| import torch
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| import numpy as np
|
| from PIL import Image
|
|
|
| class ConstrainImageforVideo:
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| """
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| A node that constrains an image to a maximum and minimum size while maintaining aspect ratio.
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| """
|
|
|
| @classmethod
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| def INPUT_TYPES(cls):
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| return {
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| "required": {
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| "images": ("IMAGE",),
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| "max_width": ("INT", {"default": 1024, "min": 0}),
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| "max_height": ("INT", {"default": 1024, "min": 0}),
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| "min_width": ("INT", {"default": 0, "min": 0}),
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| "min_height": ("INT", {"default": 0, "min": 0}),
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| "crop_if_required": (["yes", "no"], {"default": "no"}),
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| },
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| }
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|
|
| RETURN_TYPES = ("IMAGE",)
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| RETURN_NAMES = ("IMAGE",)
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| FUNCTION = "constrain_image_for_video"
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| CATEGORY = "image"
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|
|
| def constrain_image_for_video(self, images, max_width, max_height, min_width, min_height, crop_if_required):
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| crop_if_required = crop_if_required == "yes"
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| results = []
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| for image in images:
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| i = 255. * image.cpu().numpy()
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| img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)).convert("RGB")
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|
|
| current_width, current_height = img.size
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| aspect_ratio = current_width / current_height
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|
|
| constrained_width = max(min(current_width, min_width), max_width)
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| constrained_height = max(min(current_height, min_height), max_height)
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|
|
| if constrained_width / constrained_height > aspect_ratio:
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| constrained_width = max(int(constrained_height * aspect_ratio), min_width)
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| if crop_if_required:
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| constrained_height = int(current_height / (current_width / constrained_width))
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| else:
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| constrained_height = max(int(constrained_width / aspect_ratio), min_height)
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| if crop_if_required:
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| constrained_width = int(current_width / (current_height / constrained_height))
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|
|
| resized_image = img.resize((constrained_width, constrained_height), Image.LANCZOS)
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|
|
| if crop_if_required and (constrained_width > max_width or constrained_height > max_height):
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| left = max((constrained_width - max_width) // 2, 0)
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| top = max((constrained_height - max_height) // 2, 0)
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| right = min(constrained_width, max_width) + left
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| bottom = min(constrained_height, max_height) + top
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| resized_image = resized_image.crop((left, top, right, bottom))
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|
|
| resized_image = np.array(resized_image).astype(np.float32) / 255.0
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| resized_image = torch.from_numpy(resized_image)[None,]
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| results.append(resized_image)
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| all_images = torch.cat(results, dim=0)
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|
|
| return (all_images, all_images.size(0),)
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|
|
| NODE_CLASS_MAPPINGS = {
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| "ConstrainImageforVideo|pysssss": ConstrainImageforVideo,
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| }
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|
|
| NODE_DISPLAY_NAME_MAPPINGS = {
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| "ConstrainImageforVideo|pysssss": "Constrain Image for Video 🐍",
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| }
|
|
|
