PatchMatch based Inpainting

This library implements the PatchMatch based inpainting algorithm. It provides both C++ and Python interfaces. This implementation is heavily based on the implementation by Younesse ANDAM: younesse-cv/PatchMatch, with some bug fixes, and updates.

From left to right: the photo, the input of examples/py_example.py with the birdhouses painted out in white, and its result.

Installation

pip install PyPatchMatch

Wheels for Linux (glibc, x86_64 and aarch64), macOS 11+ (arm64 and x86_64) and Windows (x64) ship the compiled library with a statically linked OpenCV, so no compiler or OpenCV installation is needed. On other platforms, e.g. Alpine Linux or Windows on ARM, pip builds from the source distribution, which requires a C++17 compiler, CMake and the OpenCV development files (e.g. apt install libopencv-dev or brew install opencv).

If that build fails, PyPatchMatch is installed without the native library instead of blocking the installation: patchmatch.patchmatch_available is False and the inpainting functions raise RuntimeError. After installing the missing tools, reinstall it without the cached build:

pip install --force-reinstall --no-deps --no-cache-dir PyPatchMatch

Usage

Python (see examples/py_example.py):

import patchmatch

image = ...  # HxWx3 uint8 numpy array or PIL image
mask = ...  # HxW uint8 or bool numpy array or PIL image, non-zero marks the holes
result = patchmatch.inpaint(image, mask, patch_size=3)

The mask must have the same height and width as the image. If mask is omitted, all pure white pixels are treated as holes. Only the holes are filled; all other pixels keep their values, and the colors under the holes have no influence on the fill. Holes need hard edges: an anti-aliased brush leaves light pixels along the edge of its strokes that are not pure white, so they count as known and the fill continues them. Paint without anti-aliasing or pass a mask that covers those pixels as well. The optional keyword argument global_mask, in the same format, marks pixels that are neither filled nor used as a source; they keep their values as well, and their colors have no influence on the fill (see examples/py_example_global_mask.py). patchmatch.patchmatch_available tells whether the native library could be loaded. The previous import path from patchmatch import patch_match keeps working.

patch_size is the radius of the compared patches, which span 2 * patch_size + 1 pixels in each direction. Larger patches follow larger structures but are much slower; the default of 15 compares 31x31 patches, the examples use 3. Below 5, the coarse levels that lay out the fill are also tried with a radius of 5, which is kept where it fits the surroundings better, so that repeating patterns continue across larger holes.

patchmatch.set_random_seed(seed) sets the seed of the randomized search, modulo 2**32, and patchmatch.set_verbose(True) prints the progress of the native code to stderr. patchmatch.inpaint_regularity(image, mask, ijmap) additionally guides the search with a regularity map, an HxWx3 float32 array with the regularity coordinates of each pixel in its first two channels. Its guide_weight must be between 0 and the largest float32, about 3.4e38.

C++ (see examples/cpp_example.cpp, build and run it with examples/cpp_example_run.sh):

#include "inpaint.h"

int main() {
    cv::Mat image = ...;
    cv::Mat mask = ...;

    auto metric = PatchSSDDistanceMetric(5);
    cv::Mat result = Inpainting(image, mask, &metric).run();
}

The library is built with CMake; PATCHMATCH_BUILD_EXAMPLES builds the example.

Development

The project is managed with uv:

uv sync                        # create .venv, build the library, install dev deps
uv run pre-commit install      # enable ruff and the other hooks on commit
uv run pytest                  # run the test suite
uv run pytest -m "not e2e"     # only the fast unit tests
uv run python scripts/evaluate_inpainting.py  # measure the fill quality

The evaluation cuts holes into the images of examples/images, compares the fills with the original content and writes examples/images/evaluation.html, which shows them side by side. --baseline evaluates another build of the library as well, e.g. of the main branch; the docstring of the script explains the columns. Pull requests that change the C++ code get this comparison with their base commit in the summary of the workflow "Fill quality", and the HTML report as its artifact.

Releases are published to PyPI by creating a GitHub release; the version is taken from its tag (e.g. v2.0.0). Every released wheel and sdist carries a build provenance attestation: gh attestation verify <file> -R mauwii/PyPatchMatch.

License

PyPatchMatch is released under the MIT License. The wheels contain a statically linked build of OpenCV core (Apache-2.0) and its bundled third-party code; their licenses are included in the licenses/opencv directory of the wheel's .dist-info, and a CycloneDX SBOM of OpenCV (PEP 770) in sboms/opencv.cdx.json, so that vulnerability scanners can find it.

README and COPYRIGHT by Younesse ANDAM

@Author: Younesse ANDAM

@Contact: younesse.andam@gmail.com

Description:

This project is a personal implementation of an algorithm called PATCHMATCH that restores missing areas in an image. The algorithm is presented in the following paper PatchMatch A Randomized Correspondence Algorithm for Structural Image Editing by C.Barnes, E.Shechtman, A.Finkelstein and Dan B.Goldman ACM Transactions on Graphics (Proc. SIGGRAPH), vol.28, aug-2009

For more information please refer to https://gfx.cs.princeton.edu/pubs/Barnes_2009_PAR/

Copyright (c) 2010-2011