diff --git a/supervision/annotators/core.py b/supervision/annotators/core.py
index 5b06120b2..e12ed2044 100644
--- a/supervision/annotators/core.py
+++ b/supervision/annotators/core.py
@@ -3,7 +3,7 @@
 
 import cv2
 import numpy as np
-from PIL import Image, ImageDraw, ImageFont
+from PIL import ImageDraw, ImageFont
 
 from supervision.annotators.base import BaseAnnotator, ImageType
 from supervision.annotators.utils import (
@@ -18,7 +18,10 @@
 from supervision.draw.color import Color, ColorPalette
 from supervision.draw.utils import draw_polygon
 from supervision.geometry.core import Position
-from supervision.utils.conversion import convert_for_annotation_method
+from supervision.utils.conversion import (
+    ensure_cv2_image_for_annotation,
+    ensure_pil_image_for_annotation,
+)
 from supervision.utils.image import crop_image, overlay_image, scale_image
 
 
@@ -45,7 +48,7 @@ def __init__(
         self.thickness: int = thickness
         self.color_lookup: ColorLookup = color_lookup
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -126,7 +129,7 @@ def __init__(
         self.thickness: int = thickness
         self.color_lookup: ColorLookup = color_lookup
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -214,7 +217,7 @@ def __init__(
         self.opacity = opacity
         self.color_lookup: ColorLookup = color_lookup
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -270,8 +273,10 @@ def annotate(
             mask = detections.mask[detection_idx]
             colored_mask[mask] = color.as_bgr()
 
-        scene = cv2.addWeighted(colored_mask, self.opacity, scene, 1 - self.opacity, 0)
-        return scene.astype(np.uint8)
+        cv2.addWeighted(
+            colored_mask, self.opacity, scene, 1 - self.opacity, 0, dst=scene
+        )
+        return scene
 
 
 class PolygonAnnotator(BaseAnnotator):
@@ -301,7 +306,7 @@ def __init__(
         self.thickness: int = thickness
         self.color_lookup: ColorLookup = color_lookup
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -387,7 +392,7 @@ def __init__(
         self.color_lookup: ColorLookup = color_lookup
         self.opacity = opacity
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -426,7 +431,7 @@ def annotate(
         ![box-mask-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/box-mask-annotator-example-purple.png)
         """
-        mask_image = scene.copy()
+        scene_with_boxes = scene.copy()
         for detection_idx in range(len(detections)):
             x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
             color = resolve_color(
@@ -438,14 +443,15 @@ def annotate(
                 else custom_color_lookup,
             )
             cv2.rectangle(
-                img=scene,
+                img=scene_with_boxes,
                 pt1=(x1, y1),
                 pt2=(x2, y2),
                 color=color.as_bgr(),
                 thickness=-1,
             )
-        scene = cv2.addWeighted(
-            scene, self.opacity, mask_image, 1 - self.opacity, gamma=0
+
+        cv2.addWeighted(
+            scene_with_boxes, self.opacity, scene, 1 - self.opacity, gamma=0, dst=scene
         )
         return scene
 
@@ -481,7 +487,7 @@ def __init__(
         self.color_lookup: ColorLookup = color_lookup
         self.kernel_size: int = kernel_size
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -546,7 +552,8 @@ def annotate(
         gray = cv2.cvtColor(colored_mask, cv2.COLOR_BGR2GRAY)
         alpha = self.opacity * gray / gray.max()
         alpha_mask = alpha[:, :, np.newaxis]
-        scene = np.uint8(scene * (1 - alpha_mask) + colored_mask * self.opacity)
+        blended_scene = np.uint8(scene * (1 - alpha_mask) + colored_mask * self.opacity)
+        np.copyto(scene, blended_scene)
         return scene
 
 
@@ -579,7 +586,7 @@ def __init__(
         self.end_angle: int = end_angle
         self.color_lookup: ColorLookup = color_lookup
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -670,7 +677,7 @@ def __init__(
         self.corner_length: int = corner_length
         self.color_lookup: ColorLookup = color_lookup
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -758,7 +765,7 @@ def __init__(
         self.thickness: int = thickness
         self.color_lookup: ColorLookup = color_lookup
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -851,7 +858,7 @@ def __init__(
         self.color_lookup: ColorLookup = color_lookup
         self.outline_thickness = outline_thickness
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -910,7 +917,7 @@ def annotate(
         return scene
 
 
-class LabelAnnotator:
+class LabelAnnotator(BaseAnnotator):
     """
     A class for annotating labels on an image using provided detections.
     """
@@ -950,12 +957,12 @@ def __init__(
         self.text_anchor: Position = text_position
         self.color_lookup: ColorLookup = color_lookup
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
         detections: Detections,
-        labels: List[str] = None,
+        labels: Optional[List[str]] = None,
         custom_color_lookup: Optional[np.ndarray] = None,
     ) -> ImageType:
         """
@@ -966,7 +973,7 @@ def annotate(
                 `ImageType` is a flexible type, accepting either `numpy.ndarray`
                 or `PIL.Image.Image`.
             detections (Detections): Object detections to annotate.
-            labels (List[str]): Optional. Custom labels for each detection.
+            labels (Optional[List[str]]): Custom labels for each detection.
             custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                 Allows to override the default color mapping strategy.
 
@@ -976,7 +983,7 @@ def annotate(
 
         Example:
             ```python
-             import supervision as sv
+            import supervision as sv
 
             image = ...
             detections = sv.Detections(...)
@@ -1004,12 +1011,9 @@ def annotate(
         ).astype(int)
         if labels is not None and len(labels) != len(detections):
             raise ValueError(
-                f"The number of labels provided ({len(labels)}) does not match the "
-                f"number of detections ({len(detections)}). Each detection should have "
-                f"a corresponding label. This discrepancy can occur if the labels and "
-                f"detections are not aligned or if an incorrect number of labels has "
-                f"been provided. Please ensure that the labels array has the same "
-                f"length as the Detections object."
+                f"The number of labels ({len(labels)}) does not match the "
+                f"number of detections ({len(detections)}). Each detection "
+                f"should have exactly 1 label."
             )
 
         for detection_idx, center_coordinates in enumerate(anchors_coordinates):
@@ -1111,7 +1115,7 @@ def draw_rounded_rectangle(
         return scene
 
 
-class RichLabelAnnotator:
+class RichLabelAnnotator(BaseAnnotator):
     """
     A class for annotating labels on an image using provided detections,
     with support for Unicode characters by using a custom font.
@@ -1121,7 +1125,7 @@ def __init__(
         self,
         color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
         text_color: Color = Color.WHITE,
-        font_path: str = None,
+        font_path: Optional[str] = None,
         font_size: int = 10,
         text_padding: int = 10,
         text_position: Position = Position.TOP_LEFT,
@@ -1133,8 +1137,8 @@ def __init__(
             color (Union[Color, ColorPalette]): The color or color palette to use for
                 annotating the text background.
             text_color (Color): The color to use for the text.
-            font_path (str): Path to the font file (e.g., ".ttf" or ".otf") to use for
-                rendering text. If `None`, the default PIL font will be used.
+            font_path (Optional[str]): Path to the font file (e.g., ".ttf" or ".otf")
+                to use for rendering text. If `None`, the default PIL font will be used.
             font_size (int): Font size for the text.
             text_padding (int): Padding around the text within its background box.
             text_position (Position): Position of the text relative to the detection.
@@ -1155,15 +1159,16 @@ def __init__(
                 self.font = ImageFont.truetype(font_path, font_size)
             except OSError:
                 print(f"Font path '{font_path}' not found. Using PIL's default font.")
-                self.font = ImageFont.load_default(size=font_size)
+                self.font = self._load_default_font(font_size)
         else:
-            self.font = ImageFont.load_default(size=font_size)
+            self.font = self._load_default_font(font_size)
 
+    @ensure_pil_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
         detections: Detections,
-        labels: List[str] = None,
+        labels: Optional[List[str]] = None,
         custom_color_lookup: Optional[np.ndarray] = None,
     ) -> ImageType:
         """
@@ -1175,7 +1180,7 @@ def annotate(
                 `ImageType` is a flexible type, accepting either `numpy.ndarray`
                 or `PIL.Image.Image`.
             detections (Detections): Object detections to annotate.
-            labels (List[str]): Optional. Custom labels for each detection.
+            labels (Optional[List[str]]): Custom labels for each detection.
             custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                 Allows to override the default color mapping strategy.
 
@@ -1205,8 +1210,6 @@ def annotate(
             ```
 
         """
-        if isinstance(scene, np.ndarray):
-            scene = Image.fromarray(cv2.cvtColor(scene, cv2.COLOR_BGR2RGB))
         draw = ImageDraw.Draw(scene)
         anchors_coordinates = detections.get_anchors_coordinates(
             anchor=self.text_anchor
@@ -1215,10 +1218,7 @@ def annotate(
             raise ValueError(
                 f"The number of labels provided ({len(labels)}) does not match the "
                 f"number of detections ({len(detections)}). Each detection should have "
-                f"a corresponding label. This discrepancy can occur if the labels and "
-                f"detections are not aligned or if an incorrect number of labels has "
-                f"been provided. Please ensure that the labels array has the same "
-                f"length as the Detections object."
+                f"a corresponding label."
             )
         for detection_idx, center_coordinates in enumerate(anchors_coordinates):
             color = resolve_color(
@@ -1266,9 +1266,21 @@ def annotate(
                 font=self.font,
                 fill=self.text_color.as_rgb(),
             )
-
         return scene
 
+    @staticmethod
+    def _load_default_font(size):
+        """
+        PIL either loads a font that accepts a size (e.g. on my machine)
+        or raises an error saying `load_default` does not accept arguments
+        (e.g. in Colab).
+        """
+        try:
+            font = ImageFont.load_default(size)
+        except TypeError:
+            font = ImageFont.load_default()
+        return font
+
 
 class BlurAnnotator(BaseAnnotator):
     """
@@ -1282,7 +1294,7 @@ def __init__(self, kernel_size: int = 15):
         """
         self.kernel_size: int = kernel_size
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -1331,7 +1343,7 @@ def annotate(
         return scene
 
 
-class TraceAnnotator:
+class TraceAnnotator(BaseAnnotator):
     """
     A class for drawing trace paths on an image based on detection coordinates.
 
@@ -1367,7 +1379,7 @@ def __init__(
         self.thickness = thickness
         self.color_lookup: ColorLookup = color_lookup
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -1440,7 +1452,7 @@ def annotate(
         return scene
 
 
-class HeatMapAnnotator:
+class HeatMapAnnotator(BaseAnnotator):
     """
     A class for drawing heatmaps on an image based on provided detections.
     Heat accumulates over time and is drawn as a semi-transparent overlay
@@ -1474,7 +1486,7 @@ def __init__(
         self.top_hue = top_hue
         self.low_hue = low_hue
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(self, scene: ImageType, detections: Detections) -> ImageType:
         """
         Annotates the scene with a heatmap based on the provided detections.
@@ -1550,7 +1562,7 @@ def __init__(self, pixel_size: int = 20):
         """
         self.pixel_size: int = pixel_size
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -1641,7 +1653,7 @@ def __init__(
         self.color_lookup: ColorLookup = color_lookup
         self.outline_thickness: int = outline_thickness
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -1740,7 +1752,7 @@ def __init__(
             raise ValueError("roundness attribute must be float between (0, 1.0]")
         self.roundness: float = roundness
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -1877,7 +1889,7 @@ def __init__(
         if border_thickness is None:
             self.border_thickness = int(0.15 * self.height)
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
@@ -2050,7 +2062,7 @@ def __init__(
         self.border_thickness: int = border_thickness
         self.border_color_lookup: ColorLookup = border_color_lookup
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
diff --git a/supervision/detection/annotate.py b/supervision/detection/annotate.py
index f496b2487..eb52171c2 100644
--- a/supervision/detection/annotate.py
+++ b/supervision/detection/annotate.py
@@ -5,7 +5,7 @@
 from supervision.annotators.base import ImageType
 from supervision.detection.core import Detections
 from supervision.draw.color import Color, ColorPalette
-from supervision.utils.conversion import convert_for_annotation_method
+from supervision.utils.conversion import ensure_cv2_image_for_annotation
 from supervision.utils.internal import deprecated
 
 
@@ -46,7 +46,7 @@ def __init__(
         "`BoxAnnotator` is deprecated and will be removed in "
         "`supervision-0.22.0`. Use `BoundingBoxAnnotator` and `LabelAnnotator` instead"
     )
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(
         self,
         scene: ImageType,
diff --git a/supervision/keypoint/annotators.py b/supervision/keypoint/annotators.py
index e6ff1fcfd..256b1b621 100644
--- a/supervision/keypoint/annotators.py
+++ b/supervision/keypoint/annotators.py
@@ -11,7 +11,7 @@
 from supervision.draw.utils import draw_rounded_rectangle
 from supervision.keypoint.core import KeyPoints
 from supervision.keypoint.skeletons import SKELETONS_BY_VERTEX_COUNT
-from supervision.utils.conversion import convert_for_annotation_method
+from supervision.utils.conversion import ensure_cv2_image_for_annotation
 
 
 class BaseKeyPointAnnotator(ABC):
@@ -41,7 +41,7 @@ def __init__(
         self.color = color
         self.radius = radius
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(self, scene: ImageType, key_points: KeyPoints) -> ImageType:
         """
         Annotates the given scene with skeleton vertices based on the provided key
@@ -117,7 +117,7 @@ def __init__(
         self.thickness = thickness
         self.edges = edges
 
-    @convert_for_annotation_method
+    @ensure_cv2_image_for_annotation
     def annotate(self, scene: ImageType, key_points: KeyPoints) -> ImageType:
         """
         Annotates the given scene by drawing lines between specified key points to form
diff --git a/supervision/utils/conversion.py b/supervision/utils/conversion.py
index 8ddce9695..646d7dcf2 100644
--- a/supervision/utils/conversion.py
+++ b/supervision/utils/conversion.py
@@ -8,11 +8,13 @@
 from supervision.annotators.base import ImageType
 
 
-def convert_for_annotation_method(annotate_func):
+def ensure_cv2_image_for_annotation(annotate_func):
     """
     Decorates `BaseAnnotator.annotate` implementations, converts scene to
     an image type used internally by the annotators, converts back when annotation
     is complete.
+
+    Assumes the annotators modify the scene in-place.
     """
 
     @wraps(annotate_func)
@@ -21,19 +23,22 @@ def wrapper(self, scene: ImageType, *args, **kwargs):
             return annotate_func(self, scene, *args, **kwargs)
 
         if isinstance(scene, Image.Image):
-            scene = pillow_to_cv2(scene)
-            annotated = annotate_func(self, scene, *args, **kwargs)
-            return cv2_to_pillow(image=annotated)
+            scene_np = pillow_to_cv2(scene)
+            annotated_np = annotate_func(self, scene_np, *args, **kwargs)
+            scene.paste(cv2_to_pillow(annotated_np))
+            return scene
 
         raise ValueError(f"Unsupported image type: {type(scene)}")
 
     return wrapper
 
 
-def convert_for_image_processing(image_processing_fun):
+def ensure_cv2_image_for_processing(image_processing_fun):
     """
     Decorates image processing functions that accept np.ndarray, converting `image` to
     np.ndarray, converts back when processing is complete.
+
+    Assumes the annotators do NOT modify the scene in-place.
     """
 
     @wraps(image_processing_fun)
@@ -44,13 +49,37 @@ def wrapper(image: ImageType, *args, **kwargs):
         if isinstance(image, Image.Image):
             scene = pillow_to_cv2(image)
             annotated = image_processing_fun(scene, *args, **kwargs)
-            return cv2_to_pillow(image=annotated)
+            return cv2_to_pillow(annotated)
 
         raise ValueError(f"Unsupported image type: {type(image)}")
 
     return wrapper
 
 
+def ensure_pil_image_for_annotation(annotate_func):
+    """
+    Decorates image processing functions that accept np.ndarray, converting `image` to
+    PIL image, converts back when processing is complete.
+
+    Assumes the annotators modify the scene in-place.
+    """
+
+    @wraps(annotate_func)
+    def wrapper(self, scene: ImageType, *args, **kwargs):
+        if isinstance(scene, np.ndarray):
+            scene_pil = cv2_to_pillow(scene)
+            annotated_pil = annotate_func(self, scene_pil, *args, **kwargs)
+            np.copyto(scene, pillow_to_cv2(annotated_pil))
+            return scene
+
+        if isinstance(scene, Image.Image):
+            return annotate_func(self, scene, *args, **kwargs)
+
+        raise ValueError(f"Unsupported image type: {type(scene)}")
+
+    return wrapper
+
+
 def images_to_cv2(images: List[ImageType]) -> List[np.ndarray]:
     """
     Converts images provided either as Pillow images or OpenCV
@@ -67,7 +96,7 @@ def images_to_cv2(images: List[ImageType]) -> List[np.ndarray]:
     result = []
     for image in images:
         if issubclass(type(image), Image.Image):
-            image = pillow_to_cv2(image=image)
+            image = pillow_to_cv2(image)
         result.append(image)
     return result
 
diff --git a/supervision/utils/image.py b/supervision/utils/image.py
index 9f2e17839..d69721893 100644
--- a/supervision/utils/image.py
+++ b/supervision/utils/image.py
@@ -14,8 +14,8 @@
 from supervision.draw.utils import calculate_optimal_text_scale, draw_text
 from supervision.geometry.core import Point
 from supervision.utils.conversion import (
-    convert_for_image_processing,
     cv2_to_pillow,
+    ensure_cv2_image_for_processing,
     images_to_cv2,
 )
 from supervision.utils.iterables import create_batches, fill
@@ -25,7 +25,7 @@
 MAX_COLUMNS_FOR_SINGLE_ROW_GRID = 3
 
 
-@convert_for_image_processing
+@ensure_cv2_image_for_processing
 def crop_image(
     image: ImageType,
     xyxy: Union[npt.NDArray[int], List[int], Tuple[int, int, int, int]],
@@ -86,7 +86,7 @@ def crop_image(
     return image[y_min:y_max, x_min:x_max]
 
 
-@convert_for_image_processing
+@ensure_cv2_image_for_processing
 def scale_image(image: ImageType, scale_factor: float) -> ImageType:
     """
     Scales the given image based on the given scale factor.
@@ -143,7 +143,7 @@ def scale_image(image: ImageType, scale_factor: float) -> ImageType:
     return cv2.resize(image, (width_new, height_new), interpolation=cv2.INTER_LINEAR)
 
 
-@convert_for_image_processing
+@ensure_cv2_image_for_processing
 def resize_image(
     image: ImageType,
     resolution_wh: Tuple[int, int],
@@ -216,7 +216,7 @@ def resize_image(
     return cv2.resize(image, (width_new, height_new), interpolation=cv2.INTER_LINEAR)
 
 
-@convert_for_image_processing
+@ensure_cv2_image_for_processing
 def letterbox_image(
     image: ImageType,
     resolution_wh: Tuple[int, int],
diff --git a/test/utils/conftest.py b/test/utils/conftest.py
index cc134f8ce..4230571b2 100644
--- a/test/utils/conftest.py
+++ b/test/utils/conftest.py
@@ -11,7 +11,7 @@
 
 
 @fixture(scope="function")
-def empty_opencv_image() -> np.ndarray:
+def empty_cv2_image() -> np.ndarray:
     return np.zeros((128, 128, 3), dtype=np.uint8)
 
 
diff --git a/test/utils/test_conversion.py b/test/utils/test_conversion.py
index fb3d8faff..6ff0c630b 100644
--- a/test/utils/test_conversion.py
+++ b/test/utils/test_conversion.py
@@ -2,28 +2,28 @@
 from PIL import Image, ImageChops
 
 from supervision.utils.conversion import (
-    convert_for_image_processing,
     cv2_to_pillow,
+    ensure_cv2_image_for_processing,
     images_to_cv2,
     pillow_to_cv2,
 )
 
 
-def test_convert_for_image_processing_when_pillow_image_submitted(
-    empty_opencv_image: np.ndarray, empty_pillow_image: Image.Image
+def test_ensure_cv2_image_for_processing_when_pillow_image_submitted(
+    empty_cv2_image: np.ndarray, empty_pillow_image: Image.Image
 ) -> None:
     # given
     param_a_value = 3
     param_b_value = "some"
 
-    @convert_for_image_processing
+    @ensure_cv2_image_for_processing
     def my_custom_processing_function(
         image: np.ndarray,
         param_a: int,
         param_b: str,
     ) -> np.ndarray:
         assert np.allclose(
-            image, empty_opencv_image
+            image, empty_cv2_image
         ), "Expected conversion to OpenCV image to happen"
         assert (
             param_a == param_a_value
@@ -48,21 +48,21 @@ def my_custom_processing_function(
     )
 
 
-def test_convert_for_image_processing_when_opencv_image_submitted(
-    empty_opencv_image: np.ndarray,
+def test_ensure_cv2_image_for_processing_when_cv2_image_submitted(
+    empty_cv2_image: np.ndarray,
 ) -> None:
     # given
     param_a_value = 3
     param_b_value = "some"
 
-    @convert_for_image_processing
+    @ensure_cv2_image_for_processing
     def my_custom_processing_function(
         image: np.ndarray,
         param_a: int,
         param_b: str,
     ) -> np.ndarray:
         assert np.allclose(
-            image, empty_opencv_image
+            image, empty_cv2_image
         ), "Expected conversion to OpenCV image to happen"
         assert (
             param_a == param_a_value
@@ -74,22 +74,20 @@ def my_custom_processing_function(
 
     # when
     result = my_custom_processing_function(
-        empty_opencv_image,
+        empty_cv2_image,
         param_a_value,
         param_b=param_b_value,
     )
 
     # then
-    assert (
-        result is empty_opencv_image
-    ), "Expected to return OpenCV image without changes"
+    assert result is empty_cv2_image, "Expected to return OpenCV image without changes"
 
 
 def test_cv2_to_pillow(
-    empty_opencv_image: np.ndarray, empty_pillow_image: Image.Image
+    empty_cv2_image: np.ndarray, empty_pillow_image: Image.Image
 ) -> None:
     # when
-    result = cv2_to_pillow(image=empty_opencv_image)
+    result = cv2_to_pillow(image=empty_cv2_image)
 
     # then
     difference = ImageChops.difference(result, empty_pillow_image)
@@ -99,14 +97,14 @@ def test_cv2_to_pillow(
 
 
 def test_pillow_to_cv2(
-    empty_opencv_image: np.ndarray, empty_pillow_image: Image.Image
+    empty_cv2_image: np.ndarray, empty_pillow_image: Image.Image
 ) -> None:
     # when
     result = pillow_to_cv2(image=empty_pillow_image)
 
     # then
     assert np.allclose(
-        result, empty_opencv_image
+        result, empty_cv2_image
     ), "Conversion to OpenCV image expected not to change the content of image"
 
 
@@ -119,10 +117,10 @@ def test_images_to_cv2_when_empty_input_provided() -> None:
 
 
 def test_images_to_cv2_when_only_cv2_images_provided(
-    empty_opencv_image: np.ndarray,
+    empty_cv2_image: np.ndarray,
 ) -> None:
     # given
-    images = [empty_opencv_image] * 5
+    images = [empty_cv2_image] * 5
 
     # when
     result = images_to_cv2(images=images)
@@ -131,13 +129,13 @@ def test_images_to_cv2_when_only_cv2_images_provided(
     assert len(result) == 5, "Expected the same number of output element as input ones"
     for result_element in result:
         assert (
-            result_element is empty_opencv_image
+            result_element is empty_cv2_image
         ), "Expected CV images not to be touched by conversion"
 
 
 def test_images_to_cv2_when_only_pillow_images_provided(
     empty_pillow_image: Image.Image,
-    empty_opencv_image: np.ndarray,
+    empty_cv2_image: np.ndarray,
 ) -> None:
     # given
     images = [empty_pillow_image] * 5
@@ -149,16 +147,16 @@ def test_images_to_cv2_when_only_pillow_images_provided(
     assert len(result) == 5, "Expected the same number of output element as input ones"
     for result_element in result:
         assert np.allclose(
-            result_element, empty_opencv_image
+            result_element, empty_cv2_image
         ), "Output images expected to be equal to empty OpenCV image"
 
 
 def test_images_to_cv2_when_mixed_input_provided(
     empty_pillow_image: Image.Image,
-    empty_opencv_image: np.ndarray,
+    empty_cv2_image: np.ndarray,
 ) -> None:
     # given
-    images = [empty_pillow_image, empty_opencv_image]
+    images = [empty_pillow_image, empty_cv2_image]
 
     # when
     result = images_to_cv2(images=images)
@@ -166,8 +164,8 @@ def test_images_to_cv2_when_mixed_input_provided(
     # then
     assert len(result) == 2, "Expected the same number of output element as input ones"
     assert np.allclose(
-        result[0], empty_opencv_image
+        result[0], empty_cv2_image
     ), "PIL image should be converted to OpenCV one, equal to example empty image"
     assert (
-        result[1] is empty_opencv_image
+        result[1] is empty_cv2_image
     ), "Expected CV images not to be touched by conversion"