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https://github.com/msimecek/signal-faces

This project was built for the Anonymous Illusion art installation at Signal Festival 2017 in Prague.
https://github.com/msimecek/signal-faces

azure-functions cognitive-services uwp

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This project was built for the Anonymous Illusion art installation at Signal Festival 2017 in Prague.

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# Signal Faces

This project was built for the *Anonymous Illusion* art installation at Signal Festival 2017 in Prague.



![](images/00-tabule.jpg)



its goal was to create a space that broadened people’s minds and offered them not only an insight into illusionary anonymity but also a taste of technology of the future.



![](images/00-signal-prod.jpg)



You can find the [full story on my blog](https://codez.deedx.cz/projects/signal-faces/).



This repo hosts the client & server applications.



![](images/00-architecture.png)



## Prerequisites



Several things need to be created before you can run this system:



1. You need to have a Microsoft Azure account (if you don't, try the [free Trial](https://azure.microsoft.com/en-us/free/)).

2. Create a **Storage Account** resource in Azure.

   1. Copy **Connection String** for this account.

   2. Create a **Container** in this account (I called it *photos*).

   3. Generate and copy a **SAS token** for this container (the easiest way is to [use Storage Explorer](https://docs.microsoft.com/en-us/azure/vs-azure-tools-storage-manage-with-storage-explorer#attach-a-storage-account-by-using-an-sas)).

3. Create a **Cognitive Services Face API** resource in Azure.

   1. Copy **API Key** and **Endpoint URL**.

4. Create a **Custom Vision** model at https://customvision.ai.

   1. Copy **Prediction Key** and **Prediction Endpoint**.

5. Create a **Cosmos DB** account and a database.

   1. Copy **Connection String**.

6. Make sure you have the [**Azure Functions Tools** installed in Visual Studio](https://docs.microsoft.com/en-us/azure/azure-functions/functions-develop-vs) and Functions CLI on your PC.

7. Make sure you have the UWP tools installed in Visual Studio.

8. Create a listenting endpoint which would receive the tags from processing function (needs to be a POST API, accessible from the internet).



## Setup



Clone this repo:



```

git clone https://github.com/msimecek/Signal-Faces.git

```



**Open the *SignalFunc.sln* solution.**



Rename `local.settings.json-empty` to `local.settings.json`.



Open `local.settings.json` and fill in your connection strings, API keys etc.



*Use your Storage Account connection string for the `AzureWebJobsStorage` property. You can leave `AzureWebJobsDashboard` empty.*



Build & Run to see if everything is OK.



**Open the *SignalClient.sln* solution.**



Go to *Services\StorageService.cs* and paste your container's SAS URL to `CONTAINER_SAS`.



Connect your IP camera, so that it's accessible over the network.



Run the app.



Use the UI to configure camera's snapshot template and other parameters.



| Element             | Functionality                            |

| ------------------- | ---------------------------------------- |

| Start button        | Starts camera capture                    |

| Stop button         | Stops camera capture                     |

| Force upload        | Uploads current capture immediately      |

| Camera IP           | IP address of the camera. It's better when the camera is on the same local network. |

| Camera URL template | Every camera has a specific URL from which you can download current snapshot. Use {IpAddress} to indicate where in the string is the address. |

| Camera admin        | Administrator username for access to the camera. |

| Camera pass         | Admin password.                          |

| Capture interval    | How often will the app download a new image (in seconds). We used 2 seconds. |

| Delay after upload  | How long will the app wait after uploading to Storage before starting new capture (in seconds). |

| Upload enabled      | If unchecked, no image will be uploaded to storage. |

| Apply button        | Save configuration and restart capture.  |



![](images/01-client-app.png)



## Remarks



This project was built in less than a week and properly tested only in production :) Hence the code... Not pretty all the time. 



But hey - pull requests welcome!