| --- |
| task_categories: |
| - image-segmentation |
| - image-classification |
| language: |
| - en |
| tags: |
| - agritech |
| - hyperspectral |
| - spectroscopy |
| - fruit |
| - sub-class classification |
| - detection |
| size_categories: |
| - 10K<n<100K |
| license: other |
| --- |
| # Living Optics Hyperspectral Fruit Dataset |
|
|
|  |
|
|
| ## Overview |
| This dataset contains 100 images of various fruits and vegetables captured under controlled lighting, with the [Living Optics Camera](livingoptics.com). |
|
|
| The data consists of RGB images, sparse spectral samples and instance segmentation masks. |
|
|
| From the 100 images, we extract >430,000 spectral samples, of which >85,000 belong to one of the 19 classes in the dataset. The rest of the spectra can be used for negative sampling when training classifiers. |
|
|
| An additional 11 labelled images are provided as a validation set. |
|
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| Additionally, we provide a set of demo videos in `.lo` format which are unannotated but which can be used to qualititively test algorithms built on this dataset. |
|
|
|
|
| ### Classes |
|
|
| The training dataset contains 19 classes: |
| - 🍋 lemon - 8275 total spectral samples |
| - 🍈 melon - 9507 total spectral samples |
| - 🥒 cucumber - 227 total spectral samples |
| - 🍏 granny smith apple - 3984 total spectral samples |
| - 🍏 jazz apple - 272 total spectral samples |
| - 🍎 plastic apple - 6693 total spectral samples |
| - 🍎 pink lady apple - 17311 total spectral samples |
| - 🍎 royal gala apple - 21319 total spectral samples |
| - 🍅 tomato - 3748 total spectral samples |
| - 🍅 cherry tomato - 360 total spectral samples |
| - 🍅 plastic tomato - 569 total spectral samples |
| - 🫑 green pepper - 226 total spectral samples |
| - 🫑 yellow pepper - 4752 total spectral samples |
| - 🫑 orange pepper - 552 total spectral samples |
| - 🍊 orange - 4641 total spectral samples |
| - 🍊 easy peeler orange - 2720 total spectral samples |
| - 🍐 pear - 194 total samples |
| - 🍇 green grape - 106 total spectral samples |
| - 🍋🟩 lime - 43 total spectral samples |
|
|
|  |
|
|
| > This is a notebook showing how hyperspectral data, collected with the Living Optics camera. |
|
|
| ## Requirements |
|
|
| - [lo-sdk](https://cloud.livingoptics.com/) |
| - [datareader](https://github.com/livingoptics/datareader.git) |
|
|
|
|
| ## Download instructions |
|
|
| You can access this dataset via the [Living Optics Cloud Portal](https://cloud.livingoptics.com/shared-resources?file=data%2Fannotated-datasets%2Fhyperspectral-fruit.zip). |
|
|
| See our [Spatial Spectral ML](https://github.com/livingoptics/spatial-spectral-ml) project for an example of how to train and run a segmentation and spectral classification algoirthm using this dataset. |
|
|
| ## Usage |
|
|
| ```python |
| import os |
| import numpy as np |
| import matplotlib.pyplot as plt |
| from lo_dataset_reader import DatasetReader, spectral_coordinate_indices_in_mask, rle_to_mask |
| |
| os.environ["QT_QPA_PLATFORM"] = "xcb" |
| |
| dataset_path = "/path/to/dataset" |
| dataset = DatasetReader(dataset_path, display_fig=True) |
| |
| for idx, ((info, scene, spectra, unit, images_extern), (converted_spectra, converted_unit), annotations, library_spectra, labels) in enumerate(dataset): |
| for ann_idx, annotation in enumerate(annotations): |
| annotation["labels"] = labels |
| |
| # Visualise the annotation on the scene |
| dataset.save_annotation_visualisation(scene, annotation, images_extern, ann_idx) |
| |
| # Get spectrum stats from annotation |
| stats = annotation.get("extern", {}).get("stats", {}) |
| label = stats.get("category") |
| mean_radiance_spectrum = stats.get("mean_radiance_spectrum") |
| mean_reflectance_spectrum = stats.get("mean_reflectance_spectrum") |
| |
| # Get mask and spectral indices |
| mask = rle_to_mask(annotation["segmentation"], scene.shape) |
| spectral_indices = spectral_coordinate_indices_in_mask(mask, info.sampling_coordinates) |
| |
| # Extract spectra and converted spectra |
| spec = spectra[spectral_indices, :] |
| if converted_spectra is not None: |
| conv_spec = converted_spectra[spectral_indices, :] |
| else: |
| conv_spec = None |
| |
| # X-axis based on band index or wavelengths (optional) |
| x = np.arange(spec.shape[1]) |
| if stats.get("wavelength_min") is not None and stats.get("wavelength_max") is not None: |
| x = np.linspace(stats["wavelength_min"], stats["wavelength_max"], spec.shape[1]) |
| |
| # Determine plot layout |
| if converted_spectra is not None: |
| fig, axs = plt.subplots(2, 2, figsize=(12, 8)) |
| axs_top = axs[0] |
| axs_bottom = axs[1] |
| else: |
| fig, axs_top = plt.subplots(1, 2, figsize=(12, 4)) |
| print(f"Warning: No converted_spectra for annotation '{label}'") |
| |
| unit_label = unit.capitalize() if unit else "Radiance" |
| |
| # (1,1) Individual spectra |
| for s in spec: |
| axs_top[0].plot(x, s, alpha=0.3) |
| axs_top[0].set_title(f"{unit_label.capitalize()} Spectra") |
| axs_top[0].set_xlabel("Wavelength") |
| axs_top[0].set_ylabel(f"{unit_label.capitalize()}") |
| |
| # (1,2) Mean + Min/Max (Before conversion) |
| if mean_radiance_spectrum is not None: |
| spec_min = np.min(spec, axis=0) |
| spec_max = np.max(spec, axis=0) |
| axs_top[1].fill_between(x, spec_min, spec_max, color='lightblue', alpha=0.5, label='Min-Max Range') |
| axs_top[1].plot(x, mean_radiance_spectrum, color='blue', label=f'Mean {unit_label.capitalize()}') |
| axs_top[1].set_title(f"Extern Mean ± Range ({unit_label.capitalize()})") |
| axs_top[1].set_xlabel("Wavelength") |
| axs_top[1].set_ylabel(f"{unit_label.capitalize()}") |
| axs_top[1].legend() |
| |
| # (2,1) and (2,2) Only if converted_spectra is available |
| if converted_spectra is not None and conv_spec is not None: |
| for s in conv_spec: |
| axs_bottom[0].plot(x, s, alpha=0.3) |
| axs_bottom[0].set_title(f"{converted_unit} Spectra") |
| axs_bottom[0].set_xlabel("Wavelength") |
| axs_bottom[0].set_ylabel(f"{converted_unit}") |
| |
| if mean_reflectance_spectrum is not None: |
| conv_min = np.min(conv_spec, axis=0) |
| conv_max = np.max(conv_spec, axis=0) |
| axs_bottom[1].fill_between(x, conv_min, conv_max, color='lightgreen', alpha=0.5, label='Min-Max Range') |
| axs_bottom[1].plot(x, mean_reflectance_spectrum, color='green', label=f'Mean {converted_unit}') |
| axs_bottom[1].set_title(f"Extern Mean ± Range ({converted_unit})") |
| axs_bottom[1].set_xlabel("Wavelength") |
| axs_bottom[1].set_ylabel(f"{converted_unit}") |
| axs_bottom[1].legend() |
| |
| fig.suptitle(f"Annotation {label}", fontsize=16) |
| plt.tight_layout() |
| plt.show() |
| ``` |
|
|
|  |
| ## Citation |
|
|
| Raw data is available by request |
