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import torch
import numpy as np
from PIL import Image
class ConstrainImageforVideo:
"""
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",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "constrain_image_for_video"
CATEGORY = "image"
def constrain_image_for_video(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)
all_images = torch.cat(results, dim=0)
return (all_images, all_images.size(0),)
NODE_CLASS_MAPPINGS = {
"ConstrainImageforVideo|pysssss": ConstrainImageforVideo,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ConstrainImageforVideo|pysssss": "Constrain Image for Video 🐍",
}
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