def __init__(self, nx, ny, numCirlces=20, numImages=100):
self.numImages = numImages
# define the generator
self.generator = image_gen.GrayScaleDataProvider(nx,
ny,
cnt=numCirlces)
# number of channels and output classes
self.channels = self.generator.channels
self.n_class = self.generator.n_class
# generate the image set
self._gen_imageset()
from __future__ import division, print_function
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
plt.rcParams['image.cmap'] = 'gist_earth'
np.random.seed(98765)
from tf_unet import image_gen
from tf_unet import unet
from tf_unet import util
nx = 3072
ny = 3072
generator = image_gen.GrayScaleDataProvider(nx, ny, cnt=20)
x_test, y_test = generator(1)
print(generator.channels)
'''
fig, ax = plt.subplots(1,2, sharey=True, figsize=(8,4))
ax[0].imshow(x_test[0,...,0], aspect="auto")
ax[1].imshow(y_test[0,...,1], aspect="auto")
'''
net = unet.Unet(channels=generator.channels,
n_class=generator.n_class,
layers=3,
features_root=16)
trainer = unet.Trainer(net,
optimizer="momentum",
opt_kwargs=dict(momentum=0.2))
'''
Created on Jul 28, 2016
author: jakeret
'''
from __future__ import print_function, division, absolute_import, unicode_literals
import numpy as np
from tf_unet import image_gen
from tf_unet import unet
from tf_unet import util
if __name__ == '__main__':
np.random.seed(98765)
generator = image_gen.GrayScaleDataProvider(nx=572,
ny=572,
cnt=20,
rectangles=False)
net = unet.Unet(channels=generator.channels,
n_class=generator.n_class,
layers=3,
features_root=16)
trainer = unet.Trainer(net,
optimizer="momentum",
opt_kwargs=dict(momentum=0.2))
path = trainer.train(
generator,
"./unet_trained",
training_iters=32,
epochs=5,
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import cv2
plt.rcParams['image.cmap'] = 'gist_earth'
np.random.seed(98765)
# import unet module
from tf_unet import image_gen
from tf_unet import unet
from img_provider import simple_data_provider
from tf_unet import util
generator_artificial = image_gen.GrayScaleDataProvider(572,
572,
cnt=20,
rectangles=True)
generator_reallife = simple_data_provider(x=572,
y=572,
nclass=3,
channel='red',
test=False)
net_real = unet.Unet(channels=generator_reallife.channels,
n_class=generator_reallife.n_class,
layers=4,
features_root=16)
trainer_real = unet.Trainer(net_real,
optimizer="momentum",
opt_kwargs=dict(momentum=0.2))
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from tf_unet import image_gen
from tf_unet import unet
from tf_unet import util
plt.rcParams['image.cmap'] = 'gist_earth'
nx = 256
ny = 256
generator = image_gen.GrayScaleDataProvider(nx, ny, cnt=20, depth_3d=8)
x_test, y_test = generator(1)
net = unet.Unet3D(channels=generator.channels, n_class=generator.n_class, layers=3, features_root=16)
trainer = unet.Trainer(net,optimizer="momentum", opt_kwargs=dict(momentum=0.2))
# trainer = unet.Trainer(net,optimizer="adam", opt_kwargs=dict(learning_rate=0.1))
path = trainer.train(generator,"./unet_trained", training_iters=10, epochs=50, display_step=2)
prediction = net.predict("./unet_trained/model.cpkt", x_test)
fig, ax = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(12,5))
ax[0].imshow(x_test[0,0,...,0], aspect="auto")
ax[1].imshow(y_test[0,0,...,1], aspect="auto")
mask = prediction[0,0,...,1] > 0.9
ax[2].imshow(mask, aspect="auto")
ax[0].set_title("Input")
ax[1].set_title("Ground truth")
ax[2].set_title("Prediction")
fig.tight_layout()
fig.savefig("../docs/toy_problem.png")
