Gradient To Float

Documentation

• Class name: GradientToFloat
• Category: KJNodes/image
• Output node: False

Transforms an image into a list of float values based on sampling along its width and height axes. This process is useful for extracting numerical representations of images for further analysis or processing.

Input types

Required

• image
  • The image tensor to be sampled, where the sampling occurs along the width and height axes to generate float values.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• steps
  • Defines the number of intervals to sample along the width and height axes of the image, affecting the resolution of the output float lists.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• float_x
  • Comfy dtype: FLOAT
  • A list of float values representing the mean of the sampled points along the width of the image.
  • Python dtype: List[float]
• float_y
  • Comfy dtype: FLOAT
  • A list of float values representing the mean of the sampled points along the height of the image.
  • Python dtype: List[float]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class GradientToFloat:

    RETURN_TYPES = ("FLOAT", "FLOAT",)
    RETURN_NAMES = ("float_x", "float_y", )
    FUNCTION = "sample"
    CATEGORY = "KJNodes/image"
    DESCRIPTION = """
Calculates list of floats from image.    
"""

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE", ),
                "steps": ("INT", {"default": 10, "min": 2, "max": 10000, "step": 1}),
        },
    } 

    def sample(self, image, steps):
        # Assuming image is a tensor with shape [B, H, W, C]
        B, H, W, C = image.shape

        # Sample along the width axis (W)
        w_intervals = torch.linspace(0, W - 1, steps=steps, dtype=torch.int64)
        # Assuming we're sampling from the first batch and the first channel
        w_sampled = image[0, :, w_intervals, 0]

        # Sample along the height axis (H)
        h_intervals = torch.linspace(0, H - 1, steps=steps, dtype=torch.int64)
        # Assuming we're sampling from the first batch and the first channel
        h_sampled = image[0, h_intervals, :, 0]

        # Taking the mean across the height for width sampling, and across the width for height sampling
        w_values = w_sampled.mean(dim=0).tolist()
        h_values = h_sampled.mean(dim=1).tolist()

        return (w_values, h_values)