def predict_patch(img, patch_size, patch_step, nb_filters, nb_conv, batch_size,
wpath, spath):
"""
the cnn model for image transformation
Parameters
----------
img : array
The image need to be calculated
Returns
-------
y_img
Description.
"""
patch_shape = (patch_size, patch_size)
img = nor_data(img)
pn, iy, ix = img.shape
mdl = model_test(patch_size, patch_size, nb_filters, nb_conv)
mdl.load_weights(wpath)
for i in range(pn):
print('Processing the %s th projection' % i)
tstart = time.time()
x_img = img[i]
x_img = extract_3d(x_img, patch_shape, patch_step)
x_img = np.reshape(x_img, (len(x_img), patch_size, patch_size, 1))
y_img = mdl.predict(x_img, batch_size=batch_size)
y_img = np.reshape(y_img, (len(x_img), patch_size, patch_size))
y_img = reconstruct_patches(y_img, (iy, ix), patch_step)
fname = spath + 'prj-' + str(i)
dxchange.write_tiff(y_img, fname, dtype='float32')
print('The prediction runs for %s seconds' % (time.time() - tstart))
def train_patch(img_x, img_y, patch_size, patch_step, nb_filters, nb_conv,
batch_size, nb_epoch):
"""
Function description.
Parameters
----------
parameter_01 : type
Description.
parameter_02 : type
Description.
parameter_03 : type
Description.
Returns
-------
return_01
Description.
"""
# pn, iy, ix = img_x.shape
patch_shape = (patch_size, patch_size)
img_x = nor_data(img_x)
img_y = nor_data(img_y)
train_x = extract_3d(img_x, patch_shape, patch_step)
print train_x.shape
train_y = extract_3d(img_y, patch_shape, patch_step)
train_x = np.reshape(train_x, (len(train_x), patch_size, patch_size, 1))
train_y = np.reshape(train_y, (len(train_y), patch_size, patch_size, 1))
mdl = model_test(patch_size, patch_size, nb_filters, nb_conv)
# mdl.load_weights(ipath)
print(mdl.summary())
mdl.fit(train_x,
train_y,
batch_size=batch_size,
epochs=nb_epoch,
shuffle=True)
return mdl
def train(img_x, img_y, patch_size, patch_step, dim_img, nb_filters, nb_conv, batch_size, nb_epoch, x_test, y_test):
"""
Function description.
Parameters
----------
parameter_01 : type
Description.
parameter_02 : type
Description.
parameter_03 : type
Description.
Returns
-------
return_01
Description.
"""
# img_x = nor_data(img_x)
img_y = nor_data(img_y)
img_input = extract_patches(img_x, patch_size, 1)
img_output = extract_patches(img_y, patch_size, 1)
img_input = np.reshape(img_input, (len(img_input), 1, dim_img, dim_img))
img_output = np.reshape(img_output, (len(img_input), 1, dim_img, dim_img))
# test_x = nor_data(x_test)
test_y = nor_data(y_test)
test_x = extract_patches(x_test, patch_size, 1)
test_y = extract_patches(test_y, patch_size, 1)
test_x = np.reshape(test_x, (len(test_x), 1, dim_img, dim_img))
test_y = np.reshape(test_y, (len(test_y), 1, dim_img, dim_img))
mdl = mirror_model(dim_img, nb_filters, nb_conv)
print(mdl.summary())
mdl.fit(img_input, img_output, batch_size=batch_size, nb_epoch=nb_epoch, validation_data=(test_x,test_y))
return mdl
def train_full(img_x, img_y, nb_filters, nb_conv, batch_size, nb_epoch):
"""
Function description.
Parameters
----------
parameter_01 : type
Description.
parameter_02 : type
Description.
parameter_03 : type
Description.
Returns
-------
return_01
Description.
"""
pn, iy, ix = img_x.shape
img_x = nor_data(img_x)
img_y = nor_data(img_y)
train_x = np.reshape(img_x, (pn, iy, ix, 1))
train_y = np.reshape(img_y, (pn, iy, ix, 1))
mdl = model(iy, ix, nb_filters, nb_conv)
print(mdl.summary())
mdl.fit(train_x,
train_y,
batch_size=batch_size,
epochs=nb_epoch,
shuffle=True)
return mdl