def test_loader():
nn_input_shape = (32, ) * 3
norm_patch_shape = (32, ) * 3
preprocessors = [
AugmentFPRCandidates(
candidates_csv="candidates_V2",
tags=["luna:3d"],
output_shape=nn_input_shape,
norm_patch_shape=norm_patch_shape,
augmentation_params={
"scale": [1, 1, 1], # factor
"uniform scale": 1, # factor
"rotation": [0, 0, 0], # degrees
"shear": [0, 0, 0], # deg
"translation": [0, 0, 0], # mm
"reflection": [0, 0, 0]
}, # Bernoulli p
interp_order=1),
DefaultNormalizer(tags=["luna:3d"])
]
l = LunaDataLoader(only_positive=True,
multiprocess=False,
sets=TRAINING,
preprocessors=preprocessors)
l.prepare()
chunk_size = 1
batches = l.generate_batch(chunk_size=chunk_size,
required_input={
"luna:3d": (chunk_size, ) + nn_input_shape,
"luna:pixelspacing": (chunk_size, 3)
},
required_output={"luna:target": (chunk_size, )})
for sample in batches:
import utils.plt
print sample[INPUT]["luna:3d"].shape, sample[OUTPUT][
"luna:target"], sample[INPUT]["luna:pixelspacing"]
utils.plt.show_animate(np.clip(sample[INPUT]["luna:3d"][0] + 0.25, 0,
1),
50,
normalize=False)
#####################
training_data = LunaDataLoader(sets=TRAINING,
epochs=1,
preprocessors=preprocessors,
multiprocess=False,
crash_on_exception=True)
chunk_size = 1
training_data.prepare()
if True:
print training_data.number_of_samples
batches = training_data.generate_batch(
chunk_size=chunk_size,
required_input={},
required_output={"luna:segmentation": None},
)
# import matplotlib.pyplot as plt
import numpy as np
import utils.buffering
i = 0
np.set_printoptions(formatter={'float_kind': lambda x: "%.1f" % x})
positive_pixels = 0
zero_pixels = 0
for data in batches:
i += 1
epochs=1,
preprocessors=preprocessors,
multiprocess=False,
crash_on_exception=True)
chunk_size = 1
training_data.prepare()
if False:
print training_data.number_of_samples
batches = training_data.generate_batch(
chunk_size=chunk_size,
required_input={
"luna:shape": (chunk_size, 3),
"luna:pixelspacing": (chunk_size, 3)
}, #"luna:3d":(chunk_size,512,512,512),
required_output=dict(
) #{"luna:segmentation":None, "luna:sample_id":None},
)
# import matplotlib.pyplot as plt
import numpy as np
import utils.buffering
maximum_pixels = np.zeros(shape=(3, ))
maximum_mm = np.zeros(shape=(3, ))
i = 0
np.set_printoptions(formatter={'float_kind': lambda x: "%.1f" % x})