README.md

metadata

task_categories:
  - image-segmentation
  - image-classification
language:
  - en
tags:
  - agritech
  - hyperspectral
  - spectroscopy
  - fruit
  - sub-class classification
  - detection
size_categories:
  - 10K<n<100K

Living Optics Orchard Dataset

Overview

This dataset contains 100 images of various fruits and vegetables captured under controlled lighting, with the Living Optics Camera.

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.

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 dataset contains 19 classes:

• lemon - 8275 total samples
• melon - 9507 total samples
• yellow pepper - 4752 total samples
• cucumber - 227 total samples
• granny smith apple - 3984 total samples
• jazz apple - 272 total samples
• plastic apple - 6693 total samples
• pink lady apple - 17311 total samples
• royal gala apple - 21319 total samples
• tomato - 3748 total samples
• cherry tomato - 360 total samples
• plastic tomato - 569 total samples
• green pepper - 226 total samples
• orange - 4641 total samples
• easy peeler orange - 2720 total samples
• orange pepper - 552 total samples
• pear - 194 total samples
• green grape - 106 total samples
• lime - 43 total samples

Requirements

• lo-sdk
• lo-examples
• [lo-data] TODO

Download instructions

Command line

mkdir -p hyperspectral-fruit
huggingface-cli download LivingOptics/hyperspectral-fruit --repo-type dataset --local-dir hyperspectral-fruit

Python

from huggingface_hub import hf_hub_download
dataset_path = hf_hub_download(repo_id="LivingOptics/hyperspectral-fruit", filename="train", repo_type="dataset")
print(dataset_path)

Usage

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 lo.data.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()    

See here TODO for an example of how to train and run a segmentation and spectral classification algoirthm using this dataset.