Pixelate image. Use `pixelate.py --help` to get help message. Install required libraries `pip install opencv-python numpy typer`. General usage with default params: `pixelate.py img.png`

pixelate.py

from typing import Annotated
from pathlib import Path

import cv2
import numpy as np
import typer

app = typer.Typer(no_args_is_help=True)


@app.command()
def main(
    image: Annotated[Path, typer.Argument(help="Path to image")],
    quantization_level: Annotated[int, typer.Option("--quantization-level", "-q", help="Number of levels of quantization, 0 - disabled", rich_help_panel="Params")] = 16,
    pixel_size: Annotated[int, typer.Option("--pixel-size", "-p", help="Pixel size, 1 or 0 - disabled", rich_help_panel="Params")] = 10,
    gamma: Annotated[float, typer.Option("--gamma", "-g", help="Adjust gamma, 1.0 - disabled, 0 - auto", rich_help_panel="Params")] = 0.0,
    preview: Annotated[bool, typer.Option(help="Show preview of pixelated image", rich_help_panel="Debug")] = True,
    output_dir: Annotated[Path | None, typer.Option("--output-dir", "-d", help="Specify output directory, default - input image directory", rich_help_panel="File")] = None,
    output_name: Annotated[
        str | None,
        typer.Option("--output-name", "-n", help="Specify output file name, default - input image name + passed parameters", rich_help_panel="File"),
    ] = None,
) -> cv2.typing.MatLike:
    """
    # Convert image to pixelated image

    By default posterize image to 16 levels of quantization (improve sharpness before pixelation),
    pixelate to 10x10 pixel size and adjust gamma based of size of pixel (as it become dimmed)
    """

    im: cv2.typing.MatLike = cv2.imread(str(image))
    im2: cv2.typing.MatLike
    im3: cv2.typing.MatLike
    im4: cv2.typing.MatLike
    imf: cv2.typing.MatLike

    if 255 > quantization_level > 0:
        indices = np.arange(0, 256)  # List of all colors
        divider = np.linspace(0, 255, quantization_level + 1)[1]  # we get a divider
        quantiz = np.intp(np.linspace(0, 255, quantization_level))  # we get quantization colors
        color_levels = np.clip(np.intp(indices / divider), 0, quantization_level - 1)  # color levels 0, 1, 2...
        palette = quantiz[color_levels]  # type: ignore[palette] # Creating the palette

        im2 = palette[im]  # Applying palette on image
        im2 = cv2.convertScaleAbs(im2)  # Converting image back to uint8
    else:
        im2 = im

    if pixel_size >= 2:
        h, w, _ = im2.shape
        sw, sh = max(round(w / pixel_size), 1), max(round(h / pixel_size), 1)
        im3 = cv2.resize(im2, (sw, sh), interpolation=cv2.INTER_AREA)  # Downscale
        im4 = cv2.resize(im3, (sw * pixel_size, sh * pixel_size), interpolation=cv2.INTER_NEAREST)  # Upscale
    else:
        im4 = im2

    if gamma != 1.0:  # Adjust gamma
        # build a lookup table mapping the pixel values [0, 255] to their adjusted gamma values
        if gamma <= 0.0:
            gamma = 0.4 / pixel_size + 0.6  # Approximate influence on gamma by pixel_size
        inv_gamma = 1.0 / gamma
        table = np.array([((i / 255.0) ** inv_gamma) * 255 for i in np.arange(0, 256)]).astype("uint8")

        imf = cv2.LUT(im4, table)  # apply gamma correction using the lookup table
    else:
        imf = im4

    if preview:
        # cv2.namedWindow("preview", cv2.WINDOW_NORMAL)  # | cv2.WINDOW_KEEPRATIO
        cv2.imshow("preview", imf)
        cv2.waitKey(5_000)
        cv2.destroyAllWindows()

    file_name = output_name or f"{image.stem}.q{quantization_level}.px{pixel_size}.g{gamma:.2f}{image.suffix}"
    new_file = output_dir / file_name if output_dir else image.with_name(file_name)

    print(f"Saving pixelated file to: {new_file}")
    if new_file.suffix in (".jpg", ".jpeg"):
        cv2.imwrite(str(new_file), imf, [int(cv2.IMWRITE_JPEG_QUALITY), 90])  # Set JPEG quality to 90%
    else:
        cv2.imwrite(str(new_file), imf)

    return imf


if __name__ == "__main__":
    app()

Pixelate image

Use pixelate.py --help to get help message.

Install required libraries:

pip install opencv-python numpy typer

General usage with default params:

pixelate.py img.png

Advanced usage:

pixelate.py --quantization-level 16 --pixel-size 10 --gamma 0.5 --no-preview --output-dir "some/path" --output-name new.png img.png
pixelate.py -q 16 -p 10 -g 0.5 --no-preview -d "some/path" -n new.png img.png