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import torch
import comfy.utils
import numpy as np
from PIL import Image, ImageSequence, ImageOps
class ConstrainImageNode:
"""
A node that constrains an image to a maximum and minimum size while maintaining aspect ratio.
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images": ("IMAGE",),
"max_width": ("INT", {"default": 1024, "min": 0}),
"max_height": ("INT", {"default": 1024, "min": 0}),
"min_width": ("INT", {"default": 0, "min": 0}),
"min_height": ("INT", {"default": 0, "min": 0}),
"crop_if_required": (["yes", "no"], {"default": "no"}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "constrain_image"
CATEGORY = "tbox/Image"
OUTPUT_IS_LIST = (True,)
def constrain_image(self, images, max_width, max_height, min_width, min_height, crop_if_required):
crop_if_required = crop_if_required == "yes"
results = []
for image in images:
i = 255. * image.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)).convert("RGB")
current_width, current_height = img.size
aspect_ratio = current_width / current_height
constrained_width = max(min(current_width, min_width), max_width)
constrained_height = max(min(current_height, min_height), max_height)
if constrained_width / constrained_height > aspect_ratio:
constrained_width = max(int(constrained_height * aspect_ratio), min_width)
if crop_if_required:
constrained_height = int(current_height / (current_width / constrained_width))
else:
constrained_height = max(int(constrained_width / aspect_ratio), min_height)
if crop_if_required:
constrained_width = int(current_width / (current_height / constrained_height))
resized_image = img.resize((constrained_width, constrained_height), Image.LANCZOS)
if crop_if_required and (constrained_width > max_width or constrained_height > max_height):
left = max((constrained_width - max_width) // 2, 0)
top = max((constrained_height - max_height) // 2, 0)
right = min(constrained_width, max_width) + left
bottom = min(constrained_height, max_height) + top
resized_image = resized_image.crop((left, top, right, bottom))
resized_image = np.array(resized_image).astype(np.float32) / 255.0
resized_image = torch.from_numpy(resized_image)[None,]
results.append(resized_image)
return (results,)
# https://github.com/bronkula/comfyui-fitsize
class ImageSizeNode:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", ),
}
}
RETURN_TYPES = ("INT", "INT", "INT")
RETURN_NAMES = ("width", "height", "count")
FUNCTION = "get_size"
CATEGORY = "tbox/Image"
def get_size(self, image):
print(f'shape of image:{image.shape}')
return (image.shape[2], image.shape[1], image[0])
class ImageResizeNode:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", ),
"method": (["nearest", "bilinear", "bicubic", "area", "nearest-exact", "lanczos"],),
},
"optional": {
"width": ("INT,FLOAT", { "default": 0.0, "step": 0.1 }),
"height": ("INT,FLOAT", { "default": 0.0, "step": 0.1 }),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "resize"
CATEGORY = "tbox/Image"
def resize(self, image, method, width, height):
print(f'shape of image:{image.shape}, resolution:{width}x{height} type: {type(width)}, {type(height)}')
if width == 0 and height == 0:
s = image
else:
samples = image.movedim(-1,1)
if width == 0:
width = max(1, round(samples.shape[3] * height / samples.shape[2]))
elif height == 0:
height = max(1, round(samples.shape[2] * width / samples.shape[3]))
s = comfy.utils.common_upscale(samples, width, height, method, True)
s = s.movedim(1,-1)
return (s,)
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