CavinHuang-comfyui-nodes-docs/docs/ConstrainImageforVideo%7Cpysssss.md

Constrain Image for Video 🐍

Documentation

• Class name: ConstrainImageforVideo|pysssss
• Category: image
• Output node: False
• Repo Ref: https://github.com/pythongosssss/ComfyUI-Custom-Scripts

此节点旨在调整图像的尺寸以适应指定的最大和最小尺寸，同时保持纵横比。它提供了在必要时裁剪图像以满足尺寸约束的选项。

Input types

Required

• images
  • 要处理的图像。它们将被调整以适应指定的尺寸约束，同时保持其纵横比。
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]
• max_width
  • 图像允许的最大宽度。宽度超过此值的图像将调整为此宽度。
  • Comfy dtype: INT
  • Python dtype: int
• max_height
  • 图像允许的最大高度。高度超过此值的图像将调整为此高度。
  • Comfy dtype: INT
  • Python dtype: int
• min_width
  • 图像允许的最小宽度。宽度小于此值的图像将调整为此宽度。
  • Comfy dtype: INT
  • Python dtype: int
• min_height
  • 图像允许的最小高度。高度小于此值的图像将调整为此高度。
  • Comfy dtype: INT
  • Python dtype: int
• crop_if_required
  • 决定是否在调整大小不足以满足尺寸约束时裁剪图像。
  • Comfy dtype: COMBO[STRING]
  • Python dtype: bool

Output types

• IMAGE
  • Comfy dtype: IMAGE
  • 已处理的图像，调整大小并在必要时裁剪以适应指定的尺寸约束。
  • 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),)