DeMark

Automatic image watermark detection and removal toolkit. Multiple methods from classic CV to deep learning, pick what fits your watermark type.

What it does

Detects watermark regions in images and removes them while preserving underlying content. Ten methods are implemented:

What it does NOT do

• No batch GUI — CLI and Python API only. For a GUI, use IOPaint (23k stars) which wraps LaMa.
• No video watermark removal — for video use ProPainter (sczhou/ProPainter).
• No invisible/digital watermark removal — those require diffusion-based attacks (see [10]).
• alpha_invert is the only truly lossless method, and it requires the watermark pattern and alpha to be known.

Requirements

• Python 3.9+
• OpenCV, NumPy, SciPy, scikit-image, Pillow
• For deep learning methods: pip install demark[deep] (LaMa) or pip install demark[sd] (Stable Diffusion)

Installation

# From PyPI
pip install demark

# Optional deep learning extras (LaMa / Stable Diffusion)
pip install "demark[deep]"      # LaMa inpainting
pip install "demark[sd]"        # Stable Diffusion inpainting

# From source
git clone https://github.com/cycleuser/demark.git
cd demark
pip install -e .

Quick Start

# Auto-detect and remove
demark remove watermarked.jpg clean.jpg

# Use specific method
demark remove watermarked.jpg clean.jpg --method fft_notch

# Known watermark logo — template matching
demark remove watermarked.jpg clean.jpg --method template --template logo.png

# Known watermark overlay + alpha — lossless removal
demark remove watermarked.jpg clean.jpg --method alpha_invert --watermark wm_overlay.png --alpha 0.4

# Scale-adaptive multi-scale + auto-optimisation (works at any scale)
demark remove watermarked.jpg clean.jpg --method harmonic --bbox 1056 1249 1304 1305

# Just detect the mask
demark detect watermarked.jpg mask.png

# JSON output
demark --json remove watermarked.jpg clean.jpg

Usage

CLI

demark [-V] [-v] [-o OUTPUT] [--json] [-q] <command> ...

Commands:
  remove   <input> <output> [--method M] [--mask M.png] [--template T.png]
                         [--watermark W.png] [--alpha 0.4] [--radius 5]
  detect   <input> <output> [--method auto|edge|color|otsu]
  list-methods

Python API

from demark import remove_watermark, detect_watermark

# Auto remove
r = remove_watermark(input_path="wm.jpg", output_path="clean.jpg")
print(r.success)        # True
print(r.data["method"]) # "edge_inpaint"
print(r.data["elapsed"])# 0.123

# Specific method
r = remove_watermark(
    input_path="wm.jpg",
    output_path="clean.jpg",
    method="fft_notch"
)

# Detect only
r = detect_watermark(input_path="wm.jpg", output_path="mask.png")
print(r.data["coverage"])  # 0.0523

Agent Integration (OpenAI Function Calling)

from demark.tools import TOOLS, dispatch

# Pass TOOLS to your LLM's function-calling API
result = dispatch("demark_remove_watermark", {
    "input_path": "/data/wm.jpg",
    "output_path": "/data/clean.jpg",
    "method": "auto"
})
print(result["success"])  # True

References

Development

pip install -e ".[dev]"
pytest tests/test_unified_api.py -v
ruff check demark/

License

GPL-3.0