Constrain Image for Video 🐍

Documentation

• Class name: ConstrainImageforVideo|pysssss
• Category: image
• Output node: False

This node is designed to adjust the dimensions of an image to fit within specified maximum and minimum sizes, ensuring the aspect ratio is maintained. It offers the option to crop the image if necessary to meet the size constraints.

Input types

Required

• images
  • The images to be processed. They are adjusted to fit within the specified size constraints while maintaining their aspect ratio.
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]
• max_width
  • The maximum width allowed for the images. Images wider than this will be resized to this width.
  • Comfy dtype: INT
  • Python dtype: int
• max_height
  • The maximum height allowed for the images. Images taller than this will be resized to this height.
  • Comfy dtype: INT
  • Python dtype: int
• min_width
  • The minimum width allowed for the images. Images narrower than this will be resized to this width.
  • Comfy dtype: INT
  • Python dtype: int
• min_height
  • The minimum height allowed for the images. Images shorter than this will be resized to this height.
  • Comfy dtype: INT
  • Python dtype: int
• crop_if_required
  • Determines whether images should be cropped to meet the size constraints if resizing alone is insufficient.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: bool

Output types

• IMAGE
  • Comfy dtype: IMAGE
  • The processed images, resized and optionally cropped to fit within the specified size constraints.
  • Python dtype: List[torch.Tensor]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

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),)