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DeepDetect Webcam Tutorial

Introduction

This tutorial expands on the DeepDetect REST Tutorial. Here the Deep Detect application and REST APIs are used to connect a webcam from a host machine and use it to stream video to F1, allowing for classification of live images.

The full code of this project is in /home/centos/xfdnn_18_03_19/deepdetect/

For instructions on launching and connecting to instances, see here.

Start by launching two Terminals

Terminal 1

  1. Connect to F1

  2. Navigate to /home/centos/xfdnn_18_03_19/deepdetect/

    $ cd /home/centos/xfdnn_18_03_19/deepdetect/
$ ls
createService.sh           libs     sdaccel_profile_summary.csv   testService.sh
dede                       models   sdaccel_profile_summary.html  xclbin
demo                       run.sh   start_deepdetect_docker.sh    xfdnn_scheduler
exec_deepdetect_docker.sh  runtime  templates

  3. Execute ./start_deepdetect_docker.sh to enter application docker

  4. Navigate to /opt/deepdetect/

  5. Execute ./runDeepDetectServer.sh to start the DeepDetect Caffe REST Server

    $ ./start_deepdetect_docker.sh
# ./runDeepDetectServer.sh
DeepDetect [ commit  ]

INFO - 16:03:43 - Running DeepDetect HTTP server on 0.0.0.0:8080

    When you see the message "INFO - 16:03:43 - Running DeepDetect HTTP server on 0.0.0.0:8080", this indicates that the script has started the webserver correctly.
    When the FPGA is ready you will see XBLAS online! (d=0)

Terminal 2:

  1. Connect to F1

  2. Navigate to /home/centos/xfdnn_18_03_19/deepdetect/

    $ cd /home/centos/xfdnn_18_03_19/deepdetect/
$ ls
createService.sh           libs     sdaccel_profile_summary.csv   testService.sh
dede                       models   sdaccel_profile_summary.html  xclbin
demo                       run.sh   start_deepdetect_docker.sh    xfdnn_scheduler
exec_deepdetect_docker.sh  runtime  templates

  3. Execute ./createService.sh This initializes the DeepDetect server in Terminal 1.
    Wait for the FPGA to load xclbin in Terminal 1.
    On success you will see {"status":{"code":201,"msg":"Created"}}

  4. Navigate to /home/centos/xfdnn_18_03_19/deepdetect/demo/webcam

  5. Edit index.html (using a text editor such as vi or nano). Find the section below:

    /*******************************************
* TODO: Please update the address below
* in 'url' to point to your public IP
*******************************************/
var url = null;

    Change yourpublicdns.compute-1.amazonaws.com to your instance's public IP address from EC2.

     ```

    var url = ".compute-1.amazonaws.com:8888"; ```

    This is for the client browser to upload webcam images to server.py

  6. Navigate to /home/centos/xfdnn_18_03_19/deepdetect/

  7. Execute ./exec_deepdetect_docker.sh

  8. Navigate to /opt/deepdetect/demo/webcam/

  9. Execute python server.py This starts the webcam demo webpage and server.

     $ cd demo/webcam/
 $ ls
 css  index.html  jpeg_camera  media  README  server.py  streams  test.html
 $ nano index.html
 $ cd ../..
 $ ./exec_deepdetect_docker.sh
 # cd demo/webcam/
 # python server.py
 serving at port 8888

Once you see serving at port 8888 the application is running and ready.

Host PC:

  1. In Firefox visit http://.compute-1.amazonaws.com:8888

    Note: Firefox browser is recommended. Allow browser permissions for use of the Webcam and Adobe, if needed.

    Now you should be able to classify images via your webcam