SETUP The Environment:
$ sudo apt-get install python-pip python-dev python-virtualenv
$ sudo apt-get install virtualenv
$ virtualenv ~/venv
$ source ~/venv/bin/activate
INSTALL dependencies: Install opencv 3.4.5
(venv)$ cat requirements.txt | xargs -n 1 pip install
(venv)$ cat requirements3.txt | xargs -n 1 pip3 install
(venv)morse_code/src$ g++ ComputeGraphReconstruction.cpp -std=c++11 `pkg-config --cflags --libs opencv`
Folder Structure
Images folder : /data/Train/images/[...]_img.tif
Masks folder : /data/Train/masks2m/[...]_img.png [0,255]
Training ALBU
(venv)src/preprocessing$ python3 tif2rgb #Images_Folder <if images are grayscale>
(venv)src/preprocessing$ python3 renamer.py #Image_Folder
(venv)src/preprocessing$ python3 renamer.py #Masks_Folder png
(venv)src$ python3 preprocessing/folds4gen.py #Image_Folder
(venv)src$ python3 train_eval.py resnet34_512_02_02.json --training
The hyperparameters of the training are in src/resnet34_512_02_02.json
Generate the Data for training DM++
(venv)morse_code$ python3 wrapperALBU.py
(venv)morse_code$ python3 wrapperDM1.py <ensure the data is single channel 16-bit for MBA/ grayscale for BFI>
Input Folder Name (line 55), Output folder names (line 58) & trained model name (line 104) need to be updated in the 'wrapperALBU' code. Input Folder Name (line 238) & Output folder names (line 246) need to be updated in the 'wrapperDM1' code.
Training DM++
(venv)DM_base$ python3 createData.py
(venv)DM_base$ python3 fullModel.py
Input folder names and .npy filename needs to be updated in the code. Model name needs to be updated in the code.
Testing DM++
Generate ALBU and DM data for testing (same as training)
(venv)morse_code$ python3 wrapperALBU.py
(venv)morse_code$ python3 wrapperDM1.py <ensure the data is single channel 16-bit for MBA/ grayscale for BFI>
Input Folder Name (line 55), Output folder names (line 58) & trained model name (line 104) need to be updated in the 'wrapperALBU' code. Input Folder Name (line 238) & Output folder names (line 246) need to be updated in the 'wrapperDM1' code.
(venv)DM_base$ python3 tsting.py
Input Folder Name (line 56, 57), Output folder names (line 107) & trained model name (line 49) need to be updated in the 'wrapperALBU' code. Create the output Directory.
Evaluation
(venv)ComputeScore$ python3 cal_F1.py #AnnotatedMaskFolder #PredictedOutputFolder .
Use the mask to eliminate the injection region (/Injection Removal) and calculate the score.
Details in Mask_RCNN folder.
Details in Mask_RCNN/samples/nucleus folder
Details in bouton_code folder
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