Update soybean_dataset.py

soybean_dataset.py

@@ -236,45 +236,63 @@ class SoybeanDataset(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}),
         ]
 
-    def process_image(self,image_url):
+    def download_image(self, image_url):
+        try:
             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
+            response.raise_for_status()  # This will raise an exception for HTTP errors
             img = Image.open(BytesIO(response.content))
             return img
-        
-
+        except requests.RequestException as e:
+            logging.error(f"Error downloading {image_url}: {e}")
+            return None
+
+    def download_images_concurrently(self, image_urls):
+        images = {}
+        with ThreadPoolExecutor(max_workers=5) 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}')
+        return images
 
     def _generate_examples(self, filepath):
-    #"""Yields examples as (key, example) tuples."""
         logging.info("generating examples from = %s", 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'])
 
+        # Download all images concurrently
+        downloaded_images = self.download_images_concurrently(set(image_urls))  # Use set to avoid duplicate downloads
 
+        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)
+                original_image = downloaded_images.get(original_image_path)
+                segmentation_image = downloaded_images.get(segmentation_image_path)
+
+                if original_image is None or segmentation_image is None:
+                    logging.error(f"Missing image for {unique_id}")
+                    continue
 
-                
-                # 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'], {
+                yield unique_id, {
                     "unique_id": unique_id,
                     "sets": sets,
                     "original_image": original_image,
                     "segmentation_image": segmentation_image,
-                    # ... add other features if necessary
                 }
-