Update Populus_Stomatal_Images_Datasets.py

Populus_Stomatal_Images_Datasets.py

@@ -111,10 +111,12 @@ class NewDataset(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         
-        data_files = dl_manager.download_and_extract({
+         data_files = dl_manager.download_and_extract({
             "csv": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images.csv",
-            "zip": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images.zip"
+            "zip": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images.zip",
+            "annotations_json": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/annotations.json"
         })
+
         
         species_info = pd.read_csv(data_files["csv"])
         extracted_images_path = os.path.join(data_files["zip"], "Labeled Stomatal Images")
@@ -128,7 +130,8 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             gen_kwargs={
                 "filepaths": all_image_filenames,
                 "species_info": species_info,
-                "data_dir": extracted_images_path
+                "data_dir": extracted_images_path，
+                "annotations_file": data_files["annotations_json"]
             },
         )]
     
@@ -157,42 +160,84 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             })
         return annotations
     
-    def _generate_examples(self, filepaths, species_info, data_dir):
+    # def _generate_examples(self, filepaths, species_info, data_dir):
+    #     """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['Witdh'].values[0]
+    #             height = species_row['Heigth'].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)# Convert the PIL image to a numpy array and then to a list
+    #             # print(pics_array.shape)
+            
+    #         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
+    #         }
+
+
+      
+
+    def _generate_examples(self, filepaths, species_info, data_dir, annotations_file):
         """Yields examples as (key, example) tuples."""
+        # Load annotations from JSON file
+        with open(annotations_file, 'r') as file:
+            annotations_dict = json.load(file)
+
         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['Witdh'].values[0]
-                height = species_row['Heigth'].values[0]
+                width = species_row['Width'].values[0]  # Corrected field name from 'Witdh'
+                height = species_row['Height'].values[0]  # Corrected field name from 'Heigth'
             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)# Convert the PIL image to a numpy array and then to a list
-                # print(pics_array.shape)
-            
-            annotations = self._parse_yolo_labels(label_path, width, height)
-            
+                pics_array = np.array(img)  # Convert the PIL image to a numpy array
+
+            # Retrieve annotations for the current image from the dictionary
+            annotations = annotations_dict.get(image_id, [])
+
             # 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
+                "pics_array": pics_array.tolist(),  # Convert numpy array to list for JSON serializability
                 "image_resolution": {"width": width, "height": height},
                 "annotations": annotations
             }
-
-