from util import *
import matplotlib.pyplot as plt
X, ids = all_imgs(ret_ids=True, white=True)
y = masks_for(ids, erode=True)
s = [512, 256, 128]
for i in range(len(X)):
for size in s:
if X[i].shape[0] >= size or X[i].shape[1] >= size:
new_shape = (size, size)
break
X[i] = imresize(X[i], new_shape)
y[i] = imresize(y[i], new_shape)
gen = generator(X, y, shuffle=False)
from skimage.morphology import label
for i in range(5):
X, y = next(gen)
pred = model.predict(X)[0, :, :, 0]
pred = (pred > 0.5).astype(np.uint8)
act = y[0, :, :, 0]
print test_img(pred, act)
_, axs = plt.subplots(1, 3)
axs[0].imshow(pred, 'gray')
axs[1].imshow(y[0, :, :, 0], 'gray')
from os.path import isfile, join
name = sys.argv[1]
with open(join('models', name, 'model.json')) as f:
json = f.read()
model = model_from_json(json)
model.load_weights(join('models', name, 'model.h5'))
from util import *
import matplotlib.pyplot as plt
X, ids = all_recursive_masks(ret_ids=True)
y = masks_for(ids, erode=True)
gen = generator(X, y)
for i in range(5):
X, y = next(gen)
pred = X
# Uncomment the following blocks if you want to create a gif
# of the smoothing process.
"""
files = ['smoothed_0.png']
imsave(join('images', files[-1]), pred[0,:,:,0])
"""
for j in range(3):
pred = model.predict(pred)