046be8c5a3 | ||
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.. | ||
data | ||
models | ||
options | ||
utils | ||
README.md | ||
end-to-end.png | ||
folder_simulation.py | ||
main_finetuning_drunet.py | ||
main_test_drunet.py | ||
main_test_drunet_captures.py | ||
main_test_tempest_drunet.py | ||
main_test_trainset_drunet.py | ||
main_train_drunet.py | ||
main_train_drunet_data_train-val.py | ||
optuna_drunet.py | ||
requirement.txt | ||
tempest_evaluation.py |
README.md
End-to-End Method
Usage Guide
In general, the options to use (reference/degraded image folders, network models, output directory, etc.) are located in end-to-end/options.
Inference and Evaluation
To run inference, you need to edit the file end-to-end/options/train_drunet.json and, once the changes are made, execute:
python main_test_drunet.py
This command will output a new directory with the inferences from the input directory.
To evaluate a directory with images (both reference and model's inference), you need to edit the file end-to-end/options/evaluation.json and, once the changes are made, execute:
python tempest_evaluation.py
Training
Note: Before executing the following command, you must select which type of data to use for training
Training with Real Data
To train with real data, the file train_drunet.json must have the value "drunet_finetune" in the dataset_type field (datasets-->train).
Training with Synthetic Data
To train with synthetic data, the file train_drunet.json must have the value "drunet" in the dataset_type field (datasets-->train).
Once the data type was selected, use the following command to train the network:
python main_train_drunet.py
Generating Synthetic Captures
For synthetic captured images generation, first configure the options on tempest_simulation.json file. Be sure to include the path to the folder containing the images to run the simulation of direct capturing image from the EME of a monitor. Then run the following command:
python folder_simulation.py
Which outputs the synthetic captured in the specified folder.