Add README and dataset script

soybean_dataset.py

@@ -21,44 +21,52 @@ import os
 from typing import List
 import datasets
 import logging
+import csv
+import numpy as np
+from PIL import Image
+import os
+import io
+
 
 # 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}
+@article{chen2023dataset,
+  title={A dataset of the quality of soybean harvested by mechanization for deep-learning-based monitoring and analysis},
+  author={Chen, M and Jin, C and Ni, Y and Yang, T and Xu, J},
+  journal={Data in Brief},
+  volume={52},
+  pages={109833},
+  year={2023},
+  publisher={Elsevier},
+  doi={10.1016/j.dib.2023.109833}
 }
+
 """
 
 # 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.
+This dataset contains images captured during the mechanized harvesting of soybeans, aimed at facilitating the development of machine vision and deep learning models for quality analysis. It contains information of original soybean pictures in different forms, labels of whether the soybean belongs to training, validation, or testing datasets, segmentation class of soybean pictures in one dataset.
 """
 
 # TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = ""
+_HOMEPAGE = "https://huggingface.co/datasets/lisawen/soybean_dataset"
 
 # TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
+_LICENSE = "Under a Creative Commons 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)
-_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
-_URLS = {
-    "train": _URL + "train-v1.1.json",
-    "dev": _URL + "dev-v1.1.json",
-}
+_URL = "/content/drive/MyDrive/sta_663/soybean/dataset.csv"
+
 
 # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class SquadDataset(datasets.GeneratorBasedBuilder):
+class SoybeanDataset(datasets.GeneratorBasedBuilder):
     """TODO: Short description of my dataset."""
 
-    _URLS = _URLS
+    _URLS = _URL
     VERSION = datasets.Version("1.1.0")
 
     def _info(self):
@@ -67,22 +75,92 @@ class SquadDataset(datasets.GeneratorBasedBuilder):
           description=_DESCRIPTION,
           features=datasets.Features(
               {
-                  "id": datasets.Value("string"),
-                  "title": datasets.Value("string"),
-                  "context": datasets.Value("string"),
-                  "question": datasets.Value("string"),
-                  "answers": datasets.features.Sequence(
-                      {"text": datasets.Value("string"), "answer_start": datasets.Value("int32"),}
-                  ),
+                  "unique_id": datasets.Value("string"),
+                  "sets": datasets.Value("string"),
+                  "original_image": datasets.Value("string"),
+                  "segmentation_image": datasets.Value("string"),
+                  
               }
           ),
           # No default supervised_keys (as we have to pass both question
           # and context as input).
-          supervised_keys=None,
-          homepage="https://rajpurkar.github.io/SQuAD-explorer/",
+          supervised_keys=("original_image","segmentation_image"),
+          #homepage="https://rajpurkar.github.io/SQuAD-explorer/",
           citation=_CITATION,
       )
 
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # Since the dataset is on Google Drive, you need to implement a way to download it using the Google Drive API.
+
+        # The path to the dataset file in Google Drive
+        dataset_path = "/content/drive/MyDrive/sta_663/soybean/dataset.csv"
+
+        # Check if the file exists (you may need to mount the drive and use the appropriate path)
+        if not os.path.exists(dataset_path):
+            raise FileNotFoundError(f"{dataset_path} does not exist. Have you mounted Google Drive?")
+
+        # Since we're using a local file, we don't need to download it, so we just return the path.
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split,
+                gen_kwargs={
+                    "filepath": dataset_path
+                }
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+    #"""Yields examples as (key, example) tuples."""
+    
+        # Check if the file exists (you may need to mount the drive and use the appropriate path)
+        if not os.path.exists(filepath):
+            raise FileNotFoundError(f"{filepath} does not exist. Have you mounted Google Drive?")
+        
+        # Read the dataset.csv
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            
+            for row in reader:
+                # Assuming the 'original_image' column has the full path to the image file
+                original_image_path = row['original_image']
+                segmentation_image_path = row['segmentation_image']
+                sets = row['sets']
+
+                # Open the image and convert to numpy array
+                with open(original_image_path, "rb") as image_file:
+                    original_image = Image.open(image_file)
+                    original_image_array = np.array(original_image)
+                
+                
+                # Open the image and convert to numpy array
+                with open(segmentation_image_path, "rb") as image_file:
+                    segmentation_image = Image.open(image_file)
+                    segmentation_image_array = np.array(segmentation_image)
+
+                # Here you need to replace 'initial_radius', 'final_radius', 'initial_angle', 'final_angle', 'target'
+                # with actual columns from your CSV or additional processing you need to do
+                yield row['unique_id'], {
+                    "sets": sets,
+                    "original_image": original_image_array,
+                    "segmentation_image": segmentation_image_array,
+                    # ... add other features if necessary
+                }
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    #### origin
     def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
       urls_to_download = self._URLS
       downloaded_files = dl_manager.download_and_extract(urls_to_download)