# Imports
from collections.abc import Callable
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
import numpy as np
from numpy.typing import NDArray
from PIL import Image
# Functions
[docs]
def image_resize[T: "Image.Image | NDArray[np.number]"](
image: T,
max_result_size: int,
resampling: "Image.Resampling | None" = None,
min_or_max: Callable[[int, int], int] = max,
return_type: type[T] | str = "same",
keep_aspect_ratio: bool = True,
) -> Any:
""" Resize an image while preserving its aspect ratio by default.
Scales the image so that its largest dimension equals max_result_size.
Args:
image (Image.Image | NDArray): The image to resize.
max_result_size (int): Maximum size for the largest dimension.
resampling (Image.Resampling | None): PIL resampling filter to use (default: Image.Resampling.LANCZOS).
min_or_max (Callable): Function to use to get the minimum or maximum of the two ratios.
return_type (type | str): Type of the return value (Image.Image, np.ndarray, or "same" to match input type).
keep_aspect_ratio (bool): Whether to keep the aspect ratio.
Returns:
Image.Image | NDArray[np.number]: The resized image with preserved aspect ratio.
Examples:
>>> # Test with (height x width x channels) numpy array
>>> import numpy as np
>>> array = np.random.randint(0, 255, (100, 50, 3), dtype=np.uint8)
>>> image_resize(array, 100).shape
(100, 50, 3)
>>> image_resize(array, 100, min_or_max=max).shape
(100, 50, 3)
>>> image_resize(array, 100, min_or_max=min).shape
(200, 100, 3)
>>> # Test with PIL Image
>>> from PIL import Image
>>> pil_image: Image.Image = Image.new('RGB', (200, 100))
>>> image_resize(pil_image, 50).size
(50, 25)
>>> # Test with different return types
>>> resized_array = image_resize(array, 50, return_type=np.ndarray)
>>> isinstance(resized_array, np.ndarray)
True
>>> resized_array.shape
(50, 25, 3)
>>> # Test with different resampling methods
>>> image_resize(pil_image, 50, resampling=Image.Resampling.NEAREST).size
(50, 25)
"""
# Imports
import numpy as np
from PIL import Image
# Set default resampling method if not provided
if resampling is None:
resampling = Image.Resampling.LANCZOS
# Store original type for later conversion
original_was_pil: bool = isinstance(image, Image.Image)
# Convert numpy array to PIL Image if needed
if not original_was_pil:
image = Image.fromarray(image) # type: ignore
if keep_aspect_ratio:
# Get original image dimensions
width: int = image.size[0]
height: int = image.size[1]
# Determine which dimension to use for scaling based on min_or_max function
max_dimension: int = min_or_max(width, height)
# Calculate scaling factor
scale: float = max_result_size / max_dimension
# Calculate new dimensions while preserving aspect ratio
new_width: int = int(width * scale)
new_height: int = int(height * scale)
# Resize the image with the calculated dimensions
new_image: Image.Image = image.resize((new_width, new_height), resampling)
else:
# If not keeping aspect ratio, resize to square with max_result_size
new_image: Image.Image = image.resize((max_result_size, max_result_size), resampling)
# Return the image in the requested format
if return_type == "same":
# Return same type as input
if original_was_pil:
return new_image
else:
return np.array(new_image)
elif return_type != Image.Image:
return np.array(new_image)
else:
return new_image
