refactor: script and readme

README.md

@@ -7,6 +7,76 @@ language:
 tags:
 - finance
 - code
+dataset_info:
+  features:
+  - name: portrait_1
+    dtype: image
+  - name: photo_1
+    dtype: image
+  - name: photo_2
+    dtype: image
+  - name: photo_3
+    dtype: image
+  - name: photo_4
+    dtype: image
+  - name: photo_5
+    dtype: image
+  - name: photo_6
+    dtype: image
+  - name: photo_7
+    dtype: image
+  - name: photo_8
+    dtype: image
+  - name: photo_9
+    dtype: image
+  - name: photo_10
+    dtype: image
+  - name: photo_11
+    dtype: image
+  - name: photo_12
+    dtype: image
+  - name: photo_13
+    dtype: image
+  - name: photo_14
+    dtype: image
+  - name: photo_15
+    dtype: image
+  - name: photo_16
+    dtype: image
+  - name: photo_17
+    dtype: image
+  - name: photo_18
+    dtype: image
+  - name: photo_19
+    dtype: image
+  - name: photo_20
+    dtype: image
+  - name: photo_21
+    dtype: image
+  - name: photo_22
+    dtype: image
+  - name: photo_23
+    dtype: image
+  - name: photo_24
+    dtype: image
+  - name: photo_25
+    dtype: image
+  - name: photo_26
+    dtype: image
+  - name: worker_id
+    dtype: string
+  - name: age
+    dtype: int8
+  - name: country
+    dtype: string
+  - name: gender
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 927211725
+    num_examples: 14
+  download_size: 923699881
+  dataset_size: 927211725
 ---
 
 # The Portrait and 26 Photos (272 people)

portrait_and_26_photos.py

@@ -3,7 +3,7 @@ import pandas as pd
 
 _CITATION = """\
 @InProceedings{huggingface:dataset,
-title = {FaceSegmentation},
+title = {portrait_and_26_photos},
 author = {TrainingDataPro},
 year = {2023}
 }
@@ -14,7 +14,7 @@ An example of a dataset that we've collected for a photo edit App.
 The dataset includes 20 selfies of people (man and women)
 in segmentation masks and their visualisations.
 """
-_NAME = 'FaceSegmentation'
+_NAME = 'portrait_and_26_photos'
 
 _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
 
@@ -30,11 +30,37 @@ class FaceSegmentation(datasets.GeneratorBasedBuilder):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features({
-                'image': datasets.Image(),
-                'mask': datasets.Image(),
-                'id': datasets.Value('string'),
-                'gender': datasets.Value('string'),
-                'age': datasets.Value('int8')
+                'portrait_1': datasets.Image(),
+                'photo_1': datasets.Image(),
+                'photo_2': datasets.Image(),
+                'photo_3': datasets.Image(),
+                'photo_4': datasets.Image(),
+                'photo_5': datasets.Image(),
+                'photo_6': datasets.Image(),
+                'photo_7': datasets.Image(),
+                'photo_8': datasets.Image(),
+                'photo_9': datasets.Image(),
+                'photo_10': datasets.Image(),
+                'photo_11': datasets.Image(),
+                'photo_12': datasets.Image(),
+                'photo_13': datasets.Image(),
+                'photo_14': datasets.Image(),
+                'photo_15': datasets.Image(),
+                'photo_16': datasets.Image(),
+                'photo_17': datasets.Image(),
+                'photo_18': datasets.Image(),
+                'photo_19': datasets.Image(),
+                'photo_20': datasets.Image(),
+                'photo_21': datasets.Image(),
+                'photo_22': datasets.Image(),
+                'photo_23': datasets.Image(),
+                'photo_24': datasets.Image(),
+                'photo_25': datasets.Image(),
+                'photo_26': datasets.Image(),
+                'worker_id': datasets.Value('string'),
+                'age': datasets.Value('int8'),
+                'country': datasets.Value('string'),
+                'gender': datasets.Value('string')
             }),
             supervised_keys=None,
             homepage=_HOMEPAGE,
@@ -43,34 +69,43 @@ class FaceSegmentation(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         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_archive(images)
-        masks = dl_manager.iter_archive(masks)
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                     gen_kwargs={
                                         "images": images,
-                                        'masks': masks,
                                         'annotations': annotations
                                     }),
         ]
 
-    def _generate_examples(self, images, masks, annotations):
-        annotations_df = pd.read_csv(annotations, sep=';')
+    def _generate_examples(self, images, annotations):
+        annotations_df = pd.read_csv(annotations, sep=',')
+        images_data = pd.DataFrame(columns=['Link', 'Bytes'])
+        for idx, (image_path, image) in enumerate(images):
+            images_data.loc[idx] = {'Link': image_path, 'Bytes': image.read()}
 
-        for idx, ((image_path, image),
-                  (mask_path, mask)) in enumerate(zip(images, masks)):
-            yield idx, {
-                "image": {
-                    "path": image_path,
-                    "bytes": image.read()
-                },
-                "mask": {
-                    "path": mask_path,
-                    "bytes": mask.read()
-                },
-                'id': annotations_df['id'].iloc[idx],
-                'gender': annotations_df['gender'].iloc[idx],
-                'age': annotations_df['age'].iloc[idx]
+        annotations_df = pd.merge(annotations_df, images_data)
+        for idx, worker_id in enumerate(pd.unique(annotations_df['WorkerId'])):
+            annotation = annotations_df.loc[annotations_df['WorkerId'] ==
+                                            worker_id]
+            annotation = annotation.sort_values(['Type'])
+            data = {
+                row[5]: {
+                    'path': row[6],
+                    'bytes': row[7]
+                } for row in annotation.itertuples()
             }
+
+            age = annotation.loc[annotation['Type'] ==
+                                 'portrait_1']['Age'].values[0]
+            country = annotation.loc[annotation['Type'] ==
+                                     'portrait_1']['Country'].values[0]
+            gender = annotation.loc[annotation['Type'] ==
+                                    'portrait_1']['Gender'].values[0]
+
+            data['worker_id'] = worker_id
+            data['age'] = age
+            data['country'] = country
+            data['gender'] = gender
+            yield idx, data