Move common functions to utils.py (#1260)

* Move common functions into util.py

* Fix mypy errors

albumentations/augmentations/crops/functional.py

@@ -1,12 +1,15 @@
-import typing
-from typing import Optional, Sequence, Tuple, Union
+from typing import Optional, Sequence, Tuple
 
 import cv2
 import numpy as np
 
+from albumentations.augmentations.utils import (
+    _maybe_process_in_chunks,
+    preserve_channel_dim,
+)
+
 from ...core.bbox_utils import denormalize_bbox, normalize_bbox
-from ...core.transforms_interface import BoxType, KeypointType
-from ..functional import _maybe_process_in_chunks, preserve_channel_dim
+from ...core.transforms_interface import BoxInternalType, KeypointInternalType
 from ..geometric import functional as FGeometric
 
 __all__ = [
@@ -55,7 +58,12 @@ def random_crop(img: np.ndarray, crop_height: int, crop_width: int, h_start: flo
 
 
 def crop_bbox_by_coords(
-    bbox: BoxType, crop_coords: Tuple[int, int, int, int], crop_height: int, crop_width: int, rows: int, cols: int
+    bbox: BoxInternalType,
+    crop_coords: Tuple[int, int, int, int],
+    crop_height: int,
+    crop_width: int,
+    rows: int,
+    cols: int,
 ):
     """Crop a bounding box using the provided coordinates of bottom-left and top-right corners in pixels and the
     required height and width of the crop.
@@ -80,13 +88,15 @@ def crop_bbox_by_coords(
 
 
 def bbox_random_crop(
-    bbox: BoxType, crop_height: int, crop_width: int, h_start: float, w_start: float, rows: int, cols: int
+    bbox: BoxInternalType, crop_height: int, crop_width: int, h_start: float, w_start: float, rows: int, cols: int
 ):
     crop_coords = get_random_crop_coords(rows, cols, crop_height, crop_width, h_start, w_start)
     return crop_bbox_by_coords(bbox, crop_coords, crop_height, crop_width, rows, cols)
 
 
-def crop_keypoint_by_coords(keypoint: KeypointType, crop_coords: Tuple[int, int, int, int]):  # skipcq: PYL-W0613
+def crop_keypoint_by_coords(
+    keypoint: KeypointInternalType, crop_coords: Tuple[int, int, int, int]
+):  # skipcq: PYL-W0613
     """Crop a keypoint using the provided coordinates of bottom-left and top-right corners in pixels and the
     required height and width of the crop.
 
@@ -104,7 +114,13 @@ def crop_keypoint_by_coords(keypoint: KeypointType, crop_coords: Tuple[int, int,
 
 
 def keypoint_random_crop(
-    keypoint: KeypointType, crop_height: int, crop_width: int, h_start: float, w_start: float, rows: int, cols: int
+    keypoint: KeypointInternalType,
+    crop_height: int,
+    crop_width: int,
+    h_start: float,
+    w_start: float,
+    rows: int,
+    cols: int,
 ):
     """Keypoint random crop.
 
@@ -147,12 +163,12 @@ def center_crop(img: np.ndarray, crop_height: int, crop_width: int):
     return img
 
 
-def bbox_center_crop(bbox: BoxType, crop_height: int, crop_width: int, rows: int, cols: int):
+def bbox_center_crop(bbox: BoxInternalType, crop_height: int, crop_width: int, rows: int, cols: int):
     crop_coords = get_center_crop_coords(rows, cols, crop_height, crop_width)
     return crop_bbox_by_coords(bbox, crop_coords, crop_height, crop_width, rows, cols)
 
 
-def keypoint_center_crop(keypoint: KeypointType, crop_height: int, crop_width: int, rows: int, cols: int):
+def keypoint_center_crop(keypoint: KeypointInternalType, crop_height: int, crop_width: int, rows: int, cols: int):
     """Keypoint center crop.
 
     Args:
@@ -192,7 +208,7 @@ def crop(img: np.ndarray, x_min: int, y_min: int, x_max: int, y_max: int):
     return img[y_min:y_max, x_min:x_max]
 
 
-def bbox_crop(bbox: BoxType, x_min: int, y_min: int, x_max: int, y_max: int, rows: int, cols: int):
+def bbox_crop(bbox: BoxInternalType, x_min: int, y_min: int, x_max: int, y_max: int, rows: int, cols: int):
     """Crop a bounding box.
 
     Args:
@@ -230,9 +246,9 @@ def clamping_crop(img: np.ndarray, x_min: int, y_min: int, x_max: int, y_max: in
 @preserve_channel_dim
 def crop_and_pad(
     img: np.ndarray,
-    crop_params: Sequence[int],
-    pad_params: Sequence[int],
-    pad_value: Union[int, float],
+    crop_params: Optional[Sequence[int]],
+    pad_params: Optional[Sequence[int]],
+    pad_value: Optional[float],
     rows: int,
     cols: int,
     interpolation: int,
@@ -242,7 +258,9 @@ def crop_and_pad(
     if crop_params is not None and any(i != 0 for i in crop_params):
         img = crop(img, *crop_params)
     if pad_params is not None and any(i != 0 for i in pad_params):
-        img = FGeometric.pad_with_params(img, *pad_params, border_mode=pad_mode, value=pad_value)
+        img = FGeometric.pad_with_params(
+            img, pad_params[0], pad_params[1], pad_params[2], pad_params[3], border_mode=pad_mode, value=pad_value
+        )
 
     if keep_size:
         resize_fn = _maybe_process_in_chunks(cv2.resize, dsize=(cols, rows), interpolation=interpolation)
@@ -252,15 +270,14 @@ def crop_and_pad(
 
 
 def crop_and_pad_bbox(
-    bbox: BoxType,
+    bbox: BoxInternalType,
     crop_params: Optional[Sequence[int]],
     pad_params: Optional[Sequence[int]],
     rows,
     cols,
     result_rows,
     result_cols,
-    keep_size: bool,
-) -> BoxType:
+) -> BoxInternalType:
     x1, y1, x2, y2 = denormalize_bbox(bbox, rows, cols)[:4]
 
     if crop_params is not None:
@@ -274,15 +291,15 @@ def crop_and_pad_bbox(
 
 
 def crop_and_pad_keypoint(
-    keypoint: KeypointType,
+    keypoint: KeypointInternalType,
     crop_params: Optional[Sequence[int]],
     pad_params: Optional[Sequence[int]],
     rows: int,
     cols: int,
     result_rows: int,
     result_cols: int,
     keep_size: bool,
-) -> KeypointType:
+) -> KeypointInternalType:
     x, y, angle, scale = keypoint[:4]
 
     if crop_params is not None:

albumentations/augmentations/crops/transforms.py

@@ -7,7 +7,12 @@
 
 from albumentations.core.bbox_utils import union_of_bboxes
 
-from ...core.transforms_interface import BoxType, DualTransform, KeypointType, to_tuple
+from ...core.transforms_interface import (
+    BoxInternalType,
+    DualTransform,
+    KeypointInternalType,
+    to_tuple,
+)
 from ..geometric import functional as FGeometric
 from . import functional as F
 
@@ -450,7 +455,7 @@ def get_params_dependent_on_targets(self, params: Dict[str, Any]) -> Dict[str, i
 
         return {"x_min": x_min, "x_max": x_max, "y_min": y_min, "y_max": y_max}
 
-    def apply_to_bbox(self, bbox: BoxType, **params) -> BoxType:
+    def apply_to_bbox(self, bbox: BoxInternalType, **params) -> BoxInternalType:
         return F.bbox_crop(bbox, **params)
 
     def apply_to_keypoint(
@@ -678,11 +683,11 @@ def __init__(
     def apply(
         self,
         img: np.ndarray,
-        crop_params: Sequence[int] = None,
-        pad_params: Sequence[int] = None,
-        pad_value: Union[int, float] = None,
-        rows: int = None,
-        cols: int = None,
+        crop_params: Sequence[int] = (),
+        pad_params: Sequence[int] = (),
+        pad_value: Union[int, float] = 0,
+        rows: int = 0,
+        cols: int = 0,
         interpolation: int = cv2.INTER_LINEAR,
         **params
     ) -> np.ndarray:
@@ -695,9 +700,9 @@ def apply_to_mask(
         img: np.ndarray,
         crop_params: Optional[Sequence[int]] = None,
         pad_params: Optional[Sequence[int]] = None,
-        pad_value_mask: Union[int, float] = None,
-        rows: int = None,
-        cols: int = None,
+        pad_value_mask: float = None,
+        rows: int = 0,
+        cols: int = 0,
         interpolation: int = cv2.INTER_NEAREST,
         **params
     ) -> np.ndarray:
@@ -707,28 +712,28 @@ def apply_to_mask(
 
     def apply_to_bbox(
         self,
-        bbox: BoxType,
+        bbox: BoxInternalType,
         crop_params: Optional[Sequence[int]] = None,
         pad_params: Optional[Sequence[int]] = None,
         rows: int = 0,
         cols: int = 0,
         result_rows: int = 0,
         result_cols: int = 0,
         **params
-    ) -> BoxType:
-        return F.crop_and_pad_bbox(bbox, crop_params, pad_params, rows, cols, result_rows, result_cols, self.keep_size)
+    ) -> BoxInternalType:
+        return F.crop_and_pad_bbox(bbox, crop_params, pad_params, rows, cols, result_rows, result_cols)
 
     def apply_to_keypoint(
         self,
-        keypoint: KeypointType,
+        keypoint: KeypointInternalType,
         crop_params: Optional[Sequence[int]] = None,
         pad_params: Optional[Sequence[int]] = None,
         rows: int = 0,
         cols: int = 0,
         result_rows: int = 0,
         result_cols: int = 0,
         **params
-    ) -> KeypointType:
+    ) -> KeypointInternalType:
         return F.crop_and_pad_keypoint(
             keypoint, crop_params, pad_params, rows, cols, result_rows, result_cols, self.keep_size
         )

albumentations/augmentations/domain_adaptation.py

@@ -8,15 +8,16 @@
 from sklearn.decomposition import PCA
 from sklearn.preprocessing import MinMaxScaler, StandardScaler
 
-from ..core.transforms_interface import ImageOnlyTransform, to_tuple
-from .functional import (
+from albumentations.augmentations.utils import (
     clipped,
     get_opencv_dtype_from_numpy,
     is_grayscale_image,
     is_multispectral_image,
     preserve_shape,
+    read_rgb_image,
 )
-from .utils import read_rgb_image
+
+from ..core.transforms_interface import ImageOnlyTransform, to_tuple
 
 __all__ = [
     "HistogramMatching",

albumentations/augmentations/dropout/functional.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from ..functional import preserve_shape
+from albumentations.augmentations.utils import preserve_shape
 
 __all__ = ["cutout", "channel_dropout"]