Image Dragan Photography Filter

Documentation

• Class name: Image Dragan Photography Filter
• Category: WAS Suite/Image/Filter
• Output node: False

This node applies a Dragan effect to images, enhancing their visual appeal by adjusting saturation, contrast, sharpness, brightness, and colorization. It utilizes a high-pass filter to accentuate details and texture, creating a distinctive, dramatic look.

Input types

Required

• image
  • The input images to which the Dragan effect will be applied. This parameter is crucial for defining the visual content that will undergo transformation.
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]
• saturation
  • Controls the intensity of the image's colors, affecting the vibrancy of the output.
  • Comfy dtype: FLOAT
  • Python dtype: float
• contrast
  • Adjusts the difference between the darkest and lightest tones in the image, enhancing the visual depth.
  • Comfy dtype: FLOAT
  • Python dtype: float
• brightness
  • Modifies the overall lightness or darkness of the image, affecting its visibility and mood.
  • Comfy dtype: FLOAT
  • Python dtype: float
• sharpness
  • Determines the clarity of detail in the image, making textures more pronounced.
  • Comfy dtype: FLOAT
  • Python dtype: float
• highpass_radius
  • Specifies the radius for the high-pass filter, influencing the level of detail enhancement.
  • Comfy dtype: FLOAT
  • Python dtype: int
• highpass_samples
  • Defines the number of samples for the high-pass filter, affecting the texture's granularity.
  • Comfy dtype: INT
  • Python dtype: int
• highpass_strength
  • Sets the strength of the high-pass filter, determining the intensity of detail accentuation.
  • Comfy dtype: FLOAT
  • Python dtype: float
• colorize
  • Enables or disables colorization, adding a unique hue to the image or preserving its original colors.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: bool

Output types

• image
  • Comfy dtype: IMAGE
  • The transformed images with the applied Dragan effect, showcasing enhanced textures and dramatic visual appeal.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class WAS_Dragon_Filter:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image": ("IMAGE",),
                "saturation": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 16.0, "step": 0.01}),
                "contrast": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 16.0, "step": 0.01}),
                "brightness": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 16.0, "step": 0.01}),
                "sharpness": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 6.0, "step": 0.01}),
                "highpass_radius": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 255.0, "step": 0.01}),
                "highpass_samples": ("INT", {"default": 1, "min": 0, "max": 6.0, "step": 1}),
                "highpass_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01}),
                "colorize": (["true","false"],),
            },
        }

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

    CATEGORY = "WAS Suite/Image/Filter"

    def apply_dragan_filter(self, image, saturation, contrast, sharpness, brightness, highpass_radius, highpass_samples, highpass_strength, colorize):

        WTools = WAS_Tools_Class()

        tensor_images = []
        for img in image:
            tensor_images.append(pil2tensor(WTools.dragan_filter(tensor2pil(img), saturation, contrast, sharpness, brightness, highpass_radius, highpass_samples, highpass_strength, colorize)))
        tensor_images = torch.cat(tensor_images, dim=0)

        return (tensor_images, )