DEV Community: Valerie Sims

Anyone know how to trace a low-opacity or faint watermark using Python?

Valerie Sims — Mon, 13 Jul 2026 18:51:09 +0000

I'm working on an image-processing project and ran into a challenge.

Does anyone have experience detecting or tracing very low-opacity (faint) watermarks using Python? I'm not trying to remove them—just identify or highlight where they exist.

I've been looking into approaches like:

OpenCV (contrast enhancement, edge detection, frequency analysis)
Pillow for basic preprocessing
FFT/DCT-based techniques
Template matching (when a watermark pattern is known)

If you've solved a similar problem or know of a good library, research paper, or technique, I'd really appreciate your suggestions.

What approach worked best for you?

Building a Simple Python Script to Bulk Download Images

Valerie Sims — Mon, 13 Jul 2026 18:27:08 +0000

If you've ever needed to grab a bunch of images from a list of URLs — for a dataset, a backup, a scraping project, or just to save some references — writing your own downloader is one of those small utilities that pays for itself pretty quickly. It's also a nice beginner-friendly project if you're getting comfortable with the requests library and basic file handling in Python.

In this post, I'll walk through a lightweight script I put together that downloads single or multiple images, handles errors gracefully, and organizes everything into a folder automatically.

Why Not Just Right-Click and Save?

For one or two images, sure, manual saving is fine. But once you're dealing with dozens (or hundreds) of URLs, doing it by hand gets tedious fast — and error-prone. A script gives you:

Consistency — every image lands in the same folder with a predictable naming pattern
Resilience — one broken link doesn't stop the whole batch
Repeatability — rerun it anytime with a new list of URLs

The Script

Here's the full implementation:

import os
import requests

def download_image(url, folder="downloaded_images", filename=None):
    """
    Downloads a single image from a given URL and saves it to a folder.
    """
    os.makedirs(folder, exist_ok=True)

    if filename is None:
        filename = url.split("/")[-1].split("?")[0]
        if not filename:
            filename = "image.jpg"

    filepath = os.path.join(folder, filename)

    try:
        response = requests.get(url, stream=True, timeout=10)
        response.raise_for_status()

        with open(filepath, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)

        print(f"✅ Downloaded: {filepath}")
        return filepath

    except requests.exceptions.RequestException as e:
        print(f"❌ Failed to download {url}: {e}")
        return None


def download_multiple_images(url_list, folder="downloaded_images"):
    """
    Downloads multiple images from a list of URLs.
    """
    results = []
    for idx, url in enumerate(url_list, start=1):
        print(f"Downloading image {idx}/{len(url_list)}...")
        result = download_image(url, folder=folder)
        results.append(result)
    return results


if __name__ == "__main__":
    # Replace with your own list of image URLs
    image_urls = [
        "https://example.com/image1.jpg",
        "https://example.com/image2.png",
    ]

    download_multiple_images(image_urls)

Breaking It Down

1. Streaming the download

response = requests.get(url, stream=True, timeout=10)

Setting stream=True means the file is downloaded in chunks instead of loading the entire image into memory at once. This matters more than you'd think once you start dealing with larger files — it keeps memory usage flat regardless of image size.

The timeout=10 is a small but important detail: without it, a single unresponsive server can hang your script indefinitely.

2. Writing in chunks

with open(filepath, "wb") as f:
    for chunk in response.iter_content(chunk_size=8192):
        f.write(chunk)

Instead of writing the whole response body in one go, the script writes 8KB chunks as they arrive. This is the standard pattern for downloading binary files in requests and works well for anything from tiny thumbnails to multi-megabyte images.

3. Graceful failure

except requests.exceptions.RequestException as e:
    print(f"❌ Failed to download {url}: {e}")
    return None

RequestException is the base class for pretty much everything that can go wrong with a requests call — connection errors, timeouts, bad status codes (via raise_for_status()), and so on. Catching it here means one dead link just gets logged and skipped, rather than crashing the whole batch.

4. Auto-naming files

filename = url.split("/")[-1].split("?")[0]

This grabs the last segment of the URL path and strips off any query string, so a URL like https://example.com/photos/sunset.jpg?size=large becomes sunset.jpg. It's not bulletproof (some URLs won't have a clean filename at the end), but it covers the common case, and you can always pass an explicit filename if you need more control.

Using It

Drop your URLs into a list and run it:

image_urls = [
    "https://example.com/image1.jpg",
    "https://example.com/image2.png",
]

download_multiple_images(image_urls)

Every image gets saved into a downloaded_images/ folder in your working directory, created automatically if it doesn't already exist.

Requirements

Just one dependency:

pip install requests

Where You Could Take This Further

This script covers the basics, but there's room to grow it depending on what you need:

Parallel downloads — using concurrent.futures.ThreadPoolExecutor or asyncio + aiohttp to download many images at once instead of sequentially
Content-type validation — checking the response's Content-Type header to confirm you actually got an image before saving it
Retry logic — wrapping the request in a retry decorator for flaky connections
Progress bars — plugging in tqdm for a nicer CLI experience on large batches
Deduplication — hashing file contents to skip images you've already downloaded

Wrapping Up

It's a small script, but it's a good example of how a few well-placed lines — streaming, chunked writes, and proper exception handling — turn a "works on my machine for one file" snippet into something you can actually trust to run unattended on a list of hundreds of URLs.

If you end up extending it (especially the async version), I'd love to hear about it in the comments!

Decking Out My Coding Cave for the Holidays 💻

Valerie Sims — Mon, 13 Jul 2026 18:11:10 +0000

Okay, I'll admit it — the holidays are still weeks away and I'm already deep into decoration mode. As developers, we spend way too many hours in our home office setups, so why not make that space actually feel joyful during the holiday season instead of just... functional?

This year I'm doing something a little different. Instead of going full traditional red-and-green, I'm leaning into a modern, tech-inspired holiday aesthetic — think warm string lights, circuit-board coasters, a few 3D-printed ornaments, and the centerpiece of it all: a custom neon sign.

I recently checked out Beer Neon Sign and honestly got a little too excited scrolling through the options. Something like "Code & Cheer" or "Merry & Bright" glowing softly above my monitor sounds like exactly the vibe I want. I'm also seriously tempted by a beer neon sign for the little relaxation corner next to my desk — perfect for when I close the laptop after a long debugging session and just want to unwind with a drink and some chill lighting.

Since I'm a dev, of course I couldn't resist automating the whole thing. I've got the neon sign and string lights on a smart plug, controlled through a tiny script that syncs with my work calendar:

import schedule
import time
from smart_lights import LightController

lights = LightController(device="office-neon")

def focus_mode():
    lights.dim(20)  # subtle glow, no distractions during deep work

def holiday_mode():
    lights.set_color("warm-white")
    lights.brightness(80)  # full festive glow after hours

schedule.every().day.at("09:00").do(focus_mode)
schedule.every().day.at("18:00").do(holiday_mode)

while True:
    schedule.run_pending()
    time.sleep(60)

Nothing fancy, but it means I get a calm, dim glow while I'm heads-down in a PR review, and the full festive light show kicks in the moment my workday ends. Small thing, but it genuinely makes closing the laptop feel more rewarding.

Here's the balance I'm trying to strike overall: festive, not distracting.

Keep the neon signs on a smart plug/schedule so they dim automatically during focus hours
Pair them with warm-white string lights instead of anything too flashy or color-cycling
Keep the desk itself minimal — decorations around the perimeter, not on my keyboard

Curious how the rest of you approach this:

Anyone else decorating their home office / coding setup for the holidays already?
Traditional decor, or leaning into something more modern/tech-themed like I am?
Are you automating your lights (Home Assistant, smart plugs, custom scripts, etc.)?
Any small dev-themed decor finds worth sharing?

Would love to see everyone's setups — feel free to drop pics, scripts, or ideas below. Happy (early) holiday planning, fellow devs! 🎅