Update soybean_dataset.py

soybean_dataset.py

@@ -26,6 +26,12 @@ import numpy as np
 from PIL import Image
 import os
 import io
+import pandas as pd
+import matplotlib.pyplot as plt
+from numpy import asarray
+import requests
+from io import BytesIO
+from numpy import asarray
 
 
 # TODO: Add BibTeX citation
@@ -60,13 +66,17 @@ _LICENSE = "Under a Creative Commons license"
 # 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 = "/content/drive/MyDrive/sta_663/soybean/dataset.csv"
-
+_URLs = {
+    "train" : "https://drive.google.com/file/d/1-5Tdr_OTUUfkjf_UCa5EZOjGdlW683S-/view?usp=sharing",
+    "test": "https://drive.google.com/file/d/1-2wUyuBTeesGxLuDCvxRcUPdftL-Zen9/view?usp=sharing",
+    "valid": "https://drive.google.com/file/d/1-1DeSjBY9YlfGCl7CvoU97h7eX95R1eC/view?usp=sharing"
+}
 
 # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
 class SoybeanDataset(datasets.GeneratorBasedBuilder):
     """TODO: Short description of my dataset."""
 
-    _URLS = _URL
+    _URLS = _URLs
     VERSION = datasets.Version("1.1.0")
 
     def _info(self):
@@ -94,53 +104,56 @@ class SoybeanDataset(datasets.GeneratorBasedBuilder):
         # 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?")
+        urls_to_download = self._URLs
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
 
         # 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
-                }
-            ),
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),    
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}),
         ]
 
+    def process_image(self,image_url):
+        response = requests.get(image_url)
+        response.raise_for_status()  # This will raise an exception if there is a download error
+
+        # Open the image from the downloaded bytes
+        img = Image.open(BytesIO(response.content))
+
+        numpydata = asarray(img)
+
+        return numpydata
+        
+
+
     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
+        logging.info("generating examples from = %s", filepath)
+
         with open(filepath, encoding="utf-8") as f:
-            reader = csv.DictReader(f)
-            
-            for row in reader:
+            data = csv.DictReader(f)
+            for image_url in data["original_image"]:
+                numpydata = self.process_image(image_url)
+
+            for row in data:
                 # Assuming the 'original_image' column has the full path to the image file
+                unique_id = row['unique_id']
                 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)
+                original_image_array = self.process_image(original_image_path)
+                segmentation_image_array = self.process_image(segmentation_image_path)
 
+                
                 # 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'], {
+                    "unique_id": unique_id,
                     "sets": sets,
                     "original_image": original_image_array,
                     "segmentation_image": segmentation_image_array,
@@ -157,3 +170,41 @@ class SoybeanDataset(datasets.GeneratorBasedBuilder):
     
     
     
+    
+    
+    
+    #### 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)
+
+      return [
+          datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+          datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+      ]
+      
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logging.info("generating examples from = %s", filepath)
+        with open(filepath) as f:
+            squad = json.load(f)
+            for article in squad["data"]:
+                title = article.get("title", "").strip()
+                for paragraph in article["paragraphs"]:
+                    context = paragraph["context"].strip()
+                    for qa in paragraph["qas"]:
+                        question = qa["question"].strip()
+                        id_ = qa["id"]
+
+                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+                        answers = [answer["text"].strip() for answer in qa["answers"]]
+
+                        # Features currently used are "context", "question", and "answers".
+                        # Others are extracted here for the ease of future expansions.
+                        yield id_, {
+                            "title": title,
+                            "context": context,
+                            "question": question,
+                            "id": id_,
+                            "answers": {"answer_start": answer_starts, "text": answers,},
+                        }