825 lines
27 KiB
Python
825 lines
27 KiB
Python
import base64
|
|
import json
|
|
import numpy as np
|
|
import torch
|
|
try:
|
|
import piexif.helper
|
|
import piexif
|
|
from .exif.exif import read_info_from_image_stealth
|
|
piexif_loaded = True
|
|
except ImportError:
|
|
piexif_loaded = False
|
|
|
|
from .imgio.converter import PILHandlingHodes
|
|
from .autonode import node_wrapper, get_node_names_mappings, validate, anytype, PILImage
|
|
import time
|
|
import os
|
|
from PIL import Image
|
|
from PIL import ImageOps
|
|
from PIL import ImageEnhance
|
|
from PIL.PngImagePlugin import PngInfo
|
|
import folder_paths
|
|
from comfy.cli_args import args
|
|
|
|
fundamental_classes = []
|
|
fundamental_node = node_wrapper(fundamental_classes)
|
|
|
|
@fundamental_node
|
|
class SleepNodeAny:
|
|
FUNCTION = "sleep"
|
|
RETURN_TYPES = (anytype,)
|
|
CATEGORY = "Misc"
|
|
custom_name = "SleepNode"
|
|
@staticmethod
|
|
def sleep(interval, inputs):
|
|
time.sleep(interval)
|
|
return (inputs,)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"interval": ("FLOAT", {"default": 0.0}),
|
|
},
|
|
"optional": {
|
|
"inputs": (anytype, {"default": 0.0}),
|
|
}
|
|
}
|
|
@fundamental_node
|
|
class SleepNodeImage:
|
|
FUNCTION = "sleep"
|
|
RETURN_TYPES = (anytype,)
|
|
CATEGORY = "Misc"
|
|
custom_name = "Sleep (Image tunnel)"
|
|
@staticmethod
|
|
def sleep(interval, image):
|
|
time.sleep(interval)
|
|
return (image,)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"interval": ("FLOAT", {"default": 0.0}),
|
|
"image": (anytype,),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class ErrorNode:
|
|
FUNCTION = "raise_error"
|
|
RETURN_TYPES = ("STRING",)
|
|
CATEGORY = "Misc"
|
|
custom_name = "ErrorNode"
|
|
@staticmethod
|
|
def raise_error(error_msg = "Error"):
|
|
raise Exception("Error: {}".format(error_msg))
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"error_msg": ("STRING", {"default": "Error"}),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class DebugComboInputNode:
|
|
FUNCTION = "debug_combo_input"
|
|
RETURN_TYPES = ("STRING",)
|
|
CATEGORY = "Misc"
|
|
custom_name = "Debug Combo Input"
|
|
@staticmethod
|
|
def debug_combo_input(input1):
|
|
print(input1)
|
|
return (input1,)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"input1": (["0", "1", "2"], { "default": "0" }),
|
|
}
|
|
}
|
|
|
|
# https://github.com/comfyanonymous/ComfyUI/blob/340177e6e85d076ab9e222e4f3c6a22f1fb4031f/custom_nodes/example_node.py.example#L18
|
|
@fundamental_node
|
|
class TextPreviewNode:
|
|
"""
|
|
Displays text in the UI
|
|
"""
|
|
FUNCTION = "text_preview"
|
|
RETURN_TYPES = ()
|
|
CATEGORY = "Misc"
|
|
custom_name = "Text Preview"
|
|
RESULT_NODE = True
|
|
OUTPUT_NODE = True
|
|
|
|
def text_preview(self, text):
|
|
print(text)
|
|
# below does not work, why?
|
|
return {"ui": {"text": str(text)}}
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"text": (anytype,{"default": "text", "type" : "output"}),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class ParseExifNode:
|
|
"""
|
|
Parses exif data from image
|
|
"""
|
|
FUNCTION = "parse_exif"
|
|
RETURN_TYPES = ("STRING",)
|
|
CATEGORY = "Misc"
|
|
custom_name = "Parse Exif"
|
|
@staticmethod
|
|
def parse_exif(image):
|
|
return (read_info_from_image_stealth(image),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
}
|
|
}
|
|
|
|
def throw_if_parent_or_root_access(path):
|
|
if ".." in path or path.startswith("/") or path.startswith("\\"):
|
|
raise RuntimeError("Invalid path")
|
|
if path.startswith("~"):
|
|
raise RuntimeError("Invalid path")
|
|
if os.path.isabs(path):
|
|
raise RuntimeError("Path cannot be absolute")
|
|
|
|
@fundamental_node
|
|
class SaveImageCustomNode:
|
|
def __init__(self):
|
|
self.output_dir = folder_paths.get_output_directory()
|
|
self.type = "output"
|
|
self.prefix_append = ""
|
|
self.compress_level = 4
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required":
|
|
{"images": ("IMAGE", ),
|
|
"filename_prefix": ("STRING", {"default": "ComfyUI"}),
|
|
"subfolder_dir": ("STRING", {"default": ""}),
|
|
},
|
|
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING",) #Filename
|
|
FUNCTION = "save_images"
|
|
|
|
OUTPUT_NODE = True
|
|
RESULT_NODE = True
|
|
CATEGORY = "image"
|
|
custom_name = "Save Image Custom Node"
|
|
|
|
def save_images(self, images, filename_prefix="ComfyUI",subfolder_dir="", prompt=None, extra_pnginfo=None):
|
|
if images is None: # sometimes images is empty
|
|
images = []
|
|
filename_prefix += self.prefix_append
|
|
throw_if_parent_or_root_access(filename_prefix)
|
|
throw_if_parent_or_root_access(subfolder_dir)
|
|
output_dir = os.path.join(self.output_dir, subfolder_dir)
|
|
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_dir, images[0].shape[1], images[0].shape[0])
|
|
results = list()
|
|
for image in images:
|
|
i = 255. * image.cpu().numpy()
|
|
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
|
|
metadata = None
|
|
if not args.disable_metadata:
|
|
metadata = PngInfo()
|
|
if prompt is not None:
|
|
metadata.add_text("prompt", json.dumps(prompt))
|
|
if extra_pnginfo is not None:
|
|
for x in extra_pnginfo:
|
|
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
|
|
|
|
file = f"{filename}_{counter:05}_.png"
|
|
img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
|
|
results.append({
|
|
"filename": file,
|
|
"subfolder": subfolder,
|
|
"type": self.type
|
|
})
|
|
counter += 1
|
|
|
|
return { "ui": { "images": results }, "outputs": { "images": file.rstrip('.png') } }
|
|
|
|
@fundamental_node
|
|
class SaveTextCustomNode:
|
|
def __init__(self):
|
|
self.output_dir = folder_paths.get_output_directory()
|
|
self.type = "output"
|
|
self.prefix_append = ""
|
|
self.compress_level = 4
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required":
|
|
{"text": (anytype, ),
|
|
"filename_prefix": ("STRING", {"default": "ComfyUI"}),
|
|
"subfolder_dir": ("STRING", {"default": ""}),
|
|
"filename": ("STRING", {"default": ""}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING",) #Filename
|
|
FUNCTION = "save_text"
|
|
custom_name = "Save Text Custom Node"
|
|
CATEGORY = "text"
|
|
RESULT_NODE = True
|
|
OUTPUT_NODE = True
|
|
|
|
def save_text(self, text, filename_prefix="ComfyUI",subfolder_dir="",filename=""):
|
|
text = str(text)
|
|
throw_if_parent_or_root_access(filename_prefix)
|
|
throw_if_parent_or_root_access(subfolder_dir)
|
|
assert len(text) > 0 and len(filename) > 0, "Text and filename must be non-empty"
|
|
filename_prefix += self.prefix_append
|
|
output_dir = os.path.join(self.output_dir, subfolder_dir)
|
|
filename_merged = filename_prefix + filename + ".txt"
|
|
full_output_folder, subfolder, actual_filename = output_dir, "", filename_merged
|
|
results = list()
|
|
file = actual_filename
|
|
with open(os.path.join(full_output_folder, file), "w") as f:
|
|
f.write(text)
|
|
results.append({
|
|
"filename": file,
|
|
"subfolder": subfolder,
|
|
"type": self.type
|
|
})
|
|
counter += 1
|
|
|
|
return { "ui": { "texts": results }, "outputs": { "images": file.rstrip('.txt') } }
|
|
|
|
@fundamental_node
|
|
class SaveImageWebpCustomNode:
|
|
def __init__(self):
|
|
self.output_dir = folder_paths.get_output_directory()
|
|
self.type = "output"
|
|
self.prefix_append = ""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required":
|
|
{"images": ("IMAGE", ),
|
|
"filename_prefix": ("STRING", {"default": "ComfyUI"}),
|
|
"subfolder_dir": ("STRING", {"default": ""}),
|
|
},
|
|
"optional": {"quality": ("INT", {"default": 100}), "lossless": ("BOOLEAN", {"default": False}), "compression": ("INT", {"default": 4}), "optimize": ("BOOLEAN", {"default": False}), "metadata_string": ("STRING", {"default": ""})},
|
|
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING",) #Filename
|
|
FUNCTION = "save_images"
|
|
|
|
OUTPUT_NODE = True
|
|
RESULT_NODE = True
|
|
|
|
CATEGORY = "image"
|
|
custom_name = "Save Image Webp Node"
|
|
|
|
def save_images(self, images, filename_prefix="ComfyUI",subfolder_dir="", prompt=None, extra_pnginfo=None, quality=100, lossless=False, compression=4, optimize=False, metadata_string=""):
|
|
if images is None: # sometimes images is empty
|
|
images = []
|
|
throw_if_parent_or_root_access(filename_prefix)
|
|
throw_if_parent_or_root_access(subfolder_dir)
|
|
filename_prefix += self.prefix_append
|
|
output_dir = os.path.join(self.output_dir, subfolder_dir)
|
|
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_dir, images[0].shape[1], images[0].shape[0])
|
|
results = list()
|
|
for image in images:
|
|
i = 255. * image.cpu().numpy()
|
|
clipped = np.clip(i, 0, 255).astype(np.uint8)
|
|
if clipped.shape[0] <= 3:
|
|
clipped = np.transpose(clipped, (1, 2, 0)) #[1216, 832, 3]
|
|
img = Image.fromarray(clipped)
|
|
metadata = None
|
|
if not args.disable_metadata:
|
|
metadata = {}
|
|
if prompt is not None:
|
|
metadata["prompt"] = json.dumps(prompt)
|
|
if extra_pnginfo is not None:
|
|
for x in extra_pnginfo:
|
|
metadata[x] = json.dumps(extra_pnginfo[x])
|
|
if metadata_string:# override metadata
|
|
metadata = {}
|
|
metadata["metadata"]= metadata_string
|
|
if piexif_loaded:
|
|
exif_bytes = piexif.dump({
|
|
"Exif": {
|
|
piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(json.dumps(metadata) or "", encoding="unicode")
|
|
},
|
|
})
|
|
file = f"{filename}_{counter:05}_.webp"
|
|
img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compression, quality=quality, lossless=lossless, optimize=optimize)
|
|
if piexif_loaded:
|
|
piexif.insert(exif_bytes, os.path.join(full_output_folder, file))
|
|
|
|
results.append({
|
|
"filename": os.path.join(full_output_folder, file),
|
|
"subfolder": subfolder_dir,
|
|
"type": self.type
|
|
})
|
|
counter += 1
|
|
|
|
return { "ui": { "images": results }, "outputs": { "images": os.path.join(full_output_folder, file).rstrip('.webp') } }
|
|
|
|
@fundamental_node
|
|
class ComposeRGBAImageFromMask:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"mask": ("MASK",),
|
|
"invert": ("BOOLEAN", {"default": False}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "compose"
|
|
CATEGORY = "image"
|
|
custom_name = "Compose RGBA Image From Mask"
|
|
@staticmethod
|
|
def compose(image, mask, invert):
|
|
if invert:
|
|
mask = 1.0 - mask
|
|
|
|
# Ensure mask has shape (batch_size, height, width, 1)
|
|
mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1], 1))
|
|
# check devices, move to cpu
|
|
if hasattr(image, "device"):
|
|
image = image.cpu()
|
|
if hasattr(mask, "device"):
|
|
mask = mask.cpu()
|
|
# Resize mask to match image dimensions if necessary
|
|
if image.shape[0] != mask.shape[0] or image.shape[1] != mask.shape[1] or image.shape[2] != mask.shape[2]:
|
|
# Resize mask to match image dimensions
|
|
mask = torch.nn.functional.interpolate(
|
|
mask.permute(0, 3, 1, 2),
|
|
size=(image.shape[1], image.shape[2]),
|
|
mode="bilinear",
|
|
align_corners=False
|
|
).permute(0, 2, 3, 1)
|
|
|
|
num_channels = image.shape[-1]
|
|
if num_channels == 3:
|
|
rgba_image = torch.cat((image, mask), dim=-1)
|
|
elif num_channels == 4:
|
|
rgba_image = image.clone()
|
|
rgba_image[:, :, :, 3:] = mask
|
|
else:
|
|
raise ValueError("Image must have 3 (RGB) or 4 (RGBA) channels")
|
|
|
|
return (rgba_image,)
|
|
|
|
@fundamental_node
|
|
class ResizeImageNode:
|
|
FUNCTION = "resize_image"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Resize Image"
|
|
|
|
constants = {
|
|
"NEAREST": Image.Resampling.NEAREST,
|
|
"LANCZOS": Image.Resampling.LANCZOS,
|
|
"BICUBIC": Image.Resampling.BICUBIC,
|
|
}
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def resize_image(image, width, height, method):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
return (image.resize((width, height), ResizeImageNode.constants[method]),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"size": ("INT", {"default": 512}),
|
|
"method": (["NEAREST", "LANCZOS", "BICUBIC"],),
|
|
},
|
|
}
|
|
|
|
@fundamental_node
|
|
class ResizeImageResolution:
|
|
FUNCTION = "resize_image_resolution"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Resize Image With Resolution"
|
|
|
|
constants = {
|
|
"NEAREST": Image.Resampling.NEAREST,
|
|
"LANCZOS": Image.Resampling.LANCZOS,
|
|
"BICUBIC": Image.Resampling.BICUBIC,
|
|
}
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def resize_image_resolution(image, resolution, method):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
image_width, image_height = image.size
|
|
total_pixels = image_width * image_height
|
|
if total_pixels == 0:
|
|
raise RuntimeError("Image has no pixels")
|
|
if resolution < 256:
|
|
raise RuntimeError("Resolution must be positive and at least 256")
|
|
# get ratio
|
|
target_pixels = resolution ** 2
|
|
ratio = target_pixels / total_pixels
|
|
target_width = int(image_width * ratio)
|
|
target_height = int(image_height * ratio)
|
|
return (image.resize((target_width, target_height), ResizeImageResolution.constants[method]),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"resolution": ("INT", {"default": 512}),
|
|
"method": (["NEAREST", "LANCZOS", "BICUBIC"],),
|
|
},
|
|
}
|
|
|
|
@fundamental_node
|
|
class Base64DecodeNode:
|
|
FUNCTION = "base64_decode"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Base64 Decode to Image"
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def base64_decode(base64_string):
|
|
image = PILHandlingHodes.handle_input(base64_string) # automatically converts to PIL image
|
|
return (image,)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"base64_string": ("STRING",),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class Base64EncodeNode:
|
|
FUNCTION = "base64_encode"
|
|
RETURN_TYPES = ("STRING",)
|
|
CATEGORY = "image"
|
|
custom_name = "Image to Base64 Encode"
|
|
@staticmethod
|
|
def base64_encode(image, quality, format, gzip_compress):
|
|
image = PILHandlingHodes.to_base64(image, quality, format, gzip_compress)
|
|
return (image,)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
},
|
|
"optional": {
|
|
"quality": ("INT", {"default": 100}),
|
|
"format": (["PNG", "WEBP", "JPG"], {"default": "PNG"}),
|
|
"gzip_compress": ("BOOLEAN", {"default": False}),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class StringToBase64Node:
|
|
FUNCTION = "string_to_base64"
|
|
RETURN_TYPES = ("STRING",)
|
|
CATEGORY = "image"
|
|
custom_name = "String to Base64 Encode"
|
|
@staticmethod
|
|
def string_to_base64(string, gzip_compress):
|
|
return (PILHandlingHodes.string_to_base64(string, gzip_compress),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"string": ("STRING",),
|
|
},
|
|
"optional": {
|
|
"gzip_compress": ("BOOLEAN", {"default": False}),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class Base64ToStringNode:
|
|
FUNCTION = "base64_to_string"
|
|
RETURN_TYPES = ("STRING",)
|
|
CATEGORY = "image"
|
|
custom_name = "Base64 to String Decode"
|
|
@staticmethod
|
|
def base64_to_string(base64_string):
|
|
return (PILHandlingHodes.maybe_gzip_base64_to_string(base64_string),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"base64_string": ("STRING",),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class InvertImageNode:
|
|
FUNCTION = "invert_image"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Invert Image"
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def invert_image(image):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
return (ImageOps.invert(image),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class ResizeScaleImageNode:
|
|
FUNCTION = "resize_scale_image"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Resize Scale Image"
|
|
|
|
constants = {
|
|
"NEAREST": Image.Resampling.NEAREST,
|
|
"LANCZOS": Image.Resampling.LANCZOS,
|
|
"BICUBIC": Image.Resampling.BICUBIC,
|
|
}
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def resize_scale_image(image, scale, method):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
if scale < 0:
|
|
raise RuntimeError("Scale must be positive")
|
|
return (image.resize((int(image.width*scale), int(image.height*scale)), ResizeScaleImageNode.constants[method]),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"size": ("INT", {"default": 2}),
|
|
"method": (["NEAREST", "LANCZOS", "BICUBIC"],),
|
|
},
|
|
}
|
|
|
|
@fundamental_node
|
|
class ResizeShortestToNode:
|
|
FUNCTION = "resize_shortest_to"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Resize Shortest To"
|
|
|
|
constants = {
|
|
"NEAREST": Image.Resampling.NEAREST,
|
|
"LANCZOS": Image.Resampling.LANCZOS,
|
|
"BICUBIC": Image.Resampling.BICUBIC,
|
|
}
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def resize_shortest_to(image, size, method):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
if size < 0:
|
|
raise RuntimeError("Size must be positive")
|
|
if image.width < image.height:
|
|
return (image.resize((size, int(image.height* size/image.width)), ResizeShortestToNode.constants[method]),)
|
|
else:
|
|
return (image.resize((int(image.width* size/image.height), size), ResizeShortestToNode.constants[method]),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"size": ("INT", {"default": 512}),
|
|
"method": (["NEAREST", "LANCZOS", "BICUBIC"],),
|
|
},
|
|
}
|
|
|
|
@fundamental_node
|
|
class ResizeLongestToNode:
|
|
FUNCTION = "resize_longest_to"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Resize Longest To"
|
|
|
|
constants = {
|
|
"NEAREST": Image.Resampling.NEAREST,
|
|
"LANCZOS": Image.Resampling.LANCZOS,
|
|
"BICUBIC": Image.Resampling.BICUBIC,
|
|
}
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def resize_longest_to(image, size, method):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
if size < 0:
|
|
raise RuntimeError("Size must be positive")
|
|
if image.width > image.height:
|
|
return (image.resize((size, int(image.height* size/image.width)), ResizeLongestToNode.constants[method]),)
|
|
else:
|
|
return (image.resize((int(image.width* size/image.height), size), ResizeLongestToNode.constants[method]),)
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"size": ("INT", {"default": 512}),
|
|
"method": (["NEAREST", "LANCZOS", "BICUBIC"],),
|
|
},
|
|
}
|
|
|
|
|
|
@fundamental_node
|
|
class ConvertGreyscaleNode:
|
|
FUNCTION = "convert_greyscale"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Convert Greyscale"
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def convert_greyscale(image):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
greyscale_image = image.convert("L")
|
|
# 3 channel greyscale image
|
|
return (greyscale_image.convert("RGB"),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class RotateImageNode:
|
|
FUNCTION = "rotate_image"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Rotate Image"
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def rotate_image(image, angle):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
return (image.rotate(angle),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"angle": ("INT", {"default": 0}),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class BrightnessNode:
|
|
FUNCTION = "brightness"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Brightness"
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def brightness(image, factor):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
enhancer = ImageEnhance.Brightness(image)
|
|
return (enhancer.enhance(factor),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"factor": ("FLOAT", {"default": 1.0}),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class ContrastNode:
|
|
FUNCTION = "contrast"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Contrast"
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def contrast(image, factor):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
enhancer = ImageEnhance.Contrast(image)
|
|
return (enhancer.enhance(factor),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"factor": ("FLOAT", {"default": 1.0}),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class SharpnessNode:
|
|
FUNCTION = "sharpness"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Sharpness"
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def sharpness(image, factor):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
enhancer = ImageEnhance.Sharpness(image)
|
|
return (enhancer.enhance(factor),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"factor": ("FLOAT", {"default": 1.0}),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class ColorNode:
|
|
FUNCTION = "color"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Color"
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def color(image, factor):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
enhancer = ImageEnhance.Color(image)
|
|
return (enhancer.enhance(factor),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"factor": ("FLOAT", {"default": 1.0}),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class ConvertRGBNode:
|
|
FUNCTION = "convert_rgb"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Convert RGB"
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def convert_rgb(image):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
return (image.convert("RGB"),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
}
|
|
}
|
|
|
|
@fundamental_node
|
|
class GetImageInfoNode:
|
|
FUNCTION="get_image_info"
|
|
RETURN_TYPES=("WIDTH", "HEIGHT", "TOTAL_PIXELS")
|
|
CATEGORY="image"
|
|
custom_name="Get Image Info"
|
|
@staticmethod
|
|
def get_image_info(image):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
width, height = image.size
|
|
return (width, height, width * height)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
}
|
|
}
|
|
|
|
|
|
@fundamental_node
|
|
class ThresholdNode:
|
|
FUNCTION = "threshold"
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "image"
|
|
custom_name = "Threshold image with value"
|
|
@staticmethod
|
|
@PILHandlingHodes.output_wrapper
|
|
def threshold(image, threshold):
|
|
image = PILHandlingHodes.handle_input(image)
|
|
return (image.point(lambda p: p > threshold and 255),)
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"threshold": ("INT", {"default": 128}),
|
|
}
|
|
}
|
|
|
|
CLASS_MAPPINGS, CLASS_NAMES = get_node_names_mappings(fundamental_classes)
|
|
validate(fundamental_classes)
|