spectral-datamaker 0.3.0

CLI tool for creating hyperspectral image datasets for machine learning.

SpectralDatamaker

Python CLI tool designed to facilitate the creation of datasets with hyperspectral images for machine learning.

The dataset structure is organized as follows:

dataset_root/
├── images
│   ├── DATASET-01_image-name_0
│   ├── DATASET-01_image-name_1
│   ├── DATASET-01_image-name_2
│   └── DATASET-01_image-name_3
├── masks
│   ├── RoiMASK_image-name.csv
│   ├── PxMASK_image-name.npy
│   ├── DATASET-01_image-name_0
│   ├── DATASET-01_image-name_1
│   ├── DATASET-01_image-name_2
│   └── DATASET-01_image-name_3
├── source
│   ├── image-name.hdr
│   └── image-name.raw
└── metadata.json

This tool provides functionalities for processing the source images, generating region of interest (ROI) masks, pixel masks, labels, and cropping the images based on the generated masks.

CLI Usage

After installing the package, you can use the console command:

spectral-datamaker --help

You can also invoke the package module directly:

python -m spectral_datamaker --help

The CLI provides four main commands:

Create a complete dataset:

spectral-datamaker create <config.yaml> <output_directory>

Options:

• --dry-run: Validate configuration without executing
• --skip-validation: Skip final dataset validation
• --no-interactive: Skip interactive mask adjustment (not yet implemented)

Validate an existing dataset:

spectral-datamaker validate <dataset_directory>

Options:

• --config <file>: Validate against a specific configuration file

Inspect dataset metadata:

spectral-datamaker inspect <dataset_directory>

Options:

• --format [json|yaml|table]: Output format (default: table)
• --show-images: List all processed images

Execute individual pipeline steps:

spectral-datamaker step <step_name> <config.yaml> <dataset_directory>

Available steps: structure, roi-mask, pixel-mask, crop, metadata

Library usage (Python API)

Besides the CLI, SpectralDatamaker can be used as a Python library. The most useful classes for inspection and validation are:

• DatasetStructure: infers canonical dataset locations (images/, masks/, source/, metadata.json) from a root directory.
• Filenames: derives expected filenames and absolute paths for masks, labels, cropped outputs, and metadata.
• DatasetValidator: validates an existing dataset either from a config file or from metadata.json.

from spectral_datamaker.config import DatasetStructure, Filenames
from spectral_datamaker.processors import DatasetValidator

dataset_root = "/path/to/dataset_root"

# 1) Infer dataset structure from root directory
structure = DatasetStructure(dataset_root)
print(structure.images_dir)
print(structure.masks_dir)
print(structure.source_dir)
print(structure.metadata_file)

# 2) Derive expected file paths and names
names = Filenames(structure)
print(names.get_roi_mask("image_1.hdr", abs=True))
print(names.get_px_mask("image_1.hdr", abs=True))
print(names.get_dataset_metadata(abs=True))

# 3) Validate dataset contents
validator = DatasetValidator(structure)
validator.validate_dataset_from_config("/path/to/dataset.yaml")
# Or, if metadata already exists:
# validator.validate_dataset_from_metadata()

Dataset config file

The dataset configuration file (e.g., dataset.yaml) contains the necessary information for creating a dataset from ENVI images. The YAML file should have the following structure:

dataset:
  name: dataset-example
  description: An example dataset created with SpectralDatamaker.

  source-images:
    - path: /path/to/source/image_1.hdr
      masking:
        shape: circle
        size: 35
        num: 6

    - path: /path/to/source/image_2.hdr
      masking:
        shape: square
        size: 20
        num: 4

    - path: /path/to/source/image_n.hdr
      masking:
        shape: triangle
        size: 50
        num: 2

  segmentation:
    enabled: true
    classes:
      - type_A
      - type_B

  classification:
    enabled: false

Segmentation mode

When segmentation mode is enabled, SpectralDatamaker will generate a dataset with segmentation masks for each source image. The steps are as follows:

1. Creates ROI masks based on the specified shape, size, and number of regions in the configuration file. A napari viewer is launched to allow the user to adjust the generated masks if necessary. Masks are saved when the user closes the viewer.
2. Generates pixel masks from the ROI masks, asking the user to label each region of interest (ROI) with the corresponding class from the configuration file.
3. Crops the source images based on the generated masks and saves the cropped images, masks in the appropriate directories.

Classification mode

[!NOTE] The classification mode is currently in development is not yet available for use. The following description is based on the intended functionality.

When classification mode is enabled, SpectralDatamaker will generate a dataset with class labels for each source image. The steps are as follows:

1. Creates ROI masks based on the specified shape, size, and number of regions in the configuration file. A napari viewer is launched to allow the user to adjust the generated masks if necessary. Masks are saved when the user closes the viewer.
2. Asks the user to label each ROI with the corresponding class from the configuration file. Saves the class labels in a CSV file.
3. Crops the source images based on the generated masks and saves the cropped images in the appropriate directories.

Dataset metadata

SpectralDatamaker generates a metadata.json file containing information about the dataset, including the dataset name, description, source images, and the processing steps applied to each image. This metadata file is recognized by the SpectralDatamaker and can be used to validate the dataset structure and contents. An example of the metadata.json structure is as follows:

{
    "name": "dataset-03",
    "description": "Dataset created with one hyperespectral image.",
    "last_update": "2026-04-08 13:52:01",
    "source_images": ["/path/to/image_1.hdr"],
    "types": ["segmentation"],
    "segmentation_masking": {
        "image_1": {
            "label_map": {"0": "background", "1": "type_A", "2": "type_B"},
            "num_classes": 3,
            "classes": ["type_A", "type_B"],
            "assignments": {
                "type_A": [0,2,3],
                "type_B": [1,5,4]
            },
            "source_image": "image_1.hdr",
            "rois_file": "RoiMASK_image_1.csv",
            "mask_file": "PxMASK_image_1.npy",
            "created": "2026-04-08T13:51:33.931524",
            "format": "npy"
        }
    }
}

Validations

SpectralDatamaker includes validation checks allowing users to verify the generated dataset structure and contents, as well as validate existing datasets. The validation includes checks for the presence of required directories and expected files.