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    Use the Face Blur model with the Python SDK

    This tutorial shows you how to use the Python SDK to blur faces in video. The example blurs video files from a Cloud Storage bucket and generates blurred video outputs. These output videos are stored to the same Cloud Storage bucket as the source videos.

    Objectives

    This tutorial shows you how to do the following:

    • Create a Cloud Storage bucket.
    • Upload a local video file to the bucket.
    • Send a request using the Python SDK.
    • View blurred output videos.

    Costs

    In this document, you use the following billable components of Google Cloud:

    To generate a cost estimate based on your projected usage, use the pricing calculator .

    New Google Cloud users might be eligible for a free trial .

    When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, see Clean up .

    Before you begin

    1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

    2. Install the Google Cloud CLI.

    3. If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity .

    4. To initialize the gcloud CLI, run the following command:

      gcloud  
init

    5. Create or select a Google Cloud project .

      • Create a Google Cloud project:

        gcloud projects create PROJECT_ID

        Replace PROJECT_ID with a name for the Google Cloud project you are creating.

      • Select the Google Cloud project that you created:

        gcloud config set project PROJECT_ID

        Replace PROJECT_ID with your Google Cloud project name.

    6. Verify that billing is enabled for your Google Cloud project .

    7. Enable the Vertex AI Vision, Cloud Storage APIs:

      gcloud  
services  
 enable 
  
visionai.googleapis.com  
 storage.googleapis.com

    8. If you're using a local shell, then create local authentication credentials for your user account:

      gcloud  
auth  
application-default  
login

      You don't need to do this if you're using Cloud Shell.

      If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity .

    9. Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/visionai.editor, roles/storage.objectAdmin 

      gcloud  
projects  
add-iam-policy-binding  
 PROJECT_ID 
  
--member = 
 "user: USER_IDENTIFIER 
" 
  
--role = 
 ROLE

      Replace the following:

      • PROJECT_ID : your project ID.
      • USER_IDENTIFIER : the identifier for your user account—for example, myemail@example.com .
      • ROLE : the IAM role that you grant to your user account.

    10. Install the Google Cloud CLI.

    11. If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity .

    12. To initialize the gcloud CLI, run the following command:

      gcloud  
init

    13. Create or select a Google Cloud project .

      • Create a Google Cloud project:

        gcloud projects create PROJECT_ID

        Replace PROJECT_ID with a name for the Google Cloud project you are creating.

      • Select the Google Cloud project that you created:

        gcloud config set project PROJECT_ID

        Replace PROJECT_ID with your Google Cloud project name.

    14. Verify that billing is enabled for your Google Cloud project .

    15. Enable the Vertex AI Vision, Cloud Storage APIs:

      gcloud  
services  
 enable 
  
visionai.googleapis.com  
 storage.googleapis.com

    16. If you're using a local shell, then create local authentication credentials for your user account:

      gcloud  
auth  
application-default  
login

      You don't need to do this if you're using Cloud Shell.

      If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity .

    17. Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/visionai.editor, roles/storage.objectAdmin 

      gcloud  
projects  
add-iam-policy-binding  
 PROJECT_ID 
  
--member = 
 "user: USER_IDENTIFIER 
" 
  
--role = 
 ROLE

      Replace the following:

      • PROJECT_ID : your project ID.
      • USER_IDENTIFIER : the identifier for your user account—for example, myemail@example.com .
      • ROLE : the IAM role that you grant to your user account.
      18.
    18. Get the Vertex AI Vision SDK source code: 
      git clone https://github.com/google/visionai.git

      The Python examples are located in the visionai/python/example/ directory.

    19. Get the Python SDK: 
      wget https://github.com/google/visionai/releases/download/v0.0.5/visionai-0.0.5-py3-none-any.whl

    Add input files to Cloud Storage

    Before you can send a request using the Python SDK, create a Cloud Storage bucket and upload a local video to use as input.

    1. Create a Cloud Storage bucket:

       gcloud  
storage  
buckets  
create  
gs:// BUCKET_NAME

    2. Upload a local video file to the new bucket:

        gcloud 
 storage 
 cp 
  LOCAL_FILE 
 
 gs 
 : 
 // 
  BUCKET_NAME

    Install dependencies and send the request

    After you create your Cloud Storage bucket for input and output videos and add a local video, install the necessary dependencies and send your request.

    1. Optional. Set up your virtual environment:

      1. If not installed, install virtualenv :

         sudo  
apt-get  
install  
python3-venv

      2. Create a new virtual environment:

         python3  
-m  
venv  
 vaivenv

      3. Activate your virtual environment:

          source 
  
 vaivenv 
/bin/activate

    2. Install dependencies:

       pip3  
install  
visionai-0.0.5-py3-none-any.whl
pip3  
install  
google-cloud-storage

    3. Send your request with the Python SDK.

      Make the following variable substitutions:

      • PROJECT_ID : Your Google Cloud project ID.
      • LOCATION_ID : Your location ID. For example, us-central1 . More information . Supported regions .
      • BUCKET_NAME : The Cloud Storage bucket you created.
       python3  
visionai/python/example/blur_gcs_video.py  
 \ 
--project_id = 
 PROJECT_ID 
  
–cluster_id = 
application-cluster-0  
 \ 
–location_id = 
 LOCATION_ID 
  
–bucket_name = 
 BUCKET_NAME

      You should see output similar to the following:

        Listing 
 mp4 
 files 
 ... 
 test1 
 . 
 mp4 
 test2 
 . 
 mp4 
 Creating 
 deid 
 processes 
 ... 
 process 
 vnluvxgl 
 is 
 created 
 process 
 rvrdoucx 
 is 
 created 
 Waiting 
 for 
 processes 
 to 
 finish 
 ... 
 process 
 vnluvxgl 
 state 
 is 
 COMPLETED 
 process 
 rvrdoucx 
 state 
 is 
 COMPLETED 
 All 
 processes 
 have 
 finished 
 , 
 please 
 check 
 the 
 GCS 
 bucket 
 ! 
 ```

    Examine output

    After your video has finished processing you can examine the output in your Cloud Storage bucket. The generated blurred video files will be in the same Cloud Storage bucket as the source video.

    1. List all objects in your bucket with the gcloud storage ls command:

        gcloud 
 storage 
 ls 
 gs 
 : 
 // 
 bucket

      You should see the source files and output files similar to the following:

        test1 
 . 
 mp4 
 test2 
 . 
 mp4 
 test1_deid_output 
 . 
 mp4 
 test2_deid_output 
 . 
 mp4

    2. Optional. Download the output files locally with the gcloud storage cp command and view the blurred videos:

        gcloud 
 storage 
 cp 
 gs 
 : 
 // 
  BUCKET_NAME 
 
 / 
  FILE_NAME 
 
 .

    Clean up

    To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

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