Update new_dataset_script.py

new_dataset_script.py

@@ -121,52 +121,109 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         # 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]
-        data_dir = dl_manager.download_and_extract(urls)
+        
+        # Download and extract the dataset using Hugging Face's datasets library
+        data_files = dl_manager.download_and_extract({
+            "csv": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.csv",
+            "zip": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.zip"
+        })
+        
+        # Load the CSV file containing species and scientific names
+        species_info = pd.read_csv(data_files["csv"])
+
+        # Get the list of all image files from the CSV
+        all_files = species_info['FileName'].tolist()
+
+        # Shuffle the list for random split
+        random.shuffle(all_files)
+        
+        # Split the files into train/validation/test
+        num_files = len(all_files)
+        train_split_end = int(num_files * 0.7)
+        val_split_end = train_split_end + int(num_files * 0.15)
+        
+        train_files = all_files[:train_split_end]
+        val_files = all_files[train_split_end:val_split_end]
+        test_files = all_files[val_split_end:]
+
         return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
+            SplitGenerator(
+                name=Split.TRAIN,
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "train.jsonl"),
+                    "filepaths": train_files,
+                    "species_info": species_info,
+                    "data_dir": extracted_path,
                     "split": "train",
                 },
             ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
+            SplitGenerator(
+                name=Split.VALIDATION,
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "dev.jsonl"),
-                    "split": "dev",
+                    "filepaths": val_files,
+                    "species_info": species_info,
+                    "data_dir": extracted_path,
+                    "split": "validation",
                 },
             ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
+            SplitGenerator(
+                name=Split.TEST,
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "test.jsonl"),
-                    "split": "test"
+                    "filepaths": test_files,
+                    "species_info": species_info,
+                    "data_dir": extracted_path,
+                    "split": "test",
                 },
             ),
         ]
 
-    # 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)
-                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"],
-                    }
+    def _generate_examples(self, filepaths, species_info, data_dir, split):
+        """Yields examples as (key, example) tuples."""
+        for file_name in filepaths:
+            # Extract the base name without the file extension
+            image_id = os.path.splitext(file_name)[0]
+            
+            # Construct the full image file path
+            image_path = os.path.join(data_dir, f"{image_id}.jpg")
+            # Construct the full label file path
+            label_path = os.path.join(data_dir, f"{image_id}.txt")
+            
+            # Open image and convert to array
+            with Image.open(image_path) as img:
+                pics_array = np.array(img)
+                width, height = img.size
+            
+            # Extract species and scientific name from CSV file
+            species_row = species_info.loc[species_info['FileName'] == image_id]
+            species = species_row['Species'].values[0]
+            scientific_name = species_row['ScientificName'].values[0]
+
+            # Parse YOLO annotations
+            annotations = self._parse_yolo_labels(label_path, width, height)
+
+            # Yield the final structured example
+            yield image_id, {
+                "image_id": image_id,
+                "species": species,
+                "scientific_name": scientific_name,
+                "pics_array": pics_array,
+                "image_resolution": {"width": width, "height": height},
+                "annotations": annotations
+            }
+
+    def _parse_yolo_labels(self, label_path, width, height):
+        annotations = []
+        with open(label_path, 'r') as file:
+            yolo_data = file.readlines()
+
+        for line in yolo_data:
+            class_id, x_center_rel, y_center_rel, width_rel, height_rel = map(float, line.split())
+            annotations.append({
+                "category_id": int(class_id),
+                "bounding_box": {
+                    "x_center": x_center_rel * width,
+                    "y_center": y_center_rel * height,
+                    "width": width_rel * width,
+                    "height": height_rel * height
+                }
+            })
+        return annotatio