"""
.. 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()
"""
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()))
"""
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:: 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())