speed

soybean_dataset.py

@@ -117,69 +117,43 @@ class SoybeanDataset(datasets.GeneratorBasedBuilder):
                 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_path):
+        # Load the image from the local filesystem
+        img = Image.open(image_path)
+        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)
-
-                
-                # 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'], {
+                original_image_path = row['original_image']  # Adjust this path if necessary
+                segmentation_image_path = row['segmentation_image']  # Adjust this path if necessary
+
+                # Check if image exists locally before loading
+                if os.path.exists(original_image_path):
+                    original_image = self.process_image(original_image_path)
+                else:
+                    logging.error(f"Original image not found: {original_image_path}")
+                    continue  # or handle missing image appropriately
+
+                if os.path.exists(segmentation_image_path):
+                    segmentation_image = self.process_image(segmentation_image_path)
+                else:
+                    logging.error(f"Segmentation image not found: {segmentation_image_path}")
+                    continue  # or handle missing image appropriately
+
+                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
                 }
     
-                # 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_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,
-                #     # ... add other features if necessary
-                # }
-    
-    
-    
-