Upload plantsdataset.py

plantsdataset.py

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+# -*- coding: utf-8 -*-
+"""PlantsDataset
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1nkvgrtbJQaIBdnxYHl8WTpKVL_AzAzux
+"""
+
+import datasets
+from datasets import load_dataset, DatasetInfo, Features, Value, ClassLabel, Split, SplitGenerator, GeneratorBasedBuilder, BuilderConfig, Array3D, Version
+import os
+from PIL import Image
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import geopandas as gpd
+from datasets import (
+    GeneratorBasedBuilder, Version, DownloadManager, SplitGenerator, Split,
+    Features, Value, BuilderConfig, DatasetInfo
+)
+import matplotlib.pyplot as plt
+import seaborn as sns
+import csv
+import json
+from shapely.geometry import Point
+import base64
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import io
+import os
+from PIL import Image
+import numpy as np
+from datasets import DatasetInfo, Features, Value, ClassLabel, Split, SplitGenerator, GeneratorBasedBuilder, BuilderConfig
+from datasets import NamedSplit, Split, SplitGenerator
+import gdown
+_DRIVE_ID = "1fXgVwhdU5YGj0SPIcHxSpxkhvRh54oEH"
+_URL = f"https://drive.google.com/uc?export=download&id={_DRIVE_ID}"
+
+class PlantsDataset(GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        BuilderConfig(name="default", version=VERSION, description="Default configuration for PlantsDataset"),
+    ]
+
+    def _info(self):
+        features = Features({
+            "image": Value("string"),  # Change to Array3D to store image arrays
+            "label": ClassLabel(names=["aleo vera", "calotropis gigantea"]),
+        })
+        return DatasetInfo(
+            description="Your dataset description",
+            features=features,
+            supervised_keys=("image", "label"),
+            homepage="Your dataset homepage",
+            citation="Citation for your dataset",
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_file = dl_manager.download_and_extract(_URL)
+
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={
+                    "data_folder": os.path.join(downloaded_file, "train"),
+                },
+            ),
+            SplitGenerator(
+                name=Split.TEST,
+                gen_kwargs={
+                    "data_folder": os.path.join(downloaded_file, "test"),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_folder):
+        label_names = self.info.features['label'].names
+        for label_name in label_names:
+            subfolder_path = os.path.join(data_folder, label_name)
+            label = label_names.index(label_name)
+            for root, _, files in os.walk(subfolder_path):
+                for file_name in files:
+                    file_path = os.path.join(root, file_name)
+                    if os.path.isfile(file_path) and file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
+                        # Image ID should be unique, use filename for simplicity
+                        image_id = os.path.splitext(file_name)[0]
+                        yield image_id, {
+                            "image": file_path,  # Store file path as a string
+                            "label": label,
+                        }
+                    else:
+                        print(f"Skipped file {file_path}, since it is not an image."）
+# Instantiate the dataset builder for PlantsDataset
+plants_dataset = PlantsDataset()
+
+# Download the data and prepare the dataset
+plants_dataset.download_and_prepare()
+
+# Access the dataset as a `DatasetDict`
+dataset_dict = plants_dataset.as_dataset()
+
+# Access the train and test splits
+train_dataset = dataset_dict['train']
+test_dataset = dataset_dict['test']
+
+# Now you can use `train_dataset` and `test_dataset` as needed
+# For example, you can iterate over the dataset and access the file paths and labels
+for example in train_dataset:
+    print(example['image'], example['label'])