fix: image loading

README.md

@@ -19,10 +19,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 8715
+    num_bytes: 134736982
     num_examples: 15
-  download_size: 129578665
-  dataset_size: 8715
+  download_size: 129724970
+  dataset_size: 134736982
 ---
 # Facial Keypoints
 The dataset is designed for computer vision and machine learning tasks involving the identification and analysis of key points on a human face. It consists of images of human faces, each accompanied by key point annotations in XML format.

facial_keypoint_detection.py

@@ -103,11 +103,15 @@ class FacialKeypointDetection(datasets.GeneratorBasedBuilder):
                                     license=_LICENSE)
 
     def _split_generators(self, dl_manager):
-        images = dl_manager.download_and_extract(f"{_DATA}images.zip")
-        masks = dl_manager.download_and_extract(f"{_DATA}masks.zip")
+        # images = dl_manager.download_and_extract(f"{_DATA}images.zip")
+        # masks = dl_manager.download_and_extract(f"{_DATA}masks.zip")
+        images = dl_manager.download(f"{_DATA}images.tar.gz")
+        masks = dl_manager.download(f"{_DATA}masks.tar.gz")
         annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
-        images = dl_manager.iter_files(images)
-        masks = dl_manager.iter_files(masks)
+        # images = dl_manager.iter_files(images)
+        # masks = dl_manager.iter_files(masks)
+        images = dl_manager.iter_archive(images)
+        masks = dl_manager.iter_archive(masks)
 
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN,
@@ -120,29 +124,44 @@ class FacialKeypointDetection(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, images, masks, annotations):
         annotations_df = pd.read_csv(annotations, sep=',')
-        images_data = pd.DataFrame(
-            columns=['image_name', 'image_path', 'mask_path'])
-        for idx, (image_path, mask_path) in enumerate(zip(images, masks)):
-            images_data.loc[idx] = {
-                'image_name': image_path.split('/')[-1],
-                'image_path': image_path,
-                'mask_path': mask_path
-            }
-
-        annotations_df = pd.merge(annotations_df,
-                                  images_data,
-                                  how='left',
-                                  on=['image_name'])
-
-        annotations_df[['image_path', 'mask_path'
-                       ]] = annotations_df[['image_path',
-                                            'mask_path']].astype('string')
-
-        for row in annotations_df.sort_values(['image_name'
-                                              ]).itertuples(index=False):
+        for idx, ((image_path, image),
+                  (mask_path, mask)) in enumerate(zip(images, masks)):
             yield idx, {
-                'image_id': row[0],
-                'image': row[3],
-                'mask': row[4],
-                'key_points': row[2]
+                'image_id': annotations_df['image_id'].iloc[idx],
+                "image": {
+                    "path": image_path,
+                    "bytes": image.read()
+                },
+                "mask": {
+                    "path": mask_path,
+                    "bytes": mask.read()
+                },
+                'key_points': annotations_df['key_points'].iloc[idx]
             }
+        # images_data = pd.DataFrame(
+        #     columns=['image_name', 'image_path', 'mask_path'])
+        # for idx, ((image_path, image),
+        #           (mask_path, mask)) in enumerate(zip(images, masks)):
+        #     images_data.loc[idx] = {
+        #         'image_name': image_path.split('/')[-1],
+        #         'image_path': image_path,
+        #         'mask_path': mask_path
+        #     }
+
+        # annotations_df = pd.merge(annotations_df,
+        #                           images_data,
+        #                           how='left',
+        #                           on=['image_name'])
+
+        # annotations_df[['image_path', 'mask_path'
+        #                ]] = annotations_df[['image_path',
+        #                                     'mask_path']].astype('string')
+
+        # for row in annotations_df.sort_values(['image_name'
+        #                                       ]).itertuples(index=False):
+        #     yield idx, {
+        #         'image_id': row[0],
+        #         'image': row[3],
+        #         'mask': row[4],
+        #         'key_points': row[2]
+        #     }