Awesome

<dlimgedit>

dlimgedit is a C++ library for image painting and editing workflows which make use of deep learning.

• Simple high-level C++ API
• Flexible integration (supports dynamic loading via C interface)
• Optimized for minimal copying and overhead
• Fully C++ based neural network inference (via onnxruntime)
• Platforms: Windows, Linux
• Backends: CPU, GPU via DirectML (Windows only), GPU via CUDA (Linux/NVIDIA only)

Features

Segmentation

Identify objects in an image and generate masks for them (based on SegmentAnything)

// Load an image...
Image image = Image::load("example.png");
// ...or use existing image data:
ImageView image(pixel_data, {width, height}, Channels::rgba);

// Analyse the image
Environment env;
Segmentation segmentation = Segmentation::process(image, env);

// Query mask for the object at a certain point in the image:
Image mask = segmentation.compute_mask(Point{220, 355});

// Query mask for the largest object contained in a certain region:
Image mask = segmentation.compute_mask(Region(Point{140, 200}, Extent{300, 300}));

Performance is interactive: roughly 500ms for Segmentation::process and 80ms per mask on CPU. Running on GPU can be much faster: 50ms and 12ms respectively on RTX4070, with around 500MB of VRAM used.

Building

Building only requires CMake and a compiler with C++20 support (eg. MSVC 2022 on Windows, GCC 11+ on Linux).

Clone the repository:

git clone https://github.com/Acly/dlimgedit.git
cd dlimgedit

Configure:

mkdir build
cd build
cmake ..

Build:

cmake --build . --config Release

Documentation

The library can be added to existing CMake projects either via add_subdirectory(dlimgedit) to build from source, or by adding the target from installed binaries with find_package(dlimgedit). Packages should work out of the box on CPU. The onnxruntime shared library is installed as a required runtime dependency. Execution on GPU may require further libraries at runtime, see below.

The public API is C++14 compatible.

See the public header for API documentation.

GPU on Windows (DirectML)

Using Backend::gpu on Windows makes use of DirectML to run inference on GPU. A large range of GPUs is supported. Deploying DirectML.dll next to applications is recommended, otherwise the version of the DLL which ships with Windows will be used, and it is usually too old.

GPU on Linux (CUDA)

Using Backend::gpu on Linux makes use of CUDA to run inference on GPU. This requires the following additional libraries to be installed:

• NVIDIA CUDA Toolkit (Version 11.x)
• NVIDIA cuDNN (Version 8.x for CUDA 11.x)

Refer to NVIDIA's installation guide for detailed instructions.