V4D offers a way of writing graphical (on- and offscreen) high performance applications with OpenCV. It is light-weight and unencumbered by QT or GTK licenses. It features vector graphics using [NanoVG](https://github.com/inniyah/nanovg) a GUI based on [NanoGUI](https://github.com/mitsuba-renderer/nanogui) and (on supported systems) OpenCL/OpenGL and OpenCL/VAAPI interoperability. It should be included in [OpenCV-contrib](https://github.com/opencv/opencv_contrib) once it is ready.
# Why V4D?
Please refer to the online demos in the \ref v4d_tutorials and \ref v4d_demos section to see at a glance what it can do for you. **Note**: The online demos are slower than native builds and are sometimes missing features. If you want full performance (including hardware acceleration) you should really create a native build and test it.
Please refer to the online demos in the \ref v4d_tutorials and \ref v4d_demos section to see at a glance what it can do for you. **But note**: The online demos are slower than native builds and are sometimes missing features. If you want full performance (including hardware acceleration) you should really create a native build and test it.
* **OpenGL**: Easy access to OpenGL.
* **GUI**: Simple yet powerful user interfaces through NanoGUI.
@ -55,7 +55,7 @@ v4d->gl([](const Size sz) {
* [My NanoGUI fork](https://github.com/kallaballa/nanogui) which is included with V4D as a sub-repository (includes NanoVG).
# Optional: Dependencies for demos
* If you want CL-GL sharing on a recent Intel Platform (Gen8 - Gen12) you currently **need to build** [compute-runtime](https://github.com/intel/compute-runtime)
* (At the time of writing) If you want CL-GL interop on a recent Intel Platform you might need to build [compute-runtime](https://github.com/intel/compute-runtime). The first version of compute-runtime shipping CL-GL interop is **23.13.26032**
# Tutorials {#v4d_tutorials}
The tutorials are designed to be read one after the other to give you a good overview over the key concepts of V4D. After that you can move on to the demos.
Actually there are two ways to display an image using V4D. The most convenient way is to use the video pipeline to feed an image to V4D. That has the advantage that the image is automatically resized (preserving aspect ratio) to framebuffer size and color converted (the framebuffer is BGRA).
Actually there are several ways to display an image using V4D. The most convenient way is to use the video pipeline to feed an image to V4D. That has the advantage that the image is automatically resized (preserving aspect ratio) to framebuffer size and color converted (the framebuffer is BGRA).
| Original author | Amir Hassan (kallaballa) <amir@viel-zu.org> |
| Compatibility | OpenCV >= 4.7 |
Renders a rainbow cube on blueish background using OpenGL, applies a glow effect using OpenCV (optionally using OpenCL with native builds) and encodes the video (optionally using VAAPI and CL-VA interop with native builds).
Renders a rainbow cube on blueish background using OpenGL and applies a glow effect using OpenCV.
| Original author | Amir Hassan (kallaballa) <amir@viel-zu.org> |
| Compatibility | OpenCV >= 4.7 |
Renders a color wheel on top of an input-video using nanovg (OpenGL), does colorspace conversions using OpenCV (OpenCL) and decodes/encodes on the GPU (VAAPI).
Renders a color wheel on top of an input-video using nanovg (OpenGL) and does colorspace conversions using OpenCV.
| Original author | Amir Hassan (kallaballa) <amir@viel-zu.org> |
| Compatibility | OpenCV >= 4.7 |
Pedestrian detection using HOG with a linear SVM, non-maximal suppression and tracking using KCF. Uses nanovg for rendering (OpenGL), detects using a linear SVM (OpenCV/OpenCL), filters resuls using NMS (CPU) and tracks using KCF (CPU). Decodes/encodes on the GPU (VAAPI).
Pedestrian detection using HOG with a linear SVM, non-maximal suppression and tracking using KCF. Uses nanovg for rendering (OpenGL), detects using a linear SVM (OpenCV), filters resuls using NMS and tracks using KCF.
| Original author | Amir Hassan (kallaballa) <amir@viel-zu.org> |
| Compatibility | OpenCV >= 4.7 |
Optical flow visualization on top of a video. Uses background subtraction (OpenCV/OpenCL) to isolate areas with motion, detects features to track (OpenCV/OpenCL), calculates the optical flow (OpenCV/OpenCL), uses nanovg for rendering (OpenGL) and post-processes the video (OpenCV/OpenCL). Decodes/encodes on the video (VAAPI).
Optical flow visualization on top of a video. Uses background subtraction (OpenCV/) to isolate areas with motion, detects features to track (OpenCV), calculates the optical flow (OpenCV), uses nanovg for rendering (OpenGL) and post-processes the video (OpenCV/).
| Original author | Amir Hassan (kallaballa) <amir@viel-zu.org> |
| Compatibility | OpenCV >= 4.7 |
Face beautification using face landmark detection (OpenCV/OpenCL), nanovg (OpenGL) for drawing masks and multi-band blending (CPU) to put it all together.
Face beautification using face landmark detection (OpenCV), nanovg (OpenGL) for drawing masks and multi-band blending (CPU) to put it all together.
@ -13,14 +13,6 @@ It is possible to compile C++ (but also other languages) for the browser. The re
# So what makes it special for OpenCV and V4D?
For OpenCV there has been the possibility to run code in the browser for a while using [OpenCV.js](https://docs.opencv.org/4.x/d0/d84/tutorial_js_usage.html). But OpenCV.js merely offers the OpenCV APIs and visualization and GUI has to be done by other means (e.g. HTML5 Canvas). That is where V4D steps in because it has been written with WebAssembly in mind and cleverly uses [OpenGL](https://en.wikipedia.org/wiki/OpenGL) in a fashion that translates well to [WebGL](https://en.wikipedia.org/wiki/WebGL). V4D enables you to write graphical OpenCV applications that run native as well as in the browser.
# Online Demos
Please note that the following online demos are slower and/or have less features than the native versions.
kallaballa
2 years ago