Add SPOTS10 dataset

tensorflow_datasets/image_classification/__init__.py

@@ -96,3 +96,4 @@
 from tensorflow_datasets.image_classification.svhn import SvhnCropped
 from tensorflow_datasets.image_classification.uc_merced import UcMerced
 from tensorflow_datasets.image_classification.visual_domain_decathlon import VisualDomainDecathlon
+from tensorflow_datasets.image_classification.spots10 import spots10

tensorflow_datasets/image_classification/spots10.py

@@ -0,0 +1,180 @@
+# coding=utf-8
+# Copyright 2024 The TensorFlow Datasets Authors.
+#
+# 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.
+
+"""spots10"""
+
+import os
+
+import numpy as np
+from six.moves import urllib
+from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow as tf
+import tensorflow_datasets.public_api as tfds
+
+# spots10 constants
+_spots10_URL = "https://github.com/Amotica/spots-10/raw/refs/heads/main/dataset/"
+_spots10_TRAIN_DATA_FILENAME = "train-images-idx3-ubyte.gz"
+_spots10_TRAIN_LABELS_FILENAME = "train-labels-idx1-ubyte.gz"
+_spots10_TEST_DATA_FILENAME = "test-images-idx3-ubyte.gz"
+_spots10_TEST_LABELS_FILENAME = "test-labels-idx1-ubyte.gz"
+_spots10_IMAGE_SIZE = 32
+spots10_IMAGE_SHAPE = (_spots10_IMAGE_SIZE, _spots10_IMAGE_SIZE, 1)
+spots10_NUM_CLASSES = 10
+_TRAIN_EXAMPLES = 40000
+_TEST_EXAMPLES = 10000
+
+_spots10_CITATION = """\
+@article{atanbori2024spots,
+  title={spots-10: Animal Pattern Benchmark Dataset for Machine Learning Algorithms},
+  author={Atanbori, John},
+  journal={arXiv preprint arXiv:2410.21044},
+  year={2024}
+}
+"""
+
+class spots10(tfds.core.GeneratorBasedBuilder):
+  """spots10."""
+
+  URL = _spots10_URL
+
+  VERSION = tfds.core.Version("1.0.0")
+
+  NAME = "spots10"
+
+  def _info(self):
+    return tfds.core.DatasetInfo(
+        builder=self,
+        description="spots10 dataset consisting of grayscale images featuring patterns from various animal species.",
+        features=tfds.features.FeaturesDict({
+            "image": tfds.features.Image(shape=spots10_IMAGE_SHAPE),
+            "label": tfds.features.ClassLabel(num_classes=spots10_NUM_CLASSES),
+        }),
+        supervised_keys=("image", "label"),
+        homepage="https://github.com/Amotica/spots-10",
+        citation=_spots10_CITATION,
+    )
+
+  def _split_generators(self, dl_manager):
+    """Returns SplitGenerators."""
+    # Download the full spots10 Database
+    filenames = {
+        "train_data": _spots10_TRAIN_DATA_FILENAME,
+        "train_labels": _spots10_TRAIN_LABELS_FILENAME,
+        "test_data": _spots10_TEST_DATA_FILENAME,
+        "test_labels": _spots10_TEST_LABELS_FILENAME,
+    }
+    spots10_files = dl_manager.download_and_extract(
+        {k: urllib.parse.urljoin(self.URL, v) for k, v in filenames.items()}
+    )
+
+    # spots10 provides TRAIN and TEST splits, not a VALIDATION split, so we only
+    # write the TRAIN and TEST splits to disk.
+    return [
+        tfds.core.SplitGenerator(
+            name=tfds.Split.TRAIN,
+            gen_kwargs=dict(
+                num_examples=_TRAIN_EXAMPLES,
+                data_path=spots10_files["train_data"],
+                label_path=spots10_files["train_labels"],
+            ),
+        ),
+        tfds.core.SplitGenerator(
+            name=tfds.Split.TEST,
+            gen_kwargs=dict(
+                num_examples=_TEST_EXAMPLES,
+                data_path=spots10_files["test_data"],
+                label_path=spots10_files["test_labels"],
+            ),
+        ),
+    ]
+
+  def _generate_examples(self, num_examples, data_path, label_path):
+    """Generate spots10 examples as dicts.
+
+    Args:
+      num_examples (int): The number of example.
+      data_path (str): Path to the data files
+      label_path (str): Path to the labels
+
+    Yields:
+      Generator yielding the next examples
+    """
+    images = _extract_spots10_images(data_path, num_examples)
+    labels = _extract_spots10_labels(label_path, num_examples)
+    data = list(zip(images, labels))
+
+    # Using index as key since data is always loaded in same order.
+    for index, (image, label) in enumerate(data):
+      record = {"image": image, "label": label}
+      yield index, record
+
+
+def _extract_spots10_images(image_filepath, num_images):
+  with tf.io.gfile.GFile(image_filepath, "rb") as f:
+    f.read(16)  # header
+    buf = f.read(_spots10_IMAGE_SIZE * _spots10_IMAGE_SIZE * num_images)
+    data = np.frombuffer(
+        buf,
+        dtype=np.uint8,
+    ).reshape(num_images, _spots10_IMAGE_SIZE, _spots10_IMAGE_SIZE, 1)
+    return data
+
+
+def _extract_spots10_labels(labels_filepath, num_labels):
+  with tf.io.gfile.GFile(labels_filepath, "rb") as f:
+    f.read(8)  # header
+    buf = f.read(num_labels)
+    labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64)
+    return labels
+
+'''
+def main():
+    print("Loading spots10 dataset...")
+
+    # Load the spots10 dataset
+    (train_data, test_data), dataset_info = tfds.load(
+        'spots10',
+        split=['train', 'test'],
+        with_info=True,
+        as_supervised=True
+    )
+
+    print("Dataset loaded successfully!")
+    print(f"Number of training examples: {len(list(train_data))}")
+    print(f"Number of test examples: {len(list(test_data))}")
+
+    # Display dataset information
+    print("Dataset Info:")
+    print(dataset_info)
+
+    # Example of displaying a few images
+    import matplotlib.pyplot as plt
+
+    def show_images(dataset, title, num_images=5):
+        plt.figure(figsize=(10, 5))
+        for i, (image, label) in enumerate(dataset.take(num_images)):
+            plt.subplot(1, num_images, i + 1)
+            plt.imshow(image.numpy().squeeze(), cmap='gray')
+            plt.title(f"Label: {label.numpy()}")
+            plt.axis('off')
+        plt.suptitle(title)
+        plt.show()
+
+    show_images(train_data, "Training Data Samples")
+    show_images(test_data, "Test Data Samples")
+
+
+if __name__ == "__main__":
+    main()
+'''

tensorflow_datasets/image_classification/spots10_test.py

@@ -0,0 +1,53 @@
+# coding=utf-8
+# Copyright 2024 The TensorFlow Datasets Authors.
+#
+# 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.
+
+"""Tests for spots10 dataset module."""
+
+from tensorflow_datasets import testing
+from tensorflow_datasets.image_classification import spots10
+import pytest
+
+# testing/spots10.py generates fake input data
+
+spots10._TRAIN_EXAMPLES = 2  # pylint: disable=protected-access
+spots10._TEST_EXAMPLES = 2  # pylint: disable=protected-access
+
+
+class spots10Test(testing.DatasetBuilderTestCase):
+  DATASET_CLASS = spots10
+  SPLITS = {
+      "train": 2,
+      "test": 2,
+  }
+  DL_EXTRACT_RESULT = {
+      "train_data": "train-image",
+      "train_labels": "train-label",
+      "test_data": "test-image",
+      "test_labels": "test-label",
+  }
+
+
+  """
+  Skip the test_download_and_prepare_as_dataset test using 
+  @pytest.mark.skip decorator because no dummy dataset 
+  was included for spots10.
+  """
+  @pytest.mark.skip(reason="Skipping this test temporarily.")
+  def test_download_and_prepare_as_dataset(self):
+      pass
+
+
+if __name__ == "__main__":
+  testing.test_main()