Update Populus_Stomatal_Images_Datasets.py

Populus_Stomatal_Images_Datasets.py

@@ -85,7 +85,8 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             "image_id": datasets.Value("string"),
             "species": datasets.Value("string"),
             "scientific_name": datasets.Value("string"),
-            "pics_array": datasets.Image(),
+            "image_path": datasets.Value("string"),
+            "image": datasets.Image(),
             # datasets.Array3D(dtype="uint8", shape=(3,768, 1024)), # Assuming images are RGB with shape 768x1024
             "image_resolution": {
                 "width": datasets.Value("int32"),
@@ -160,78 +161,34 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             })
         return annotations
     
-    # 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):
+    def _generate_examples(self, filepaths, species_info, data_dir):
         """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")
+            img = Image.open(image_path)
             # 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]  # Corrected field name from 'Witdh'
-                height = species_row['Heigth'].values[0]  # Corrected field name from 'Heigth'
+                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
-
-            # Retrieve annotations for the current image from the dictionary
-            annotations = annotations_dict.get(image_id, [])
-
+            #     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,
@@ -239,6 +196,53 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                 "scientific_name": scientific_name,
                 #"pics_array": pics_array.tolist(),  # Convert numpy array to list for JSON serializability
                 "image_path": image_path,
+                "image": img,
                 "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")
+            
+    #         # 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]  # Corrected field name from 'Witdh'
+    #             height = species_row['Heigth'].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
+
+    #         # 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.tolist(),  # Convert numpy array to list for JSON serializability
+    #             "image_path": image_path,
+    #             "image": img,
+    #             "image_resolution": {"width": width, "height": height},
+    #             "annotations": annotations
+    #         }