Sharpen an image with an unsharp mask

Before you begin

Python 3.12 or newer
Pillow installed in the same Python environment

What this script changesCreates new image files or a new frame directory. Inputs and outputs are limited to 20 million pixels per image; animated tasks also cap frame count. Ordinary transforms process the first frame and normalize EXIF orientation. Color profiles and original metadata are not preserved reliably; use source copies for archival work.

Set up your example

Save the complete code as image-sharpen.py in a working folder.
Save the provided sample files in that same folder; run commands from that folder.
Install Pillow: python -m pip install Pillow
Generate the original fixtures once: python "make_samples.py". Choose an empty folder so the fixture files do not exist.

Sample files in this download:

make_samples.py

Download the ZIP, extract it, and open a terminal inside the extracted folder. The source file is image-sharpen.py.

Run the script

python "image-sharpen.py" --input "sample.png" --radius 2 --percent 150 --output "sharp.png"

If your system uses python3 instead of python, use that command for Python examples. Keep paths containing spaces in quotes. Required inputs and optional settings are described in the code and setup steps.

What to expect

Emphasize local image detail using a bounded unsharp mask. The fixture's shape edges become more pronounced.

The complete script

#!/usr/bin/env python3
'Sharpen an image with an unsharp mask'
import argparse
import json
from pathlib import Path

def write_json(path, value):
    data = json.dumps(value, indent=2, ensure_ascii=False, allow_nan=False) + "\n"
    with Path(path).open("x", encoding="utf-8", newline="") as handle:
        handle.write(data)

import math

try:
    from PIL import Image, ImageOps, ImageEnhance, ImageFilter, ImageDraw, ImageFont, ImageChops
except ImportError:
    raise SystemExit("Install Pillow first: python -m pip install Pillow")
MAX_PIXELS=20_000_000
def check_size(size):
    if min(size)<1 or size[0]*size[1]>MAX_PIXELS:
        raise ValueError("Image dimensions must be positive and at most 20 million pixels.")
def read_image(path):
    with Image.open(path) as original:
        check_size(original.size)
        return ImageOps.exif_transpose(original).convert("RGBA")
def save_image(path,image,format="PNG"):
    check_size(image.size)
    with Path(path).open("xb") as handle:
        image.save(handle,format=format)

def main():
    parser = argparse.ArgumentParser(description='Sharpen an image with an unsharp mask')
    parser.add_argument("--input",required=True,help="Local source image")
    parser.add_argument("--output",required=True,help="New output file or directory")
    parser.add_argument("--radius",type=float,default=2)
    parser.add_argument("--percent",type=int,default=150)
    args = parser.parse_args()
    if not 0<args.radius<=20 or not 0<=args.percent<=500: raise ValueError("Use radius 0..20 excluding zero and percent 0..500.")
    save_image(args.output,read_image(args.input).filter(ImageFilter.UnsharpMask(radius=args.radius,percent=args.percent,threshold=3)))

if __name__ == "__main__":
    try:
        main()
    except (OSError, ValueError, KeyError, TypeError, ArithmeticError, IndexError, LookupError) as exc:
        raise SystemExit(str(exc))

Open source as plain text · Download source and sample files

How it works

Pillow reads the local image; the source is left unchanged and existing destinations are refused.
Radius sets the neighborhood and percent sets sharpening strength; threshold 3 avoids amplifying tiny differences.
The fixture's shape edges become more pronounced.

Make it your own

--percent

Reduce --percent to 75 for a gentler effect.

Adapt the processing

Edit main's threshold value to control which tonal differences are sharpened.

Change one setting at a time, run it again on sample data, and compare the output. The customization guide walks through this process.

If something goes wrong

Halos appear around shapes.

Reduce radius and percent; strong sharpening can create visible halos.

The destination already exists.

Choose a new --output path. The script deliberately refuses to replace an existing result.

Reference & next steps

These official references explain the underlying tools. The Scriptage example and guide are original instructional content. See verification notes for what has been checked and the limits of those checks.

Copies a template for your own notes. It does not send anything.