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 >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
• lo-examples

Download instructions

Command line

huggingface-cli ...

Python

from huggingface_hub import hf_hub_download
...

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 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()