Update soybean_dataset.py

soybean_dataset.py

@@ -21,7 +21,7 @@ import os
 from typing import List
 import datasets
 import logging
-import csv
+import zipfile
 import numpy as np
 from PIL import Image
 import os
@@ -32,11 +32,6 @@ from numpy import asarray
 import requests
 from io import BytesIO
 from numpy import asarray
-from concurrent.futures import ThreadPoolExecutor, as_completed
-import requests
-import asyncio
-from functools import wraps
-import logging
 
 
 # TODO: Add BibTeX citation
@@ -70,11 +65,11 @@ _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 = "/content/drive/MyDrive/sta_663/soybean/dataset.csv"
+
 _URLs = {
-    "train" : "https://raw.githubusercontent.com/lisawen0707/soybean/main/train_dataset.csv",
-    "test": "https://raw.githubusercontent.com/lisawen0707/soybean/main/test_dataset.csv",
-    "valid": "https://raw.githubusercontent.com/lisawen0707/soybean/main/valid_dataset.csv"
+    "train" : "https://huggingface.co/datasets/lisawen/soybean_dataset/resolve/main/train.zip?download=true",
+    "test": "https://huggingface.co/datasets/lisawen/soybean_dataset/resolve/main/test.zip?download=true",
+    "valid": "https://huggingface.co/datasets/lisawen/soybean_dataset/resolve/main/valid.zip?download=true"
 }
 
 # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
@@ -90,8 +85,7 @@ class SoybeanDataset(datasets.GeneratorBasedBuilder):
           description=_DESCRIPTION,
           features=datasets.Features(
               {
-                  "unique_id": datasets.Value("string"),
-                  "sets": datasets.Value("string"),
+                  
                   "original_image": datasets.Image(),
                   "segmentation_image": datasets.Image(),
                   
@@ -121,49 +115,44 @@ class SoybeanDataset(datasets.GeneratorBasedBuilder):
             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 and return the PIL Image
-            img = Image.open(BytesIO(response.content))
-            return img
+    # 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 and return the PIL Image
+    #         img = Image.open(BytesIO(response.content))
+    #         return img
         
 
 
     def _generate_examples(self, filepath):
-    #"""Yields examples as (key, example) tuples."""
         logging.info("generating examples from = %s", filepath)
-
-        with open(filepath, encoding="utf-8") as f:
-            data = csv.DictReader(f)
-
-
-            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']
-
-                original_image = self.process_image(original_image_path)
-                segmentation_image = self.process_image(segmentation_image_path)
-
+        # Open the zip file
+        with zipfile.ZipFile(filepath, 'r') as zip_ref:
+            # List all the contents of the zip file
+            zip_list = zip_ref.namelist()
+            
+            # Create pairs of original and segmentation images
+            images_pairs = [(f, f.replace('_original.jpg', '_segmentation.png')) for f in zip_list if '_original.jpg' in f]
+            
+            # Iterate over the pairs and yield examples
+            for original_image_name, segmentation_image_name in images_pairs:
+                # The unique_id is derived from the original image name by stripping away the file extension and the suffix
+                unique_id = original_image_name.split('_')[0]
+                
+                # Extract and process the original image
+                with zip_ref.open(original_image_name) as original_file:
+                    original_image = Image.open(original_file)
                 
-                # 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,
+                # Extract and process the segmentation image
+                with zip_ref.open(segmentation_image_name) as segmentation_file:
+                    segmentation_image = Image.open(segmentation_file)
+                
+                yield unique_id, {
                     "original_image": original_image,
                     "segmentation_image": segmentation_image,
-                    # ... add other features if necessary
-                }
-    
-
-    
-            
+                }