| --- |
| license: other |
| license_name: lo-license |
| license_link: >- |
| https://customers.livingoptics.com/hubfs/Outbound/Legal/Living%20Optics%20EULA.pdf |
| task_categories: |
| - image-segmentation |
| - image-classification |
| language: |
| - en |
| tags: |
| - forensics |
| - blood detection |
| - blood classification |
| - hyperspectral |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Living Optics Forensics Dataset |
|
|
|  |
|
|
| ## Overview |
|
|
| This dataset contains **224 images** captured during a **forensics application investigation** using the **Living Optics Camera**. |
|
|
| The data includes: |
| - **RGB images** |
| - **Sparse spectral samples** |
| - **Instance segmentation masks** |
| - **White reference spectra** |
| - **Libary spectra** |
|
|
| It is derived from over **200 unique raw files**, corresponding to 224 frames. The dataset has **not** been split into training/validation sets — the choice of split is left to the developer. |
|
|
| Annotation example can be seen below: |
|
|
|  |
|
|
|
|
| ### Contents |
| - **249 instances** of horse blood captured on various surfaces. |
| - **167 instances** of blood confusers (e.g., fake blood, ketchup) across **22 different surfaces**. |
| - A **total of 416 labeled instances**. |
|
|
| Additionally, the dataset contains **library spectra** captured with a spectrometer covering the wavelength range **350–1000 nm**, sampled at a higher resolution than the Living Optics camera. |
| These spectra can be used for: |
| - Spectral lookup–style algorithms |
| - Outlier filtering |
| - **Negative sampling** when spectra do not fall within labeled segmentation masks |
|
|
| Extra **unlabeled data** is available upon request. |
|
|
| ## Classes |
|
|
| The dataset contains **25 classes**: |
|
|
| | ID | Class Name | |
| |-------|------------| |
| | 104 | Horse blood (sample) | |
| | 103 | Tomato ketchup (sample) | |
| | 106 | Red food dye (sample) | |
| | 107 | Fake blood (sample) | |
| | 1015 | 100% Cotton Shirt (White) (surface) | |
| | 1013 | 100% Cotton Shirt (Black) (surface) | |
| | 1012 | Light Fabric Lined Plywood (EF64) – 3 mm (surface) | |
| | 1010 | PVC (EF9) Black Plywood – 3 mm (surface) | |
| | 1007 | PVC (EF50) Light Woodgrain Plywood – 3 mm (surface) | |
| | 1016 | 100% Cotton Shirt (Brown) (surface) | |
| | 1011 | Normal Plywood – 3 mm (surface) | |
| | 1008 | PVC Walnut Woodgrain Plywood (EF326) – 3 mm (surface) | |
| | 1006 | PVC Leather (Black) (surface) | |
| | 1005 | PVC Leather (White) (surface) | |
| | 1004 | PVC Leather (Brown) (surface) | |
| | 1003 | PVC Leather (Red) (surface) | |
| | 1019 | Skinny Jeans (Light Blue) (surface) | |
| | 1024 | Dri-fit Shirt (Brown) (surface) | |
| | 1022 | Dri-fit Shirt (White) (surface) | |
| | 1018 | Skinny Jeans (Black) (surface) | |
| | 1017 | Skinny Jeans (Grey) (surface) | |
| | 1021 | Dri-fit Shirt (Red) (surface) | |
| | 1020 | Skinny Jeans (Dark Blue) (surface) | |
| | 1009 | PVC White Plywood – 3 mm (surface) | |
| | 1023 | Dri-fit Shirt (Black) (surface) | |
| | 1014 | 100% Cotton Shirt (Maroon) (surface) | |
|
|
| Unlabeled or background regions can be grouped into a single `"background"` class. |
|
|
| ## Visualization |
|
|
|  |
|
|
| ## 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?downloadFile=data%2Fannotated-datasets%2FForensics-Dataset.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() |
| ``` |
|
|
| For more details on the dataset format and reader see: [dataset format](https://github.com/livingoptics/datareader/blob/main/docs/lo_format_dataset.md) |
|
|
| ## Citation |
|
|
| Raw data is available by request |
