Update soybean_dataset.py

soybean_dataset.py

@@ -122,51 +122,38 @@ class SoybeanDataset(datasets.GeneratorBasedBuilder):
         ]
 
 
-    def __init__(self, max_workers=5):
-        # Initialize a ThreadPoolExecutor with the desired number of workers
-        self.executor = ThreadPoolExecutor(max_workers=max_workers)
-
-    def process_image(self, image_url):
-        # This function is now a static method that doesn't need self
-        response = requests.get(image_url)
-        response.raise_for_status()  # This will raise an exception if there is a download error
-        img = Image.open(BytesIO(response.content))
-        return img
-
-    def download_images(self, image_urls):
-        # Use the executor to download images concurrently
-        # and return a future to image map
-        future_to_url = {self.executor.submit(self.process_image, url): url for url in image_urls}
-        return future_to_url
+    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)
 
-            # Create a set to collect all unique image URLs to download
-            image_urls = {row['original_image'] for row in data}
-            image_urls.update(row['segmentation_image'] for row in data)
-            
-            # Start the batch download
-            future_to_url = self.download_images(image_urls)
-
-            # Reset the file pointer to the start for the second pass
-            f.seek(0)
-            next(data)  # Skip header
 
             for row in data:
+                # Assuming the 'original_image' column has the full path to the image file
                 unique_id = row['unique_id']
-                original_image_url = row['original_image']
-                segmentation_image_url = row['segmentation_image']
+                original_image_path = row['original_image']
+                segmentation_image_path = row['segmentation_image']
                 sets = row['sets']
 
-                # Wait for the individual image futures to complete and get the result
-                original_image = future_to_url[self.executor.submit(self.process_image, original_image_url)].result()
-                segmentation_image = future_to_url[self.executor.submit(self.process_image, segmentation_image_url)].result()
+                original_image = self.process_image(original_image_path)
+                segmentation_image = self.process_image(segmentation_image_path)
 
-                yield unique_id, {
+                
+                # 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,
@@ -174,6 +161,8 @@ class SoybeanDataset(datasets.GeneratorBasedBuilder):
                     # ... add other features if necessary
                 }
     
+
+