Commit a7c0e69f authored by MOEBEL Emmanuel's avatar MOEBEL Emmanuel
Browse files

added LICENSE and headers for source files

parent 31ee2a71
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# ============================================================================================
# DeepFinder - a deep learning approach to localize macromolecules in cryo electron tomograms
# ============================================================================================
# Copyright (c) 2019 - now
# Inria - Centre de Rennes Bretagne Atlantique, France
# Author: Emmanuel Moebel (serpico team)
# License: GPL v3.0. See <https://www.gnu.org/licenses/>
# ============================================================================================
import numpy as np
import time
......
# ============================================================================================
# DeepFinder - a deep learning approach to localize macromolecules in cryo electron tomograms
# ============================================================================================
# Copyright (c) 2019 - now
# Inria - Centre de Rennes Bretagne Atlantique, France
# Author: Emmanuel Moebel (serpico team)
# License: GPL v3.0. See <https://www.gnu.org/licenses/>
# ============================================================================================
from keras import backend as K
import numpy as np
# Ref: salehi17, "Twersky loss function for image segmentation using 3D FCDN"
# -> the score is computed for each class separately and then summed
......
# ============================================================================================
# DeepFinder - a deep learning approach to localize macromolecules in cryo electron tomograms
# ============================================================================================
# Copyright (c) 2019 - now
# Inria - Centre de Rennes Bretagne Atlantique, France
# Author: Emmanuel Moebel (serpico team)
# License: GPL v3.0. See <https://www.gnu.org/licenses/>
# ============================================================================================
from keras.layers import Input, concatenate
from keras.models import Model
from keras.layers.convolutional import Conv3D, MaxPooling3D, UpSampling3D
......
# ============================================================================================
# DeepFinder - a deep learning approach to localize macromolecules in cryo electron tomograms
# ============================================================================================
# Copyright (c) 2019 - now
# Inria - Centre de Rennes Bretagne Atlantique, France
# Author: Emmanuel Moebel (serpico team)
# License: GPL v3.0. See <https://www.gnu.org/licenses/>
# ============================================================================================
import h5py
import numpy as np
import time
......
# ============================================================================================
# DeepFinder - a deep learning approach to localize macromolecules in cryo electron tomograms
# ============================================================================================
# Copyright (c) 2019 - now
# Inria - Centre de Rennes Bretagne Atlantique, France
# Author: Emmanuel Moebel (serpico team)
# License: GPL v3.0. See <https://www.gnu.org/licenses/>
# ============================================================================================
import os
import numpy as np
import h5py
......
# ============================================================================================
# DeepFinder - a deep learning approach to localize macromolecules in cryo electron tomograms
# ============================================================================================
# Copyright (c) 2019 - now
# Inria - Centre de Rennes Bretagne Atlantique, France
# Author: Emmanuel Moebel (serpico team)
# License: GPL v3.0. See <https://www.gnu.org/licenses/>
# ============================================================================================
# This file contains classes/functions that are judged not necessary for the user.
import numpy as np
......
# ============================================================================================
# DeepFinder - a deep learning approach to localize macromolecules in cryo electron tomograms
# ============================================================================================
# Copyright (c) 2019 - now
# Inria - Centre de Rennes Bretagne Atlantique, France
# Author: Emmanuel Moebel (serpico team)
# License: GPL v3.0. See <https://www.gnu.org/licenses/>
# ============================================================================================
import numpy as np
import warnings
......
# ============================================================================================
# DeepFinder - a deep learning approach to localize macromolecules in cryo electron tomograms
# ============================================================================================
# Copyright (c) 2019 - now
# Inria - Centre de Rennes Bretagne Atlantique, France
# Author: Emmanuel Moebel (serpico team)
# License: GPL v3.0. See <https://www.gnu.org/licenses/>
# ============================================================================================
import numpy as np
import h5py
......
......@@ -136,7 +136,6 @@ objects. The minimum size of validation set should be **at least** few dozen obj
.. figure:: ../../images/gui_train.png
:align: center
:height: 400
Training GUI
......
......@@ -5,7 +5,7 @@ This part describes how to reproduce the segmentations obtained in our `paper <h
First, please follow installation `instructions <https://gitlab.inria.fr/serpico/deep-finder>`_ .
Next, launch the segmentation GUI: :code:`/path/to/deep-finder/bin/segment`
Next, launch the segmentation GUI by typing following command into the terminal: :code:`/path/to/deep-finder/bin/segment`
.. figure:: ../../images/gui_segment.png
:align: center
......@@ -30,4 +30,7 @@ Download the example tomogram `here <https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-3
* **Net weights path**: :code:`/path/to/deep-finder/examples/training/out/net_weights_chlamydomonas.h5`
* **Number of classes**: 4
After setting the patch size and your output path, click on button **Launch**. Progress about computation should be printed in the box below the button. Once computation is finished, the display window should pop up, showing the obtained segmentation, super-imposed with the tomogram. Here you can inspect the result. For more details about segmentation and display windows, please see our :ref:`guide`.
\ No newline at end of file
After setting the patch size and your output path, click on button **Launch**. Progress about computation should be
printed in the box below the button. Once computation is finished, the display window should pop up, showing the
obtained segmentation, super-imposed with the tomogram, allowing you to inspect the result. For more details about
segmentation and display windows, please see our :ref:`guide`.
\ No newline at end of file
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