Datasets:
Tasks:
Image Segmentation
Sub-tasks:
instance-segmentation
Size Categories:
n<1K
Annotations Creators:
no-annotation
Source Datasets:
original
License:
apache-2.0
| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ | |
| This dataset contains example data for running through the multiplexed imaging data pipeline in | |
| Ark Analysis: https://github.com/angelolab/ark-analysis. | |
| Dataset Fov renaming: | |
| TMA2_R8C3 -> fov0 | |
| TMA6_R4C5 -> fov1 | |
| TMA7_R5C4 -> fov2 | |
| TMA10_R7C3 -> fov3 | |
| TMA11_R9C6 -> fov4 | |
| TMA13_R8C5 -> fov5 | |
| TMA17_R9C2 -> fov6 | |
| TMA18_R9C2 -> fov7 | |
| TMA21_R2C5 -> fov8 | |
| TMA21_R12C6 -> fov9 | |
| TMA24_R9C1 -> fov10 | |
| """ | |
| import datasets | |
| import pathlib | |
| # Find for instance the citation on arxiv or on the dataset repo/website | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {Ark Analysis Example Dataset}, | |
| author={Angelo Lab}, | |
| year={2022} | |
| } | |
| """ | |
| # TODO: Add description of the dataset here | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| This dataset contains 11 Field of Views (FOVs), each with 22 channels. | |
| """ | |
| _HOMEPAGE = "https://github.com/angelolab/ark-analysis" | |
| _LICENSE = "https://github.com/angelolab/ark-analysis/blob/main/LICENSE" | |
| # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. | |
| # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
| _URL_DATA = { | |
| "image_data": "./data/image_data.zip", | |
| "cell_table": "./data/segmentation/cell_table.zip", | |
| "deepcell_output": "./data/segmentation/deepcell_output.zip", | |
| "example_pixel_output_dir": "./data/pixie/example_pixel_output_dir.zip", | |
| "example_cell_output_dir": "./data/pixie/example_cell_output_dir.zip", | |
| "spatial_lda": "./data/spatial_analysis/spatial_lda.zip", | |
| "post_clustering": "./data/post_clustering.zip", | |
| "ome_tiff": "./data/ome_tiff.zip", | |
| } | |
| _URL_DATASET_CONFIGS = { | |
| "segment_image_data": {"image_data": _URL_DATA["image_data"]}, | |
| "cluster_pixels": { | |
| "image_data": _URL_DATA["image_data"], | |
| "cell_table": _URL_DATA["cell_table"], | |
| "deepcell_output": _URL_DATA["deepcell_output"], | |
| }, | |
| "cluster_cells": { | |
| "image_data": _URL_DATA["image_data"], | |
| "cell_table": _URL_DATA["cell_table"], | |
| "deepcell_output": _URL_DATA["deepcell_output"], | |
| "example_pixel_output_dir": _URL_DATA["example_pixel_output_dir"], | |
| }, | |
| "post_clustering": { | |
| "image_data": _URL_DATA["image_data"], | |
| "cell_table": _URL_DATA["cell_table"], | |
| "deepcell_output": _URL_DATA["deepcell_output"], | |
| "example_cell_output_dir": _URL_DATA["example_cell_output_dir"], | |
| }, | |
| "fiber_segmentation": { | |
| "image_data": _URL_DATA["image_data"], | |
| }, | |
| "LDA_preprocessing": { | |
| "image_data": _URL_DATA["image_data"], | |
| "cell_table": _URL_DATA["cell_table"], | |
| }, | |
| "LDA_training_inference": { | |
| "image_data": _URL_DATA["image_data"], | |
| "cell_table": _URL_DATA["cell_table"], | |
| "spatial_lda": _URL_DATA["spatial_lda"], | |
| }, | |
| "neighborhood_analysis": { | |
| "image_data": _URL_DATA["image_data"], | |
| "cell_table": _URL_DATA["cell_table"], | |
| "deepcell_output": _URL_DATA["deepcell_output"], | |
| }, | |
| "pairwise_spatial_enrichment": { | |
| "image_data": _URL_DATA["image_data"], | |
| "cell_table": _URL_DATA["cell_table"], | |
| "deepcell_output": _URL_DATA["deepcell_output"], | |
| "post_clustering": _URL_DATA["post_clustering"], | |
| }, | |
| "ome_tiff": { | |
| "ome_tiff": _URL_DATA["ome_tiff"], | |
| }, | |
| } | |
| # Note: Name of the dataset usually match the script name with CamelCase instead of snake_case | |
| class ArkExample(datasets.GeneratorBasedBuilder): | |
| """The Dataset consists of 11 FOVs""" | |
| VERSION = datasets.Version("0.0.5") | |
| # You will be able to load one or the other configurations in the following list with | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig( | |
| name="segment_image_data", | |
| version=VERSION, | |
| description="This configuration contains data used by notebook 1 - Segment Image Data.", | |
| ), | |
| datasets.BuilderConfig( | |
| name="cluster_pixels", | |
| version=VERSION, | |
| description="This configuration contains data used by notebook 2 - Pixel Clustering (Pixie Pipeline #1).", | |
| ), | |
| datasets.BuilderConfig( | |
| name="cluster_cells", | |
| version=VERSION, | |
| description="This configuration contains data used by notebook 3 - Cell Clustering (Pixie Pipeline #2).", | |
| ), | |
| datasets.BuilderConfig( | |
| name="post_clustering", | |
| version=VERSION, | |
| description="This configuration contains data used by notebook 4 - Post Clustering.", | |
| ), | |
| datasets.BuilderConfig( | |
| name="fiber_segmentation", | |
| version=VERSION, | |
| description="This configuration contains data used by the Fiber Segmentation Notebook.", | |
| ), | |
| datasets.BuilderConfig( | |
| name="LDA_preprocessing", | |
| version=VERSION, | |
| description="This configuration contains data used by the Spatial LDA - Preprocessing Notebook." | |
| ), | |
| datasets.BuilderConfig( | |
| name="LDA_training_inference", | |
| version=VERSION, | |
| description="This configuration contains data used by the Spatial LDA - Training and Inference Notebook." | |
| ), | |
| datasets.BuilderConfig( | |
| name="neighborhood_analysis", | |
| version=VERSION, | |
| description="This configuration contains data used by the Neighborhood Analysis Notebook." | |
| ), | |
| datasets.BuilderConfig( | |
| name="pairwise_spatial_enrichment", | |
| version=VERSION, | |
| description="This configuration contains data used by the Pairwise Spatial Enrichment Notebook." | |
| ), | |
| datasets.BuilderConfig( | |
| name="ome_tiff", | |
| version=VERSION, | |
| description="This configuration contains an OME-TIFF format of FOV1. Intended to be used with the OME-TIFF Conversion Notebook." | |
| ) | |
| ] | |
| def _info(self): | |
| # This is the name of the configuration selected in BUILDER_CONFIGS above | |
| if self.config.name in list(_URL_DATASET_CONFIGS.keys()): | |
| features = datasets.Features( | |
| {f: datasets.Value("string") for f in _URL_DATASET_CONFIGS[self.config.name].keys()} | |
| ) | |
| else: | |
| ValueError(f"Dataset name is incorrect, options include {list(_URL_DATASET_CONFIGS.keys())}") | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=_DESCRIPTION, | |
| # This defines the different columns of the dataset and their types | |
| features=features, # Here we define them above because they are different between the two configurations | |
| # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and | |
| # specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
| # supervised_keys=("sentence", "label"), | |
| # Homepage of the dataset for documentation | |
| homepage=_HOMEPAGE, | |
| # License for the dataset if available | |
| license=_LICENSE, | |
| # Citation for the dataset | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration | |
| urls = _URL_DATASET_CONFIGS[self.config.name] | |
| data_dirs = {} | |
| for data_name, url in urls.items(): | |
| dl_path = pathlib.Path(dl_manager.download_and_extract(url)) | |
| data_dirs[data_name] = dl_path | |
| return [ | |
| datasets.SplitGenerator( | |
| name=self.config.name, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={"dataset_paths": data_dirs}, | |
| ), | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, dataset_paths): | |
| yield self.config.name, dataset_paths | |