Use colour checks configuration.

Author: KelSolaar

.coveragerc

@@ -5,4 +5,5 @@ sigterm = True
 exclude_lines =
     pragma: no cover
     if __name__ == .__main__.:
+    if TYPE_CHECKING:
     pass

colour_checker_detection/__init__.py

@@ -67,7 +67,7 @@
 __major_version__ = "0"
 __minor_version__ = "2"
 __change_version__ = "1"
-__version__ = ".".join((__major_version__, __minor_version__, __change_version__))
+__version__ = f"{__major_version__}.{__minor_version__}.{__change_version__}"
 
 try:
     _version = (
@@ -79,7 +79,7 @@
         .strip()
         .decode("utf-8")
     )
-except Exception:
+except Exception:  # noqa: BLE001
     _version = __version__
 
 colour.utilities.ANCILLARY_COLOUR_SCIENCE_PACKAGES[  # pyright: ignore

colour_checker_detection/detection/common.py

@@ -19,25 +19,27 @@
 
 from __future__ import annotations
 
+import typing
 from dataclasses import dataclass
 
 import cv2
 import numpy as np
 from colour.algebra import linear_conversion
 from colour.characterisation import CCS_COLOURCHECKERS
-from colour.hints import (
-    Any,
-    ArrayLike,
-    Dict,
-    DTypeFloat,
-    DTypeInt,
-    Literal,
-    NDArrayFloat,
-    NDArrayInt,
-    Tuple,
-    Type,
-    cast,
-)
+
+if typing.TYPE_CHECKING:
+    from colour.hints import (
+        Any,
+        ArrayLike,
+        Dict,
+        DTypeFloat,
+        DTypeInt,
+        Literal,
+        NDArrayReal,
+        Type,
+    )
+
+from colour.hints import NDArrayFloat, NDArrayInt, NDArrayReal, Tuple, cast
 from colour.models import XYZ_to_RGB, xyY_to_XYZ
 from colour.utilities import (
     MixinDataclassIterable,
@@ -275,7 +277,7 @@ def swatch_masks(
     offset_v = height / swatches_v / 2
     for j in np.linspace(offset_v, height - offset_v, swatches_v):
         for i in np.linspace(offset_h, width - offset_h, swatches_h):
-            masks.append(
+            masks.append(  # noqa: PERF401
                 as_int32_array(
                     [
                         j - samples_half,
@@ -352,7 +354,7 @@ def reformat_image(
         cv2.WARP_FILL_OUTLIERS,  # pyright: ignore
         cv2.WARP_INVERSE_MAP,  # pyright: ignore
     ] = cv2.INTER_CUBIC,
-) -> NDArrayInt | NDArrayFloat:
+) -> NDArrayReal:
     """
     Reformat given image so that it is horizontal and resizes it to given target
     width.
@@ -432,10 +434,10 @@ def reformat_image(
 
 
 def transform_image(
-    image,
-    translation=np.array([0, 0]),
-    rotation=0,
-    scale=np.array([1, 1]),
+    image: ArrayLike,
+    translation: ArrayLike = (0, 0),
+    rotation: float = 0,
+    scale: ArrayLike = (1, 1),
     interpolation_method: Literal[
         cv2.INTER_AREA,  # pyright: ignore
         cv2.INTER_CUBIC,  # pyright: ignore
@@ -448,7 +450,7 @@ def transform_image(
         cv2.WARP_FILL_OUTLIERS,  # pyright: ignore
         cv2.WARP_INVERSE_MAP,  # pyright: ignore
     ] = cv2.INTER_CUBIC,
-) -> NDArrayInt | NDArrayFloat:
+) -> NDArrayReal:
     """
     Transform given image using given translation, rotation and scale values.
 
@@ -550,7 +552,7 @@ def transform_image(
     transform += as_float32_array([[0, 0, t_x], [0, 0, t_y]])
 
     return cast(
-        NDArrayInt | NDArrayFloat,
+        NDArrayReal,
         cv2.warpAffine(
             image,
             transform,
@@ -563,7 +565,7 @@ def transform_image(
 
 def detect_contours(
     image: ArrayLike, additional_data: bool = False, **kwargs: Any
-) -> Tuple[NDArrayInt] | Tuple[Tuple[NDArrayInt], NDArrayInt | NDArrayFloat]:
+) -> Tuple[NDArrayInt] | Tuple[Tuple[NDArrayInt], NDArrayReal]:
     """
     Detect the contours of given image using given settings.
 
@@ -648,7 +650,7 @@ def detect_contours(
         iterations=settings.convolution_iterations,
     )
 
-    image_k = cast(NDArrayInt | NDArrayFloat, image_k)
+    image_k = cast(NDArrayReal, image_k)
 
     # Detecting contours.
     contours, _hierarchy = cv2.findContours(
@@ -659,8 +661,7 @@ def detect_contours(
 
     if additional_data:
         return contours, image_k
-    else:
-        return contours
+    return contours
 
 
 def is_square(contour: ArrayLike, tolerance: float = 0.015) -> bool:
@@ -730,13 +731,11 @@ def contour_centroid(contour: ArrayLike) -> Tuple[float, float]:
 
     moments = cv2.moments(contour)
 
-    centroid = (
+    return (
         moments["m10"] / moments["m00"],
         moments["m01"] / moments["m00"],
     )
 
-    return centroid
-
 
 def scale_contour(contour: ArrayLike, factor: ArrayLike) -> NDArrayFloat:
     """
@@ -773,9 +772,7 @@ def scale_contour(contour: ArrayLike, factor: ArrayLike) -> NDArrayFloat:
 
     centroid = contour_centroid(contour)
 
-    scaled_contour = (contour - centroid) * factor + centroid
-
-    return scaled_contour
+    return (contour - centroid) * factor + centroid
 
 
 def approximate_contour(
@@ -995,7 +992,11 @@ class DataDetectionColourChecker(MixinDataclassIterable):
 
 
 def sample_colour_checker(
-    image: ArrayLike, quadrilateral, rectangle, samples=32, **kwargs
+    image: ArrayLike,
+    quadrilateral: ArrayLike,
+    rectangle: ArrayLike,
+    samples: int = 32,
+    **kwargs: Any,
 ) -> DataDetectionColourChecker:
     """
     Sample the colour checker using the given source quadrilateral, i.e.,

colour_checker_detection/detection/inference.py

@@ -17,19 +17,23 @@
 import subprocess
 import sys
 import tempfile
+import typing
 
 import cv2
 import numpy as np
-from colour.hints import (
-    Any,
-    ArrayLike,
-    Callable,
-    Dict,
-    NDArrayFloat,
-    NDArrayInt,
-    Tuple,
-    cast,
-)
+
+if typing.TYPE_CHECKING:
+    from colour.hints import (
+        Any,
+        ArrayLike,
+        Callable,
+        Dict,
+        Literal,
+        NDArrayFloat,
+        Tuple,
+    )
+
+from colour.hints import NDArrayReal, cast
 from colour.io import convert_bit_depth, read_image, write_image
 from colour.models import eotf_inverse_sRGB, eotf_sRGB
 from colour.plotting import CONSTANTS_COLOUR_STYLE, plot_image
@@ -136,7 +140,7 @@ def inferencer_default(
     cctf_encoding: Callable = eotf_inverse_sRGB,
     apply_cctf_encoding: bool = True,
     show: bool = False,
-) -> NDArrayInt | NDArrayFloat:
+) -> NDArrayReal:
     """
     Predict the colour checker rectangles in given image using
     *Ultralytics YOLOv8*.
@@ -224,17 +228,60 @@ def inferencer_default(
 """Inferred classes."""
 
 
+@typing.overload
+def detect_colour_checkers_inference(
+    image: str | ArrayLike,
+    samples: int = ...,
+    cctf_decoding: Callable = ...,
+    apply_cctf_decoding: bool = ...,
+    inferencer: Callable = ...,
+    inferencer_kwargs: dict | None = ...,
+    show: bool = ...,
+    additional_data: Literal[True] = True,
+    **kwargs: Any,
+) -> Tuple[DataDetectionColourChecker, ...]: ...
+
+
+@typing.overload
+def detect_colour_checkers_inference(
+    image: str | ArrayLike,
+    samples: int = ...,
+    cctf_decoding: Callable = ...,
+    apply_cctf_decoding: bool = ...,
+    inferencer: Callable = ...,
+    inferencer_kwargs: dict | None = ...,
+    show: bool = ...,
+    *,
+    additional_data: Literal[False],
+    **kwargs: Any,
+) -> Tuple[NDArrayFloat, ...]: ...
+
+
+@typing.overload
+def detect_colour_checkers_inference(
+    image: str | ArrayLike,
+    samples: int,
+    cctf_decoding: Callable,
+    apply_cctf_decoding: bool,
+    inferencer: Callable,
+    inferencer_kwargs: dict | None,
+    show: bool,
+    additional_data: Literal[False],
+    **kwargs: Any,
+) -> Tuple[NDArrayFloat, ...]: ...
+
+
 def detect_colour_checkers_inference(
     image: str | ArrayLike,
     samples: int = 32,
-    cctf_decoding=eotf_sRGB,
+    cctf_decoding: Callable = eotf_sRGB,
     apply_cctf_decoding: bool = False,
     inferencer: Callable = inferencer_default,
     inferencer_kwargs: dict | None = None,
     show: bool = False,
     additional_data: bool = False,
     **kwargs: Any,
-) -> Tuple[DataDetectionColourChecker | NDArrayFloat, ...]:
+) -> Tuple[DataDetectionColourChecker, ...] | Tuple[NDArrayFloat, ...]:
     """
     Detect the colour checkers swatches in given image using inference.
 
@@ -375,7 +422,7 @@ def detect_colour_checkers_inference(
     if apply_cctf_decoding:
         image = cctf_decoding(image)
 
-    image = cast(NDArrayInt | NDArrayFloat, image)
+    image = cast(NDArrayReal, image)
 
     rectangle = as_int32_array(
         [
@@ -443,8 +490,8 @@ def detect_colour_checkers_inference(
 
     if additional_data:
         return tuple(colour_checkers_data)
-    else:
-        return tuple(
-            colour_checker_data.swatch_colours
-            for colour_checker_data in colour_checkers_data
-        )
+
+    return tuple(
+        colour_checker_data.swatch_colours
+        for colour_checker_data in colour_checkers_data
+    )