Open Image Debugger: Enabling visualization of in-memory buffers on GDB/LLDB

Open Image Debugger is a tool for visualizing in-memory buffers during debug sessions, compatible with both GDB and LLDB. It works out of the box with instances of the OpenCV Mat class and Eigen matrices, but can also be customized to work with any arbitrary data structure.

Prefer VS Code or a fork (Cursor, VSCodium, Windsurf, …)? Skip the manual build — install the extension from the VS Code Marketplace or Open VSX. See Installation below.

New — using CLion or Android Studio? The Open Image Debugger plugin is now on the JetBrains Marketplace: plot buffers straight from the native C/C++ debugger, inside the IDE. See Installation below.

New — declarative custom types. You can now describe your own buffer types in a .oid/types.json file instead of writing Python; the same file works in gdb, lldb, and the VS Code and JetBrains extensions. See doc/declarative-types.md.

Download (experimental)

A bit experimental, better to compile manually

Features

• GUI interactivity:
  • Scroll to zoom, left click+drag to move the buffer around;
  • Rotate buffers 90° clockwise or counterclockwise;
  • Go-to widget that quickly takes you to any arbitrary pixel location;
• Buffer values: Zoom in close enough to inspect the numerical contents of any pixel.
• Auto update: Whenever a breakpoint is hit, the buffer view is automatically updated.
• Auto contrast: The entire range of values present in the buffer can be automatically mapped to the visualization range [0, 1], where 0 represents black and 1 represents white.
• The contrast range can be manually adjusted, which is useful for inspecting buffers with extreme values (e.g. infinity, nan and other outliers).
• Link views together, moving all watched buffers simultaneously when any single buffer is moved on the screen
• Supported buffer types: uint8_t, int16_t, uint16_t, int32_t, uint32_t, float and double
• Supported buffer channels: Up to four channels (Grayscale, two-channels, RGB and RGBA)
• GPU accelerated
• Supports large buffers whose dimensions exceed GL_MAX_TEXTURE_SIZE.
• Supports data structures that map to a ROI of a larger buffer.
• Exports buffers as png images (with auto contrast) or octave/matlab matrix files (unprocessed).
• Auto-load buffers being visualized in the previous debug session
• Designed to scale well for HighDPI displays
• Works on Linux, macOS X and Windows (experimental)

Supported OSes

• OID is developed with Ubuntu as the main target. The goal is to support the two latest LTS versions at a given time.
  • Ubuntu is also used as a basis for the minimum versions of the dependencies: we try to support the default versions of the packages you get via apt install
• There are currently no plans to support other Linux distros. OID may or may not compile on your favorite distro, your mileage may vary.
• Support for MacOS and Windows are somewhat experimental now - the code should be able to compile (see https://github.com/OpenImageDebugger/OpenImageDebugger/releases), but the binaries are not actively tested - in fact we currently have no automated tests at all for any OS - help is more than welcome in this regard. Also, we haven't come up with a simple installation/usage guides for these OSes yet.

Requirements

• A C++20 compliant compiler
• GDB 15.0.50+ or LLDB 18.1.3+
• CMake 3.28.3+
• Python 3.12.3+ development packages
• OpenGL 2.1+ support
• Linux only: Wayland and X11 development packages (needed to build the bundled GLFW) and GTK 3 development packages (needed by the native file-open dialog); see the apt install command below. Alternatively, configure with -DNFD_PORTAL=ON to use the xdg-desktop-portal (D-Bus) dialog backend instead of GTK.

All other third-party libraries are bundled as git submodules and built from source, so they don't need to be installed:

• Dear ImGui — viewer UI
• GLFW — window and OpenGL context management
• Eigen — linear algebra for the visualization layer
• Asio (standalone) — IPC between the debugger bridge and the viewer
• nlohmann/json — settings persistence
• stb — image decoding for opening files, plus PNG export and text rendering (stb_image, stb_image_write, stb_truetype)
• nanosvg — toolbar icon rasterization
• nativefiledialog-extended — native OS dialog for File → Open (native builds only)
• GoogleTest — unit tests

Note: this list might get out-of-date by accident. For a more accurate list of requirements, please check what is used in https://github.com/OpenImageDebugger/OpenImageDebugger/blob/main/.github/workflows/build.yml and in the CI container images defined in https://github.com/OpenImageDebugger/dockerfiles.

Installation

VS Code and forks

The quickest way to get started is the Open Image Debugger extension, available for VS Code and compatible forks (Cursor, VSCodium, Windsurf, and others):

• VS Code Marketplace
• Open VSX

CLion and Android Studio

The Open Image Debugger plugin brings the same viewer to JetBrains IDEs with a native C/C++ debugger — CLion, and Android Studio with the NDK:

• JetBrains Marketplace

If you'd rather build and integrate the desktop version manually, follow the steps below.

Ubuntu Linux dependencies

On Ubuntu, you can install most of the dependencies with the following command:

sudo apt install build-essential cmake libgl1-mesa-dev libgtk-3-dev libpython3-dev \
    python3-dev libwayland-dev libxcursor-dev libxi-dev libxinerama-dev \
    libxkbcommon-dev libxrandr-dev pkg-config

Building the Open Image Debugger

Clone the source code to any folder you prefer and initialize the submodules:

git clone https://github.com/OpenImageDebugger/OpenImageDebugger.git --recurse-submodules

Now run the following commands to build it:

cmake -S . -B build -DCMAKE_INSTALL_PREFIX=/path/to/installation/folder
cmake --build build --config Release --target install -j 4

GDB integration: Edit the file ~/.gdbinit (create it if it doesn't exist) and append the following line:

source /path/to/OpenImageDebugger/oid.py

LLDB integration: Edit the file ~/.lldbinit (create it if it doesn't exist) and append the following line:

command script import /path/to/OpenImageDebugger/oid.py

MacOS Installation

At the moment, the MacOS build is only known to work with python3 and lldb installed from Homebrew (the system-provided LLDB from the Xcode Command Line Tools is not supported). Install them with:

brew install python3 llvm

Make sure python3 resolves to the Homebrew one — run which python3 and confirm it points under the Homebrew prefix (/opt/homebrew on Apple Silicon, /usr/local on Intel; brew --prefix prints it), rather than a pyenv, conda or system Python. The standard Homebrew install puts that prefix's bin on your PATH.

Then debug your program using the Homebrew LLDB, for example:

BREW_PREFIX=$(brew --prefix)
"$BREW_PREFIX"/opt/llvm/bin/lldb /path/to/your/executable

Testing your installation

After compiling the plugin, you can test it by running the following command (use the same Python 3 interpreter CMake found when building):

python3 /path/to/OpenImageDebugger/oid.py --test

On MacOS, invoke the test with the full path to the Homebrew python3, for example:

BREW_PREFIX=$(brew --prefix)
"$BREW_PREFIX"/bin/python3 /path/to/OpenImageDebugger/oid.py --test

If the installation was succesful, you should see the Open Image Debugger window with the buffers sample_buffer_1 and sample_buffer_2.

Using plugin

When the debugger hits a breakpoint, the Open Image Debugger window will be opened. You only need to type the name of the buffer to be watched in the "add symbols" input, and press <enter>.

Opening image files directly

You can also open an image or NumPy array in the viewer without a debugger session at all, either fro