Enable dataset download, not yet finishing the nesting enumerating of folders

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data.tar.gz filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+download_testing.py
+zipping.sh

HyperForensics-plus-plus.py

@@ -15,6 +15,8 @@
 import csv
 import json
 import os
+import numpy as np
+import tifffile as tiff  # Install with `pip install tifffile`
 
 import datasets
 
@@ -64,12 +66,12 @@ class HyperForensicsPlusPlus(datasets.GeneratorBasedBuilder):
     def _info(self):
         features = datasets.Features(
             {
-                "origin": datasets.Image(), # The original HSI
+                "origin": datasets.Array3D(dtype="int16", shape=(256, 256, 172)), # The original HSI
                 "label": datasets.Value("string"), # The label of the image
-                "forgery": datasets.Image(), # The HSI after forgery
+                "forgery": datasets.Array3D(dtype="int16", shape=(256, 256, 172)), # 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)
+                "bbox": datasets.Sequence(feature=datasets.Value(dtype='int16'), length=6)
             }
         )
  
@@ -95,12 +97,14 @@ class HyperForensicsPlusPlus(datasets.GeneratorBasedBuilder):
         # 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 = _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, "metadata.jsonl"),
+                    "filepath": data_dir,
                     "split": "train",
                 },
             ),
@@ -108,36 +112,58 @@ class HyperForensicsPlusPlus(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "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, "metadata.jsonl"),
-                    "split": "test"
+                    "filepath": data_dir,
+                    "split": "validation",
                 },
             ),
+            #datasets.SplitGenerator(
+            #    name=datasets.Split.TEST,
+            #    # These kwargs will be passed to _generate_examples
+            #    gen_kwargs={
+            #        "filepath": data_dir,
+            #        "split": "testing"
+            #    },
+            #),
         ]
+    
+    def _load_npy_as_image(self, npy_path):
+        """
+        Load a .npy file and convert it to a PIL Image for datasets.Image(). Not using in current scope.
+        """
+        array = np.load(npy_path)  # Load the .npy file as a NumPy array
+        image = tiff.imread(npy_path)  # Convert to a PIL Image
+        return image
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split):
-        
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                if self.config.name == "first_domain":
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option1": data["option1"],
-                        "answer": "" if split == "test" else data["answer"],
-                    }
-                else:
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option2": data["option2"],
-                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
-                    }
+        filepath = os.path.join(filepath, "data_testing")
+        with open(os.path.join(filepath, "metadata.jsonl"), encoding="utf-8") as f:
+            metadata = json.load(f)  # Load the nested JSON object (train, validation, testing)
+
+            # Select the appropriate split (train, validation, or testing)
+            records = metadata[split]
+
+            for key, record in enumerate(records):
+                file_prefix = record["file_prefix"]
+                label = record["label"]
+                bbox = record["bbox"]
+
+                # Construct paths for the origin and forgery files
+                origin_path = os.path.join(
+                    filepath, "ADMM-ADAM", "config0", f"{file_prefix}_inpaint_result(0).npy"
+                )
+                forgery_path = os.path.join(
+                    filepath, "ADMM-ADAM", "config0", f"{file_prefix}_inpaint_result(0).npy"
+                )
+
+                # Load the .npy files as images
+                origin_image = np.load(origin_path) #np.load(origin_path)
+                forgery_image = np.load(forgery_path) #np.load(forgery_path)
+                # Yield the example
+                yield key, {
+                    "origin": origin_image,
+                    "label": label,
+                    "forgery": forgery_image,
+                    "method": "ADMM-ADAM",  # Hardcoded for now; can be dynamic if needed
+                    "bbox": bbox,
+                }

data.tar.gz

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:50c1387476e53b21681a90c897e45d6c404995b9cc21e2d2e4f468c10068884c
-size 4638814869
+oid sha256:313ddddbb44d0946609a73c368eee0216919ba55c2229f5def9a754ae0c63a67
+size 4651559462

download_testing.py

@@ -1,6 +0,0 @@
-import datasets
-
-dl_manager = datasets.DownloadManager()
-data_dir = dl_manager.download_and_extract("https://huggingface.co/datasets/OtoroLin/HyperForensics-plus-plus/resolve/main/data.tar.gz")
-
-print(data_dir)

zipping.sh

@@ -1,12 +0,0 @@
-#!/bin/bash
-
-# Define the folder to zip and the output zip file
-FOLDER_TO_ZIP="../data_testing"
-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"