CavinHuang-comfyui-nodes-docs/docs/Inference_Core_CannyEdgePreprocessor.md

Documentation

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

Canny Edge预处理器节点是专为图像边缘检测设计的,它利用Canny边缘检测算法来突出图像中的轮廓和边缘。这一预处理步骤对于需要清晰描绘物体边界的任务至关重要,比如图像分割或特征提取过程。

Input types

Required

• image
  • 需要进行边缘检测处理的输入图像。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Optional

• low_threshold
  • 指定Canny边缘检测算法中滞后阈值步骤的下限,用于控制弱边缘的检测。
  • Comfy dtype: INT
  • Python dtype: int
• high_threshold
  • 定义滞后阈值步骤的上限,用于确定图像中强边缘的检测。
  • Comfy dtype: INT
  • Python dtype: int
• resolution
  • 在应用Canny边缘检测算法之前,输入图像将被调整到的分辨率,影响检测到的边缘的细节水平。
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • 使用Canny边缘检测技术处理后的图像,其中边缘被突出显示。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class Canny_Edge_Preprocessor:
    @classmethod
    def INPUT_TYPES(s):
        return create_node_input_types(
            low_threshold=("INT", {"default": 100, "min": 0, "max": 255, "step": 1}),
            high_threshold=("INT", {"default": 200, "min": 0, "max": 255, "step": 1})
        )

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

    CATEGORY = "ControlNet Preprocessors/Line Extractors"

    def execute(self, image, low_threshold, high_threshold, resolution=512, **kwargs):
        from controlnet_aux.canny import CannyDetector

        return (common_annotator_call(CannyDetector(), image, low_threshold=low_threshold, high_threshold=high_threshold, resolution=resolution), )