Skip to content

2-ObjectDetection

Directory actions

More options

Directory actions

More options

Latest commit

 

History

History
 
 

2-ObjectDetection

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
assets
assets
 
 
.DS_Store
.DS_Store
 
 
README.md
README.md
 
 

README.md

Object Detection Using Deep Lens

Create Your Project

  1. Using your browser, open the AWS DeepLens console at https://console.aws.amazon.com/deeplens/.
  2. Choose Projects, then choose Create new project.
  3. On the Choose project type screen
  • Choose Use a project template, then choose Object detection.

  • Scroll to the bottom of the screen, then choose Next.
  1. On the Specify project details screen

    • In the Project information section:

      • Either accept the default name for the project, or type a name you prefer.
      • Either accept the default description for the project, or type a description you prefer.

    • In the Project content section:

      • Model—make sure the model is deeplens-object-detection. If it isn't, remove the current model then choose Add model. From the list of models, choose deeplens-object-detection.
      • Function—make sure the function is deeplens-object-detection. If it isn't, remove the current function then choose Add function. From the list of functions, choose deeplens-object-detection.

  • Choose Create.

This returns you to the Projects screen where the project you just created is listed with your other projects.

Deploy your project

Next you will deploy the Object Detection project you just created.

  1. From Deeplens console, On the Projects screen, choose the radio button to the left of your project name, then choose Deploy to device.

  1. On the Target device screen, from the list of AWS DeepLens devices, choose the radio button to the left of the device that you want to deploy this project to. An AWS DeepLens device can have only one project deployed to it at a time.

  1. Choose Review.

    This will take you to the Review and deploy screen.

    If a project is already deployed to the device, you will see an error message "There is an existing project on this device. Do you want to replace it? If you Deploy, AWS DeepLens will remove the current project before deploying the new project."

  2. On the Review and deploy screen, review your project and choose Deploy to deploy the project.

    This will take you to to device screen, which shows the progress of your project deployment.

View your project output

You need MPlayer to view the project output from Deeplens device. Instructions are provided below for installing this on Mac and Windows.

For Mac

  1. Install MPlayer by using command below in the terminal window:
brew install mplayer
  1. Wait until the project is deployed and you see the message Deployment of project Face-detection, version 0 succeeded. After the project is successfully deployed, use the command below from terminal window to view project output stream:
ssh aws_cam@<IP Address of your deeplens device> cat /tmp/results.mjpeg | mplayer -demuxer lavf -lavfdopts format=mjpeg:probesize=32 -

Example:

ssh aws_cam@192.168.86.120 cat /tmp/results.mjpeg | mplayer -demuxer lavf -lavfdopts format=mjpeg:probesize=32 -

For Windows

  1. You will need to use 7zip to extract the MPlayer executable. You can install 7zip by running the executable at the following link:
  1. After you have installed 7zip, download the MPlayer 7z archive at the following link, and then use 7zip to extract the contents of the archive:

Remember the directory in which you extracted the contents of the archive, because in the next step, you will need to provide the full path to the MPlayer executable that you extracted from that archive.

To view the project output stream

Wait until the project is deployed and you see the message Deployment of project Face-detection, version 0 succeeded. After the project is successfully deployed, follow these steps to view the project output stream:

Option 1. If you want to use Windows Subsystem for Linux (e.g. running bash or any Linux distro installed from Windows Store), use the following command in your Windows command prompt:

ssh aws_cam@<IP Address of your deeplens device> cat /tmp/results.mjpeg | /mnt/c/your-path-to-mplayer-exe-here/mplayer.exe -demuxer lavf -lavfdopts format=mjpeg:probesize=32 –

Example:

ssh aws_cam@10.57.226.58 cat /tmp/results.mjpeg | /mnt/c/your-path-to-mplayer-exe-here/mplayer.exe -demuxer lavf -lavfdopts format=mjpeg:probesize=32 –

Option 2. If you have ssh installed directly in Windows 10 (using Fall Creators Update or later, in optional features), use the following command in your Windows command prompt:

ssh aws_cam@<IP Address of your deeplens device> cat /tmp/results.mjpeg | c:\Your-path-to-mplayer-exe-here\mplayer.exe -demuxer lavf -lavfdopts format=mjpeg:probesize=32 –

Example:

ssh aws_cam@192.168.86.120 cat /tmp/results.mjpeg | c:\Your-path-to-mplayer-exe-here\mplayer.exe -demuxer lavf -lavfdopts format=mjpeg:probesize=32 –

View your project log messages in IoT

You can also view the log messages that your project's Lambda function running on DeepLens device sends to IoT topic.

  1. Go to DeepLens in AWS console and then Devices at https://console.aws.amazon.com/deeplens/home?region=us-east-1#devices
  2. Click on the name of your DeepLens device and on the next screen note the IoT topic under Device settings.

  1. Go to IoT in AWS Console at https://console.aws.amazon.com/iot/home?region=us-east-1#/dashboard
  2. Click on Test in the left navigation.
  3. Enter the IoT topic in the textbox under Subscription topic and click Subscribe to topic
  4. You should now see log messages published from DeepLens device to IoT.

Completion

You have create and deployed object detection project to your Deeplens device. In the next module, Hot Dog Not Hot Dog, you will learn how to edit a pre-trained object recognition model in SageMaker to perform a binary classification and deploy newly trained model to the device.