targetran - npm Package Compare versions

Comparing version 0.11.9 to 0.12.0

PKG-INFO

 Metadata-Version: 2.1
 Name: targetran
-Version: 0.11.9
+Version: 0.12.0
 Summary: Target transformation for data augmentation in objection detection
@@ -195,2 +195,3 @@ Home-page: https://github.com/bhky/targetran
     selected_probabilities=[0.5, 0.0, 0.3, 0.2, 0.0],  # Must sum up to 1.0, if given.
+    keep_order=True,  # If True, the selected steps must be performed in the given order.
     probability=1.0  # Probability to apply this single combined transformation.
@@ -302,2 +303,3 @@ )
     selected_probabilities=[0.5, 0.0, 0.3, 0.2, 0.0],  # Must sum up to 1.0, if given.
+    keep_order=True,  # If True, the selected steps must be performed in the given order.
     probability=1.0  # Probability to apply this single combined transformation.
@@ -304,0 +306,0 @@ )

README.md

@@ -181,2 +181,3 @@ ![logo](logo/targetran_logo.png)
     selected_probabilities=[0.5, 0.0, 0.3, 0.2, 0.0],  # Must sum up to 1.0, if given.
+    keep_order=True,  # If True, the selected steps must be performed in the given order.
     probability=1.0  # Probability to apply this single combined transformation.
@@ -288,2 +289,3 @@ )
     selected_probabilities=[0.5, 0.0, 0.3, 0.2, 0.0],  # Must sum up to 1.0, if given.
+    keep_order=True,  # If True, the selected steps must be performed in the given order.
     probability=1.0  # Probability to apply this single combined transformation.
@@ -290,0 +292,0 @@ )

targetran.egg-info/PKG-INFO

 Metadata-Version: 2.1
 Name: targetran
-Version: 0.11.9
+Version: 0.12.0
 Summary: Target transformation for data augmentation in objection detection
@@ -195,2 +195,3 @@ Home-page: https://github.com/bhky/targetran
     selected_probabilities=[0.5, 0.0, 0.3, 0.2, 0.0],  # Must sum up to 1.0, if given.
+    keep_order=True,  # If True, the selected steps must be performed in the given order.
     probability=1.0  # Probability to apply this single combined transformation.
@@ -302,2 +303,3 @@ )
     selected_probabilities=[0.5, 0.0, 0.3, 0.2, 0.0],  # Must sum up to 1.0, if given.
+    keep_order=True,  # If True, the selected steps must be performed in the given order.
     probability=1.0  # Probability to apply this single combined transformation.
@@ -304,0 +306,0 @@ )

targetran/__init__.py

@@ -1,3 +0,3 @@
-__version__ = "0.11.9"
+__version__ = "0.12.0"
 __author__ = "Bosco Yung"
 __license__ = "MIT"

targetran/_np_functional.py

@@ -16,4 +16,12 @@ """
 )
+from targetran.utils import Interpolation
 
+_INTERPOLATION_DICT = {
+    Interpolation.NEAREST: cv2.INTER_NEAREST,  # pylint: disable=no-member
+    Interpolation.BILINEAR: cv2.INTER_LINEAR,  # pylint: disable=no-member
+    Interpolation.BICUBIC: cv2.INTER_CUBIC,  # pylint: disable=no-member
+    Interpolation.AREA: cv2.INTER_AREA,  # pylint: disable=no-member
+}
 
+
 def _np_convert(x: ArrayLike) -> NDFloatArray:
@@ -58,3 +66,4 @@ if isinstance(x, np.ndarray):
         image: NDFloatArray,
-        dest_size: Tuple[int, int]
+        dest_size: Tuple[int, int],
+        interpolation: Interpolation
 ) -> NDFloatArray:
@@ -67,3 +76,3 @@ """
         dsize=(dest_size[1], dest_size[0]),
-        interpolation=cv2.INTER_AREA  # pylint: disable=no-member
+        interpolation=_INTERPOLATION_DICT[interpolation]
     )
@@ -70,0 +79,0 @@ return resized_image

targetran/_tf_functional.py

@@ -9,4 +9,12 @@ """
 from targetran._typing import ArrayLike
+from targetran.utils import Interpolation
 
+_TF_INTERPOLATION_DICT = {
+    Interpolation.NEAREST: tf.image.ResizeMethod.NEAREST_NEIGHBOR,
+    Interpolation.BILINEAR: tf.image.ResizeMethod.BILINEAR,
+    Interpolation.BICUBIC: tf.image.ResizeMethod.BICUBIC,
+    Interpolation.AREA: tf.image.ResizeMethod.AREA,
+}
 
+
 def _tf_convert(x: ArrayLike) -> tf.Tensor:
@@ -46,3 +54,4 @@ if isinstance(x, tf.Tensor):
         image: tf.Tensor,
-        dest_size: Tuple[int, int]
+        dest_size: Tuple[int, int],
+        interpolation: Interpolation
 ) -> tf.Tensor:
@@ -53,3 +62,3 @@ """
     return tf.image.resize(
-        image, size=dest_size, method=tf.image.ResizeMethod.AREA
+        image, size=dest_size, method=_TF_INTERPOLATION_DICT[interpolation]
     )
@@ -56,0 +65,0 @@

targetran/_transform.py

@@ -71,2 +71,3 @@ """
         interpolation: Interpolation,
+        fill_value: float,
         d: _AffineDependency
@@ -91,6 +92,12 @@ ) -> Tuple[T, T, T]:
 
-    # Pad image to provide a zero-value pixel frame for clipping use below.
+    # Pad image to provide a fill-value pixel frame for clipping use below.
+    fill_image = d.ones_like_fn(image) * fill_value
+
     pad_offsets = d.convert_fn([1, 1, 1, 1])
     image = d.pad_image_fn(image, pad_offsets)
+    fill_image = d.pad_image_fn(fill_image, pad_offsets)
 
+    fill_image = (fill_image - fill_value) * -1.0
+    image = image + fill_image
+
     # References:
@@ -174,3 +181,3 @@ # https://www.kaggle.com/cdeotte/rotation-augmentation-gpu-tpu-0-96
     else:
-        raise ValueError("Undefined interpolation option.")
+        raise ValueError("Unsupported interpolation option.")
 
@@ -278,3 +285,2 @@ new_image = d.reshape_fn(values, (height, width, num_channels))
         labels: T,
-        interpolation: Interpolation,
         d: _AffineDependency
@@ -292,3 +298,3 @@ ) -> Tuple[T, T, T]:
         image, bboxes, labels, image_dest_flip_lr_mat, bboxes_flip_lr_mat,
-        interpolation, d
+        Interpolation.NEAREST, 0.0, d  # Dummy interpolation and fill_value.
     )
@@ -317,3 +323,2 @@
         labels: T,
-        interpolation: Interpolation,
         d: _AffineDependency
@@ -331,3 +336,3 @@ ) -> Tuple[T, T, T]:
         image, bboxes, labels, image_dest_flip_ud_mat, bboxes_flip_ud_mat,
-        interpolation, d
+        Interpolation.NEAREST, 0.0, d  # Dummy interpolation and fill_value.
     )
@@ -366,2 +371,3 @@
         interpolation: Interpolation,
+        fill_value: float,
         d: _AffineDependency
@@ -380,3 +386,3 @@ ) -> Tuple[T, T, T]:
         image, bboxes, labels, image_dest_rot_mat, bboxes_rot_mat,
-        interpolation, d
+        interpolation, fill_value, d
     )
@@ -414,2 +420,3 @@
         interpolation: Interpolation,
+        fill_value: float,
         d: _AffineDependency
@@ -428,3 +435,3 @@ ) -> Tuple[T, T, T]:
         image, bboxes, labels, image_dest_shear_mat, bboxes_shear_mat,
-        interpolation, d
+        interpolation, fill_value, d
     )
@@ -458,2 +465,3 @@
         interpolation: Interpolation,
+        fill_value: float,
         d: _AffineDependency
@@ -473,3 +481,3 @@ ) -> Tuple[T, T, T]:
         image, bboxes, labels, image_dest_translate_mat, bboxes_translate_mat,
-        interpolation, d
+        interpolation, fill_value, d
     )
@@ -603,6 +611,7 @@
         dest_size: Tuple[int, int],
+        interpolation: Interpolation,
         convert_fn: Callable[..., T],
         shape_fn: Callable[[T], Sequence[int]],
         reshape_fn: Callable[[T, Sequence[int]], T],
-        resize_image_fn: Callable[[T, Tuple[int, int]], T],
+        resize_image_fn: Callable[[T, Tuple[int, int], Interpolation], T],
         concat_fn: Callable[[List[T], int], T],
@@ -619,8 +628,5 @@ ) -> Tuple[T, T, T]:
     )
-
     image_shape = shape_fn(image)
-    if image_shape[0] == dest_size[0] and image_shape[1] == dest_size[1]:
-        return image, bboxes, labels
 
-    image = resize_image_fn(image, dest_size)
+    image = resize_image_fn(image, dest_size, interpolation)
 
@@ -627,0 +633,0 @@ w = convert_fn(dest_size[1] / image_shape[1])

targetran/np/_np.py

 """
 API for NumPy usage.
 """
-import functools
 from typing import Any, Callable, List, Optional, Sequence, Tuple
@@ -66,7 +65,8 @@
         bboxes_tran_mat: NDFloatArray,
-        interpolation: Interpolation
+        interpolation: Interpolation,
+        fill_value: float
 ) -> Tuple[NDFloatArray, NDFloatArray, NDFloatArray]:
     return _affine_transform(
         image, bboxes, labels, image_dest_tran_mat, bboxes_tran_mat,
-        interpolation, _np_get_affine_dependency()
+        interpolation, fill_value, _np_get_affine_dependency()
     )
@@ -81,4 +81,3 @@
     return _flip_left_right(
-        image, bboxes, labels,
-        Interpolation.NEAREST, _np_get_affine_dependency()
+        image, bboxes, labels, _np_get_affine_dependency()
     )
@@ -93,4 +92,3 @@
     return _flip_up_down(
-        image, bboxes, labels,
-        Interpolation.NEAREST, _np_get_affine_dependency()
+        image, bboxes, labels, _np_get_affine_dependency()
     )
@@ -104,7 +102,8 @@
         angle_deg: float,
-        interpolation: Interpolation = Interpolation.BILINEAR
+        interpolation: Interpolation = Interpolation.BILINEAR,
+        fill_value: float = 0.0
 ) -> Tuple[NDFloatArray, NDFloatArray, NDFloatArray]:
     return _rotate(
         image, bboxes, labels, _np_convert(angle_deg), np.cos, np.sin,
-        interpolation, _np_get_affine_dependency()
+        interpolation, fill_value, _np_get_affine_dependency()
     )
@@ -119,2 +118,3 @@
         interpolation: Interpolation = Interpolation.BILINEAR,
+        fill_value: float = 0.0,
         _check_input: bool = True
@@ -126,3 +126,3 @@ ) -> Tuple[NDFloatArray, NDFloatArray, NDFloatArray]:
         image, bboxes, labels, _np_convert(angle_deg), np.tan,
-        interpolation, _np_get_affine_dependency()
+        interpolation, fill_value, _np_get_affine_dependency()
     )
@@ -138,2 +138,3 @@
         interpolation: Interpolation = Interpolation.BILINEAR,
+        fill_value: float = 0.0,
         _check_input: bool = True
@@ -146,3 +147,3 @@ ) -> Tuple[NDFloatArray, NDFloatArray, NDFloatArray]:
         _np_convert(translate_height), _np_convert(translate_width),
-        interpolation, _np_get_affine_dependency()
+        interpolation, fill_value, _np_get_affine_dependency()
     )
@@ -189,6 +190,7 @@
         labels: NDFloatArray,
-        dest_size: Tuple[int, int]
+        dest_size: Tuple[int, int],
+        interpolation: Interpolation = Interpolation.BILINEAR
 ) -> Tuple[NDFloatArray, NDFloatArray, NDFloatArray]:
     return _resize(
-        image, bboxes, labels, dest_size,
+        image, bboxes, labels, dest_size, interpolation,
         _np_convert, np.shape, np.reshape, _np_resize_image, np.concatenate
@@ -210,3 +212,4 @@ )
         self.probability = probability
-        self._rng = np.random.default_rng(seed=seed)
+        self.seed = seed
+        self._rng = np.random.default_rng(seed=self.seed)
         self.name = name
@@ -250,3 +253,5 @@ self.is_affine = is_affine
             selected_probabilities: Optional[List[float]] = None,
+            keep_order: bool = False,
             interpolation: Interpolation = Interpolation.BILINEAR,
+            fill_value: float = 0.0,
             probability: float = 1.0,
@@ -273,6 +278,5 @@ seed: Optional[int] = None
         self._selected_probabilities = selected_probabilities
+        self._keep_order = keep_order
         self._interpolation = interpolation
-        self._identity_mat = np.expand_dims(np.array([
-            [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]
-        ]), axis=0)
+        self._fill_value = fill_value
 
@@ -289,3 +293,3 @@ def _get_mats(
 
-        if self._num_selected_transforms:
+        if self._num_selected_transforms is not None:
             indices = self._rng.choice(
@@ -295,21 +299,31 @@ len(self._transforms),
                 replace=False, p=self._selected_probabilities
-            ).tolist()
+            )
+        else:
+            conditions = rand_fn() < probs
+            indices = np.arange(len(probs), dtype=np.int32)[conditions]
+
+        if len(indices) > 1:
+            if self._keep_order:
+                indices.sort()
+            else:
+                self._rng.shuffle(indices)
+
+            indices = indices.tolist()
             image_dest_tran_mats = np.take(image_dest_tran_mats, indices, 0)
             bboxes_tran_mats = np.take(bboxes_tran_mats, indices, 0)
+
+            image_dest_tran_mat = np.linalg.multi_dot(image_dest_tran_mats)
+            # Note the reversed order for the bboxes tran matrices.
+            bboxes_tran_mat = np.linalg.multi_dot(bboxes_tran_mats[::-1])
+
+        elif len(indices) == 1:
+            image_dest_tran_mat = image_dest_tran_mats[0]
+            bboxes_tran_mat = bboxes_tran_mats[0]
         else:
-            conditions = np.reshape(rand_fn() < probs, (len(probs), 1, 1))
-            image_dest_tran_mats = np.where(  # type: ignore
-                conditions, image_dest_tran_mats, self._identity_mat
-            )
-            bboxes_tran_mats = np.where(  # type: ignore
-                conditions, bboxes_tran_mats, self._identity_mat
-            )
+            # This happens when conditions are all False, or
+            # num_selected_transforms is 0.
+            assert len(indices) == 0
+            image_dest_tran_mat = np.identity(3)
+            bboxes_tran_mat = np.identity(3)
 
-        image_dest_tran_mat = functools.reduce(
-            np.matmul, image_dest_tran_mats
-        )
-        # Note the reversed order for the bboxes tran matrices.
-        bboxes_tran_mat = functools.reduce(
-            np.matmul, bboxes_tran_mats[::-1]
-        )
         return image_dest_tran_mat, bboxes_tran_mat
@@ -330,3 +344,3 @@
             image, bboxes, labels, image_dest_tran_mat, bboxes_tran_mat,
-            self._interpolation
+            self._interpolation, self._fill_value
         )
@@ -399,2 +413,3 @@
             interpolation: Interpolation = Interpolation.BILINEAR,
+            fill_value: float = 0.0,
             probability: float = 0.9,
@@ -407,2 +422,3 @@ seed: Optional[int] = None
         self.interpolation = interpolation
+        self.fill_value = fill_value
 
@@ -437,3 +453,3 @@ def _get_angle_deg(
             image, bboxes, labels, self._get_angle_deg(self._rand_fn),
-            self.interpolation
+            self.interpolation, self.fill_value
         )
@@ -448,2 +464,3 @@
             interpolation: Interpolation = Interpolation.BILINEAR,
+            fill_value: float = 0.0,
             probability: float = 0.9,
@@ -456,2 +473,3 @@ seed: Optional[int] = None
         self.interpolation = interpolation
+        self.fill_value = fill_value
 
@@ -484,3 +502,3 @@ def _get_angle_deg(
             image, bboxes, labels, self._get_angle_deg(self._rand_fn),
-            self.interpolation, False
+            self.interpolation, self.fill_value, False
         )
@@ -496,2 +514,3 @@
             interpolation: Interpolation = Interpolation.BILINEAR,
+            fill_value: float = 0.0,
             probability: float = 0.9,
@@ -512,2 +531,3 @@ seed: Optional[int] = None
         self.interpolation = interpolation
+        self.fill_value = fill_value
 
@@ -555,3 +575,3 @@ def _get_translate_height_and_width(
             image, bboxes, labels, translate_height, translate_width,
-            self.interpolation, False
+            self.interpolation, self.fill_value, False
         )
@@ -603,4 +623,9 @@
 
-    def __init__(self, dest_size: Tuple[int, int]) -> None:
+    def __init__(
+            self,
+            dest_size: Tuple[int, int],
+            interpolation: Interpolation = Interpolation.BILINEAR
+    ) -> None:
         self.dest_size = dest_size
+        self.interpolation = interpolation
         self.name = "Resize"
@@ -615,2 +640,2 @@ self.is_affine = False
     ) -> Tuple[NDFloatArray, NDFloatArray, NDFloatArray]:
-        return resize(image, bboxes, labels, self.dest_size)
+        return resize(image, bboxes, labels, self.dest_size, self.interpolation)

targetran/tf/_tf.py

 """
 API for TensorFlow usage.
 """
-import functools
 import itertools
@@ -116,7 +115,8 @@ from typing import Any, Callable, List, Optional, Sequence, Tuple
         bboxes_tran_mat: tf.Tensor,
-        interpolation: Interpolation
+        interpolation: Interpolation,
+        fill_value: float
 ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
     return _affine_transform(
         image, bboxes, labels, image_dest_tran_mat, bboxes_tran_mat,
-        interpolation, _tf_get_affine_dependency()
+        interpolation, fill_value, _tf_get_affine_dependency()
     )
@@ -131,4 +131,3 @@
     return _flip_left_right(
-        image, bboxes, labels,
-        Interpolation.NEAREST, _tf_get_affine_dependency()
+        image, bboxes, labels, _tf_get_affine_dependency()
     )
@@ -143,4 +142,3 @@
     return _flip_up_down(
-        image, bboxes, labels,
-        Interpolation.NEAREST, _tf_get_affine_dependency()
+        image, bboxes, labels, _tf_get_affine_dependency()
     )
@@ -154,7 +152,8 @@
         angle_deg: float,
-        interpolation: Interpolation = Interpolation.BILINEAR
+        interpolation: Interpolation = Interpolation.BILINEAR,
+        fill_value: float = 0.0
 ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
     return _rotate(
         image, bboxes, labels, _tf_convert(angle_deg), tf.cos, tf.sin,
-        interpolation, _tf_get_affine_dependency()
+        interpolation, fill_value, _tf_get_affine_dependency()
     )
@@ -169,2 +168,3 @@
         interpolation: Interpolation = Interpolation.BILINEAR,
+        fill_value: float = 0.0,
         _check_input: bool = True
@@ -176,3 +176,3 @@ ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
         image, bboxes, labels, _tf_convert(angle_deg), tf.tan,
-        interpolation, _tf_get_affine_dependency()
+        interpolation, fill_value, _tf_get_affine_dependency()
     )
@@ -188,2 +188,3 @@
         interpolation: Interpolation = Interpolation.BILINEAR,
+        fill_value: float = 0.0,
         _check_input: bool = True
@@ -198,3 +199,3 @@ ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
         _tf_convert(translate_height), _tf_convert(translate_width),
-        interpolation, _tf_get_affine_dependency()
+        interpolation, fill_value, _tf_get_affine_dependency()
     )
@@ -241,6 +242,7 @@
         labels: tf.Tensor,
-        dest_size: Tuple[int, int]
+        dest_size: Tuple[int, int],
+        interpolation: Interpolation = Interpolation.BILINEAR
 ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
     return _resize(
-        image, bboxes, labels, dest_size,
+        image, bboxes, labels, dest_size, interpolation,
         _tf_convert, tf.shape, tf.reshape, _tf_resize_image, tf.concat
@@ -262,5 +264,4 @@ )
         self.probability = probability
-        self._rng = tf.random.Generator.from_seed(
-            seed if seed is not None else np.random.randint(1e6)  # type: ignore
-        )
+        self.seed = seed if seed is not None else np.random.randint(1e6)  # type: ignore
+        self._rng = tf.random.Generator.from_seed(self.seed)
         self.name = name
@@ -316,3 +317,3 @@ self.is_affine = is_affine
     ))
-    _, indices = tf.nn.top_k(logits + z, num_selected_indices)
+    _, indices = tf.math.top_k(logits + z, num_selected_indices, sorted=False)
     return indices
@@ -328,3 +329,5 @@
             selected_probabilities: Optional[List[float]] = None,
+            keep_order: bool = False,
             interpolation: Interpolation = Interpolation.BILINEAR,
+            fill_value: float = 0.0,
             probability: float = 1.0,
@@ -351,6 +354,5 @@ seed: Optional[int] = None
         self._selected_probabilities = selected_probabilities
+        self._keep_order = keep_order
         self._interpolation = interpolation
-        self._identity_mat = tf.expand_dims(tf.constant([
-            [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]
-        ]), axis=0)
+        self._fill_value = fill_value
 
@@ -367,3 +369,6 @@ def _get_mats(
 
-        if self._num_selected_transforms:
+        if self._num_selected_transforms is not None:
+            if self._num_selected_transforms == 0:
+                return tf.eye(3), tf.eye(3)
+
             indices = _get_random_indices(
@@ -375,24 +380,33 @@ self._rng,
             )
-            image_dest_tran_mats = tf.gather(  # pylint: disable=no-value-for-parameter
-                image_dest_tran_mats, indices
-            )
-            bboxes_tran_mats = tf.gather(  # pylint: disable=no-value-for-parameter
-                bboxes_tran_mats, indices
-            )
         else:
-            conditions = tf.reshape(rand_fn() < probs, (len(probs), 1, 1))
-            image_dest_tran_mats = tf.where(
-                conditions, image_dest_tran_mats, self._identity_mat
+            # It looks like in graph mode we cannot check if a tensor is empty
+            # and then do short circuit. Therefore, to prevent empty indices
+            # which tf.gather could not handle, an identity matrix is appended.
+            image_dest_tran_mats += (tf.eye(3),)
+            bboxes_tran_mats += (tf.eye(3),)
+            probs += (1.0,)
+
+            conditions = rand_fn() < probs
+            indices = tf.boolean_mask(
+                tf.range(len(probs), dtype=tf.int32), conditions
             )
-            bboxes_tran_mats = tf.where(
-                conditions, bboxes_tran_mats, self._identity_mat
-            )
 
-        image_dest_tran_mat = functools.reduce(
-            tf.matmul, tf.unstack(image_dest_tran_mats)
+        if self._keep_order:
+            indices = tf.sort(indices)
+        else:
+            indices = tf.random.shuffle(indices, seed=self.seed)
+
+        image_dest_tran_mats = tf.gather(  # pylint: disable=no-value-for-parameter
+            image_dest_tran_mats, indices
         )
+        bboxes_tran_mats = tf.gather(  # pylint: disable=no-value-for-parameter
+            bboxes_tran_mats, indices
+        )
+
+        def matmul(a: tf.Tensor, b: tf.Tensor) -> tf.Tensor:
+            return tf.matmul(a, b)
+
+        image_dest_tran_mat = tf.scan(matmul, image_dest_tran_mats)[-1]
         # Note the reversed order for the bboxes tran matrices.
-        bboxes_tran_mat = functools.reduce(
-            tf.matmul, tf.unstack(bboxes_tran_mats)[::-1]
-        )
+        bboxes_tran_mat = tf.scan(matmul, bboxes_tran_mats[::-1])[-1]
         return image_dest_tran_mat, bboxes_tran_mat
@@ -413,3 +427,3 @@
             image, bboxes, labels, image_dest_tran_mat, bboxes_tran_mat,
-            self._interpolation
+            self._interpolation, self._fill_value
         )
@@ -482,2 +496,3 @@
             interpolation: Interpolation = Interpolation.BILINEAR,
+            fill_value: float = 0.0,
             probability: float = 0.9,
@@ -490,2 +505,3 @@ seed: Optional[int] = None
         self.interpolation = interpolation
+        self.fill_value = fill_value
 
@@ -515,3 +531,3 @@ def _get_angle_deg(self, rand_fn: Callable[..., tf.Tensor]) -> tf.Tensor:
             image, bboxes, labels, self._get_angle_deg(self._rand_fn),
-            self.interpolation
+            self.interpolation, self.fill_value
         )
@@ -526,2 +542,3 @@
             interpolation: Interpolation = Interpolation.BILINEAR,
+            fill_value: float = 0.0,
             probability: float = 0.9,
@@ -534,2 +551,3 @@ seed: Optional[int] = None
         self.interpolation = interpolation
+        self.fill_value = fill_value
 
@@ -559,3 +577,3 @@ def _get_angle_deg(self, rand_fn: Callable[..., tf.Tensor]) -> tf.Tensor:
             image, bboxes, labels, self._get_angle_deg(self._rand_fn),
-            self.interpolation, False
+            self.interpolation, self.fill_value, False
         )
@@ -571,2 +589,3 @@
             interpolation: Interpolation = Interpolation.BILINEAR,
+            fill_value: float = 0.0,
             probability: float = 0.9,
@@ -589,2 +608,3 @@ seed: Optional[int] = None
         self.interpolation = interpolation
+        self.fill_value = fill_value
 
@@ -628,3 +648,3 @@ def _get_translate_height_and_width(
             image, bboxes, labels, translate_height, translate_width,
-            self.interpolation, False
+            self.interpolation, self.fill_value, False
         )
@@ -676,4 +696,9 @@
 
-    def __init__(self, dest_size: Tuple[int, int]) -> None:
+    def __init__(
+            self,
+            dest_size: Tuple[int, int],
+            interpolation: Interpolation = Interpolation.BILINEAR
+    ) -> None:
         self.dest_size = dest_size
+        self.interpolation = interpolation
         self.name = "TFResize"
@@ -688,2 +713,2 @@ self.is_affine = False
     ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
-        return tf_resize(image, bboxes, labels, self.dest_size)
+        return tf_resize(image, bboxes, labels, self.dest_size, self.interpolation)

targetran/utils/_utils.py

@@ -13,2 +13,4 @@ """
     BILINEAR = auto()
+    BICUBIC = auto()
+    AREA = auto()
 
@@ -15,0 +17,0 @@

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Improved metrics

Total package byte prevSize

117091

3.28%

Lines of code

1966

3.36%