Update building script

HyperForensics-plus-plus.py

@@ -1,5 +1,3 @@
-# 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
@@ -11,7 +9,7 @@
 # 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.
-"""TODO: Add a description here."""
+"""HyperForensics++ dataset"""
 
 
 import csv
@@ -20,22 +18,17 @@ import os
 
 import datasets
 
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.
-},
-year={2020}
+@InProceedings{hyperforensics:dataset,
+title={HyperForensics++: Toward Adversarial Perturbed and Object Replacement in Hyperspectral Imaging Dataset},
+author={Chih-Chung Hsu, Chia-Ming Lee, Min-Tzo Ko, En-Chao Liu, Yi-Ching Cheng, Ming-Ching Chang},
+year={2025}
 }
 """
 
-# TODO: Add description of the dataset here
-# You can copy an official description
 _DESCRIPTION = """\
-This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+The HyperForensics++ dataset is an advanced benchmark designed for hyperspectral image (HSI) forgery detection.
+It builds upon the foundational HyperForensics dataset by introducing new manipulation scenarios and enhanced techniques.
 """
 
 _HOMEPAGE = "https://huggingface.co/datasets/OtoroLin/HyperForensics-plus-plus"
@@ -43,17 +36,11 @@ _HOMEPAGE = "https://huggingface.co/datasets/OtoroLin/HyperForensics-plus-plus"
 # TODO: Add the licence for the dataset here if you can find it
 _LICENSE = ""
 
-# TODO: Add link to the official dataset URLs here
-# 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)
-_URLS = {
-    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
-    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
-}
+_URL = "https://huggingface.co/datasets/OtoroLin/HyperForensics-plus-plus/resolve/main/data.tar.gz"
 
 
 class HyperForensicsPlusPlus(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
+    """HyperForensics++ dataset"""
 
     VERSION = datasets.Version("1.1.0")
 
@@ -65,32 +52,27 @@ class HyperForensicsPlusPlus(datasets.GeneratorBasedBuilder):
     # data = datasets.load_dataset('my_dataset', 'first_domain')
     # data = datasets.load_dataset('my_dataset', 'second_domain')
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
-        datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
+        datasets.BuilderConfig(
+            name="data",
+            version=VERSION,
+            description="Full dataset with all the HSI in npy format",),
+        # datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
     ]
 
-    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "data"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option1": datasets.Value("string"),
-                    "answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option2": datasets.Value("string"),
-                    "second_domain_answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
+        features = datasets.Features(
+            {
+                "origin": datasets.Image(), # The original HSI
+                "label": datasets.Value("string"), # The label of the image
+                "forgery": datasets.Image(), # The HSI after forgery
+                "method": datasets.Value("string"), # The forgery method used
+                # The bounding box of the forgery area, in the format [x1, x2, y1, y2, z1, y2]
+                "bbox": datasets.Sequence(feature=datasets.Value(dtype='int8'), length=6)
+            }
+        )
+ 
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
@@ -108,20 +90,17 @@ class HyperForensicsPlusPlus(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _URLS[self.config.name]
+        urls = _URL
         data_dir = dl_manager.download_and_extract(urls)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "train.jsonl"),
+                    "filepath": os.path.join(data_dir, "metadata.jsonl"),
                     "split": "train",
                 },
             ),
@@ -129,15 +108,15 @@ class HyperForensicsPlusPlus(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "dev.jsonl"),
-                    "split": "dev",
+                    "filepath": os.path.join(data_dir, "metadata.jsonl"),
+                    "split": "val",
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "test.jsonl"),
+                    "filepath": os.path.join(data_dir, "metadata.jsonl"),
                     "split": "test"
                 },
             ),
@@ -145,8 +124,7 @@ class HyperForensicsPlusPlus(datasets.GeneratorBasedBuilder):
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        
         with open(filepath, encoding="utf-8") as f:
             for key, row in enumerate(f):
                 data = json.loads(row)

zipping.sh

@@ -0,0 +1,12 @@
+#!/bin/bash
+
+# Define the folder to zip and the output zip file
+FOLDER_TO_ZIP="../data"
+OUTPUT_TAR="./data.tar.gz"
+
+# Create the zip file
+echo "Zipping folder: $FOLDER_TO_ZIP"
+tar -czvf "$OUTPUT_TAR" "$FOLDER_TO_ZIP"
+
+# Confirm completion
+echo "Folder zipped successfully to: $OUTPUT_TAR"