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import PIL.Image
import cv2
import numpy
import dgenerate.textprocessing as _textprocessing
import dgenerate.types as _types
from dgenerate.imageprocessors import imageprocessor as _imageprocessor
__doc__ = """This module provides some basic image processing capabilities using OpenCV."""
[docs]
class DilateProcessor(_imageprocessor.ImageProcessor):
"""
Apply morphological dilation to the input image using OpenCV.
Dilation is a morphological operation that expands white regions (foreground objects)
in a binary or grayscale image. It's commonly used to fill small holes inside objects
or to connect nearby objects.
The "size" argument specifies the size of the structuring element used for dilation.
It can be either an odd integer (e.g., 3, 5, 7, etc.) representing both width and height,
or a string specifying different dimensions like "5x3" for width x height.
All dimensions must be odd positive integers.
The "steps" argument specifies how many times the dilation operation is applied.
More steps result in more expansion.
The "shape" argument specifies the shape of the structuring element:
NOWRAP!
- "r" or "rect" or "rectangle": rectangular kernel (default)
- "c" or "circle" or "ellipse": elliptical kernel
- "+" or "cross": cross-shaped kernel
The "pre-resize" argument determines if the processing occurs before or after dgenerate resizes the image.
This defaults to False, meaning the image is processed after dgenerate is done resizing it.
"""
NAMES = ['dilate']
# hide inherited arguments
# that are device related
HIDE_ARGS = ['device', 'model-offload']
OPTION_ARGS = {
'shape': ['r', 'rect', 'rectangle', 'c', 'circle', 'ellipse', '+', 'cross'],
}
[docs]
def __init__(self,
size: int | str = 3,
steps: int = 1,
shape: str = 'rectangle',
pre_resize: bool = False,
**kwargs):
"""
:param size: size of the structuring element (must be odd integer or string like "5x3")
:param steps: number of times to apply the dilation operation
:param shape: shape of the structuring element ("r" or "rect" or "rectangle", "c" or "circle" or "ellipse", or "+" or "cross")
:param pre_resize: process the image before it is resized, or after? default is False (after)
:param kwargs: forwarded to base class
"""
super().__init__(**kwargs)
# Parse kernel_size
if isinstance(size, str):
try:
dimensions = _textprocessing.parse_dimensions(size)
except ValueError as e:
raise self.argument_error(f'Could not parse "size" as dimensions: {e}') from e
if len(dimensions) == 0:
raise self.argument_error('Argument "size" must specify at least one dimension.')
if len(dimensions) > 2:
raise self.argument_error('Argument "size" cannot have more than 2 dimensions.')
if len(dimensions) == 1:
kernel_width = kernel_height = dimensions[0]
else:
kernel_width, kernel_height = dimensions
else:
kernel_width = kernel_height = size
# Validate dimensions
if kernel_width < 1 or kernel_height < 1:
raise self.argument_error('Argument "size" dimensions must be positive integers.')
if kernel_width % 2 == 0 or kernel_height % 2 == 0:
raise self.argument_error('Argument "size" dimensions must be odd integers.')
if steps < 1:
raise self.argument_error('Argument "steps" must be a positive integer.')
self._kernel_width = kernel_width
self._kernel_height = kernel_height
self._iterations = steps
self._pre_resize = pre_resize
# Create the kernel based on shape
if shape.lower() in {'+', 'cross'}:
self._kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (kernel_width, kernel_height))
else:
try:
parsed_shape = _textprocessing.parse_basic_mask_shape(shape)
except ValueError:
parsed_shape = None
if parsed_shape == _textprocessing.BasicMaskShape.RECTANGLE:
self._kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_width, kernel_height))
elif parsed_shape == _textprocessing.BasicMaskShape.ELLIPSE:
self._kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_width, kernel_height))
else:
raise self.argument_error(f'Unknown kernel shape: {shape}')
def _process(self, image: PIL.Image.Image) -> PIL.Image.Image:
"""
Apply dilation to the image using OpenCV.
:param image: PIL image to process
:return: processed PIL image
"""
# Convert PIL image to numpy array
cv_img = cv2.cvtColor(numpy.array(image), cv2.COLOR_RGB2BGR)
# Apply dilation
dilated = cv2.dilate(cv_img, self._kernel, iterations=self._iterations)
# Convert back to PIL image
dilated_rgb = cv2.cvtColor(dilated, cv2.COLOR_BGR2RGB)
return PIL.Image.fromarray(dilated_rgb)
[docs]
def impl_pre_resize(self, image: PIL.Image.Image, resize_resolution: _types.OptionalSize):
"""
Pre resize, dilation may or may not occur here depending
on the boolean value of the processor argument "pre-resize"
:param image: image to process
:param resize_resolution: purely informational, is unused by this processor
:return: possibly a dilated image, or the input image
"""
if self._pre_resize:
return self._process(image)
return image
[docs]
def impl_post_resize(self, image: PIL.Image.Image):
"""
Post resize, dilation may or may not occur here depending
on the boolean value of the processor argument "pre-resize"
:param image: image to process
:return: possibly a dilated image, or the input image
"""
if not self._pre_resize:
return self._process(image)
return image
[docs]
def to(self, device) -> "DilateProcessor":
"""
Does nothing for this processor.
:param device: the device
:return: this processor
"""
return self
[docs]
class GaussianBlurProcessor(_imageprocessor.ImageProcessor):
"""
Apply Gaussian blur to the input image using OpenCV.
Gaussian blur is a widely used effect in image processing that reduces image noise
and detail by convolving the image with a Gaussian kernel.
The "size" argument specifies the size of the Gaussian kernel.
It can be either an odd integer (e.g., 3, 5, 7, etc.) representing both width and height,
or a string specifying different dimensions like "5x3" for width x height.
All dimensions must be odd positive integers. Larger kernel sizes produce more blur.
The "sigma-x" argument specifies the standard deviation in the X direction.
If 0, it's calculated from the kernel size.
The "sigma-y" argument specifies the standard deviation in the Y direction.
If 0, it's set to the same value as sigma-x.
The "pre-resize" argument determines if the processing occurs before or after dgenerate resizes the image.
This defaults to False, meaning the image is processed after dgenerate is done resizing it.
"""
NAMES = ['gaussian-blur']
# hide inherited arguments
# that are device related
HIDE_ARGS = ['device', 'model-offload']
[docs]
def __init__(self,
size: int | str = 5,
sigma_x: float = 0.0,
sigma_y: float = 0.0,
pre_resize: bool = False,
**kwargs):
"""
:param size: size of the Gaussian kernel (must be odd integer or string like "5x3")
:param sigma_x: standard deviation in X direction (0 = calculate from kernel size)
:param sigma_y: standard deviation in Y direction (0 = same as sigma_x)
:param pre_resize: process the image before it is resized, or after? default is False (after)
:param kwargs: forwarded to base class
"""
super().__init__(**kwargs)
# Parse kernel_size
if isinstance(size, str):
try:
dimensions = _textprocessing.parse_dimensions(size)
except ValueError as e:
raise self.argument_error(f'Could not parse "size" as dimensions: {e}') from e
if len(dimensions) == 0:
raise self.argument_error('Argument "size" must specify at least one dimension.')
if len(dimensions) > 2:
raise self.argument_error('Argument "size" cannot have more than 2 dimensions.')
if len(dimensions) == 1:
kernel_width = kernel_height = dimensions[0]
else:
kernel_width, kernel_height = dimensions
else:
kernel_width = kernel_height = size
# Validate dimensions
if kernel_width < 1 or kernel_height < 1:
raise self.argument_error('Argument "size" dimensions must be positive integers.')
if kernel_width % 2 == 0 or kernel_height % 2 == 0:
raise self.argument_error('Argument "size" dimensions must be odd integers.')
if sigma_x < 0:
raise self.argument_error('Argument "sigma-x" must be non-negative.')
if sigma_y < 0:
raise self.argument_error('Argument "sigma-y" must be non-negative.')
self._kernel_width = kernel_width
self._kernel_height = kernel_height
self._sigma_x = sigma_x
self._sigma_y = sigma_y
self._pre_resize = pre_resize
def _process(self, image: PIL.Image.Image) -> PIL.Image.Image:
"""
Apply Gaussian blur to the image using OpenCV.
:param image: PIL image to process
:return: processed PIL image
"""
# Convert PIL image to numpy array
cv_img = cv2.cvtColor(numpy.array(image), cv2.COLOR_RGB2BGR)
# Apply Gaussian blur
blurred = cv2.GaussianBlur(cv_img, (self._kernel_width, self._kernel_height),
self._sigma_x, sigmaY=self._sigma_y)
# Convert back to PIL image
blurred_rgb = cv2.cvtColor(blurred, cv2.COLOR_BGR2RGB)
return PIL.Image.fromarray(blurred_rgb)
[docs]
def impl_pre_resize(self, image: PIL.Image.Image, resize_resolution: _types.OptionalSize):
"""
Pre resize, Gaussian blur may or may not occur here depending
on the boolean value of the processor argument "pre-resize"
:param image: image to process
:param resize_resolution: purely informational, is unused by this processor
:return: possibly a blurred image, or the input image
"""
if self._pre_resize:
return self._process(image)
return image
[docs]
def impl_post_resize(self, image: PIL.Image.Image):
"""
Post resize, Gaussian blur may or may not occur here depending
on the boolean value of the processor argument "pre-resize"
:param image: image to process
:return: possibly a blurred image, or the input image
"""
if not self._pre_resize:
return self._process(image)
return image
[docs]
def to(self, device) -> "GaussianBlurProcessor":
"""
Does nothing for this processor.
:param device: the device
:return: this processor
"""
return self
__all__ = _types.module_all()