Add a new from_image API to create face stylizer dataset from a single image. Also deprecate the from_folder API since we only support one-shot use case now.

PiperOrigin-RevId: 558912896

mediapipe/model_maker/python/vision/face_stylizer/dataset.py

@@ -51,71 +51,41 @@ class Dataset(classification_dataset.ClassificationDataset):
   """Dataset library for face stylizer fine tuning."""
 
   @classmethod
-  def from_folder(
+  def from_image(
-      cls, dirname: str
+      cls, filename: str
   ) -> classification_dataset.ClassificationDataset:
-    """Loads images from the given directory.
+    """Creates a dataset from single image.
 
-    The style image dataset directory is expected to contain one subdirectory
+    Supported input image formats include 'jpg', 'jpeg', 'png'.
-    whose name represents the label of the style. There can be one or multiple
-    images of the same style in that subdirectory. Supported input image formats
-    include 'jpg', 'jpeg', 'png'.
 
     Args:
-      dirname: Name of the directory containing the image files.
+      filename: Name of the image file.
 
     Returns:
-      Dataset containing images and labels and other related info.
+      Dataset containing image and label and other related info.
-    Raises:
-      ValueError: if the input data directory is empty.
     """
-    data_root = os.path.abspath(dirname)
+    file_path = os.path.abspath(filename)
+    image_filename = os.path.basename(filename)
+    image_name, ext_name = os.path.splitext(image_filename)
 
-    # Assumes the image data of the same label are in the same subdirectory,
+    if not ext_name.endswith(('.jpg', '.jpeg', '.png')):
-    # gets image path and label names.
+      raise ValueError('Unsupported image formats: %s' % ext_name)
-    all_image_paths = list(tf.io.gfile.glob(data_root + r'/*/*'))
-    all_image_size = len(all_image_paths)
-    if all_image_size == 0:
-      raise ValueError('Invalid input data directory')
-    if not any(
-        fname.endswith(('.jpg', '.jpeg', '.png')) for fname in all_image_paths
-    ):
-      raise ValueError('No images found under given directory')
 
-    image_data = _preprocess_face_dataset(all_image_paths)
+    image_data = _preprocess_face_dataset([file_path])
-    label_names = sorted(
+    label_names = [image_name]
-        name
-        for name in os.listdir(data_root)
-        if os.path.isdir(os.path.join(data_root, name))
-    )
-    all_label_size = len(label_names)
-    index_by_label = dict(
-        (name, index) for index, name in enumerate(label_names)
-    )
-    # Get the style label from the subdirectory name.
-    all_image_labels = [
-        index_by_label[os.path.basename(os.path.dirname(path))]
-        for path in all_image_paths
-    ]
 
     image_ds = tf.data.Dataset.from_tensor_slices(image_data)
 
     # Load label
-    label_ds = tf.data.Dataset.from_tensor_slices(
+    label_ds = tf.data.Dataset.from_tensor_slices(tf.cast([0], tf.int64))
-        tf.cast(all_image_labels, tf.int64)
-    )
 
     # Create a dataset of (image, label) pairs
     image_label_ds = tf.data.Dataset.zip((image_ds, label_ds))
 
-    logging.info(
+    logging.info('Create dataset for style: %s.', image_name)
-        'Load images dataset with size: %d, num_label: %d, labels: %s.',
+
-        all_image_size,
-        all_label_size,
-        ', '.join(label_names),
-    )
     return Dataset(
         dataset=image_label_ds,
         label_names=label_names,
-        size=all_image_size,
+        size=1,
     )

mediapipe/model_maker/python/vision/face_stylizer/dataset_test.py

@@ -25,24 +25,17 @@ class DatasetTest(tf.test.TestCase):
   def setUp(self):
     super().setUp()
 
-  def test_from_folder(self):
+  def test_from_image(self):
-    test_data_dirname = 'input/style'
+    test_image_file = 'input/style/cartoon/cartoon.jpg'
-    input_data_dir = test_utils.get_test_data_path(test_data_dirname)
+    input_data_dir = test_utils.get_test_data_path(test_image_file)
-    data = dataset.Dataset.from_folder(dirname=input_data_dir)
+    data = dataset.Dataset.from_image(filename=input_data_dir)
-    self.assertEqual(data.num_classes, 2)
+    self.assertEqual(data.num_classes, 1)
-    self.assertEqual(data.label_names, ['cartoon', 'sketch'])
+    self.assertEqual(data.label_names, ['cartoon'])
-    self.assertLen(data, 2)
+    self.assertLen(data, 1)
 
-  def test_from_folder_raise_value_error_for_invalid_path(self):
+  def test_from_image_raise_value_error_for_invalid_path(self):
-    with self.assertRaisesRegex(ValueError, 'Invalid input data directory'):
+    with self.assertRaisesRegex(ValueError, 'Unsupported image formats: .zip'):
-      dataset.Dataset.from_folder(dirname='invalid')
+      dataset.Dataset.from_image(filename='input/style/cartoon/cartoon.zip')
-
-  def test_from_folder_raise_value_error_for_valid_no_data_path(self):
-    input_data_dir = test_utils.get_test_data_path('face_stylizer')
-    with self.assertRaisesRegex(
-        ValueError, 'No images found under given directory'
-    ):
-      dataset.Dataset.from_folder(dirname=input_data_dir)
 
 
 if __name__ == '__main__':

mediapipe/model_maker/python/vision/face_stylizer/face_stylizer_test.py

@@ -24,11 +24,13 @@ from mediapipe.tasks.python.test import test_utils
 
 class FaceStylizerTest(tf.test.TestCase):
 
-  def _load_data(self):
+  def _create_training_dataset(self):
-    """Loads training dataset."""
+    """Creates training dataset."""
-    input_data_dir = test_utils.get_test_data_path('input/style')
+    input_style_image_file = test_utils.get_test_data_path(
+        'input/style/cartoon/cartoon.jpg'
+    )
 
-    data = face_stylizer.Dataset.from_folder(dirname=input_data_dir)
+    data = face_stylizer.Dataset.from_image(filename=input_style_image_file)
     return data
 
   def _evaluate_saved_model(self, model: face_stylizer.FaceStylizer):
@@ -41,7 +43,7 @@ class FaceStylizerTest(tf.test.TestCase):
 
   def setUp(self):
     super().setUp()
-    self._train_data = self._load_data()
+    self._train_data = self._create_training_dataset()
 
   def test_finetuning_face_stylizer_with_single_input_style_image(self):
     with self.test_session(use_gpu=True):