rs-hello-realsense example demonstrates the basics of connecting to an Intel RealSense device and taking advantage of depth data by printing the distance to object in the center of camera field of view.
Assuming camera is connected you should see
"The camera is facing an object X meters away" line being continuously updated. X is the distance in meters to the object in the center of camera field of view.
First, we include the Intel® RealSense™ Cross-Platform API.
All but advanced functionality is provided through a single header:
// Include Intel RealSense Cross Platform API
Next, we create and start RealSense pipeline. Pipeline is the primary high level primitive controlling camera enumeration and streaming.
// Create a Pipeline - this serves as a top-level API for streaming and processing frames rs2::pipeline p; // Configure and start the pipeline p.start();
Once pipeline is configured, we can loop waiting for new frames.
Intel RealSense cameras usually offer multiple video, motion or pose streams.
wait_for_frames function will block until next set of coherent frames from various configured streams.
// Block program until frames arrive rs2::frameset frames = p.wait_for_frames();
To get first frame from the depth data stream, you can use
get_depth_frame helper function:
// Try to get a frame of a depth image rs2::depth_frame depth = frames.get_depth_frame();
Next we query the default depth frame dimensions (these may differ from sensor to sensor):
// Get the depth frame's dimensions float width = depth.get_width(); float height = depth.get_height();
To get distance at specific pixel (center of the frame), you can use
// Query the distance from the camera to the object in the center of the image float dist_to_center = depth.get_distance(width / 2, height / 2);
The only thing left is to print the resulting distance to the screen:
// Print the distance std::cout << "The camera is facing an object " << dist_to_center << " meters away \r";
Updated 4 months ago