Update soybean_dataset.py

soybean_dataset.py

@@ -250,55 +250,55 @@ class SoybeanDataset(datasets.GeneratorBasedBuilder):
             logging.error(f"Error downloading {image_url}: {e}")
             return None
 
-    def download_images_concurrently(self, image_urls):
+    def download_images_in_batch(self, image_urls):
         images = {}
-        with ThreadPoolExecutor(max_workers=5) as executor:
+        with ThreadPoolExecutor() as executor:
             future_to_url = {executor.submit(self.download_image, url): url for url in image_urls}
             for future in as_completed(future_to_url):
                 url = future_to_url[future]
                 try:
                     image = future.result()
-                    images[url] = image
-                except Exception as exc:
-                    logging.error(f'{url} generated an exception: {exc}')
+                    if image:
+                        images[url] = image
+                except Exception as e:
+                    logging.error(f"Error processing {url}: {e}")
         return images
 
     def _generate_examples(self, filepath):
-        logging.info("generating examples from = %s", filepath)
+        logging.info(f"Generating examples from = {filepath}")
 
-        image_urls = []
         with open(filepath, encoding="utf-8") as f:
             data = csv.DictReader(f)
-            for row in data:
-                image_urls.append(row['original_image'])
-                image_urls.append(row['segmentation_image'])
+            image_urls = [row['original_image'] for row in data] + [row['segmentation_image'] for row in data]
+            # Remove duplicates and None values
+            image_urls = list(set(filter(None, image_urls)))
 
-        # Download all images concurrently
-        downloaded_images = self.download_images_concurrently(set(image_urls))  # Use set to avoid duplicate downloads
+            # Download images in batch
+            images = self.download_images_in_batch(image_urls)
 
-        with open(filepath, encoding="utf-8") as f:
+            # Reset file pointer to the beginning to iterate again
+            f.seek(0)
             data = csv.DictReader(f)
+
             for row in data:
                 unique_id = row['unique_id']
-                original_image_path = row['original_image']
-                segmentation_image_path = row['segmentation_image']
-                sets = row['sets']
+                original_image_url = row['original_image']
+                segmentation_image_url = row['segmentation_image']
 
-                original_image = downloaded_images.get(original_image_path)
-                segmentation_image = downloaded_images.get(segmentation_image_path)
+                original_image = images.get(original_image_url)
+                segmentation_image = images.get(segmentation_image_url)
 
-                if original_image is None or segmentation_image is None:
-                    logging.error(f"Missing image for {unique_id}")
+                if not original_image or not segmentation_image:
+                    logging.warning(f"Missing image for {unique_id}, skipping example.")
                     continue
 
                 yield unique_id, {
                     "unique_id": unique_id,
-                    "sets": sets,
+                    "sets": row['sets'],
                     "original_image": original_image,
                     "segmentation_image": segmentation_image,
-                }
-
-    
+                    # ... add other features if necessary
+                } 
     
     
     
@@ -310,7 +310,4 @@ class SoybeanDataset(datasets.GeneratorBasedBuilder):
     
     
     
-    
-    
-