Python で画像フィルタパラメータを blackbox 最適化で探索してみるメモ

from benderopt import minimize
import numpy as np
import logging
import subprocess
import parse

logging.basicConfig(level=logging.DEBUG) # logging.INFO will print less information

blurred_filename = "shimokita-blur.jpg" # convert -blur 27x20
ref_filename = "shimokita.jpeg"

k_num_eval = 10000

def extract_result(lines):
    for line in lines:
        print(line.decode("utf-8"))
        ret = parse.parse("{:g} ({:g})", line.decode("utf-8"))
        if ret:
            return ret[0]

    raise RuntimeError("Failed to extract value from result.")

count = 0

def f(radius, sigma):
    global count

    count += 1

    print("run {} of {} ...".format(count, k_num_eval))
    tmp_filename = "shimokita-tmp.jpg"
    cmd = "convert {} -blur {}x{} {}".format(ref_filename, radius, sigma, tmp_filename)
    ret = subprocess.run(cmd, shell=True)


    # Compare two images using RMSE
    cmp_cmd = "compare -metric rmse {} {} null:".format(tmp_filename, blurred_filename)
    ret = subprocess.run(cmp_cmd, shell=True, capture_output=True)
    # `compare`(ImageMagick) outputs result into stderr, not stdout
    lines = ret.stderr.splitlines()

    val = extract_result(lines)

    return val


# We define the parameters we want to optimize:
optimization_problem_parameters = [
    {
        "name": "radius",
        "category": "uniform",
        "search_space": {
            "low": 0,
            "high": 100,
        }
    },
    {
        "name": "sigma",
        "category": "uniform",
        "search_space": {
            "low": 0,
            "high": 100,
        }
    }
]

# We launch the optimization
best_sample = minimize(f, optimization_problem_parameters, number_of_evaluation=k_num_eval)

print("radius", best_sample["radius"])
print("sigma", best_sample["sigma"])
print("err = ", f(best_sample["radius"], best_sample["sigma"]))

radius 27.993241302558445
sigma 19.957452459359814

err =  49.5512