Image Intensity

Documentation

• Class name: ImageIntensityDetector
• Category: ControlNet Preprocessors/Recolor
• Output node: False

The ImageIntensityDetector node is designed for preprocessing images by adjusting their intensity levels. This adjustment is achieved through gamma correction, enhancing the image's overall visibility and contrast before further processing.

Input types

Required

• image
  • The input image to be processed for intensity adjustment through gamma correction.
  • Comfy dtype: IMAGE
  • Python dtype: numpy.ndarray

Optional

• gamma_correction
  • Specifies the gamma correction factor to adjust the image's intensity. A higher value brightens the image, while a lower value darkens it, significantly impacting the visual quality of the output.
  • Comfy dtype: FLOAT
  • Python dtype: float
• resolution
  • The resolution to which the image is resized before applying the intensity adjustment, affecting the detail level of the output.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The output is an image with adjusted intensity levels, suitable for visual analysis or further image processing tasks.
  • Python dtype: numpy.ndarray

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class ImageIntensityDetector:
    @classmethod
    def INPUT_TYPES(s):
        #https://github.com/Mikubill/sd-webui-controlnet/blob/416c345072c9c2066101e225964e3986abe6945e/scripts/processor.py#L1229
        return create_node_input_types(
            gamma_correction=("FLOAT", {"default": 1.0, "min": 0.1, "max": 2.0, "step": 0.001})
        )

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

    CATEGORY = "ControlNet Preprocessors/Recolor"

    def execute(self, image, gamma_correction, resolution=512, **kwargs):
        from controlnet_aux.recolor import Recolorizer
        return (common_annotator_call(Recolorizer(), image, mode="intensity", gamma_correction=gamma_correction , resolution=resolution), )