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import modules.scripts as scripts
import gradio as gr
from modules import images
from modules.processing import process_images
from modules.shared import opts
import numpy as np
class Script(scripts.Script):
def title(self):
return "txt2palette"
def show(self, is_img2img):
return not is_img2img
def ui(self, is_img2img):
palette_size = gr.Slider(minimum=1, maximum=64, step=1, value=0,
label="Palette size")
method = gr.Radio(choices=['Median cut', 'KMeans'], value='Median cut', label='Palette extraction method')
sort_by = gr.Radio(choices=["luminance", "hue", "saturation", "value", "lightness"], value="luminance", label="Sort colors by")
overwrite = gr.Checkbox(False, label="Overwrite existing files")
return [palette_size, method, sort_by, overwrite]
def run(self, p, palette_size, method, sort_by, overwrite):
import colorsys
from PIL import Image
try:
from sklearn.cluster import KMeans
except ImportError:
if method == 'KMeans':
print('"sklearn" library is not installed, switching the extraction method to Median cut.')
method = "Median cut"
class Color:
luminance_weights = np.array([0.2126, 0.7152, 0.0722])
def __init__(self, RGB, frequency):
self.rgb = tuple([c for c in RGB])
self.freq = frequency
def display(self, w=50, h=50):
"""
Displays the represented color in a w x h window.
:param w: width in pixels
:param h: height in pixels
"""
img = Image.new("RGB", size=(w, h), color=self.rgb)
img.show()
def __lt__(self, other):
return self.freq < other.freq
def get_colors(self, colorspace="rgb"):
"""
Get the color in terms of a colorspace (string).
:param colorspace: rgb/hsv/hls
:return: corresponding color values
"""
colors = {"rgb": self.rgb, "hsv": self.hsv, "hls": self.hls}
return colors[colorspace]
@property
def hsv(self):
return colorsys.rgb_to_hsv(*self.rgb)
@property
def hls(self):
return colorsys.rgb_to_hls(*self.rgb)
@property
def luminance(self):
return np.dot(self.luminance_weights, self.rgb)
class ColorBox:
"""
Represents a box in the RGB color space, with associated attributes, used in the Median Cut algorithm.
"""
def __init__(self, colors):
"""
Initialize with a numpy array of RGB colors.
:param colors: np.ndarray (width * height, 3)
"""
self.colors = colors
self._get_min_max()
def _get_min_max(self):
min_channel = np.min(self.colors, axis=0)
max_channel = np.max(self.colors, axis=0)
self.min_channel = min_channel
self.max_channel = max_channel
def __lt__(self, other):
"""
Compare cubes by volume
:param other:
"""
return self.size < other.size
@property
def size(self):
return self.volume
def _get_dominant_channel(self):
dominant_channel = np.argmax(self.max_channel - self.min_channel)
return dominant_channel
@property
def average(self):
"""
Returns the average color contained in ColorBox
:return: [R, G, B]
"""
return np.mean(self.colors, axis=0)
@property
def volume(self):
return np.prod(
self.max_channel - self.min_channel,
)
def split(self):
"""
Splits the ColorBox into two ColorBoxes at the median of the dominant color channel.
:return: [ColorBox1, ColorBox2]
"""
# get the color channel with highest range
dominant_channel = self._get_dominant_channel()
# sorting colors by the dominant channel
self.colors = self.colors[self.colors[:, dominant_channel].argsort()]
median_index = len(self.colors) // 2
return [
ColorBox(self.colors[:median_index]),
ColorBox(self.colors[median_index:]),
]
class Palette:
def __init__(self, colors):
"""
Initializes a color palette with a list of Color objects.
:param colors: a list of Color-objects
"""
self.colors = colors
self.frequencies = [c.freq for c in colors]
self.number_of_colors = len(colors)
def get_image(self, w=50, h=50):
img = Image.new("RGB", size=(w * self.number_of_colors, h))
arr = np.asarray(img).copy()
for i in range(self.number_of_colors):
c = self.colors[i]
arr[:, i * h : (i + 1) * h, :] = c.rgb
img = Image.fromarray(arr, "RGB")
return img
def k_means_extraction(arr, height, width, palette_size):
"""
Extracts a color palette using KMeans.
:param arr: pixel array (height, width, 3)
:param height: height
:param width: width
:param palette_size: number of colors
:return: a palette of colors sorted by frequency
"""
arr = np.reshape(arr, (width * height, -1))
model = KMeans(n_clusters=palette_size)
labels = model.fit_predict(arr)
palette = np.array(model.cluster_centers_, dtype=int)
color_count = np.bincount(labels)
color_frequency = color_count / float(np.sum(color_count))
colors = []
for color, freq in zip(palette, color_frequency):
colors.append(Color(color, freq))
return colors
def median_cut_extraction(arr, height, width, palette_size):
"""
Extracts a color palette using the median cut algorithm.
:param arr:
:param height:
:param width:
:param palette_size:
:return:
"""
arr = arr.reshape((width * height, -1))
c = [ColorBox(arr)]
full_box_size = c[0].size
# Each iteration, find the largest box, split it, remove original box from list of boxes, and add the two new boxes.
while len(c) < palette_size:
largest_c_idx = np.argmax(c)
# add the two new boxes to the list, while removing the split box.
c = c[:largest_c_idx] + c[largest_c_idx].split() + c[largest_c_idx + 1 :]
colors = [Color(map(int, box.average), box.size / full_box_size) for box in c]
return colors
sort_methods = {
"luminance": lambda c: c.luminance,
"hue": lambda c: c.hsv[0],
"saturation": lambda c: c.hsv[1],
"value": lambda c: c.hsv[2],
"lightness": lambda c: c.hls[2],
}
def extract_colors(image, palette_size=5, resize=True, mode="Median cut", sort_mode=None):
"""
Extracts a set of 'palette_size' colors from the given image.
:param image: PIL.Image object of path to Image file
:param palette_size: number of colors to extract
:param resize: whether to resize the image before processing, yielding faster results with lower quality
:param mode: the color quantization algorithm to use. Currently supports K-Means (KM) and Median Cut (MC)
:param sort_mode: sort colors by luminance, or by frequency
:return: a list of the extracted colors
"""
if isinstance(image, Image.Image):
img = image
else:
img = Image.open(image)
img = img.convert("RGB")
if resize:
img = img.resize((256, 256))
width, height = img.size
arr = np.asarray(img)
if mode == "KMeans":
colors = k_means_extraction(arr, height, width, palette_size)
elif mode == "Median cut":
colors = median_cut_extraction(arr, height, width, palette_size)
else:
raise NotImplementedError("Extraction mode not implemented!")
if sort_mode in sort_methods:
colors.sort(key=sort_methods.get(sort_mode), reverse=False)
else:
raise NotImplementedError("Sorting mode not implemented!")
return Palette(colors)
if(not overwrite):
basename = f"_palette_{palette_size}x"
else:
p.do_not_save_samples = True
proc = process_images(p)
#do not make palettes out of grids
if len(proc.images) > 1:
iter_offset = 1
iter_num = len(proc.images) - 1
else:
iter_offset = 0
iter_num = 1
for i in range(iter_num):
pal = extract_colors(proc.images[i+iter_offset], palette_size=palette_size, sort_mode=sort_by, mode=method)
proc.images[i+iter_offset] = pal.get_image()
images.save_image(proc.images[i+iter_offset], p.outpath_samples, basename,
proc.seed + i, proc.prompt, opts.samples_format, info= proc.info, p=p)
return proc |