def train(img_x, img_y, patch_size, patch_step, dim_img, 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.
"""
img_x = utils.nor_data(img_x)
img_y = utils.nor_data(img_y)
img_input = utils.extract_patches(img_x, patch_size, patch_step)
img_output = utils.extract_patches(img_y, patch_size, patch_step)
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))
mdl = model(dim_img, nb_filters, nb_conv)
mdl.fit(img_input, img_output, batch_size=batch_size, nb_epoch=nb_epoch)
return mdl
def train(img_x, img_y, patch_size, patch_step, dim_img, 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.
"""
img_x = utils.nor_data(img_x)
img_y = utils.nor_data(img_y)
img_input = utils.extract_patches(img_x, patch_size, patch_step)
img_output = utils.extract_patches(img_y, patch_size, patch_step)
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))
mdl = model(dim_img, nb_filters, nb_conv)
mdl.fit(img_input, img_output, batch_size=batch_size, nb_epoch=nb_epoch)
return mdl
def predict(mdl, img, patch_size, patch_step, batch_size, dim_img):
"""
the cnn model for image transformation
Parameters
----------
img : array
The image need to be calculated
patch_size : (int, int)
The patches dimension
dim_img : int
The input image dimension
Returns
-------
img_rec
Description.
"""
img = np.float16(utils.nor_data(img))
img_y, img_x = img.shape
x_img = utils.extract_patches(img, patch_size, patch_step)
x_img = np.reshape(x_img, (len(x_img), 1, dim_img, dim_img))
y_img = mdl.predict(x_img, batch_size=batch_size)
del x_img
y_img = np.reshape(y_img, (len(y_img), dim_img, dim_img))
img_rec = utils.reconstruct_patches(y_img, (img_y, img_x), patch_step)
return img_rec
def predict(mdl, img, patch_size, patch_step, batch_size, dim_img):
"""
the cnn model for image transformation
Parameters
----------
img : array
The image need to be calculated
patch_size : (int, int)
The patches dimension
dim_img : int
The input image dimension
Returns
-------
img_rec
Description.
"""
img = np.float16(utils.nor_data(img))
img_y, img_x = img.shape
x_img = utils.extract_patches(img, patch_size, patch_step)
x_img = np.reshape(x_img, (len(x_img), 1, dim_img, dim_img))
y_img = mdl.predict(x_img, batch_size=batch_size)
del x_img
y_img = np.reshape(y_img, (len(y_img), dim_img, dim_img))
img_rec = utils.reconstruct_patches(y_img, (img_y, img_x), patch_step)
return img_rec
mdl.load_weights('weight_center.h5')
print('The model loading time is %s seconds' % (time.time() - start_time))
Y_score = np.zeros((50, 501))
for i in range(50):
slice_num = (i + 2) * 20
datapath = '/home/oxygen/YANGX/Globus/center/test_04/slice' + str(
slice_num) + '/*.tiff'
# print(datapath)
fnames = glob.glob(datapath)
fnames = np.sort(fnames)
# print(fnames)
for j in range(len(fnames)):
img = dxchange.read_tiff(fnames[j])
img = -nor_data(img)
# X_evl = np.zeros((nb_evl, dim_img, dim_img))
# for k in range(nb_evl):
# X_evl[k] = img_window(img[360:1660, 440:1640], dim_img)
X_evl = extract_patches(img[360:1660, 440:1640], (128, 128),
step=64,
max_patches=None,
random_state=None)
X_evl = X_evl.reshape(X_evl.shape[0], 1, dim_img, dim_img)
Y_evl = mdl.predict(X_evl, batch_size=batch_size)
Y_score[i, j] = sum(np.dot(Y_evl, [0, 1]))
# print(Y_score[i])
#print('The evaluate score is:', Y_score[i])
#Y_score = sum(np.round(Y_score))/len(Y_score)
nb_epoch = 12
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
nb_pool = 2
# convolution kernel size
nb_conv = 3
fname = '../../test/test_data/1038.tiff'
ind_uncenter1 = range(1038, 1047)
ind_uncenter2 = range(1049, 1057)
uncenter1 = dxchange.read_tiff_stack(fname, ind=ind_uncenter1, digit=4)
uncenter2 = dxchange.read_tiff_stack(fname, ind=ind_uncenter2, digit=4)
uncenter = np.concatenate((uncenter1, uncenter2), axis=0)
uncenter = nor_data(uncenter)
print(uncenter.shape)
uncenter = img_window(uncenter[:, 360:1460, 440:1440], 200)
print(uncenter.shape)
uncenter_patches = extract_3d(uncenter, patch_size, 1)
np.random.shuffle(uncenter_patches)
print(uncenter_patches.shape)
# print uncenter_patches.shape
center_img = dxchange.read_tiff('../../test/test_data/1048.tiff')
center_img = nor_data(center_img)
print(center_img.shape)
center_img = img_window(center_img[360:1460, 440:1440], 400)
center_patches = extract_3d(center_img, patch_size, 1)
np.random.shuffle(center_patches)
print(center_patches.shape)
# plt.imshow(center_img, cmap='gray', interpolation= None)
nb_epoch = 12
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
nb_pool = 2
# convolution kernel size
nb_conv = 3
fname = '../../test/test_data/1038.tiff'
ind_uncenter1 = range(1038, 1047)
ind_uncenter2 = range(1049, 1057)
uncenter1 = dxchange.read_tiff_stack(fname, ind=ind_uncenter1, digit=4)
uncenter2 = dxchange.read_tiff_stack(fname, ind=ind_uncenter2, digit=4)
uncenter = np.concatenate((uncenter1, uncenter2), axis=0)
uncenter = nor_data(uncenter)
print (uncenter.shape)
uncenter = img_window(uncenter[:, 360:1460, 440:1440], 200)
print (uncenter.shape)
uncenter_patches = extract_3d(uncenter, patch_size, 1)
np.random.shuffle(uncenter_patches)
print (uncenter_patches.shape)
# print uncenter_patches.shape
center_img = dxchange.read_tiff('../../test/test_data/1048.tiff')
center_img = nor_data(center_img)
print (center_img.shape)
center_img = img_window(center_img[360:1460, 440:1440], 400)
center_patches = extract_3d(center_img, patch_size, 1)
np.random.shuffle(center_patches)
print (center_patches.shape)
# plt.imshow(center_img, cmap='gray', interpolation= None)
nb_evl = 100
start_time = time.time()
fnames = glob.glob('../../test/test_data/*.tiff')
fnames = np.sort(fnames)
mdl = model(dim_img, nb_filters, nb_conv, nb_classes)
mdl.load_weights('classify_training_weights.h5')
print('The model loading time is %s seconds'%(time.time()-start_time))
start_time = time.time()
Y_score = np.zeros((len(fnames)))
for i in range(len(fnames)):
img = dxchange.read_tiff(fnames[i])
img = nor_data(img)
X_evl = np.zeros((nb_evl, dim_img, dim_img))
for j in range(nb_evl):
X_evl[j] = img_window(img[360:1460, 440:1440], dim_img)
X_evl = X_evl.reshape(X_evl.shape[0], 1, dim_img, dim_img)
Y_evl = mdl.predict(X_evl, batch_size=batch_size)
Y_score[i] = sum(np.dot(Y_evl, [0, 1]))
#print('The evaluate score is:', Y_score[i])
#Y_score = sum(np.round(Y_score))/len(Y_score)
ind_max = np.argmax(Y_score)
print('The well-centered reconstruction is:', fnames[ind_max])
print('The prediction runs for %s seconds'%(time.time()-start_time))
plt.plot(Y_score)
mdl.load_weights('weight_center.h5')
print('The model loading time is %s seconds'%(time.time()-start_time))
Y_score = np.zeros((50, 501))
for i in range(50):
slice_num = (i+2)*20
datapath = '/home/oxygen/YANGX/Globus/center/test_04/slice'+str(slice_num)+'/*.tiff'
# print(datapath)
fnames = glob.glob(datapath)
fnames = np.sort(fnames)
# print(fnames)
for j in range(len(fnames)):
img = dxchange.read_tiff(fnames[j])
img = -nor_data(img)
# X_evl = np.zeros((nb_evl, dim_img, dim_img))
# for k in range(nb_evl):
# X_evl[k] = img_window(img[360:1660, 440:1640], dim_img)
X_evl = extract_patches(img[360:1660, 440:1640],
(128, 128), step=64, max_patches=None, random_state=None)
X_evl = X_evl.reshape(X_evl.shape[0], 1, dim_img, dim_img)
Y_evl = mdl.predict(X_evl, batch_size=batch_size)
Y_score[i, j] = sum(np.dot(Y_evl, [0, 1]))
# print(Y_score[i])
#print('The evaluate score is:', Y_score[i])
#Y_score = sum(np.round(Y_score))/len(Y_score)
ind_max = np.argmax(Y_score[i, :])
print('The well-centered reconstruction is:', fnames[ind_max])