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Merge pull request #1277 from roboflow/feat/patch-rich-label-annotator 

Patch RichLabelAnnotator, fix in-place functionality of other annotators
This commit is contained in:
Piotr Skalski 2024-07-03 13:39:10 +02:00 committed by GitHub
parent 0bb25fdd91 d35830ddeb
commit f06dcf73a5
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7 changed files with 138 additions and 99 deletions
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122
supervision/annotators/core.py
View File

@ -3,7 +3,7 @@ from typing import List, Optional, Tuple, Union
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.detection.utils import clip_boxes, mask_to_polygons
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 @@ class BoundingBoxAnnotator(BaseAnnotator):
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 @@ class OrientedBoxAnnotator(BaseAnnotator):
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 @@ class MaskAnnotator(BaseAnnotator):
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 @@ class MaskAnnotator(BaseAnnotator):
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 @@ class PolygonAnnotator(BaseAnnotator):
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 @@ class ColorAnnotator(BaseAnnotator):
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 @@ class ColorAnnotator(BaseAnnotator):
![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 @@ class ColorAnnotator(BaseAnnotator):
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 @@ class HaloAnnotator(BaseAnnotator):
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 @@ class HaloAnnotator(BaseAnnotator):
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 @@ class EllipseAnnotator(BaseAnnotator):
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 @@ class BoxCornerAnnotator(BaseAnnotator):
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 @@ class CircleAnnotator(BaseAnnotator):
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 @@ class DotAnnotator(BaseAnnotator):
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 @@ class DotAnnotator(BaseAnnotator):
return scene
class LabelAnnotator:
class LabelAnnotator(BaseAnnotator):
"""
A class for annotating labels on an image using provided detections.
"""
@ -950,12 +957,12 @@ class LabelAnnotator:
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 @@ class LabelAnnotator:
`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 @@ class LabelAnnotator:
Example:
```python
import supervision as sv
import supervision as sv
image = ...
detections = sv.Detections(...)
@ -1004,12 +1011,9 @@ class LabelAnnotator:
).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 @@ class LabelAnnotator:
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 @@ class RichLabelAnnotator:
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 @@ class RichLabelAnnotator:
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 @@ class RichLabelAnnotator:
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 @@ class RichLabelAnnotator:
`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 @@ class RichLabelAnnotator:
```
"""
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 @@ class RichLabelAnnotator:
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 @@ class RichLabelAnnotator:
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 @@ class BlurAnnotator(BaseAnnotator):
"""
self.kernel_size: int = kernel_size
@convert_for_annotation_method
@ensure_cv2_image_for_annotation
def annotate(
self,
scene: ImageType,
@ -1331,7 +1343,7 @@ class BlurAnnotator(BaseAnnotator):
return scene
class TraceAnnotator:
class TraceAnnotator(BaseAnnotator):
"""
A class for drawing trace paths on an image based on detection coordinates.
@ -1367,7 +1379,7 @@ class TraceAnnotator:
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 @@ class TraceAnnotator:
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 @@ class HeatMapAnnotator:
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 @@ class PixelateAnnotator(BaseAnnotator):
"""
self.pixel_size: int = pixel_size
@convert_for_annotation_method
@ensure_cv2_image_for_annotation
def annotate(
self,
scene: ImageType,
@ -1641,7 +1653,7 @@ class TriangleAnnotator(BaseAnnotator):
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 @@ class RoundBoxAnnotator(BaseAnnotator):
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 @@ class PercentageBarAnnotator(BaseAnnotator):
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 @@ class CropAnnotator(BaseAnnotator):
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,

4
supervision/detection/annotate.py
View File

@ -5,7 +5,7 @@ import cv2
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 @@ class BoxAnnotator:
"`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,

6
supervision/keypoint/annotators.py
View File

@ -11,7 +11,7 @@ from supervision.draw.color import Color
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 @@ class VertexAnnotator(BaseKeyPointAnnotator):
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 @@ class EdgeAnnotator(BaseKeyPointAnnotator):
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

43
supervision/utils/conversion.py
View File

@ -8,11 +8,13 @@ from PIL import Image
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 convert_for_annotation_method(annotate_func):
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 convert_for_image_processing(image_processing_fun):
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

10
supervision/utils/image.py
View File

@ -14,8 +14,8 @@ from supervision.draw.color import Color, unify_to_bgr
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 @@ RelativePosition = Literal["top", "bottom"]
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],

2
test/utils/conftest.py
View File

@ -11,7 +11,7 @@ ALL_IMAGES_LIST = [os.path.join(ASSETS_DIR, f"{i}.jpg") for i in range(1, 6)]
@fixture(scope="function")
def empty_opencv_image() -> np.ndarray:
def empty_cv2_image() -> np.ndarray:
return np.zeros((128, 128, 3), dtype=np.uint8)

50
test/utils/test_conversion.py
View File

@ -2,28 +2,28 @@ import numpy as np
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 test_convert_for_image_processing_when_pillow_image_submitted(
)
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 test_convert_for_image_processing_when_opencv_image_submitted(
# 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"