Update README.md

README.md

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----
-license: other
-license_name: lo-license
-license_link: >-
-  https://customers.livingoptics.com/hubfs/Outbound/Legal/Living%20Optics%20EULA.pdf
----
+---
+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
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66aa0ad8f46d069c6339c72c/oVtV8fWMlXljp9BKQEaGq.png)
+
+## Overview
+
+This dataset contains **217 images** captured during a **forensics application investigation** using the **Living Optics Camera**.
+
+The data includes:
+- **RGB images**
+- **Sparse spectral samples**
+- **Instance segmentation masks**
+
+It is derived from over **200 unique raw files**, corresponding to 217 frames. The dataset has **not** been split into training/validation sets — the choice of split is left to the developer.
+
+### Contents
+- **242 instances** of horse blood captured on various surfaces.
+- **166 instances** of blood confusers (e.g., fake blood, ketchup) across **21 different surfaces**.
+- A **total of 408 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) |
+
+Unlabeled or background regions can be grouped into a single `"background"` class.
+
+## Visualization
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/668ff5cfd5298d417a272a59/CzsXVbg8dfpVC6eAhcWVN.png)
+
+## 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-Full-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