deep-tempest/end-to-end

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.

You can download at this link pre-trained model with the real image captures detailed at previews the Data section.

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.