SampleColorHSV

Documentation

• Class name: SampleColorHSV
• Category: Bmad/CV/Color A.
• Output node: False

This node is designed to sample pixels from an RGB image and convert these samples into the HSV color space. It aims to facilitate color analysis and processing by providing a representative subset of the image's color distribution in HSV format.

Input types

Required

• rgb_image
  • The RGB image from which pixels will be sampled. This image is the primary input for color sampling and subsequent conversion to HSV.
  • Comfy dtype: IMAGE
  • Python dtype: numpy.ndarray
• sample_size
  • Determines the number of pixels to be sampled from the RGB image. A larger sample size provides a more representative color distribution but requires more processing time.
  • Comfy dtype: INT
  • Python dtype: int
• sampling_seed
  • A seed value for the random number generator used in pixel sampling. This ensures reproducibility of the sample selection across different runs.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• hsv_samples
  • Comfy dtype: HSV_SAMPLES
  • The output is a collection of HSV color space samples derived from the sampled RGB image pixels. This facilitates further color analysis or processing.
  • Python dtype: HSV_Samples

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class SampleColorHSV:
    @classmethod
    def INPUT_TYPES(cls):
        import sys
        return {"required": {
            "rgb_image": ("IMAGE",),
            "sample_size": ("INT", {"default": 1000, "min": 1, "max": 256 * 256, }),
            "sampling_seed": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1})
        }}

    RETURN_TYPES = ("HSV_SAMPLES",)
    FUNCTION = "sample"
    CATEGORY = f"{cv_category_path}/Color A."

    def sample(self, rgb_image, sample_size, sampling_seed):
        image = tensor2opencv(rgb_image, 3)
        image_width = image.shape[1]

        # sample pixels
        rng = np.random.default_rng(seed=sampling_seed)
        random_indices = rng.choice(image.shape[0] * image_width, sample_size, replace=False)
        sample_pixels = np.array([image[i // image_width, i % image_width] for i in random_indices])
        sample_pixels = sample_pixels.reshape((1, -1, 3))

        # only convert samples to HSV
        sample_pixels_hsv = cv.cvtColor(sample_pixels, cv.COLOR_RGB2HSV)
        samples_object = HSV_Samples(sample_pixels_hsv[0, :, :])
        return (samples_object,)