Moved image filters used by soft inpainting into soft_inpainting.py from images.py
This commit is contained in:
CodeHatchling
parent
8dbacc7d01
commit
56604f08a1
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@ -792,193 +792,3 @@ def flatten(img, bgcolor):
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return img.convert('RGB')
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def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, percentile_max=1.0, min_width=1.0):
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"""
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Generalization convolution filter capable of applying
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weighted mean, median, maximum, and minimum filters
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parametrically using an arbitrary kernel.
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Args:
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img (nparray):
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The image, a 2-D array of floats, to which the filter is being applied.
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kernel (nparray):
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The kernel, a 2-D array of floats.
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kernel_center (nparray):
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The kernel center coordinate, a 1-D array with two elements.
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percentile_min (float):
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The lower bound of the histogram window used by the filter,
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from 0 to 1.
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percentile_max (float):
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The upper bound of the histogram window used by the filter,
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from 0 to 1.
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min_width (float):
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The minimum size of the histogram window bounds, in weight units.
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Must be greater than 0.
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Returns:
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(nparray): A filtered copy of the input image "img", a 2-D array of floats.
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"""
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# Converts an index tuple into a vector.
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def vec(x):
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return np.array(x)
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kernel_min = -kernel_center
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kernel_max = vec(kernel.shape) - kernel_center
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def weighted_histogram_filter_single(idx):
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idx = vec(idx)
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min_index = np.maximum(0, idx + kernel_min)
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max_index = np.minimum(vec(img.shape), idx + kernel_max)
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window_shape = max_index - min_index
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class WeightedElement:
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"""
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An element of the histogram, its weight
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and bounds.
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"""
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def __init__(self, value, weight):
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self.value: float = value
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self.weight: float = weight
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self.window_min: float = 0.0
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self.window_max: float = 1.0
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# Collect the values in the image as WeightedElements,
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# weighted by their corresponding kernel values.
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values = []
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for window_tup in np.ndindex(tuple(window_shape)):
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window_index = vec(window_tup)
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image_index = window_index + min_index
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centered_kernel_index = image_index - idx
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kernel_index = centered_kernel_index + kernel_center
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element = WeightedElement(img[tuple(image_index)], kernel[tuple(kernel_index)])
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values.append(element)
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def sort_key(x: WeightedElement):
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return x.value
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values.sort(key=sort_key)
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# Calculate the height of the stack (sum)
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# and each sample's range they occupy in the stack
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sum = 0
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for i in range(len(values)):
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values[i].window_min = sum
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sum += values[i].weight
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values[i].window_max = sum
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# Calculate what range of this stack ("window")
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# we want to get the weighted average across.
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window_min = sum * percentile_min
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window_max = sum * percentile_max
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window_width = window_max - window_min
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# Ensure the window is within the stack and at least a certain size.
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if window_width < min_width:
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window_center = (window_min + window_max) / 2
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window_min = window_center - min_width / 2
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window_max = window_center + min_width / 2
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if window_max > sum:
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window_max = sum
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window_min = sum - min_width
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if window_min < 0:
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window_min = 0
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window_max = min_width
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value = 0
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value_weight = 0
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# Get the weighted average of all the samples
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# that overlap with the window, weighted
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# by the size of their overlap.
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for i in range(len(values)):
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if window_min >= values[i].window_max:
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continue
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if window_max <= values[i].window_min:
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break
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s = max(window_min, values[i].window_min)
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e = min(window_max, values[i].window_max)
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w = e - s
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value += values[i].value * w
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value_weight += w
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return value / value_weight if value_weight != 0 else 0
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img_out = img.copy()
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# Apply the kernel operation over each pixel.
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for index in np.ndindex(img.shape):
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img_out[index] = weighted_histogram_filter_single(index)
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return img_out
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def smoothstep(x):
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"""
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The smoothstep function, input should be clamped to 0-1 range.
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Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
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"""
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return x * x * (3 - 2 * x)
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def smootherstep(x):
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"""
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The smootherstep function, input should be clamped to 0-1 range.
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Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
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"""
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return x * x * x * (x * (6 * x - 15) + 10)
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def get_gaussian_kernel(stddev_radius=1.0, max_radius=2):
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"""
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Creates a Gaussian kernel with thresholded edges.
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Args:
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stddev_radius (float):
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Standard deviation of the gaussian kernel, in pixels.
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max_radius (int):
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The size of the filter kernel. The number of pixels is (max_radius*2+1) ** 2.
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The kernel is thresholded so that any values one pixel beyond this radius
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is weighted at 0.
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Returns:
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(nparray, nparray): A kernel array (shape: (N, N)), its center coordinate (shape: (2))
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"""
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# Evaluates a 0-1 normalized gaussian function for a given square distance from the mean.
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def gaussian(sqr_mag):
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return math.exp(-sqr_mag / (stddev_radius * stddev_radius))
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# Helper function for converting a tuple to an array.
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def vec(x):
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return np.array(x)
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"""
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Since a gaussian is unbounded, we need to limit ourselves
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to a finite range.
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We taper the ends off at the end of that range so they equal zero
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while preserving the maximum value of 1 at the mean.
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"""
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zero_radius = max_radius + 1.0
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gauss_zero = gaussian(zero_radius * zero_radius)
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gauss_kernel_scale = 1 / (1 - gauss_zero)
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def gaussian_kernel_func(coordinate):
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x = coordinate[0] ** 2.0 + coordinate[1] ** 2.0
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x = gaussian(x)
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x -= gauss_zero
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x *= gauss_kernel_scale
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x = max(0.0, x)
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return x
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size = max_radius * 2 + 1
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kernel_center = max_radius
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kernel = np.zeros((size, size))
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for index in np.ndindex(kernel.shape):
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kernel[index] = gaussian_kernel_func(vec(index) - kernel_center)
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return kernel, kernel_center
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@ -1,4 +1,6 @@
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import numpy as np
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import gradio as gr
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import math
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from modules.ui_components import InputAccordion
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import modules.scripts as scripts
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@ -101,7 +103,6 @@ def apply_adaptive_masks(
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width, height,
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paste_to):
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import torch
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import numpy as np
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import modules.processing as proc
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import modules.images as images
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from PIL import Image, ImageOps, ImageFilter
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@ -115,15 +116,15 @@ def apply_adaptive_masks(
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latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1)
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kernel, kernel_center = images.get_gaussian_kernel(stddev_radius=1.5, max_radius=2)
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kernel, kernel_center = get_gaussian_kernel(stddev_radius=1.5, max_radius=2)
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masks_for_overlay = []
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for i, (distance_map, overlay_image) in enumerate(zip(latent_distance, overlay_images)):
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converted_mask = distance_map.float().cpu().numpy()
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converted_mask = images.weighted_histogram_filter(converted_mask, kernel, kernel_center,
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converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center,
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percentile_min=0.9, percentile_max=1, min_width=1)
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converted_mask = images.weighted_histogram_filter(converted_mask, kernel, kernel_center,
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converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center,
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percentile_min=0.25, percentile_max=0.75, min_width=1)
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# The distance at which opacity of original decreases to 50%
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@ -131,7 +132,7 @@ def apply_adaptive_masks(
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# converted_mask = converted_mask / half_weighted_distance
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converted_mask = 1 / (1 + converted_mask ** 2)
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converted_mask = images.smootherstep(converted_mask)
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converted_mask = smootherstep(converted_mask)
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converted_mask = 1 - converted_mask
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converted_mask = 255. * converted_mask
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converted_mask = converted_mask.astype(np.uint8)
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@ -166,7 +167,6 @@ def apply_masks(
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width, height,
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paste_to):
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import torch
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import numpy as np
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import modules.processing as proc
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import modules.images as images
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from PIL import Image, ImageOps, ImageFilter
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@ -202,6 +202,196 @@ def apply_masks(
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return masks_for_overlay
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def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, percentile_max=1.0, min_width=1.0):
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"""
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Generalization convolution filter capable of applying
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weighted mean, median, maximum, and minimum filters
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parametrically using an arbitrary kernel.
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Args:
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img (nparray):
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The image, a 2-D array of floats, to which the filter is being applied.
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kernel (nparray):
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The kernel, a 2-D array of floats.
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kernel_center (nparray):
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The kernel center coordinate, a 1-D array with two elements.
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percentile_min (float):
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The lower bound of the histogram window used by the filter,
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from 0 to 1.
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percentile_max (float):
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The upper bound of the histogram window used by the filter,
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from 0 to 1.
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min_width (float):
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The minimum size of the histogram window bounds, in weight units.
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Must be greater than 0.
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Returns:
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(nparray): A filtered copy of the input image "img", a 2-D array of floats.
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"""
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# Converts an index tuple into a vector.
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def vec(x):
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return np.array(x)
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kernel_min = -kernel_center
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kernel_max = vec(kernel.shape) - kernel_center
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def weighted_histogram_filter_single(idx):
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idx = vec(idx)
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min_index = np.maximum(0, idx + kernel_min)
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max_index = np.minimum(vec(img.shape), idx + kernel_max)
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window_shape = max_index - min_index
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class WeightedElement:
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"""
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An element of the histogram, its weight
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and bounds.
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"""
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def __init__(self, value, weight):
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self.value: float = value
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self.weight: float = weight
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self.window_min: float = 0.0
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self.window_max: float = 1.0
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# Collect the values in the image as WeightedElements,
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# weighted by their corresponding kernel values.
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values = []
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for window_tup in np.ndindex(tuple(window_shape)):
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window_index = vec(window_tup)
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image_index = window_index + min_index
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centered_kernel_index = image_index - idx
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kernel_index = centered_kernel_index + kernel_center
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element = WeightedElement(img[tuple(image_index)], kernel[tuple(kernel_index)])
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values.append(element)
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def sort_key(x: WeightedElement):
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return x.value
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values.sort(key=sort_key)
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# Calculate the height of the stack (sum)
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# and each sample's range they occupy in the stack
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sum = 0
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for i in range(len(values)):
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values[i].window_min = sum
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sum += values[i].weight
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values[i].window_max = sum
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# Calculate what range of this stack ("window")
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# we want to get the weighted average across.
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window_min = sum * percentile_min
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window_max = sum * percentile_max
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window_width = window_max - window_min
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# Ensure the window is within the stack and at least a certain size.
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if window_width < min_width:
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window_center = (window_min + window_max) / 2
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window_min = window_center - min_width / 2
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window_max = window_center + min_width / 2
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if window_max > sum:
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window_max = sum
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window_min = sum - min_width
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if window_min < 0:
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window_min = 0
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window_max = min_width
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value = 0
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value_weight = 0
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# Get the weighted average of all the samples
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# that overlap with the window, weighted
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# by the size of their overlap.
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for i in range(len(values)):
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if window_min >= values[i].window_max:
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continue
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if window_max <= values[i].window_min:
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break
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s = max(window_min, values[i].window_min)
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e = min(window_max, values[i].window_max)
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w = e - s
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value += values[i].value * w
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value_weight += w
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return value / value_weight if value_weight != 0 else 0
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img_out = img.copy()
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# Apply the kernel operation over each pixel.
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for index in np.ndindex(img.shape):
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img_out[index] = weighted_histogram_filter_single(index)
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return img_out
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def smoothstep(x):
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"""
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The smoothstep function, input should be clamped to 0-1 range.
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Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
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"""
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return x * x * (3 - 2 * x)
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def smootherstep(x):
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"""
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The smootherstep function, input should be clamped to 0-1 range.
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Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
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"""
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return x * x * x * (x * (6 * x - 15) + 10)
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def get_gaussian_kernel(stddev_radius=1.0, max_radius=2):
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"""
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Creates a Gaussian kernel with thresholded edges.
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Args:
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stddev_radius (float):
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Standard deviation of the gaussian kernel, in pixels.
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max_radius (int):
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The size of the filter kernel. The number of pixels is (max_radius*2+1) ** 2.
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The kernel is thresholded so that any values one pixel beyond this radius
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is weighted at 0.
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Returns:
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(nparray, nparray): A kernel array (shape: (N, N)), its center coordinate (shape: (2))
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"""
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# Evaluates a 0-1 normalized gaussian function for a given square distance from the mean.
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def gaussian(sqr_mag):
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return math.exp(-sqr_mag / (stddev_radius * stddev_radius))
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# Helper function for converting a tuple to an array.
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def vec(x):
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return np.array(x)
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"""
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Since a gaussian is unbounded, we need to limit ourselves
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to a finite range.
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We taper the ends off at the end of that range so they equal zero
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while preserving the maximum value of 1 at the mean.
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"""
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zero_radius = max_radius + 1.0
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gauss_zero = gaussian(zero_radius * zero_radius)
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gauss_kernel_scale = 1 / (1 - gauss_zero)
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def gaussian_kernel_func(coordinate):
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x = coordinate[0] ** 2.0 + coordinate[1] ** 2.0
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x = gaussian(x)
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x -= gauss_zero
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x *= gauss_kernel_scale
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x = max(0.0, x)
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return x
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size = max_radius * 2 + 1
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kernel_center = max_radius
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kernel = np.zeros((size, size))
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for index in np.ndindex(kernel.shape):
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kernel[index] = gaussian_kernel_func(vec(index) - kernel_center)
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return kernel, kernel_center
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# ------------------- Constants -------------------
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@ -232,6 +422,9 @@ el_ids = SoftInpaintingSettings(
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"inpaint_detail_preservation")
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# -----
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class Script(scripts.Script):
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def __init__(self):
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