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README.md

@@ -1,149 +1,25 @@
 ---
-annotations_creators:
-- expert-generated
-language: []
-language_creators:
-- expert-generated
-license:
-- agpl-3.0
-multilinguality: []
-pretty_name: SDH staining muscle fiber histology images used to train MyoQuant model.
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-tags:
-- myology
-- biology
-- histology
-- muscle
-- cells
-- fibers
-- myopathy
-- SDH
-- myoquant
-task_categories:
-- image-classification
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          0: control
+          1: sick
+  config_name: SDH_16k
+  splits:
+  - name: test
+    num_bytes: 683067
+    num_examples: 3358
+  - name: train
+    num_bytes: 2466024
+    num_examples: 12085
+  - name: validation
+    num_bytes: 281243
+    num_examples: 1344
+  download_size: 2257836789
+  dataset_size: 3430334
 ---
-# Dataset Card for MyoQuant SDH Data
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:**
-- **Repository:**
-- **Paper:**
-- **Leaderboard:**
-- **Point of Contact:**
-
-### Dataset Summary
-
-[More Information Needed]
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed]
-
-### Languages
-
-[More Information Needed]
-
-## Dataset Structure
-
-### Data Instances
-
-[More Information Needed]
-
-### Data Fields
-
-[More Information Needed]
-
-### Data Splits
-
-[More Information Needed]
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed]
-
-### Licensing Information
-
-[More Information Needed]
-
-### Citation Information
-
-[More Information Needed]
-
-### Contributions
-
-Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

SDH_16k.py

@@ -0,0 +1,133 @@
+# 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.
+"""MyoQuant-SDH-Data: The MyoQuant SDH Model Data."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{Meyer,
+title = {MyoQuant SDH Data},
+author={Corentin Meyer},
+year={2022}
+}
+"""
+_NAMES = ["control", "sick"]
+
+_DESCRIPTION = """\
+This dataset is used to train the SDH model of MyoQuant to detect and quantify anomaly in the mitochondria repartition in SDH stained muscle fiber with myopathy disorders.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data"
+
+_LICENSE = "agpl-3.0"
+
+_URLS = {
+    "SDH_16k": "https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data/resolve/main/SDH_16k/SDH_16k.zip"
+}
+_METADATA_URL = {
+    "SDH_16k_metadata": "https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data/resolve/main/SDH_16k/metadata.jsonl"
+}
+
+
+class SDH_16k(datasets.GeneratorBasedBuilder):
+    """This dataset is used to train the SDH model of MyoQuant to detect and quantify anomaly in the mitochondria repartition in SDH stained muscle fiber with myopathy disorders."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+
+    DEFAULT_CONFIG_NAME = "SDH_16k"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(num_classes=2, names=_NAMES),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+            task_templates=[
+                datasets.ImageClassification(image_column="image", label_column="label")
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download(_URLS)
+        split_metadata_path = dl_manager.download(_METADATA_URL)
+        files_metadata = {}
+        with open(split_metadata_path["SDH_16k_metadata"], encoding="utf-8") as f:
+            for lines in f.read().splitlines():
+                file_json_metdata = json.loads(lines)
+                files_metadata.setdefault(file_json_metdata["split"], []).append(
+                    file_json_metdata
+                )
+        downloaded_files = dl_manager.download_and_extract(archive_path)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "download_path": downloaded_files["SDH_16k"],
+                    "metadata": files_metadata["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "download_path": downloaded_files["SDH_16k"],
+                    "metadata": files_metadata["validation"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "download_path": downloaded_files["SDH_16k"],
+                    "metadata": files_metadata["test"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, download_path, metadata):
+        """Generate images and labels for splits."""
+        for single_metdata in metadata:
+            img_path = os.path.join(
+                download_path,
+                single_metdata["split"],
+                single_metdata["label"],
+                single_metdata["file_name"],
+            )
+            yield single_metdata["file_name"], {
+                "image": {"path": img_path, "bytes": open(img_path, "rb").read()},
+                "label": single_metdata["label"],
+            }