|
import torch
|
|
import numpy as np
|
|
from PIL import Image
|
|
|
|
class ConstrainImage:
|
|
"""
|
|
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 = "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,)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"ConstrainImage|pysssss": ConstrainImage,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"ConstrainImage|pysssss": "Constrain Image 🐍",
|
|
}
|
|
|
