# Imports
from .common import Any, NDArray, check_image, cv2, np
# Functions
[docs]
def translate_image(image: NDArray[Any], x: float, y: float, padding: int = 0, ignore_dtype: bool = False) -> NDArray[Any]:
""" Translate an image
Args:
image (NDArray[Any]): Image to translate
x (float): Translation along the x axis (between -1 and 1)
y (float): Translation along the y axis (between -1 and 1)
padding (int): Padding that has been added to the image before calling this function
ignore_dtype (bool): Ignore the dtype check
Returns:
NDArray[Any]: Translated image
>>> ## Basic tests
>>> image = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]).astype(np.uint8)
>>> translate_image(image, 0.5, 0.5).tolist()
[[0, 0, 0], [0, 1, 2], [0, 4, 5]]
>>> translate_image(image, 0, -2/3).tolist()
[[7, 8, 9], [0, 0, 0], [0, 0, 0]]
>>> ## Test invalid inputs
>>> translate_image(image, 2, 0)
Traceback (most recent call last):
...
AssertionError: x must be between -1 and 1, got 2
>>> translate_image(image, 0, 2)
Traceback (most recent call last):
...
AssertionError: y must be between -1 and 1, got 2
>>> translate_image("not an image", 0, 0)
Traceback (most recent call last):
...
AssertionError: Image must be a numpy array
>>> translate_image(image, 0, 0, padding=-1)
Traceback (most recent call last):
...
AssertionError: padding must be positive, got -1
"""
# Check input data
check_image(image, ignore_dtype=ignore_dtype)
assert isinstance(x, float | int), f"x must be a number, got {type(x)}"
assert isinstance(y, float | int), f"y must be a number, got {type(y)}"
assert -1 <= x <= 1, f"x must be between -1 and 1, got {x}"
assert -1 <= y <= 1, f"y must be between -1 and 1, got {y}"
assert isinstance(padding, int), f"padding must be an integer, got {type(padding)}"
assert padding >= 0, f"padding must be positive, got {padding}"
# Get image dimensions
height, width = image.shape[:2]
original_width: int = width - 2 * padding
original_height: int = height - 2 * padding
# Convert relative translations to absolute pixels
x_pixels: int = int(x * original_width)
y_pixels: int = int(y * original_height)
# Create translation matrix
translation_matrix: NDArray[Any] = np.array([[1, 0, x_pixels], [0, 1, y_pixels]], dtype=np.float32)
# Apply affine transformation
return cv2.warpAffine(image, translation_matrix, (width, height))
