restored pixel art Python script

restored_pixel_art.py

#!/usr/bin/env python
"""\
This script is from Returning Pixel Art to their original size by Antonio Neves. Written for Python via ChatGPT-4o.

Original: Retrieving the original pixel size for digital arts and NFTs
        <https://community.wolfram.com/groups/-/m/t/2407109>

---

该脚本，来自 Antonio Neves 的 Returning Pixel Art to their original size。经过 ChatGPT-4o 编写为了 Python 版本。

原始手册：Retrieving the original pixel size for digital arts and NFTs
        <https://community.wolfram.com/groups/-/m/t/2407109>

Usage: python restored_pixel_art.py path/to/pixle/art.png
"""
import numpy as np
from PIL import Image, ImageOps
import sys

def restore_pixel_art(image_path):
    # read image
    # 读取图像
    input_image = Image.open(image_path)

    # Convert to grayscale image for Fourier transform
    # 转换为灰度图像以进行傅立叶变换
    gray_image = ImageOps.grayscale(input_image)

    # Convert grayscale image to NumPy array
    # 将灰度图像转换为 NumPy 数组
    gray_image_np = np.array(gray_image)

    # Perform Fourier transform
    # 进行傅立叶变换
    f_transform = np.fft.fft2(gray_image_np)
    f_shift = np.fft.fftshift(f_transform)
    magnitude_spectrum = np.log(np.abs(f_shift) + 1)

    # Get image center
    # 获取图像中心
    rows, cols = gray_image_np.shape
    crow, ccol = rows // 2, cols // 2

    # Take a quarter of the spectrum
    # 取四分之一大小的频谱
    adjusted_spectrum = f_shift[crow - rows // 4:crow + rows // 4, ccol - cols // 4:ccol + cols // 4]

    # Calculate the horizontal and vertical projection
    # 计算水平和垂直方向上的投影
    horizontal_projection = np.sum(np.abs(adjusted_spectrum), axis=0)
    vertical_projection = np.sum(np.abs(adjusted_spectrum), axis=1)

    # Background removal
    # 背景去除
    horizontal_projection -= np.min(horizontal_projection)
    vertical_projection -= np.min(vertical_projection)

    # Find the maximum peak value
    # 找到最大峰值
    h_peak = np.argmax(horizontal_projection)
    v_peak = np.argmax(vertical_projection)

    # Calculate scaling ratio
    # 计算缩放比例
    scaling_factor_h = cols / h_peak
    scaling_factor_v = rows / v_peak
    scaling_factor = min(scaling_factor_h, scaling_factor_v)

    # Reduce the original image
    # 缩小原图像
    new_size = (int(cols / scaling_factor), int(rows / scaling_factor))
    resized_image = input_image.resize(new_size, Image.NEAREST)

    # Save results
    # 保存结果
    output_image_path = f"{image_path.split('.')[0]}_restored.png"
    resized_image.save(output_image_path)
    print(f"The restored image was saved as {output_image_path}")

if __name__ == "__main__":
    if len(sys.argv) != 2:
        print("Usage: python restored_pixel_art.py path/to/pexel/art.png")
        sys.exit(1)

    image_path = sys.argv[1]
    restore_pixel_art(image_path)