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rhizonymph
2024-10-12 23:03:48 -07:00
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commit 7abd93e080
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.gitignore
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__pycache__
*.pyc
README.md
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#ComfyUI-ColorWheel
I made this to show the dominant colors in an image on a color wheel.
The image to color node is from [https://github.com/JayLyu/ComfyUI_BaiKong_Node](https://github.com/JayLyu/ComfyUI_BaiKong_Node)
I copied it over here because it's the only node from the pack needed for what this node was designed for.
What is was designed for is color studies, particularly for sunsets but it will work for anything. The workflow it's used for is in
the file colorstudy-workflow.json. It looks like this:
colorstudy-workflow.json
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{
"last_node_id": 21,
"last_link_id": 31,
"nodes": [
{
"id": 8,
"type": "PreviewImage",
"pos": {
"0": 1951.1346435546875,
"1": -221.58975219726562
},
"size": {
"0": 210,
"1": 246
},
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 18
}
],
"outputs": [],
"properties": {
"Node name for S&R": "PreviewImage"
},
"widgets_values": []
},
{
"id": 6,
"type": "PreviewImage",
"pos": {
"0": 1711.134765625,
"1": -221.58975219726562
},
"size": {
"0": 210,
"1": 246
},
"flags": {},
"order": 3,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 9
}
],
"outputs": [],
"properties": {
"Node name for S&R": "PreviewImage"
},
"widgets_values": []
},
{
"id": 3,
"type": "PreviewImage",
"pos": {
"0": 1711.134765625,
"1": 78.41023254394531
},
"size": {
"0": 210,
"1": 246
},
"flags": {},
"order": 9,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 2
}
],
"outputs": [],
"properties": {
"Node name for S&R": "PreviewImage"
},
"widgets_values": []
},
{
"id": 19,
"type": "AccurateColorWheelNode",
"pos": {
"0": 939.3956298828125,
"1": 229.64263916015625
},
"size": {
"0": 290.7735290527344,
"1": 69.09516143798828
},
"flags": {},
"order": 10,
"mode": 0,
"inputs": [
{
"name": "color_string",
"type": "STRING",
"link": 27,
"widget": {
"name": "color_string"
}
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
31
],
"slot_index": 0,
"shape": 3
}
],
"properties": {
"Node name for S&R": "AccurateColorWheelNode"
},
"widgets_values": [
"#beb6bb,#c38064,#9a99ac,#e9b68b,#ead5c2",
512
]
},
{
"id": 12,
"type": "BK_Img2Color",
"pos": {
"0": 589.3956298828125,
"1": 82.73787689208984
},
"size": {
"0": 315,
"1": 216
},
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
"name": "input_image",
"type": "IMAGE",
"link": 21
}
],
"outputs": [
{
"name": "COLORS",
"type": "STRING",
"links": [
24
],
"slot_index": 0,
"shape": 3
},
{
"name": "SELECT_COLOR",
"type": "STRING",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "BK_Img2Color"
},
"widgets_values": [
6,
false,
80,
"#feffff",
1,
"#fefeff, #f7ad89, #c5c1ce, #fbca57, #d1adae, #f2c2ae,#fefeff"
]
},
{
"id": 7,
"type": "MaskToImage",
"pos": {
"0": 939.3956298828125,
"1": -187.26214599609375
},
"size": {
"0": 293.2176513671875,
"1": 26.86839485168457
},
"flags": {},
"order": 1,
"mode": 0,
"inputs": [
{
"name": "mask",
"type": "MASK",
"link": 8
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
9
],
"slot_index": 0,
"shape": 3
}
],
"properties": {
"Node name for S&R": "MaskToImage"
},
"widgets_values": []
},
{
"id": 2,
"type": "ColorPreprocessor",
"pos": {
"0": 939.3956298828125,
"1": -27.262130737304688
},
"size": {
"0": 292.6774597167969,
"1": 58
},
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"name": "image",
"type": "IMAGE",
"link": 4
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
2
],
"slot_index": 0,
"shape": 3
}
],
"properties": {
"Node name for S&R": "ColorPreprocessor"
},
"widgets_values": [
1024
]
},
{
"id": 10,
"type": "LayerUtility: ImageAutoCrop V2",
"pos": {
"0": 179.3956298828125,
"1": -187.26214599609375
},
"size": {
"0": 378,
"1": 434
},
"flags": {},
"order": 2,
"mode": 0,
"inputs": [
{
"name": "image",
"type": "IMAGE",
"link": 16
},
{
"name": "mask",
"type": "MASK",
"link": 17,
"shape": 7
}
],
"outputs": [
{
"name": "cropped_image",
"type": "IMAGE",
"links": [
18,
19,
21
],
"slot_index": 0,
"shape": 3
},
{
"name": "box_preview",
"type": "IMAGE",
"links": null,
"shape": 3
},
{
"name": "cropped_mask",
"type": "MASK",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "LayerUtility: ImageAutoCrop V2"
},
"widgets_values": [
true,
"#FFFFFF",
"1:1",
1,
1,
"None",
1024,
"min_bounding_rect",
100,
0,
"RMBG 1.4",
"sam_vit_h (2.56GB)",
"GroundingDINO_SwinT_OGC (694MB)",
0.3,
"subject"
],
"color": "rgba(38, 73, 116, 0.7)"
},
{
"id": 4,
"type": "ImageResize+",
"pos": {
"0": 589.3956298828125,
"1": -187.26214599609375
},
"size": {
"0": 315,
"1": 218
},
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "image",
"type": "IMAGE",
"link": 19
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
4
],
"slot_index": 0,
"shape": 3
},
{
"name": "width",
"type": "INT",
"links": null,
"shape": 3
},
{
"name": "height",
"type": "INT",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "ImageResize+"
},
"widgets_values": [
1024,
1024,
"nearest",
"keep proportion",
"always",
0
]
},
{
"id": 16,
"type": "ShowText|pysssss",
"pos": {
"0": 939.3956298828125,
"1": 82.73787689208984
},
"size": {
"0": 290.13885498046875,
"1": 76
},
"flags": {},
"order": 8,
"mode": 0,
"inputs": [
{
"name": "text",
"type": "STRING",
"link": 24,
"widget": {
"name": "text"
}
}
],
"outputs": [
{
"name": "STRING",
"type": "STRING",
"links": [
27
],
"slot_index": 0,
"shape": 6
}
],
"properties": {
"Node name for S&R": "ShowText|pysssss"
},
"widgets_values": [
"",
"#fefeff, #f7ad89, #c5c1ce, #fbca57, #d1adae, #f2c2ae"
]
},
{
"id": 18,
"type": "PreviewImage",
"pos": {
"0": 1951,
"1": 78
},
"size": {
"0": 210,
"1": 246
},
"flags": {},
"order": 11,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 31
}
],
"outputs": [],
"properties": {
"Node name for S&R": "PreviewImage"
},
"widgets_values": []
},
{
"id": 1,
"type": "LoadImage",
"pos": {
"0": 1281.134765625,
"1": -221.58975219726562
},
"size": {
"0": 401.2361755371094,
"1": 540.2031860351562
},
"flags": {},
"order": 0,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
16
],
"slot_index": 0,
"shape": 3
},
{
"name": "MASK",
"type": "MASK",
"links": [
8,
17
],
"slot_index": 1,
"shape": 3
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"clipspace-mask-200317.png [temp]",
"image"
]
}
],
"links": [
[
2,
2,
0,
3,
0,
"IMAGE"
],
[
4,
4,
0,
2,
0,
"IMAGE"
],
[
8,
1,
1,
7,
0,
"MASK"
],
[
9,
7,
0,
6,
0,
"IMAGE"
],
[
16,
1,
0,
10,
0,
"IMAGE"
],
[
17,
1,
1,
10,
1,
"MASK"
],
[
18,
10,
0,
8,
0,
"IMAGE"
],
[
19,
10,
0,
4,
0,
"IMAGE"
],
[
21,
10,
0,
12,
0,
"IMAGE"
],
[
24,
12,
0,
16,
0,
"STRING"
],
[
27,
16,
0,
19,
0,
"STRING"
],
[
31,
19,
0,
18,
0,
"IMAGE"
]
],
"groups": [
{
"title": "Group",
"bounding": [
169,
-261,
1073,
570
],
"color": "#3f789e",
"font_size": 24,
"flags": {}
},
{
"title": "Group",
"bounding": [
1271,
-296,
900,
630
],
"color": "#3f789e",
"font_size": 24,
"flags": {}
}
],
"config": {},
"extra": {
"ds": {
"scale": 1.4122927695244532,
"offset": [
-1208.2183921954272,
321.92843210193087
]
}
},
"version": 0.4
}
nodes.py
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@@ -4,16 +4,29 @@ from PIL import Image, ImageDraw, ImageFont
import colorsys
import math
class AccurateColorWheelNode:
from typing import Tuple, List
import sys
import os
import torch
from sklearn.cluster import KMeans
from numpy import ndarray
import numpy as np
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
class ColorWheelNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"color_string": ("STRING", {"default": "#beb6bb,#c38064,#9a99ac,#e9b68b,#ead5c2"}),
"color_string": ("STRING", {"default": "#ead5c2,#9a99ac,#e9b68b"}),
"size": ("INT", {"default": 512, "min": 128, "max": 2048}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "create_accurate_color_wheel"
CATEGORY = "image/generation"
@@ -39,55 +52,165 @@ class AccurateColorWheelNode:
saturation = distance / radius
r, g, b = colorsys.hsv_to_rgb(angle, saturation, 1)
h, s, v = colorsys.rgb_to_hsv(r, g, b)
# Convert HSV to 2D vector
vector_x = s * math.cos(h * 2 * math.pi)
vector_y = s * math.sin(h * 2 * math.pi)
# Map to image coordinates
img_x = int((vector_x + 1) * center)
img_y = int((vector_y + 1) * center)
if 0 <= img_x < size and 0 <= img_y < size:
if v > value_map[img_y, img_x]:
output[img_y, img_x] = [r, g, b]
value_map[img_y, img_x] = v
# Create PIL Image for drawing
img = Image.fromarray((output * 255).astype(np.uint8))
draw = ImageDraw.Draw(img)
# Place color markers and labels
for color in colors:
rgb = self.hex_to_rgb(color)
h, s, v = colorsys.rgb_to_hsv(rgb[0]/255, rgb[1]/255, rgb[2]/255)
angle = h * 2 * math.pi
distance = s * radius
x = center + int(distance * math.cos(angle))
y = center + int(distance * math.sin(angle))
# Draw color circle
circle_radius = int(size * 0.03)
circle_radius = int(size * 0.02)
draw.ellipse((x - circle_radius, y - circle_radius,
x + circle_radius, y + circle_radius), fill=color, outline='white')
# Add color label
label_distance = radius + int(size * 0.08)
label_x = center + int(label_distance * math.cos(angle))
label_y = center + int(label_distance * math.sin(angle))
draw.text((label_x, label_y), color, fill='black', anchor='mm', stroke_width=2, stroke_fill='white')
# Add palette block below the wheel
palette_height = int(size * 0.2) # Reduced height
palette_width = size
palette_block = Image.new('RGB', (palette_width, palette_height), color='white')
palette_draw = ImageDraw.Draw(palette_block)
# Convert to numpy array and then to PyTorch tensor
np_image = np.array(img).astype(np.float32) / 255.0
font_size = int(size * 0.025)
try:
font = ImageFont.truetype("arial.ttf", font_size)
except IOError:
font = ImageFont.load_default()
block_size = int(palette_height * 0.5) # Smaller block size
block_spacing = int(palette_height * 0.25)
for i, color in enumerate(colors):
block_x = i * (block_size + block_spacing) + block_spacing
block_y = int(palette_height * 0.25)
palette_draw.rectangle((block_x, block_y, block_x + block_size, block_y + block_size), fill=color)
label_x = block_x
label_y = block_y + block_size + int(palette_height * 0.05)
palette_draw.text((label_x, label_y), color, fill='black', font=font)
combined_img = Image.new('RGB', (size, size + palette_height), color='white')
combined_img.paste(img, (0, 0))
combined_img.paste(palette_block, (0, size))
np_image = np.array(combined_img).astype(np.float32) / 255.0
torch_image = torch.from_numpy(np_image)[None,]
return (torch_image,)
class BK_Img2Color:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input_image": ("IMAGE",),
},
"optional": {
"num_colors": ("INT", {"default": 1, "min": 1, }),
"get_complementary_color": (
"BOOLEAN",
{
"default": False,
"label_off": "false",
"label_on": "true",
},
),
"accuracy": (
"INT",
{
"default": 80,
"display": "slider",
"min": 1,
"max": 100,
},
),
"exclude_colors": (
"STRING",
{
"default": "",
},
),
"select_color": ("INT", {
"default": 1,
"min": 1,
}),
}
}
RETURN_TYPES = ("STRING", "STRING",)
RETURN_NAMES = ("COLORS", "SELECT_COLOR",)
CATEGORY = "⭐️ Baikong/Color"
FUNCTION = "main"
# OUTPUT_NODE = True
DESCRIPTION = "从输入图像中提取主要颜色，可指定颜色数量，支持排除特定颜色，并可选择生成互补色"
def __init__(self):
pass
def main(self, input_image: torch.Tensor, num_colors: int = 5, accuracy: int = 80,
get_complementary_color: bool = False, exclude_colors: str = "", select_color: int = 1) -> Tuple[str, str]:
self.exclude = [color.strip().lower() for color in exclude_colors.strip().split(
",")] if exclude_colors.strip() else []
self.num_iterations = int(512 * (accuracy / 100))
original_colors = self.interrogate_colors(input_image, num_colors)
rgb = self.ndarrays_to_rgb(original_colors)
if get_complementary_color:
rgb = self.rgb_to_complementary(rgb)
hex_colors = [
f"#{color[0]:02x}{color[1]:02x}{color[2]:02x}" for color in rgb]
out = self.join_and_exclude(hex_colors)
# 处理 select_color
color_list = out.split(", ")
selected_color = color_list[-1] if select_color > len(
color_list) else color_list[select_color - 1]
# 指定的输出格式 {"ui": {"text": (value1, value2)}, "result": (value1, value2)}
return {"ui": {"text": (out, selected_color)}, "result": (out, selected_color)}
def join_and_exclude(self, colors: List[str]) -> str:
return ", ".join(
[str(color)
for color in colors if color.lower() not in self.exclude]
)
def rgb_to_complementary(
self, colors: List[Tuple[int, int, int]]
) -> List[Tuple[int, int, int]]:
return [(255 - color[0], 255 - color[1], 255 - color[2]) for color in colors]
def ndarrays_to_rgb(self, colors: List[ndarray]) -> List[Tuple[int, int, int]]:
return [(int(color[0]), int(color[1]), int(color[2])) for color in colors]
def interrogate_colors(self, image: torch.Tensor, num_colors: int) -> List[ndarray]:
pixels = image.view(-1, image.shape[-1]).numpy()
kmeans = KMeans(n_clusters=num_colors, algorithm="lloyd",
max_iter=self.num_iterations, n_init=10)
colors = kmeans.fit(pixels).cluster_centers_ * 255
return colors
NODE_CLASS_MAPPINGS = {
"AccurateColorWheelNode": AccurateColorWheelNode
"AccurateColorWheelNode": ColorWheelNode
}
NODE_DISPLAY_NAME_MAPPINGS = {
"AccurateColorWheelNode": "Accurate Color Wheel Generator"
"AccurateColorWheelNode": "Color Wheel Generator"
}
requirements.txt
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torch==2.4.1
numpy==1.26.4
pillow==10.4.0
scikit-learn==1.5.2