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nodes.py
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import cv2
import numpy as np
import torch
import torchvision
from .PixelOE.src.pixeloe.legacy.pixelize import pixelize
from .PixelOE.src.pixeloe.torch import env as pixeloe_env
from .PixelOE.src.pixeloe.torch.pixelize import pixelize as pixelize_torch
from .PixelOE.src.pixeloe.torch.utils import pre_resize
class PixelOE:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"img": ("IMAGE",),
"mode": (["contrast", "center", "k-centroid", "bicubic", "nearest"],),
"target_size": ("INT", {
"default": 256,
"min": 0,
"max": 4096,
"step": 1,
"display": "number",
}),
"patch_size": ("INT", {
"default": 6,
"min": 0,
"max": 4096,
"step": 1,
"display": "number",
}),
"pixel_size": ("INT", {
"default": 0,
"min": 0,
"max": 4096,
"step": 1,
"display": "number",
}),
"thickness": ("INT", {
"default": 1,
"min": 0,
"max": 4096,
"step": 1,
"display": "number",
}),
"color_matching": ("BOOLEAN", {
"default": False,
}),
"contrast": ("FLOAT", {
"default": 1.0,
"min": 0.0,
"max": 10.0,
"step": 0.01,
"round": 0.001,
"display": "number",
}),
"saturation": ("FLOAT", {
"default": 1.0,
"min": 0.0,
"max": 10.0,
"step": 0.01,
"round": 0.001,
"display": "number",
}),
"colors": ("INT", {
"default": 256,
"min": 1,
"max": 256,
"step": 1,
"display": "number",
}),
"color_quant_method": (["kmeans", "maxcover"],),
"colors_with_weight": ("BOOLEAN", {
"default": False,
}),
"no_upscale": ("BOOLEAN", {
"default": False,
}),
"no_downscale": ("BOOLEAN", {
"default": False,
}),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "process"
CATEGORY = "image/pixelize"
@staticmethod
def process(img, mode, target_size, patch_size, pixel_size, thickness,
color_matching, contrast, saturation, colors, color_quant_method,
colors_with_weight, no_upscale, no_downscale):
if pixel_size == 0:
pixel_size = None
img = img.squeeze().numpy()
img = (img * 255).astype(np.uint8)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
img_pix = pixelize(
img=img, mode=mode, target_size=target_size, patch_size=patch_size, pixel_size=pixel_size, thickness=thickness,
color_matching=color_matching, contrast=contrast, saturation=saturation, colors=colors, color_quant_method=color_quant_method,
colors_with_weight=colors_with_weight, no_upscale=no_upscale, no_downscale=no_downscale,
)
img_pix = cv2.cvtColor(img_pix, cv2.COLOR_BGR2RGB)
img_pix_t = np.array(img_pix).astype(np.float32) / 255.0
img_pix_t = torch.from_numpy(img_pix_t)[None,]
return (img_pix_t,)
class PixelOETorch:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"img": ("IMAGE",),
"mode": (["contrast", "center", "k-centroid", "bicubic", "nearest"],),
"target_size": ("INT", {
"default": 256,
"min": 0,
"max": 4096,
"step": 1,
"display": "number",
}),
"patch_size": ("INT", {
"default": 6,
"min": 0,
"max": 4096,
"step": 1,
"display": "number",
}),
"pixel_size": ("INT", {
"default": 6,
"min": 0,
"max": 4096,
"step": 1,
"display": "number",
}),
"thickness": ("INT", {
"default": 3,
"min": 0,
"max": 4096,
"step": 1,
"display": "number",
}),
"do_color_match": ("BOOLEAN", {
"default": True,
}),
"do_quant": ("BOOLEAN", {
"default": False,
}),
"num_colors": ("INT", {
"default": 32,
"min": 1,
"max": 256,
"step": 1,
"display": "number",
}),
"quant_mode": (["kmeans", "weighted-kmeans", "repeat-kmeans"],),
"dither_mode": (["ordered", "error_diffusion", "no"],),
"torch_compile": ("BOOLEAN", {
"default": True,
}),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "process_torch"
CATEGORY = "image/pixelize"
@staticmethod
def process_torch(img, target_size, patch_size, pixel_size, thickness, mode, do_color_match,
do_quant, num_colors, quant_mode, dither_mode, torch_compile):
if pixel_size == 0:
pixel_size = None
pixeloe_env.TORCH_COMPILE = torch_compile
img_t = img.squeeze().permute(2, 0, 1)
img_pil = torchvision.transforms.functional.to_pil_image(img_t)
img_t = pre_resize(
img_pil=img_pil,
target_size=target_size,
patch_size=patch_size,
)
img_pix_t = pixelize_torch(
img_t=img_t, pixel_size=pixel_size, thickness=thickness, mode=mode,
do_color_match=do_color_match, do_quant=do_quant, num_colors=num_colors,
quant_mode=quant_mode, dither_mode=dither_mode,
)
img_pix_t = img_pix_t.permute(0, 2, 3, 1)
return (img_pix_t,)
NODE_CLASS_MAPPINGS = {
"PixelOE": PixelOE,
"PixelOETorch": PixelOETorch,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"PixelOE": "PixelOE",
"PixelOETorch": "PixelOETorch",
}