Update Populus_Stomatal_Images_Datasets.py

Populus_Stomatal_Images_Datasets.py

@@ -131,71 +131,62 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             },
         )]
 
- 
-def save_metadata_as_json(image_id, annotations, species, scientific_name, json_path):
-    metadata = {
-        "image_id": image_id,
-        "species": species,
-        "scientific_name": scientific_name,
-        "annotations": annotations
-    }
-    with open(json_path, 'w') as json_file:
-        json.dump(metadata, json_file)
-
-def _parse_yolo_labels(self, label_path, width, height):
-    annotations = []
-    with open(label_path, 'r') as file:
-        yolo_data = file.readlines()
-
-    for line in yolo_data:
-        class_id, x_center_rel, y_center_rel, width_rel, height_rel = map(float, line.split())
-        x_min = (x_center_rel - width_rel / 2) * width
-        y_min = (y_center_rel - height_rel / 2) * height
-        x_max = (x_center_rel + width_rel / 2) * width
-        y_max = (y_center_rel + height_rel / 2) * height
-        annotations.append({
-            "category_id": int(class_id),
-            "bounding_box": {
-                "x_min": x_min,
-                "y_min": y_min,
-                "x_max": x_max,
-                "y_max": y_max
-            }
-        })
-    return annotations
-
-def _generate_examples(self, filepaths, species_info, data_dir, split):
-    """Yields examples as (key, example) tuples."""
-    for file_name in filepaths:
-        image_id = os.path.splitext(file_name)[0]  # Extract the base name without the file extension
-        image_path = os.path.join(data_dir, f"{image_id}.jpg")
-        label_path = os.path.join(data_dir, f"{image_id}.txt")
-        
-        # Find the corresponding row in the CSV for the current image
-        species_row = species_info.loc[species_info['FileName'] == image_id]
-        if not species_row.empty:
-            species = species_row['Species'].values[0]
-            scientific_name = species_row['ScientificName'].values[0]
-            width = species_row['Width'].values[0]
-            height = species_row['Height'].values[0]
-        else:
-            # Default values if not found
-            species = None
-            scientific_name = None
-            width = 1024  # or some default value
-            height = 768   # or some default value
-        
-        with Image.open(image_path) as img:
-            pics_array = np.array(img)  # Convert the PIL image to a numpy array
 
-        annotations = self._parse_yolo_labels(label_path, width, height)
-        
-        yield image_id, {
-            "image_id": image_id,
-            "species": species,
-            "scientific_name": scientific_name,
-            "pics_array": pics_array,
-            "image_resolution": {"width": width, "height": height},
-            "annotations": annotations,
-            "image": img  # Return the PIL image
-        }
+    def _parse_yolo_labels(self, label_path, width, height):
+        annotations = []
+        with open(label_path, 'r') as file:
+            yolo_data = file.readlines()
+    
+        for line in yolo_data:
+            class_id, x_center_rel, y_center_rel, width_rel, height_rel = map(float, line.split())
+            x_min = (x_center_rel - width_rel / 2) * width
+            y_min = (y_center_rel - height_rel / 2) * height
+            x_max = (x_center_rel + width_rel / 2) * width
+            y_max = (y_center_rel + height_rel / 2) * height
+            annotations.append({
+                "category_id": int(class_id),
+                "bounding_box": {
+                    "x_min": x_min,
+                    "y_min": y_min,
+                    "x_max": x_max,
+                    "y_max": y_max
+                }
+            })
+        return annotations
+    
+    def _generate_examples(self, filepaths, species_info, data_dir, split):
+        """Yields examples as (key, example) tuples."""
+        for file_name in filepaths:
+            image_id = os.path.splitext(file_name)[0]  # Extract the base name without the file extension
+            image_path = os.path.join(data_dir, f"{image_id}.jpg")
+            label_path = os.path.join(data_dir, f"{image_id}.txt")
+            
+            # Find the corresponding row in the CSV for the current image
+            species_row = species_info.loc[species_info['FileName'] == image_id]
+            if not species_row.empty:
+                species = species_row['Species'].values[0]
+                scientific_name = species_row['ScientificName'].values[0]
+                width = species_row['Width'].values[0]
+                height = species_row['Height'].values[0]
+            else:
+                # Default values if not found
+                species = None
+                scientific_name = None
+                width = 1024  # Default value
+                height = 768   # Default value
+    
+            pics_array = None
+            with Image.open(image_path) as img:
+                pics_array = np.array(img).tolist()  # Convert the PIL image to a numpy array and then to a list
+    
+            annotations = self._parse_yolo_labels(label_path, width, height)
+            
+            # Yield the dataset example
+            yield image_id, {
+                "image_id": image_id,
+                "species": species,
+                "scientific_name": scientific_name,
+                "pics_array": pics_array,  # Should be a list for JSON serializability
+                "image_resolution": {"width": width, "height": height},
+                "annotations": annotations
+            }