rgb-to-hyper

end-to-end implementation of microplastics detection in water using hyperspectral imaging.

How it works

Summary of the Training and Prediction Flow

A. Training Flow (mode = "global"):

1. Model and Optimizer Initialization:

   • Instantiate Generator and Discriminator.
   • Initialize their respective Adam optimizers.

2. Logging Setup:

   • Create a summary_writer for TensorBoard to log training metrics.

3. Checkpoint Path Determination:

   • Set checkpoint_path to 'global_ckpt' for global training.

4. Training Execution:

   • Call train_gan with all necessary parameters.
   • Inside train_gan:
     • Checkpoint Restoration: Load existing checkpoints if available.
     • Data Loading and Preparation: Load and preprocess paired RGB and HSI images.
     • Epoch Loop: For each epoch, shuffle data and iterate over batches.
       • Batch Processing: Perform augmentation, train discriminator, train generator, compute metrics, and log progress.
       • Checkpoint Saving: Save model states at the end of each epoch.
     • Post-Training: Save final metrics and optionally generate sample outputs.

B. Prediction Flow (mode = "predict")

1. Checkpoint Restoration:

• Use load_model_and_predict to load the Generator model from the 'global_ckpt' directory.

2. Data Loading:

   • Load RGB images designated for prediction.

3. HSI Generation:

• Use the restored Generator to create HSI images from the RGB inputs.

4. Output Saving:
   • Save the generated HSI images as TIFF files in the specified directory.