filters.py

import cv2
import numpy as np
from registry import registry


@registry.register("Original")
def original(image):
    return image

@registry.register("Dot Effect", defaults={
    "dot_size": 10,
    "dot_spacing": 2,
    "invert": False,
}, min_vals={
    "dot_size": 1,
    "dot_spacing": 1,
}, max_vals={
    "dot_size": 20,
    "dot_spacing": 10,
}, step_vals={
    "dot_size": 1,
    "dot_spacing": 1,
})
def dot_effect(image, dot_size: int = 10, dot_spacing: int = 2, invert: bool = False):
    # Convert to grayscale if image is color
    if len(image.shape) == 3:
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    else:
        gray = image

    # Apply adaptive thresholding to improve contrast
    gray = cv2.adaptiveThreshold(
        gray,
        255,
        cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
        cv2.THRESH_BINARY,
        25,  # Block size
        5    # Constant subtracted from mean
    )

    height, width = gray.shape
    canvas = np.zeros_like(gray) if not invert else np.full_like(gray, 255)

    y_dots = range(0, height, dot_size + dot_spacing)
    x_dots = range(0, width, dot_size + dot_spacing)

    dot_color = 255 if not invert else 0
    for y in y_dots:
        for x in x_dots:
            region = gray[y:min(y+dot_size, height), x:min(x+dot_size, width)]
            if region.size > 0:
                brightness = np.mean(region)

                # Dynamic dot sizing based on brightness
                relative_brightness = brightness / 255.0
                if invert:
                    relative_brightness = 1 - relative_brightness

                # Draw circle with size proportional to brightness
                radius = int((dot_size/2) * relative_brightness)
                if radius > 0:
                    cv2.circle(canvas,
                               (x + dot_size//2, y + dot_size//2),
                               radius,
                               (dot_color),
                               -1)

    return canvas