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HuggingFaceDataset-Binary.py

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+# Copyright 2024 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.
+
+import json
+import os
+import csv
+from PIL import Image
+import pandas as pd
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{gautam2024kvasirvqa,
+  title={Kvasir-VQA: A Text-Image Pair GI Tract Dataset},
+  author={Gautam, Sushant and Storås, Andrea and Midoglu, Cise and Hicks, Steven A. and Thambawita, Vajira and Halvorsen, Pål and Riegler, Michael A.},
+  booktitle={Proceedings of the First International Workshop on Vision-Language Models for Biomedical Applications (VLM4Bio '24)},
+  year={2024},
+  location={Melbourne, VIC, Australia},
+  publisher={ACM},
+  doi={10.1145/3689096.3689458}
+}
+"""
+
+_DESCRIPTION = """\
+The Kvasir-VQA dataset is an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations. This dataset is designed to facilitate advanced machine learning tasks in gastrointestinal (GI) diagnostics, including image captioning, Visual Question Answering (VQA), and text-based generation of synthetic medical images.
+"""
+
+_HOMEPAGE = "https://datasets.simula.no/kvasir-vqa/"
+
+_LICENSE = "cc-by-nc-4.0"
+
+
+class KvasirVQADataset(datasets.GeneratorBasedBuilder):
+    """Kvasir-VQA: A Text-Image Pair GI Tract Dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="kvasir_vqa", version=VERSION, description="Kvasir-VQA dataset containing text-image pairs with question-and-answer annotations"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "kvasir_vqa"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image": datasets.Image(),
+                "source": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "answer": datasets.Value("string"),
+                "img_id": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = "."
+        return [
+            datasets.SplitGenerator(
+                name="raw_annotations",
+                gen_kwargs={
+                    "metadata_file": os.path.join(data_dir, "metadata.csv"),
+                    "image_dir": data_dir,
+                },
+            )
+        ]
+    
+    def _generate_examples(self, metadata_file, image_dir):
+        image_cache = {}
+        df = pd.read_csv(metadata_file, encoding='utf-8')
+        # shuffled_df = df.sample(frac=1, random_state=42).reset_index(drop=True)
+        shuffled_df = df
+        for idx, row in shuffled_df.iterrows():
+                image_file = row["file_name"]
+                image_path = os.path.join(image_dir, image_file)
+                
+                if image_file not in image_cache:
+                    if os.path.exists(image_path):
+                        with open(image_path, "rb") as img_file:
+                            image_cache[image_file] = img_file.read()
+                    else:
+                        continue  # Skip if the image file does not exist
+
+                yield idx, {
+                    "image": image_cache[image_file],
+                    "source": row["source"],
+                    "question": row["question"],
+                    "answer": row["answer"],
+                    "img_id": image_file.replace(".jpg", "").replace("images/", ""),
+                }
+
+# RUN: datasets-cli test  HuggingFaceDataset-Binary.py --save_info --all_configs
+
+## upload to huggingface, it will save as arrow
+
+# huggingface-cli upload SimulaMet-HOST/xxKvasir-VQA  . .  --repo-type dataset  xxx
+
+## then convert the arrow to parqueet
+
+# datasets-cli convert_to_parquet  SimulaMet-HOST/xxKvasir-VQA 
+
+
+# The file names were weird. I had to rename them to make it more readable.
+# cloned the repo to local and pushed again to huggingface
+