feat: upload script

face_masks.py → facial_keypoint_detection.py

@@ -93,7 +93,8 @@ class FacialKeypointDetection(datasets.GeneratorBasedBuilder):
                                     features=datasets.Features({
                                         'image_id': datasets.Value('uint32'),
                                         'image': datasets.Image(),
-                                        'mask': datasets.Image()
+                                        'mask': datasets.Image(),
+                                        'key_points': datasets.Value('string')
                                     }),
                                     supervised_keys=None,
                                     homepage=_HOMEPAGE,
@@ -105,44 +106,38 @@ class FacialKeypointDetection(datasets.GeneratorBasedBuilder):
         masks = dl_manager.download_and_extract(f"{_DATA}masks.zip")
         annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
         images = dl_manager.iter_files(images)
+        masks = dl_manager.iter_files(masks)
+
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                     gen_kwargs={
                                         "images": images,
+                                        "masks": masks,
                                         'annotations': annotations
                                     }),
         ]
 
-    def _generate_examples(self, images, annotations):
+    def _generate_examples(self, images, masks, annotations):
         annotations_df = pd.read_csv(annotations, sep=',')
-        images_data = pd.DataFrame(columns=['Link', 'Path'])
-        for idx, image_path in enumerate(images):
+        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] = {
-                'Link': '/'.join(image_path.split('/')[-2:]),
-                'Path': image_path
+                '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=['Link'])
-        for idx, worker_id in enumerate(pd.unique(annotations_df['WorkerId'])):
-            annotation: pd.DataFrame = annotations_df.loc[
-                annotations_df['WorkerId'] == worker_id]
-            annotation = annotation.sort_values(['Link'])
-            data = {
-                f'photo_{row[5]}': load_image_file(row[7])
-                for row in annotation.itertuples()
+                                  on=['image_name'])
+
+        for row in annotations_df.sort_values(['image_name'
+                                              ]).itertuples(index=False):
+            yield idx, {
+                'image_id': row[0],
+                'image': load_image_file(row[3]),
+                'mask': load_image_file(row[4]),
+                'key_points': row[2]
             }
-
-            age = annotation.loc[annotation['Type'] == 1]['Age'].values[0]
-            country = annotation.loc[annotation['Type'] ==
-                                     1]['Country'].values[0]
-            sex = annotation.loc[annotation['Type'] == 1]['Sex'].values[0]
-
-            data['worker_id'] = worker_id
-            data['age'] = age
-            data['country'] = country
-            data['sex'] = sex
-
-            yield idx, data