> [!NOTE]
> **Note:** To complete this tutorial you need access to video data with human faces, either Real Time Streaming Protocol ([RTSP](https://en.wikipedia.org/wiki/Real_Time_Streaming_Protocol)) video data from a live Internet Protocol ([IP](https://en.wikipedia.org/wiki/IP_camera)) camera, or a local video file.
Gemini Enterprise Agent Platform Vision is an AI-powered platform you can use to ingest, analyze, and
store video data. Gemini Enterprise Agent Platform Vision lets you build
and deploy AI applications. You can build end-to-end Gemini Enterprise Agent Platform Vision solutions
by leveraging Gemini Enterprise Agent Platform Vision's integration with other product components.
To start implementing solutions using the Gemini Enterprise Agent Platform Vision platform,
review the following Agent Platform Vision concepts and components:
- **Streams**: Represent a video streaming layer from your solution. The stream
source can be a live video (for example, an IP camera) or a video file
(for example, an MP4 file).
- **Applications**: Enable the connection between a stream and an AI processor
to perform a machine learning operation on the video. For example, you can
connect a camera stream to an AI model that counts people passing in front of
it.
- **Media warehouses**: Store the video ingested by streams out to
Google Cloud storage. Storing data out to this destination lets you
query analysis
output and metadata from the AI processors used on data from the ingested
streams.
## Objectives
This tutorial shows you how to do the following:
- Create a data input stream resource.
- Begin streaming video data into this stream resource.
- Create an empty application.
- Add nodes to your application to stream data, modify data, and store data.
- Deploy your app for you to use.
- View processed data output in the Google Cloud console.
Before you begin this tutorial, you must have a streaming video resource
to send data to your Gemini Enterprise Agent Platform Vision app. This resource can
be either a local video or an RTSP feed. This video data must
contain human faces that the sample app can then blur.
## Costs
In this document, you use the following billable components of Google Cloud:
- [Agent Platform Vision](https://docs.cloud.google.com/vision-ai/pricing) (Streams - Data ingested, Streams - Data consumed, Models - Person / face blur, Warehouse - Video storage)
To generate a cost estimate based on your projected usage,
use the [pricing calculator](https://docs.cloud.google.com/products/calculator).
New Google Cloud users might be eligible for a [free trial](https://docs.cloud.google.com/free).
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](https://docs.cloud.google.com/vision-ai/docs/face-blur-tutorial.md.txt#clean-up).
## Before you begin
1. Get the location of your streaming video source, either locally (for example, `./sample_video.mp4`) or IP address of the live RTSP feed (for example, `rtsp://192.168.1.180:540`). You need this information to begin ingesting data into a stream after you create the stream resource.
2. Installing the `vaictl` tool to ingest data into a stream (OS: Debian GNU/Linux, CPU architecture: x86_64):
> [!NOTE]
> **Note** : You can also get a Docker image that has the Gemini Enterprise Agent Platform Vision SDK and all its dependencies already pre-installed. For information about getting a Docker image with the Gemini Enterprise Agent Platform Vision SDK, [Set up a
> project and a development environment](https://docs.cloud.google.com/vision-ai/docs/cloud-environment#get-docker).
1. Download the required package:
```
wget https://github.com/google/visionai/releases/download/v0.0.5/visionai_0.0-5_amd64.deb
```
2. After downloading the package, run the following command in the directory you downloaded the file:
```
sudo apt install ./visionai_0.0-5_amd64.deb
```
3. Verify installation:
```
vaictl --help
```
## Create a stream
To create a streaming video analysis app, you must first create and register a stream resource. As the resource that receives the video data from the users, the stream is required on any scenario you build using Gemini Enterprise Agent Platform Vision.
### Console
To create a new stream in the Google Cloud console, use the following steps.
1. Open the **Streams** tab of the Gemini Enterprise Agent Platform Vision dashboard.
[Go to the Streams tab](https://console.cloud.google.com/ai/vision-ai/video-streams)
2. Click **Register**.
3. Enter `input-stream` as the stream name and select the region where you
want to create your stream.
4. Click **Register** to create one or more streams.
## Ingest video into the stream
After you create a stream resource, you can use the `vaictl` command-line tool
to send video data to the stream.
> [!NOTE]
> **Note:** If you installed the `vaictl` tool using the Docker container option, you must [run your container](https://docs.docker.com/engine/reference/commandline/container_run/) and sign in before proceeding with the next steps.
### IP camera
If you're testing using a live IP camera, you need to get the IP address of
the camera. You must provide this information with the request, along with
other variable substitutions:
- PROJECT_ID: Your Google Cloud project ID.
- LOCATION_ID: Your location ID. For example, `us-central1`. For more information, see [Cloud locations](https://cloud.google.com/about/locations).
- RTSP_ADDRESS: The address of your Real Time Streaming Protocol ([RTSP](https://en.wikipedia.org/wiki/Real_Time_Streaming_Protocol)) feed. For example, `rtsp://192.168.1.180:540`.
This command sends an RTSP feed into the stream. You must run this
command in the network that has direct access to the RTSP feed.
```
vaictl -p PROJECT_ID \
-l LOCATION_ID \
-c application-cluster-0 \
--service-endpoint visionai.googleapis.com \
send rtsp to streams input-stream --rtsp-uri RTSP_ADDRESS
```
If the command runs successfully, you get the following output:
```
[...]
Waiting for long running operation projects/your-project/locations/us-central1/operations/operation-1651364156981-5dde82db7e4a9-dfb17ca5-1051eb20 ⠙
I20220430 21:16:28.024988 211449 gstvaisink.cc:417] cluster-id=application-cluster-0
I20220430 21:16:28.025032 211449 gstvaisink.cc:418] cluster-endpoint=c8khq35ftg78mn61ef50.us-central1.visionai.goog
I20220430 21:16:28.025040 211449 gstvaisink.cc:419] event-id=ev-1651364114183255223
I20220430 21:16:28.025048 211449 gstvaisink.cc:420] stream-id=input-stream
I20220430 21:16:28.025053 211449 gstvaisink.cc:421] series-id=ev-1651364114183255223--input-stream
I20220430 21:16:28.025060 211449 gstvaisink.cc:422] Sending data
```
### Local video file
You can also send video file data to a stream instead of a live video
feed. This option can be useful if you don't have access to an IP
camera.
The only difference in this option is the `vaictl` command
parameters. Instead of passing the IP camera information, pass
the path for the local video file. Make the following variable
substitutions:
- PROJECT_ID: Your Google Cloud project ID.
- LOCATION_ID: Your location ID. For example, `us-central1`. [More
information](https://cloud.google.com/about/locations).
- LOCAL_FILE.EXT: The filename of a local video file. For example, `my-video.mp4`.
- `--loop` flag: Optional. Loops file data to simulate streaming.
This command streams a video file to a stream. If using the
`--loop` flag, the video is looped into the stream until you
stop the command:
```
vaictl -p PROJECT_ID \
-l LOCATION_ID \
-c application-cluster-0 \
--service-endpoint visionai.googleapis.com \
send video-file to streams 'input-stream' --file-path LOCAL_FILE.EXT --loop
```
It might take \~100 seconds between starting the `vaictl` ingest operation and
the video appearing in the dashboard.
After the stream ingestion is available,
you can see the video feed in the **Streams** tab of the Gemini Enterprise Agent Platform Vision
dashboard by selecting the `input-stream` stream.
[Go to the Streams tab](https://console.cloud.google.com/ai/vision-ai/video-streams)
 *Live view of video being ingested into the stream in the Google Cloud console. Video credit:* [Tima Miroshnichenko](https://www.pexels.com/video/young-women-reading-books-while-sitting-on-the-bed-6860996/) on [Pexels](https://www.pexels.com/) (*pixelation added*).
## Create a face blur application
After you create a stream and ingest data into the stream, it's time to create
a Gemini Enterprise Agent Platform Vision app to process the data.
An app can be thought of as an
automated pipeline that connects the following:
- **Data ingestion**: A video feed is ingested into a stream.
- **Data analysis**: An AI model can be added after the ingestion. Any computer vision operation can be performed on the ingested video information.
- **Data storage**: The two versions of the video feed (the original stream and the stream processed by the AI model) can be stored in a media warehouse.
In the Google Cloud console an app is represented as a graph.
Additionally, in Gemini Enterprise Agent Platform Vision an app graph must have at least two nodes: a
video source node (stream), and *at least* one more node (a processing model or
output destination).
### Create an empty application
Before you can populate the app graph, you must first create an empty app.
### Console
Create an app in the Google Cloud console.
1. Open the **Applications** tab of the Gemini Enterprise Agent Platform Vision dashboard.
[Go to the Applications tab](https://console.cloud.google.com/ai/vision-ai/applications)
2. Click the **Create** button.
3. Enter `person-blur-app` as the app name and choose your region.
4. Click **Create**.
### Add app component nodes
After you have created the empty application, you can then add the three nodes
to the app graph:
1. **Ingestion node**: The stream resource that's already ingesting data.
2. **Processing node**: The person blur model that acts on ingested data.
3. **Storage node**: The media warehouse that stores processed videos, and also serves as a metadata store. The warehouse allows analytics information to be generated about ingested video data, as well as stores information inferred about the data by the AI models.
### Console
Add component nodes to your app in the console.
1. Open the **Applications** tab of the Gemini Enterprise Agent Platform Vision dashboard.
[Go to the Applications tab](https://console.cloud.google.com/ai/vision-ai/applications)
2. Next to the name of your `person-blur-app` application in the list,
select **View app**. This takes
you to the graph visualization of the processing pipeline.
**Add a data ingestion node**
1. To add the input stream node, select the **Streams** option in the
**Connectors** section of the side menu.
2. In the **Source** section of the **Stream** menu that opens, select
**Add streams**.
3. In the **Add streams** menu, choose
**Select from
existing streams** and select `person-blur-app` from the list of stream
resources.
4. To add the stream to the app graph, click **Add streams**.
**Add a data processing node**
1. To add the person blur model node, select the **Person blur** option in the
**General processors** section of the side menu.
2. In the "Person blur" option menu that opens, leave
**Full occlusion**
selected and enable the
**Blur faces only** option.
**Add a data storage node**
1. To add the output destination (storage) node, select the
**Gemini Enterprise Agent Platform Vision's Media Warehouse** option in the **Connectors** section of the side menu.
2. In the **Gemini Enterprise Agent Platform Vision's Media Warehouse** menu, click **Connect warehouse**.
3. In the **Connect warehouse** menu, select
**Create new
warehouse** . Name the warehouse `person-blur-app`, and leave
the TTL duration at 14 days.
4. To add the warehouse, click **Create**.
## Deploy your application
After you have built your end-to-end app with all the necessary components, the last step to using the app is to deploy it.
### Console
1. Open the **Applications** tab of the Gemini Enterprise Agent Platform Vision dashboard.
[Go to the Applications tab](https://console.cloud.google.com/ai/vision-ai/applications)
2. Next to the name of your `person-blur-app` application in the list,
select **View app**.
3. From the application graph builder page, click the
**Deploy** button.
4. In the following confirmation dialog, select **Deploy**.
The deploy operation might take several minutes to complete. After
deployment finishes, green check marks appear next to the nodes.
## View processed output data
### Console
1. Open the **Warehouses** tab of the Gemini Enterprise Agent Platform Vision dashboard.
[Go to the Warehouses tab](https://console.cloud.google.com/ai/vision-ai/media-warehouse)
2. Find the `person-blur-output-storage` warehouse in the list, and click
**View assets**.
## 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.
### Delete the project
> [!CAUTION]
> **Caution** : Deleting a project has the following effects:
>
> - **Everything in the project is deleted.** If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
> - **Custom project IDs are lost.** When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an `appspot.com` URL, delete selected resources inside the project instead of deleting the whole project.
>
>
> If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects
> can help you avoid exceeding project quota limits.
1. In the Google Cloud console, go to the **Manage resources** page.
[Go to Manage resources](https://console.cloud.google.com/iam-admin/projects)
2. In the project list, select the project that you want to delete, and then click **Delete**.
3. In the dialog, type the project ID, and then click **Shut down** to delete the project.
### Delete individual resources
#### Delete a warehouse
1. In the Google Cloud console, go to the **Warehouses** page.
[Go to the Warehouses tab](https://console.cloud.google.com/ai/vision-ai/media-warehouse)
2. Locate your `person-blur-output-storage` warehouse.
3. To delete the warehouse, click **Actions** , click **Delete warehouse**, and then follow the instructions.
#### Delete a stream
1. In the Google Cloud console, go to the **Streams** page.
[Go to the Streams tab](https://console.cloud.google.com/ai/vision-ai/video-streams)
2. Locate your `input-stream` stream.
3. To delete the stream, click **Actions** , click **Delete stream**, and then follow the instructions.
#### Delete an app
1. In the Google Cloud console, go to the **Applications** page.
[Go to the Applications tab](https://console.cloud.google.com/ai/vision-ai/applications)
> [!NOTE]
> **Note:** You must first undeploy your app before you can delete it.
2. Locate your `person-blur-app` app.
3. To delete the app, click **Actions** , click **Delete application**, and then follow the instructions.
## What's next
- Read more about [Responsible AI practices](https://ai.google/responsibilities/responsible-ai-practices/).
- Learn about other components you can add to an app in [Build an app](https://docs.cloud.google.com/vision-ai/docs/build-app.md.txt).
- Learn about other output storage and processing options in [Connect app output to a data destination](https://docs.cloud.google.com/vision-ai/docs/connect-data-destination.md.txt).
- Read about how to [Search Warehouse data in the console](https://docs.cloud.google.com/vision-ai/docs/search-streaming-warehouse.md.txt).
- Explore reference architectures, diagrams, and best practices about Google Cloud. Take a look at our [Cloud Architecture Center](https://docs.cloud.google.com/architecture).