from cnn_train import load_samples, load_names
train_samples, _ = load_samples()
convert_label = ConvertLabel(1, load_names())
fnames = (Enumerate() >> Zip(train_samples >> Get(1)) >> convert_label >>
Format('{0}_{1}') >> Print())
imagepath = 'images/img*.png'
train_samples >> Take(10) >> WriteImage(0, imagepath, fnames) >> Consume()
from __future__ import print_function
from nutsflow import Consume, Zip, Unzip, Map, ArgMax, nut_filter
from nutsml import TransformImage, BuildBatch, ViewImageAnnotation
BATCH_SIZE = 128
if __name__ == "__main__":
from mlp_train import create_network, load_samples
TransformImage.register('flatten', lambda img: img.flatten())
transform = (TransformImage(0).by('rerange', 0, 255, 0, 1,
'float32').by('flatten'))
show_image = ViewImageAnnotation(0, (1, 2), pause=3, figsize=(3, 3))
pred_batch = BuildBatch(BATCH_SIZE).by(0, 'vector', 'float32')
IsMisclassified = nut_filter(lambda (i, t, p): p != t)
print('loading samples ...')
train_samples, test_samples = load_samples()
print('loading network...')
network = create_network()
network.load_weights()
print('predicting...')
samples = train_samples + test_samples
images, trues = samples >> Unzip()
preds = (samples >> transform >> pred_batch >> network.predict() >> Map(
ArgMax()))
images >> Zip(trues, preds) >> IsMisclassified() >> show_image >> Consume()
"""
.. module:: view_train_images
:synopsis: Example nuts-ml pipeline reading and viewing image data
"""
from nutsflow import Take, Consume, MapCol
from nutsml import ViewImageAnnotation, PrintColType, ConvertLabel
if __name__ == "__main__":
from cnn_train import load_samples, load_names
train_samples, _ = load_samples()
convert_label = ConvertLabel(1, load_names())
show_image = ViewImageAnnotation(0,
1,
pause=1,
figsize=(2, 2),
interpolation='spline36')
(train_samples >> Take(10) >> convert_label >> PrintColType() >> show_image
>> Consume())
"""
.. module:: view_data
:synopsis: Example nuts-ml pipeline viewing CIFAR-10 image data
"""
from nutsflow import Take, Consume
from nutsml import ViewImage
if __name__ == "__main__":
from cnn_train import load_samples
train_samples, test_samples = load_samples()
samples = train_samples + test_samples
show_image = ViewImage(0, pause=1, figsize=(2, 2), interpolation='spline36')
samples >> Take(10) >> show_image >> Consume()
"""
.. module:: view_train_images
:synopsis: Example for showing images with transformation
"""
from nutsflow import Take, Consume, GetCols
from nutsml import ViewImage, TransformImage
if __name__ == "__main__":
from mlp_train import load_samples
samples, _ = load_samples()
transform = (TransformImage(0).by('elastic', 5, 100))
(samples >> GetCols(0, 0, 1) >> Take(1000) >> transform >> ViewImage(
(0, 1), pause=1) >> Consume())
"""
.. module:: view_train_images
:synopsis: Example for showing images with annotation
"""
from nutsflow import Take, Consume
from nutsml import ViewImageAnnotation
if __name__ == "__main__":
from mlp_train import load_samples
samples, _ = load_samples()
(samples >> Take(10) >> ViewImageAnnotation(0, 1, pause=1) >> Consume())
img = img.copy()
msk = np.zeros(img.shape[:2])
img[sr:er, sc:ec, ...] = color
msk[sr:er, sc:ec] = C_MASK
return img, msk
def gen_samples(config, ir, ic):
for _ in range(config['samples']):
img = create_background(ir, ic)
sr, sc = rnd.uniform(0.7, 0.8), rnd.uniform(0.7, 0.8)
rp, cp = int(ir * sr), int(ic * sc)
img, ball_msk = draw_ball(img, rp, cp, 10, C_RED)
sr, sc = rnd.uniform(0.1, 0.4), rnd.uniform(0.1, 0.4)
rp, cp = int(ir * sr), int(ic * sc)
img, box_msk = draw_box(img, rp, cp, 10, C_GREEN)
yield (img, box_msk, ball_msk)
if __name__ == '__main__':
from nutsflow import Consume, Map
from nutsml import ViewImage, PrintColType
ir, ic = 64, 64
config = {'samples': 20,
'objects': {'box': [1, 1], 'ball': [1, 1]}}
gen_samples(config, ir, ic) >> ViewImage((0, 1, 2), pause=10) >> Consume()