#!/usr/bin/env python3
import argparse
from pathlib import Path

import cv2
import numpy as np


def odd_positive(value: str) -> int:
    kernel_size = int(value)
    if kernel_size < 1 or kernel_size % 2 == 0:
        raise argparse.ArgumentTypeError("kernel size must be a positive odd integer")
    return kernel_size


def high_frequency_score(image: np.ndarray) -> float:
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return float(cv2.Laplacian(gray, cv2.CV_64F).var())


parser = argparse.ArgumentParser(description="Apply Gaussian blur to an image with OpenCV.")
parser.add_argument("input_image", type=Path)
parser.add_argument("output_image", type=Path)
parser.add_argument("--kernel", type=odd_positive, default=9)
parser.add_argument("--sigma", type=float, default=0.0)
args = parser.parse_args()

image = cv2.imread(str(args.input_image), cv2.IMREAD_COLOR)
if image is None:
    raise SystemExit(f"could not read image: {args.input_image}")

blurred = cv2.GaussianBlur(image, (args.kernel, args.kernel), args.sigma)

args.output_image.parent.mkdir(parents=True, exist_ok=True)
if not cv2.imwrite(str(args.output_image), blurred):
    raise SystemExit(f"could not write image: {args.output_image}")

before_score = high_frequency_score(image)
after_score = high_frequency_score(blurred)

print(f"input: {args.input_image}")
print(f"kernel: {args.kernel}x{args.kernel}")
print(f"sigma: {args.sigma:.1f}")
print(f"high-frequency score before: {before_score:.2f}")
print(f"high-frequency score after: {after_score:.2f}")
print(f"output: {args.output_image}")