[Inference.Core] Binary Lines

Documentation

• Class name: Inference_Core_BinaryPreprocessor
• Category: ControlNet Preprocessors/Line Extractors
• Output node: False

The Binary Preprocessor node is designed for image preprocessing within the ControlNet Preprocessors/Line Extractors category, specifically focusing on converting images to binary format based on a threshold value. This node applies a binary thresholding technique to extract significant lines or edges from images, facilitating further processing or analysis.

Input types

Required

• image
  • The input image to be processed. It serves as the primary data for the binary thresholding operation, aiming to extract significant lines or edges.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Optional

• bin_threshold
  • Specifies the threshold value for the binary conversion of images. It determines the cutoff point for classifying pixel values as either black or white, playing a crucial role in the extraction of lines or edges from the image.
  • Comfy dtype: INT
  • Python dtype: int
• resolution
  • The resolution to which the input image is resized before applying the binary thresholding. This parameter can influence the detail level of the extracted lines or edges.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The processed image in binary format, where significant lines or edges are highlighted by converting the image based on the specified binary threshold.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class Binary_Preprocessor:
    @classmethod
    def INPUT_TYPES(s):
        return create_node_input_types(
            bin_threshold=("INT", {"default": 100, "min": 0, "max": 255, "step": 1})
        )

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "execute"

    CATEGORY = "ControlNet Preprocessors/Line Extractors"

    def execute(self, image, bin_threshold, resolution=512, **kwargs):
        from controlnet_aux.binary import BinaryDetector

        return (common_annotator_call(BinaryDetector(), image, bin_threshold=bin_threshold, resolution=resolution), )