text: # rs-dnn Sample
  
  ## Overview
  This example shows how to use Intel RealSense cameras with existing [Deep Neural Network](https://en.wikipedia.org/wiki/Deep_learning) algorithms. The demo is derived from [MobileNet Single-Shot Detector example](https://github.com/opencv/opencv/blob/3.4.0/samples/dnn/ssd_mobilenet_object_detection.cpp) provided with `opencv`. We modify it to work with Intel RealSense cameras and take advantage of depth data (in a very basic way). 
  
  
  The demo will load existing [Caffe model](https://github.com/chuanqi305/MobileNet-SSD) (see another tutorial [here](https://docs.opencv.org/3.3.0/d5/de7/tutorial_dnn_googlenet.html)) and use it to classify objects within the RGB image. Once object is detected, the demo will calculate approximate distance to the object using the depth data:
  
  <p align="center"><img src="res/1.PNG" /></p>
  
  ## Implementation Details
  
  Unlike the other samples, this demo requires access to the exact depth values. We generate a matrix of floating point values (in meters) using the following helper function:
  ```cpp
  auto depth_mat = depth_frame_to_meters(pipe, depth_frame);
  ```
  
  