ImageCompositeRelative

Documentation

• Class name: ImageCompositeRelative
• Category: image/composite
• Output node: False

The ImageCompositeRelative node is designed for compositing two images relative to each other's dimensions and positions, allowing for dynamic image overlays and manipulations based on specified coordinates and sizes. This node facilitates the creation of composite images by calculating the relative positions and sizes of input images to achieve the desired layout and visual effect.

Input types

Required

• images_a
  • This parameter represents the first set of images to be composited onto the container. It plays a crucial role in the overlay process, determining the visual content and layering order in the composite image.
  • Comfy dtype: IMAGE
  • Python dtype: numpy.ndarray
• images_b
  • Similar to images_a, this parameter represents the second set of images to be overlaid onto the container. It allows for additional visual elements to be introduced, enhancing the complexity and depth of the composite image.
  • Comfy dtype: IMAGE
  • Python dtype: numpy.ndarray
• images_a_x
  • Specifies the horizontal position (as a percentage of container width) for the first set of images, influencing their placement within the composite image.
  • Comfy dtype: FLOAT
  • Python dtype: float
• images_a_y
  • Specifies the vertical position (as a percentage of container height) for the first set of images, affecting their alignment and layering within the composite.
  • Comfy dtype: FLOAT
  • Python dtype: float
• images_b_x
  • Determines the horizontal position (as a percentage of container width) for the second set of images, guiding their placement and interaction with the first set of images.
  • Comfy dtype: FLOAT
  • Python dtype: float
• images_b_y
  • Determines the vertical position (as a percentage of container height) for the second set of images, influencing their layering and visual relationship with the first set.
  • Comfy dtype: FLOAT
  • Python dtype: float
• background
  • The background parameter specifies which set of images (either images_a or images_b) should be considered the background layer in the composite image. This choice affects the visual hierarchy and layering of the composite image.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• container_size_type
  • Defines the strategy for determining the container's size, which can be based on the maximum dimensions of the input images or their sum, affecting the overall dimensions of the composite image.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• method
  • Defines the method or algorithm used for compositing the images. This parameter influences the blending and overlay techniques applied, impacting the final appearance of the composite image.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• image
  • Comfy dtype: IMAGE
  • The resulting composite image, created by blending and overlaying the specified images according to the given positions, sizes, and method. This output represents the final visual product of the compositing process.
  • Python dtype: numpy.ndarray

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class ImageCompositeRelative:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images_a": ("IMAGE",),
                "images_b": ("IMAGE",),
                "images_a_x": ("FLOAT", {
                    "default": 0.0,
                    "max": 1.0,
                    "step": 0.01
                }),
                "images_a_y": ("FLOAT", {
                    "default": 0.0,
                    "max": 1.0,
                    "step": 0.01
                }),
                "images_b_x": ("FLOAT", {
                    "default": 0.0,
                    "max": 1.0,
                    "step": 0.01
                }),
                "images_b_y": ("FLOAT", {
                    "default": 0.0,
                    "max": 1.0,
                    "step": 0.01
                }),
                "background": (["images_a", "images_b"],),
                "container_size_type": (["max", "sum", "sum_width", "sum_height"],),
                "method": (["pair", "matrix"],),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "node"
    CATEGORY = "image/composite"

    def node(
            self,
            images_a,
            images_b,
            images_a_x,
            images_a_y,
            images_b_x,
            images_b_y,
            background,
            container_size_type,
            method
    ):
        def offset_by_percent(container_size: int, image_size: int, percent: float):
            return int((container_size - image_size) * percent)

        img_a_height, img_a_width = images_a[0, :, :, 0].shape
        img_b_height, img_b_width = images_b[0, :, :, 0].shape

        if container_size_type == "max":
            container_width = max(img_a_width, img_b_width)
            container_height = max(img_a_height, img_b_height)
        elif container_size_type == "sum":
            container_width = img_a_width + img_b_width
            container_height = img_a_height + img_b_height
        elif container_size_type == "sum_width":
            container_width = img_a_width + img_b_width
            container_height = max(img_a_height, img_b_height)
        elif container_size_type == "sum_height":
            container_width = max(img_a_width, img_b_width)
            container_height = img_a_height + img_a_height
        else:
            raise ValueError()

        return ImageCompositeAbsolute().node(
            images_a,
            images_b,
            offset_by_percent(container_width, img_a_width, images_a_x),
            offset_by_percent(container_height, img_a_height, images_a_y),
            offset_by_percent(container_width, img_b_width, images_b_x),
            offset_by_percent(container_height, img_b_height, images_b_y),
            container_width,
            container_height,
            background,
            method
        )