# Imports
from collections.abc import Callable
from typing import TYPE_CHECKING, Any, cast
if TYPE_CHECKING:
import numpy as np
from numpy.typing import NDArray
from PIL import Image
# Functions
[docs]
def auto_crop[T: "Image.Image | NDArray[np.number]"](
image: T,
mask: "NDArray[np.bool_] | None" = None,
threshold: int | float | Callable[["NDArray[np.number]"], int | float] | None = None,
return_type: type[T] | str = "same",
contiguous: bool = True,
) -> Any:
""" Automatically crop an image to remove zero or uniform regions.
This function crops the image to keep only the region where pixels are non-zero
(or above a threshold). It can work with a mask or directly analyze the image.
Args:
image (Image.Image | NDArray): The image to crop.
mask (NDArray[bool] | None): Optional binary mask indicating regions to keep.
threshold (int | float | Callable): Threshold value or function (default: np.min).
return_type (type | str): Type of the return value (Image.Image, NDArray[np.number], or "same" to match input type).
contiguous (bool): If True (default), crop to bounding box. If False, remove entire rows/columns with no content.
Returns:
Image.Image | NDArray[np.number]: The cropped image.
Examples:
>>> # Test with numpy array with zeros on edges
>>> import numpy as np
>>> array = np.zeros((100, 100, 3), dtype=np.uint8)
>>> array[20:80, 30:70] = 255 # White rectangle in center
>>> cropped = auto_crop(array, return_type=np.ndarray)
>>> cropped.shape
(60, 40, 3)
>>> # Test with custom mask
>>> mask = np.zeros((100, 100), dtype=bool)
>>> mask[10:90, 10:90] = True
>>> cropped_with_mask = auto_crop(array, mask=mask, return_type=np.ndarray)
>>> cropped_with_mask.shape
(80, 80, 3)
>>> # Test with PIL Image
>>> from PIL import Image
>>> pil_image = Image.new('RGB', (100, 100), (0, 0, 0))
>>> from PIL import ImageDraw
>>> draw = ImageDraw.Draw(pil_image)
>>> draw.rectangle([25, 25, 75, 75], fill=(255, 255, 255))
>>> cropped_pil = auto_crop(pil_image)
>>> cropped_pil.size
(51, 51)
>>> # Test with threshold
>>> array_gray = np.ones((100, 100), dtype=np.uint8) * 10
>>> array_gray[20:80, 30:70] = 255
>>> cropped_threshold = auto_crop(array_gray, threshold=50, return_type=np.ndarray)
>>> cropped_threshold.shape
(60, 40)
>>> # Test with callable threshold (using lambda to avoid min value)
>>> array_gray2 = np.ones((100, 100), dtype=np.uint8) * 10
>>> array_gray2[20:80, 30:70] = 255
>>> cropped_max = auto_crop(array_gray2, threshold=lambda x: 50, return_type=np.ndarray)
>>> cropped_max.shape
(60, 40)
>>> # Test with non-contiguous crop
>>> array_sparse = np.zeros((100, 100, 3), dtype=np.uint8)
>>> array_sparse[10, 10] = 255
>>> array_sparse[50, 50] = 255
>>> array_sparse[90, 90] = 255
>>> cropped_contiguous = auto_crop(array_sparse, contiguous=True, return_type=np.ndarray)
>>> cropped_contiguous.shape # Bounding box from (10,10) to (90,90)
(81, 81, 3)
>>> cropped_non_contiguous = auto_crop(array_sparse, contiguous=False, return_type=np.ndarray)
>>> cropped_non_contiguous.shape # Only rows/cols 10, 50, 90
(3, 3, 3)
>>> # Test with 3D crop on depth dimension
>>> array_3d = np.zeros((50, 50, 10), dtype=np.uint8)
>>> array_3d[10:40, 10:40, 2:8] = 255 # Content only in depth slices 2-7
>>> cropped_3d = auto_crop(array_3d, contiguous=True, return_type=np.ndarray)
>>> cropped_3d.shape # Should crop all 3 dimensions
(30, 30, 6)
"""
# Imports
import numpy as np
from PIL import Image
# Convert to numpy array and store original type
original_was_pil: bool = isinstance(image, Image.Image)
image_array: NDArray[np.number] = np.array(image) if original_was_pil else image # type: ignore
# Create mask if not provided
if mask is None:
if threshold is None:
threshold = cast(Callable[["NDArray[np.number]"], int | float], np.min)
threshold_value: int | float = threshold(image_array) if callable(threshold) else threshold
# Create a 2D mask for both 2D and 3D arrays
if image_array.ndim == 2:
mask = image_array > threshold_value
else: # 3D array
mask = np.any(image_array > threshold_value, axis=2)
# Find rows, columns, and depth with content
rows_with_content: NDArray[np.bool_] = np.any(mask, axis=1)
cols_with_content: NDArray[np.bool_] = np.any(mask, axis=0)
# For 3D arrays, also find which depth slices have content
depth_with_content: NDArray[np.bool_] | None = None
if image_array.ndim == 3:
# Create a 1D mask for depth dimension
depth_with_content = np.any(image_array > (threshold(image_array) if callable(threshold) else threshold if threshold is not None else np.min(image_array)), axis=(0, 1))
# Return original if no content found
if not (np.any(rows_with_content) and np.any(cols_with_content)):
return image_array if return_type != Image.Image else (image if original_was_pil else Image.fromarray(image_array))
# Crop based on contiguous parameter
if contiguous:
row_idx, col_idx = np.where(rows_with_content)[0], np.where(cols_with_content)[0]
if image_array.ndim == 3 and depth_with_content is not None and np.any(depth_with_content):
depth_idx = np.where(depth_with_content)[0]
cropped_array: NDArray[np.number] = image_array[row_idx[0]:row_idx[-1]+1, col_idx[0]:col_idx[-1]+1, depth_idx[0]:depth_idx[-1]+1]
else:
cropped_array: NDArray[np.number] = image_array[row_idx[0]:row_idx[-1]+1, col_idx[0]:col_idx[-1]+1]
else:
if image_array.ndim == 3 and depth_with_content is not None:
# np.ix_ needs index arrays, not boolean arrays
row_indices = np.where(rows_with_content)[0]
col_indices = np.where(cols_with_content)[0]
depth_indices = np.where(depth_with_content)[0]
ix = np.ix_(row_indices, col_indices, depth_indices)
else:
row_indices = np.where(rows_with_content)[0]
col_indices = np.where(cols_with_content)[0]
ix = np.ix_(row_indices, col_indices)
cropped_array = image_array[ix]
# Return in requested format
if return_type == "same":
return Image.fromarray(cropped_array) if original_was_pil else cropped_array
return cropped_array if return_type != Image.Image else Image.fromarray(cropped_array)
