nodes/TashaSkyUp-EternalKernelPytorchNodes
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added the ability to render text in any found font to an image

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Tasha Upchurch
2023-06-01 23:59:26 -04:00
parent f56354cf9b
commit f4d51c07a8
1 changed files with 194 additions and 26 deletions
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image.py
+194 -26
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@@ -1,10 +1,13 @@
import doctest
try:
import comfy.samplers
# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
from nodes import CLIPTextEncode, VAEEncode, VAEDecode, KSampler, CheckpointLoaderSimple, EmptyLatentImage, \
SaveImage
from custom_nodes.ComfyUI_ADV_CLIP_emb.nodes import AdvancedCLIPTextEncode as CLIPTextEncodeAdvanced
from nodes import common_ksampler
if __name__ != "__main__":
import comfy.samplers
# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
from nodes import CLIPTextEncode, VAEEncode, VAEDecode, KSampler, CheckpointLoaderSimple, EmptyLatentImage, \
SaveImage
from custom_nodes.ComfyUI_ADV_CLIP_emb.nodes import AdvancedCLIPTextEncode as CLIPTextEncodeAdvanced
from nodes import common_ksampler
except ImportError as e:
print("ETK> comfy.samplers not found, skipping comfyui")
SaveImage = None
@@ -32,6 +35,42 @@ avoid numpy and PIL as much as possible
"""
def get_fonts():
# Directory paths for fonts in Windows and Linux
windows_fonts_dir = 'C:\\Windows\\Fonts\\'
linux_fonts_dir = '/usr/share/fonts/'
# Use the appropriate directory based on the current platform
if os.name == 'nt': # Windows
fonts_dir = windows_fonts_dir
else: # Linux and others
fonts_dir = linux_fonts_dir
# Initialize the list of fonts
fonts = []
# Check if the directory exists
if os.path.exists(fonts_dir):
try:
# Traverse the directory recursively and add all .ttf and .otf files to the list
for dirpath, dirnames, filenames in os.walk(fonts_dir):
for filename in filenames:
if filename.endswith('.ttf') or filename.endswith('.otf'):
fonts.append(os.path.join(dirpath, filename))
except PermissionError:
print(f"Permission denied to access the directory: {fonts_dir}")
except Exception as e:
print(f"An error occurred while accessing the directory: {fonts_dir}")
print(e)
else:
print(f"The directory does not exist: {fonts_dir}")
return fonts
fonts = get_fonts()
def rgba_per_channel_norm(image):
""" normalize per channel """
print("normalizing per channel")
@@ -57,8 +96,12 @@ def rgba_per_channel_norm(image):
def torch_image_show(image):
"""Show a torch image"""
from PIL import Image
image = torch.mul(image[0], 255, )
image = image.to(torch.uint8) # Convert tensor to uint8 data type
if image.size().__len__() == 4:
image = image[0]
if image.dtype != torch.uint8:
image = torch.mul(image, 255, )
image = image.to(torch.uint8) # Convert tensor to uint8 data type
# must have at least 3 channels
if image.shape[-1] == 1:
@@ -115,7 +158,7 @@ class TinyTxtToImg:
CATEGORY = "ETK"
RETURN_TYPES = ("IMAGE","FUNC",)
RETURN_TYPES = ("IMAGE", "FUNC",)
FUNCTION = "tinytxt2img"
def tinytxt2img(self, prompt, neg_prompt, name="tinytxt2img", overrides: str = "",
@@ -201,7 +244,7 @@ class TinyTxtToImg:
image = VAEDecode.decode(None, self.vae, samples)[0]
image = image.detach().cpu()
return (image,lambda : self.tinytxt2img(prompt,
return (image, lambda: self.tinytxt2img(prompt,
neg_prompt,
name,
overrides,
@@ -213,7 +256,7 @@ class TinyTxtToImg:
,)
if SaveImage != None:
if "SaveImage" in globals():
class PreviewImageTest(SaveImage):
def __init__(self):
self.output_dir = folder_paths.get_temp_directory()
@@ -255,10 +298,10 @@ class ExecWidget:
{
"text_to_eval": ("STRING",
{"multiline": True,
"default":"int_out=int_out\n"
"float_out=float_out\n"
"string_out=string_out\n"
"image_out=image_out\n"
"default": "int_out=int_out\n"
"float_out=float_out\n"
"string_out=string_out\n"
"image_out=image_out\n"
}),
"image1_in": ("IMAGE", {"multiline": False}),
"float1_in": ("FLOAT", {"multiline": False}),
@@ -278,7 +321,7 @@ class ExecWidget:
FUNCTION = "exec_handler"
def exec_handler(self, text_to_eval, image1_in: torch.Tensor = None, float1_in: float = 0.0, string1_in: str = "",
int1_in: int = 0,name: str = "exec_func"):
int1_in: int = 0, name: str = "exec_func"):
"""
>>> ExecWidget().exec_handler("2 + 3")
'5'
@@ -1215,13 +1258,6 @@ class rgba_merge:
return (torch.cat((r, g, b, a), dim=3),)
class rgba_split:
"""splits the channels of an RGBA image"""
@@ -1301,7 +1337,139 @@ class KSampler:
denoise=denoise, one_seed_per_batch=one_seed_per_batch)
if __name__ == "__main__":
import doctest
from PIL import Image, ImageDraw, ImageFont
import torch
import numpy as np
doctest.testmod()
class TextRender:
"""
renders text to an image
"""
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"text": ("STRING", {"default": "Hello World!"}),
"x": ("INT", {
"default": 128,
"min": 0,
"max": 1024,
"step": 1
}),
"y": ("INT", {
"default": 128,
"min": 0,
"max": 1024,
"step": 1
}),
"width": ("INT", {
"default": 100,
"min": 1,
"max": 1024,
"step": 32
}),
"height": ("INT", {
"default": 100,
"min": 1,
"max": 1024,
"step": 32
}),
"font": (fonts,),
"size": ("INT", {
"default": 16,
"min": 1,
"max": 200,
"step": 1
}),
"color": ("STRING", {
"default": "#000000"
})
}
}
CATEGORY = "ETK/text"
RETURN_TYPES = ("IMAGE", "IMAGE",)
FUNCTION = "render_text"
def render_text(self, text, x, y, width, height, font='Arial', size=16, color='#000000'):
"""
This function renders the provided text at specified location, with the given width and height.
The text is rendered in the provided font, size, and color.
>>> tr = TextRender()
>>> result = tr.render_text('Hello, world!', 128, 128, 512, 512,"Arial",16,"#FF11000")
>>> torch_image_show(result[0][0])
"""
# Create an empty image with RGBA channels
image = Image.new('RGBA', (width, height))
# Get a drawing context
d = ImageDraw.Draw(image)
# Define font - this assumes Arial is available.
# For different font, a full path to .ttf or .otf file may be required.
fallback_list = ['Arial', 'FreeSans', 'DejaVu Sans', 'Liberation Sans', 'Bitstream Vera Sans', 'sans-serif']
try:
font = ImageFont.truetype(font, size)
except IOError:
# Fallback
try:
font = ImageFont.truetype(fallback_list, size)
except IOError:
# Fallback
font = ImageFont.load_default()
# the text may be multi line draw each line
lines = text.split('\n')
for i,line in enumerate(lines):
# Get the width and height of the text
w, h = d.textsize(line, font=font)
# Calculate the position of the text as an offset from the given x,y and the current line we are rendering i
y_offset = y + (i * h)
x_offset = x + (width - w) / 2
# Draw the text
d.text((x_offset, y_offset),
line, font=font, fill="#FFFFFF")
# Convert the image to numpy array
image_array = np.array(image)
# Convert the numpy array to PyTorch tensor and add batch dimension
image_tensor = torch.from_numpy(image_array).unsqueeze(0).float() / 255.0
# set the alpha to the first channel to make a mask
image_tensor[..., 3] = image_tensor[..., 0]
# now set the text to the correct color
color_arr = torch.zeros(1, 1, 1, 3)
color_arr[0, 0, 0, 0] = int(color[1:3], 16) / 255.0
color_arr[0, 0, 0, 1] = int(color[3:5], 16) / 255.0
color_arr[0, 0, 0, 2] = int(color[5:7], 16) / 255.0
# use color_arr to set the color
image_tensor[..., 0:3] *= color_arr
# make a copy for the RGB image
image_rgb = image_tensor[..., :3]
return (image_rgb, image_tensor,)
if __name__ == "__main__":
# test TextRender
tr = TextRender()
result = tr.render_text('Hello, world!', 128, 128, 512, 512, "Arial", 16, "#FFFF000")[0]
torch_image_show(result)
print(result.shape)