https://github.com/deep-neural/libmediadevice
https://github.com/deep-neural/libmediadevice
Last synced: 6 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/deep-neural/libmediadevice
- Owner: deep-neural
- License: mit
- Created: 2025-04-02T01:30:15.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-04-02T02:20:24.000Z (about 2 months ago)
- Last Synced: 2025-04-02T03:18:56.293Z (about 2 months ago)
- Language: C
- Size: 88.9 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
libMediaDevice
A modern C/C++ library for interfacing with low-level media devices across multiple platforms
### New Release
libMediaDevice v1.0.0 has been released! See the [release notes](https://github.com/deep-neural/libmediadevice/wiki/[email protected]) to learn about new features, enhancements, and breaking changes.
If you aren't ready to upgrade yet, check the [tags](https://github.com/deep-neural/libmediadevice/tags) for previous stable releases.
We appreciate your feedback! Feel free to open GitHub issues or submit changes to stay updated in development and connect with the maintainers.
-----
### Usage
libMediaDevice is distributed as a pure C/C++ library. To integrate it into your project, ensure you have a compatible C/C++ compiler and the necessary build tools. Clone the repository and link against the library in your build system.
## Simple API
Video Device
Audio Device
```cpp
#include
// NVIDIA Device
media::VideoDeviceConfig config;
config.type = media::VideoDeviceType::NVFBC;
config.capture_cursor = true;
config.display_id = ":0";
auto video_device = media::VideoDevice::Create(config);
if (!video_device) {
std::cerr << "Failed to create NVFBC video device!" << std::endl;
std::cerr << "Note: NVFBC requires NVIDIA GPU and drivers" << std::endl;
return 1;
}
std::cout << "Successfully created NVFBC video device" << std::endl;
std::cout << "Display resolution: " << video_device->GetWidth() << "x" << video_device->GetHeight() << std::endl;
std::vector nv12_buffer;
bool nv12_success = false;
// NV12 format
nv12_success = video_device->GetFrameNV12(&nv12_buffer);
if (nv12_success) {
std::cout << "Successfully captured NV12 frame " << (i + 1)
<< " (" << nv12_buffer.size() << " bytes)" << std::endl;
}
```
```cpp
#include
media::AudioDeviceConfig config;
config.type = media::AudioDeviceType::PULSE;
config.sample_rate = 44100;
config.channels = 2;
config.buffer_ms = 100;
config.device_id = ""; // Use default audio device
// Create the audio device
auto audio_device = media::AudioDevice::Create(config);
if (!audio_device) {
std::cerr << "Failed to create PulseAudio device!" << std::endl;
return 1;
}
int bytes_per_frame = actual_config.channels * 2;
int expected_bytes = (actual_config.sample_rate * bytes_per_frame * actual_config.buffer_ms) / 1000;
// audio frames
std::vector audio_buffer;
if (audio_device->GetFrameS16LE(&audio_buffer)) {
std::cout << "Successfully captured audio frame " << (i + 1)
<< " (" << audio_buffer.size() << " bytes)" << std::endl;
}
```
**[Example Applications](examples/README.md)** contain code samples demonstrating common use cases with libMediaDevice.
**[API Documentation](https://github.com/deep-neural/libmediadevice/docs)** provides a comprehensive reference of our Public APIs.
Now go build something amazing! Here are some ideas to spark your creativity:
* Create cross-platform media stream applications with consistent APIs.
* Develop GPU hardware-accelerated media streaming solutions.
* Implement digital signage solutions with direct hardware access.
## Building
See [BUILDING.md](https://github.com/deep-neural/libmediadevice/blob/master/BUILDING.md) for building instructions.
### Features
#### Cross-Platform Device Access
* Uniform API for cameras, microphones, displays, and audio outputs across platforms.
* Direct hardware access with modern abstraction layers for Windows, macOS, Linux, and more.
#### Modern Interface Design
* Simplified, consistent API that hides platform-specific complexity.
* Event-driven architecture with callbacks for device state changes.
#### Performance Focused
* Zero-copy operations where supported by hardware.
* Minimal overhead for maximum throughput and reduced latency.
#### Device Control
* Comprehensive device parameter control (exposure, gain, color settings, etc.).
* Hardware capability discovery and feature negotiation.
#### Pure C/C++
* Written entirely in C/C++ with minimal dependencies for maximum performance.
* Wide platform support: Windows, macOS, Linux, FreeBSD, Android, and more.
### Contributing
Check out the [contributing guide](https://github.com/deep-neural/libmediadevice/wiki/Contributing) to join the team of dedicated contributors making this project possible.
### License
MIT License - see [LICENSE](LICENSE) for full text