clean/DataModule.py

+import pytorch_lightning as pl
+from torch.utils.data import Dataset, DataLoader
+import os 
+import numpy as np
+
+### DATASET STRUCTURE
+### --- IMAGES ONLY ---
+# -- labels folder
+# * example file : mask_0.png with 0,1,2,...
+# -- data folder
+# * example file :  
+
+class VCSELDataset(Dataset):
+    
+    def __init__(self, image_paths, label_paths, transform=None):
+        """
+        Args:
+            image_paths (list): List of paths to hyperspectral images.
+            label_paths (list): List of paths to corresponding segmentation labels.
+            transform (callable, optional): Optional transform to be applied on a sample.
+        """
+        self.image_paths = image_paths
+        self.label_paths = label_paths
+        self.transform = transform
+
+    def __len__(self):
+        pass
+
+    def __getitem__(self, idx):
+        hyperspectral_cube = self.load_hyperspectral_cube(idx)
+
+        if self.transform:
+            hyperspectral_cube = self.transform(hyperspectral_cube)
+
+        return hyperspectral_cube
+
+    def load_hyperspectral_cube(self, idx):
+        spectra = np.load(os.path.join(self.image_paths[idx], "spectra.npy"))
+        map = np.load(os.path.join(self.image_paths[idx], "map.npy"))
+
+        hyperspectral_cube  = self.generate_hyperspectral_cube(spectra, map)
+        return hyperspectral_cube
+    
+    def generate_hyperspectral_cube(self, spectra, map):
+        nb_of_spectra = spectra.shape[0]
+        # Ensure map values are within valid range
+        map = np.clip(map, 0, nb_of_spectra - 1)
+
+        # Index spectra with the map to generate the hyperspectral cube
+        hyperspectral_cube = spectra[map]
+        return hyperspectral_cube
+
+
+class VCSELDataModule(pl.LightningDataModule):
+
+    def __init__(self,data_dir,batch_size,num_workers):
+        self.data_dir = data_dir
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+
+    def prepare_data(self):
+        pass
+
+    def setup(stage):
+        pass
+
+    def train_dataloader(self):
+        pass
+
+    def val_dataloader(self):
+        pass
+
+    def test_dataloader(self):
+        pass
+

clean/generate_acquisition_V1.py

+
+#--- LOADING DATA
+# Load spectra
+# Load ground truth png
+
+#--- PREPROCESSING DATA
+# Add smooth noise to the spectra (impurities in the materials) : ie add a std depending on lambda for each spectrum
+#    --> TO BE CONSIDERED CAREFULLY
+#   * should we generate various spectra for each material to take into account non regular surface ?
+# Generate spectral filters using one of the following methods : PCA+LDA or neural network
+# 
+