Utilizzare il modello Sfocatura del volto con l'SDK Python

Questo tutorial mostra come utilizzare l'SDK Python per sfocare i volti nei video. L'esempio sfoca i file video di un bucket Cloud Storage e genera output video sfocati. Questi video di output vengono archiviati nello stesso bucket Cloud Storage dei video di origine.

Obiettivi

Questo tutorial mostra come:

• Creare un bucket Cloud Storage.
• Carica un file video locale nel bucket.
• Invia una richiesta utilizzando l'SDK Python.
• Visualizzare i video di output sfocati.

Costi

In questo documento vengono utilizzati i seguenti componenti fatturabili di Google Cloud:

• Vertex AI Vision (Models - Person / face blur)
• Cloud Storage

Per generare una stima dei costi in base all'utilizzo previsto, utilizza il Calcolatore prezzi.

I nuovi utenti di Google Cloud potrebbero avere diritto a una prova gratuita.

Al termine delle attività descritte in questo documento, puoi evitare l'addebito di ulteriori costi eliminando le risorse che hai creato. Per saperne di più, consulta Esegui la pulizia.

Prima di iniziare

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.
Install the Google Cloud CLI.
Se utilizzi un provider di identità (IdP) esterno, devi prima accedere a gcloud CLI con la tua identità federata.
Per inizializzare gcloud CLI, esegui questo comando:
gcloud init
Create or select a Google Cloud project.
Roles required to select or create a project
• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.
Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the 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.
Verify that billing is enabled for your Google Cloud project.
Enable the Vertex AI Vision, Cloud Storage APIs:
Roles required to enable APIs
To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.
gcloud services enable visionai.googleapis.com storage.googleapis.com
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.
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, [e-mail].
• ROLE: The IAM role that you grant to your user account.

Install the Google Cloud CLI.

Se utilizzi un provider di identità (IdP) esterno, devi prima accedere a gcloud CLI con la tua identità federata.

Per inizializzare gcloud CLI, esegui questo comando:

gcloud init

Create or select a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the 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.

Verify that billing is enabled for your Google Cloud project.

Enable the Vertex AI Vision, Cloud Storage APIs:

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

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

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.

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, [e-mail].
• ROLE: The IAM role that you grant to your user account.
1. Recupera il codice sorgente dell'SDK Vertex AI Vision:

   git clone https://github.com/google/visionai.git

   Gli esempi Python si trovano nella directory visionai/python/example/.

2. Scarica l'SDK Python:

   wget https://github.com/google/visionai/releases/download/v0.0.5/visionai-0.0.5-py3-none-any.whl

Aggiungi file di input a Cloud Storage

Prima di poter inviare una richiesta utilizzando l'SDK Python, crea un bucket Cloud Storage e carica un video locale da utilizzare come input.

1. Crea un bucket Cloud Storage:

   gcloud storage buckets create gs://BUCKET_NAME
   

2. Carica un file video locale nel nuovo bucket:

   gcloud storage cp LOCAL_FILE gs://BUCKET_NAME
   

Installa le dipendenze e invia la richiesta

Dopo aver creato il bucket Cloud Storage per i video di input e output e aggiunto un video locale, installa le dipendenze necessarie e invia la richiesta.

1. Facoltativo. Configura l'ambiente virtuale:

   1. Se non è installato, installa virtualenv:

      sudo apt-get install python3-venv
      

   2. Crea un nuovo ambiente virtuale:

      python3 -m venv vaivenv
      

   3. Attiva l'ambiente virtuale:

      source vaivenv/bin/activate
      

2. Installa le dipendenze:

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

3. Invia la richiesta con l'SDK Python.

   Effettua le seguenti sostituzioni delle variabili:

   • PROJECT_ID: l'ID progetto Google Cloud .
   • LOCATION_ID: il tuo ID sede. Ad esempio, us-central1. Scopri di più. Regioni supportate.
   • BUCKET_NAME: il bucket Cloud Storage che hai creato.

   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
   

   Dovresti vedere un output simile al seguente:

    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!
    ```
   

Esamina l'output

Una volta completata l'elaborazione del video, puoi esaminare l'output nel tuo bucket Cloud Storage. I file video sfocati generati si troveranno nello stesso bucket Cloud Storage del video di origine.

1. Elenca tutti gli oggetti nel bucket con il comando gcloud storage ls:

   gcloud storage ls gs://bucket
   

   Dovresti vedere i file di origine e i file di output simili ai seguenti:

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

2. Facoltativo. Scarica i file di output localmente con il comando gcloud storage cp e visualizza i video sfocati:

   gcloud storage cp gs://BUCKET_NAME/FILE_NAME .
   

Esegui la pulizia

Per evitare che al tuo account Google Cloud vengano addebitati costi relativi alle risorse utilizzate in questo tutorial, elimina il progetto che contiene le risorse oppure mantieni il progetto ed elimina le singole risorse.

Passaggi successivi

• Scopri di più sul modello di sfocatura delle persone.
• Esplora architetture, diagrammi e best practice di riferimento su Google Cloud. Consulta il nostro Cloud Architecture Center.