Create README.md

README.md

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+---
+task_categories:
+- image-segmentation
+- image-classification
+language:
+- en
+tags:
+- agritech
+- hyperspectral
+- spectroscopy
+- fruit
+- sub-class classification
+- detection
+size_categories:
+- n<1K
+---
+# Living Optics Orchard 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 >400,000 spectral samples, of which >83,000 belong to one of the 15 classes in the dataset. The rest of the spectra can be used for negative sampling when training classifiers.
+
+### Classes
+
+The dataset contains 15 classes:
+- Pink Lady Apple
+- Granny Smith Apple
+- Royal Gala Apple
+- Plastic Apple
+- Cucumber
+- Melon
+- Yellow Pepper
+- Green Pepper
+- Orange Pepper
+- Lemon
+- Tomato
+- Cherry Tomato
+- Plastic Tomato
+- Orange
+- Easy Peeler Orange
+
+## Requirements
+
+- [lo-sdk](https://www.livingoptics.com/register-for-download-sdk/)
+- [lo-examples](https://github.com/livingoptics/python-examples)
+
+
+## Download instructions
+
+### Command line
+```commandline
+huggingface-cli ...
+```
+
+### Python
+```python 
+from huggingface_hub import hf_hub_download
+...
+```
+
+## Usage
+
+```python
+import os.path as op
+import numpy.typing as npt
+from typing import List, Dict, Generator
+from lo_data_tools import Annotation, LODataItem, LOJSONDataset, draw_annotations
+from dataset_visualisation import get_object_spectra, plot_labelled_spectra
+from lo.sdk.api.acquisition.io.open import open as lo_open
+# Load the dataset
+path_to_download = op.expanduser("~/Downloads/LivingOpticsOrchardData")
+dataset = LOJSONDataset(path_to_download)
+
+# Get the training data as an iterator 
+training_data: List[LODataItem] = dataset.load("train")
+
+# Inspect the data
+
+lo_data_item: LODataItem
+for lo_data_item in training_data[:3]:
+
+    draw_annotations(lo_data_item)
+
+    ann: Annotation
+    for ann in lo_data_item.annotations:
+        print(ann.class_name, ann.category, ann.subcategories)
+
+# Plot the spectra for each class
+fig, ax = plt.subplots(1)
+object_spectra_dict = {}
+class_numbers_to_labels = {0: "background_class"}
+for lo_data_item in training_data:
+    object_spectra_dict, class_numbers_to_labels = get_object_spectra(
+        lo_data_item, object_spectra_dict, class_numbers_to_labels
+    )
+
+plot_labelled_spectra(object_spectra_dict, class_numbers_to_labels, ax)
+plt.show()
+    
+```