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--- |
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task_categories: |
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- image-segmentation |
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- image-classification |
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language: |
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- en |
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tags: |
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- agritech |
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- hyperspectral |
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- spectroscopy |
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- fruit |
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- sub-class classification |
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- detection |
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size_categories: |
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- 10K<n<100K |
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license: other |
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--- |
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# Living Optics Orchard Dataset |
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## Overview |
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This dataset contains 435 images of captured in one of the UK's largest orchards, using the Living Optics Camera. |
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The data consists of RGB images, sparse spectral samples and instance segmentation masks. |
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The dataset is derived from 44 unique raw files corresponding to 435 frames. |
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Therefore, multiple frames could originate from the same raw file. |
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This structure emphasized the need for a split strategy that avoided data leakage. |
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To ensure robust evaluation, the dataset was divided using an 8:2 split, with splitting performed at the raw file level rather than the frame level. |
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This strategy guaranteed that all frames associated with a specific raw file were confined to either the training set or the test set, eliminating the |
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risk of overlapping information between the two sets. |
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The dataset contains 3,785 instances of Royal Gala Apples, 2,523 instances of Pears, and 73 instances of Cox Apples, summing to a total of 6,381 labelled instances. |
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The spectra which do not lie within a labelled segmentation mask can be used for negative sampling when training classifiers. |
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Additional unlabelled data is available upon request. |
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## Classes |
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The training dataset contains 3 classes: |
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- 🍎 cox apple - 3,605 total spectral samples |
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- 🍎 royal gala apple - 13,282 total spectral samples |
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- 🍐 pear - 34,398 total spectral samples |
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The remaining 1,855,755 spectra are unlabelled and can be considered a single "background " class. |
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> This is a notebook showing how hyperspectral data, collected with the Living Optics camera. |
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## Requirements |
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- [lo-sdk](https://cloud.livingoptics.com/) |
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- [datareader](https://github.com/livingoptics/datareader.git) |
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## Download instructions |
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You can access this dataset via the [Living Optics Cloud Portal](https://cloud.livingoptics.com/shared-resources?file=data%2Fannotated-datasets%2Fhyperspectral-orchard.zip). |
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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. |
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## Usage |
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```python |
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import os |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from lo_dataset_reader import DatasetReader, spectral_coordinate_indices_in_mask, rle_to_mask |
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os.environ["QT_QPA_PLATFORM"] = "xcb" |
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dataset_path = "/path/to/dataset" |
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dataset = DatasetReader(dataset_path, display_fig=True) |
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for idx, ((info, scene, spectra, unit, images_extern), (converted_spectra, converted_unit), annotations, library_spectra, labels) in enumerate(dataset): |
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for ann_idx, annotation in enumerate(annotations): |
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annotation["labels"] = labels |
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# Visualise the annotation on the scene |
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dataset.save_annotation_visualisation(scene, annotation, images_extern, ann_idx) |
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# Get spectrum stats from annotation |
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stats = annotation.get("extern", {}).get("stats", {}) |
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label = stats.get("category") |
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mean_radiance_spectrum = stats.get("mean_radiance_spectrum") |
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mean_reflectance_spectrum = stats.get("mean_reflectance_spectrum") |
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# Get mask and spectral indices |
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mask = rle_to_mask(annotation["segmentation"], scene.shape) |
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spectral_indices = spectral_coordinate_indices_in_mask(mask, info.sampling_coordinates) |
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# Extract spectra and converted spectra |
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spec = spectra[spectral_indices, :] |
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if converted_spectra is not None: |
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conv_spec = converted_spectra[spectral_indices, :] |
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else: |
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conv_spec = None |
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# X-axis based on band index or wavelengths (optional) |
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x = np.arange(spec.shape[1]) |
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if stats.get("wavelength_min") is not None and stats.get("wavelength_max") is not None: |
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x = np.linspace(stats["wavelength_min"], stats["wavelength_max"], spec.shape[1]) |
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# Determine plot layout |
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if converted_spectra is not None: |
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fig, axs = plt.subplots(2, 2, figsize=(12, 8)) |
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axs_top = axs[0] |
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axs_bottom = axs[1] |
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else: |
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fig, axs_top = plt.subplots(1, 2, figsize=(12, 4)) |
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print(f"Warning: No converted_spectra for annotation '{label}'") |
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unit_label = unit.capitalize() if unit else "Radiance" |
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# (1,1) Individual spectra |
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for s in spec: |
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axs_top[0].plot(x, s, alpha=0.3) |
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axs_top[0].set_title(f"{unit_label.capitalize()} Spectra") |
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axs_top[0].set_xlabel("Wavelength") |
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axs_top[0].set_ylabel(f"{unit_label.capitalize()}") |
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# (1,2) Mean + Min/Max (Before conversion) |
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if mean_radiance_spectrum is not None: |
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spec_min = np.min(spec, axis=0) |
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spec_max = np.max(spec, axis=0) |
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axs_top[1].fill_between(x, spec_min, spec_max, color='lightblue', alpha=0.5, label='Min-Max Range') |
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axs_top[1].plot(x, mean_radiance_spectrum, color='blue', label=f'Mean {unit_label.capitalize()}') |
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axs_top[1].set_title(f"Extern Mean ± Range ({unit_label.capitalize()})") |
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axs_top[1].set_xlabel("Wavelength") |
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axs_top[1].set_ylabel(f"{unit_label.capitalize()}") |
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axs_top[1].legend() |
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# (2,1) and (2,2) Only if converted_spectra is available |
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if converted_spectra is not None and conv_spec is not None: |
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for s in conv_spec: |
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axs_bottom[0].plot(x, s, alpha=0.3) |
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axs_bottom[0].set_title(f"{converted_unit} Spectra") |
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axs_bottom[0].set_xlabel("Wavelength") |
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axs_bottom[0].set_ylabel(f"{converted_unit}") |
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if mean_reflectance_spectrum is not None: |
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conv_min = np.min(conv_spec, axis=0) |
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conv_max = np.max(conv_spec, axis=0) |
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axs_bottom[1].fill_between(x, conv_min, conv_max, color='lightgreen', alpha=0.5, label='Min-Max Range') |
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axs_bottom[1].plot(x, mean_reflectance_spectrum, color='green', label=f'Mean {converted_unit}') |
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axs_bottom[1].set_title(f"Extern Mean ± Range ({converted_unit})") |
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axs_bottom[1].set_xlabel("Wavelength") |
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axs_bottom[1].set_ylabel(f"{converted_unit}") |
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axs_bottom[1].legend() |
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fig.suptitle(f"Annotation {label}", fontsize=16) |
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plt.tight_layout() |
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plt.show() |
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``` |
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## Citation |
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Raw data is available by request |
