This is a subset of SDK examples, for full list see readme.md
Name | Language | Description | Experience Level | Technology |
---|---|---|---|---|
Hello-RealSense | C++ | Demonstrates the basics of connecting to a RealSense device and using depth data | ⭐ | |
Distance | C | Equivalent to hello-realsense but rewritten for C users |
⭐ | |
Color | C | Demonstrate how to stream color data and prints some frame information | ⭐ | |
Capture | C++ | Shows how to synchronize and render multiple streams: left, right, depth and RGB streams | ⭐ | |
Save To Disk | C++ | Demonstrate how to render and save video streams on headless systems without graphical user interface (GUI) | ⭐ | |
Pointcloud | C++ | Showcase Projection API while generating and rendering 3D pointcloud | ⭐ | |
ImShow | C++ & OpenCV | Minimal OpenCV application for visualizing depth data | ⭐ | |
Multicam | C++ | Present multiple cameras depth streams simultaneously, in separate windows | ⭐ | |
Depth | C | Demonstrates how to stream depth data and prints a simple text-based representation of the depth image | ⭐⭐ | |
Spatial Alignment | C++ | Introduces the concept of spatial stream alignment, using depth-color mapping | ⭐⭐ | |
Advanced Alignment | C++ | Show a simple method for dynamic background removal from video | ⭐⭐ | |
Measure | C++ | Lets the user measure the dimensions of 3D objects in a stream | ⭐⭐ | |
Post Processing | C++ | Demonstrating usage of post processing filters for depth images | ⭐⭐ | |
Record & Playback | C++ | Demonstrating usage of the recorder and playback devices | ⭐⭐ | |
Motion | C++ | Demonstrates how to use data from gyroscope and accelerometer to compute the rotation of the camera | ⭐⭐ | |
DNN | C++ & OpenCV | Intel RealSense camera used for real-time object-detection | ⭐⭐ | |
Software Device | C++ | Shows how to create a custom rs2::device |
⭐⭐⭐ | |
Sensor Control | C++ | A tutorial for using the rs2::sensor API |
⭐⭐⭐ | |
GrabCuts | C++ & OpenCV | Simple background removal using the GrabCut algorithm | ⭐⭐⭐ | |
Latency | C++ & OpenCV | Basic latency estimation using computer vision | ⭐⭐⭐ |