From 0776d27d8e92f2b24e3b99830363ef48eb89c0c4 Mon Sep 17 00:00:00 2001 From: JessamyT Date: Tue, 5 Mar 2024 15:30:12 -0800 Subject: [PATCH 1/8] DOCS-1804: Add hard hat detection tutorial --- .../tutorials/helmet/datacapture-config.png | Bin 0 -> 152583 bytes assets/tutorials/helmet/email-received.png | Bin 0 -> 100691 bytes assets/tutorials/helmet/filtercam-config.png | Bin 0 -> 128475 bytes assets/tutorials/helmet/hardhat-data.png | Bin 0 -> 966732 bytes assets/tutorials/helmet/model-location.png | Bin 0 -> 122964 bytes assets/tutorials/helmet/no-hard-hat1.png | Bin 0 -> 385895 bytes assets/tutorials/helmet/ppe-hooks.png | Bin 0 -> 880738 bytes assets/tutorials/helmet/synced-data.png | Bin 0 -> 557093 bytes docs/tutorials/projects/helmet.md | 447 ++++++++++++++++++ static/tutorials/helmet/hardhat.mp4 | Bin 0 -> 595779 bytes static/tutorials/helmet/hardhat.webm | Bin 0 -> 685577 bytes 11 files changed, 447 insertions(+) create mode 100644 assets/tutorials/helmet/datacapture-config.png 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detected and labeled as No-Hardhat. Then he puts on a hard hat and a bounding box labeled Hardhat appears. He gives a thumbs-up to the camera." +tags: ["tutorial"] +authors: [Jessamy Taylor] +languages: [] +viamresources: ["camera", "data_manager", "mlmodel", "vision"] +level: "Intermediate" +date: "2024-03-05" +cost: 120 +--- + +{{}} + +We all know personal protective equipment (PPE) helps keep us safe. +But, sometimes we need a reminder to use it consistently. +Luckily, you can address this problem using Viam's integrated [data capture](/data/capture/), [computer vision](/ml/vision/), and [webhooks](/build/configure/#webhooks), along with a hard hat detection model. + +Through this tutorial you will build a system to look out for you and your team, sending an email notification when someone isn't wearing a hard hat. + +{{}} + +First, you'll set up and test the computer vision functionality. +Next, you'll set up data capture to pull images of people without hard hats into the cloud. +Then, you'll write a serverless function capable of sending email notifications. +Finally, you'll configure a webhook to trigger the serverless function when someone isn't wearing a hard hat. + +## Requirements + +### Required hardware + +- A camera such as a standard USB webcam. + You can also test the hard hat detection system using the webcam built into your laptop. +- A computer capable of running [`viam-server`](/get-started/installation/). + You can use a personal computer running macOS or Linux, or a single-board computer (SBC) running 64-bit Linux. + +### Optional hardware + +If you want to set up your camera far away from your personal computer, you can use a webcam plugged into a single-board computer, powered by batteries. +You could mount your machine in a stationary location like on a pole, or you could mount it on a rover. +This tutorial covers the software side; you can get creative with the hardware. + +Note that your machine must be connected to the internet for data sync and email notifications to work. + +### Required software + +- [`viam-server`](/get-started/installation/) +- [Ultralytics YOLOv8](https://docs.ultralytics.com/), integrated with Viam using the [YOLOv8 modular service](https://github.com/viam-labs/YOLOv8) +- [YOLOv8 Hard Hat Detection model](https://huggingface.co/keremberke/yolov8s-hard-hat-detection) +- [`objectfilter-camera`](https://github.com/felixreichenbach/objectfilter-camera) {{< glossary_tooltip term_id="modular resource" text="modular resource" >}} +- [Google Cloud Functions](https://cloud.google.com/functions) +- Python 3.8 +- [SendGrid](https://sendgrid.com/en-us) + +## Set up your helmet monitor + +Plug your webcam into your computer. + +Make sure your computer (whether it's a personal computer or an SBC) is connected to adequate power, and turn it on. + +If you haven't already, [install `viam-server`](/get-started/installation/) on the computer you're using for this project, and set up the connection between your machine and the [Viam app](https://app.viam.com). + +## Configure the camera and computer vision + +### Configure your physical camera + +Configure your [webcam](/components/camera/webcam/) so that your machine knows where to pull images from: + +1. On the [Viam app](https://app.viam.com), navigate to your machine's page. + Check that the **Last online** indicator reads "Live"; this indicates that your machine is turned on and that its instance of `viam-server` is in contact with the Viam app. + +2. Click **Create component** in the lower-left corner of the page. + Start typing "webcam" and select **camera / webcam**. + Give your camera a name. + This tutorial uses the name `my_webcam` in all example code. + Click **Create**. + +3. Click the **video path** dropdown and select the webcam you'd like to use for this project from the list of suggestions. + +4. At the bottom of the screen, click **Save config**. + +### Test your physical camera + +To test your camera, go to the **Control** tab and click to expand your camera's panel. + +Toggle **View `my_webcam`** to the "on" position. +The video feed should display. +If it doesn't, double-check that your config is saved correctly, and check the **Logs** tab for errors. + +### Configure the vision service + +Now that you know the camera is properly connected to your machine, it is time to add computer vision by configuring the [vision service](/ml/vision/) on your machine. +Viam's built-in [`mlmodel` vision service](/ml/vision/mlmodel/) works with Tensor Flow Lite models, but since this tutorial uses a YOLOv8 model, we will use a {{< glossary_tooltip term_id="module" text="module" >}} from the [modular resource registry](/registry/) that augments Viam with YOLOv8 integration: + +1. Navigate to the **Services** subtab of your machine's **Config** tab. + +2. Click **Create service**. + Start typing `yolo` and select **vision / yolov8** from the registry options. + Click **Add module**. + You can find more details about this module in [its readme](https://github.com/viam-labs/YOLOv8). + +3. Give your vision service a name, for example `yolo`, and click **Create**. + +4. In the **Attributes** box of your new vision service, paste the following JSON: + + ```json {class="line-numbers linkable-line-numbers"} + { + "model_location": "keremberke/yolov8n-hard-hat-detection" + } + ``` + + This tells the vision service where to look for [the hard hat detection model](https://huggingface.co/keremberke/yolov8s-hard-hat-detection) we are using for this tutorial. + + Your vision service config should now resemble the following: + + {{}} + +5. Click **Save config** at the bottom of the screen. + +### Configure the `objectfilter` module + +The physical camera is working and the vision service is set up. +Now you will pull them together with the [`objectfilter`](https://app.viam.com/module/felixreichenbach/object-filter) {{< glossary_tooltip term_id="module" text="module" >}}. +This module takes a vision service (in this case, your hard hat detector) and applies it to your webcam feed. +It outputs a stream with bounding boxes around the hard hats (and people without hard hats) in your camera's view so that you can see the detector working. +This module also filters the output so that later, when you configure data management, you can save only the images that contain people without hard hats. + +1. Navigate to the **Components** subtab of your machine's **Config** tab. + +2. Click **Create component**. + Start typing `objectfilter` and select **camera / objectfilter** from the results. + +3. Name your filtering camera something like `objectfilter-cam` and click **Create**. + +4. Paste the following into the attributes box: + + ```json {class="line-numbers linkable-line-numbers"} + { + "filter_data": true, + "camera": "my_webcam", + "vision_services": ["yolo"], + "labels": ["Hardhat", "NO-Hardhat"], + "confidence": 0.5, + "display_boxes": true + } + ``` + + If you named your detector something other than "yolo," edit the `detector_name` line accordingly. + You can also edit the confidence threshold. + If you change it to `0.2` for example, you will see detections of over 20% confidence. + + Setting the `filter_data` attribute to `true` means that later, when you configure data capture on this camera, only images that contain one or more of the labels will be captured. + + Your `objectfilter` camera configuration should now resemble the following: + + {{}} + +5. Click **Save config** at the bottom of the screen. + +### Test the detector + +Now that the detector is configured, it's time to test it! + +1. Navigate to the **Control** tab. + +2. Click the **objectfilter_cam** panel to open your detector camera controls. + +3. Toggle **View objectfilter_cam** to the "on" position. + This displays a live feed from your webcam with detection bounding boxes overlaid on it. + +4. The detector can recognize humans with and without hard hats on. + Try positioning yourself in front of the camera without anything on your head, and then with a helmet on, and watch the bounding boxes appear. + + {{}} + +## Configure data capture and sync + +Viam's built-in [data management service](/data/) allows you to, among other things, capture images and sync them to the cloud. +Configure data capture on the `objectfilter` camera to capture images of people without hard hats: + +1. First, you need to add the data service to your machine to make it available to capture data on your camera. + + Navigate to the **Services** subtab of your machine's **Config** tab. + + Click **Create service**. + Type "data" and click **data management / RDK**. + Name your data management service `data-manager` and click **Create**. + + Leave all the default data service attributes as they are and click **Save config**. + +2. Now you're ready to enable data capture on your detector camera. + Navigate to the **Components** subtab. + Locate the `objectfilter-cam` panel. + +3. Click **Add method**. + Click the **Type** dropdown and select **ReadImage**. + Set the capture frequency to `0.2` images per second (equivalent to one image every 5 seconds). + You can always change the frequency to suit your use case. + + {{}} + +### Test data capture and sync + +To make sure the detector camera is capturing and syncing labeled images: + +1. Position yourself in front of your webcam for 30 seconds or so to let it capture a few images of a person without a hard hat on. + +2. Navigate to your [**DATA** page](https://app.viam.com/data/view?view=images) in the Viam app. + You should see some images with bounding boxes on them. + If you do not, try refreshing the page. + + {{}} + +3. You can also try this with a hard hat on your head. + + {{}} + +### Modify the detector config to sync only images without hard hats + +So far you've been identifying people without hard hats as well as people with hard hats. +Now that you have verified that the detector and data sync are working, modify your config so that only images with people _without_ hard hats are captured: + +1. Navigate to your `objectfilter-cam` card on the **Config** tab. + +2. Delete the `"Hardhat",` line from the `"labels"` array. + +3. Click **Save config**. + +## Set up email notifications + +[Webhooks](https://en.wikipedia.org/wiki/Webhook) are a way to trigger a custom function to run when a certain event happens. +For the purposes of this project, you will use a webhook to run a function that sends you an email when an image of someone without a hard hat is uploaded to the cloud. + +Before you configure a webhook on your machine, you need to create a serverless function for the webhook to call. + +### Create a serverless function + +A serverless function is a simple script that is hosted by a service such as [Google Cloud Functions](https://cloud.google.com/functions) or [AWS Lambda](https://aws.amazon.com/pm/lambda). +You don't need to host it on your machine; instead, it is always available and runs only when an event triggers it. + +#### Set up your cloud function scaffold + +For this project, we used Google Cloud Functions, and wrote our function in Python. +If you are new to cloud functions, you may find [this getting started guide](https://cloud.google.com/functions/docs/console-quickstart) useful. + +1. Create a Google Cloud (GCP) account. +2. Create a project, then search for **Cloud Functions** in the Google Cloud console search bar. +3. Click **CREATE FUNCTION**. +4. Choose **1st gen** as the **Environment**. +5. Give your function a name. + We called ours "hat-email." +6. Choose your region. +7. For trigger type, choose **HTTP**. +8. Click **NEXT**. +9. For **Runtime**, choose Python 3.8. +10. Click **Deploy**. + The demo function won't do anything exciting yet; keep going through the next few sections before testing it. + +#### Configure email credentials + +To write a function that sends an email, you need to configure credentials to authenticate to the sending email account. +For this project we used [SendGrid](https://sendgrid.com) (which has a free tier) to make configuration of your email notifications simpler: + +1. [Enable SendGrid Email API](https://console.cloud.google.com/marketplace/details/sendgrid-app/sendgrid-email) through the Google Cloud Marketplace. +1. Create a SendGrid account and confirm your email. +1. Create a [SendGrid API key](https://docs.sendgrid.com/ui/account-and-settings/api-keys) with **Mail Send > Full Access** permissions. +1. Store your API key with the Google Cloud Function console: + 1. In the top banner of your console, click **Edit**. + 2. Click to expand **Runtime, build, connections and security settings**. + 3. Under **Runtime environment variables**, click **ADD VARIABLE**. + For the name, enter `EMAIL_API_KEY`. + For the value, paste the API key you generated with SendGrid. + 4. Click **NEXT**. + +#### Write the cloud function code + +SendGrid's [Email API Quickstart](https://docs.sendgrid.com/for-developers/sending-email/quickstart-python) contains more information about the functionality of the SendGrid API. +The following code is adapted from that example. + +1. Copy and paste the following code into your cloud function source code `main.py` file: + + ```python {class="line-numbers linkable-line-numbers"} + import functions_framework + import os + import sendgrid + from sendgrid import SendGridAPIClient + from sendgrid.helpers.mail import Mail, Email, To, Content + from python_http_client.exceptions import HTTPError + + def email(request): + + sg = sendgrid.SendGridAPIClient(api_key=os.environ.get('EMAIL_API_KEY')) + + message = Mail( + from_email='youremailaddresstosendfrom@example.com', # Change to the email you created the API key for + to_emails='youremailaddresstosendto@example.com', # Change to whichever email should receive the notification + subject='Put on a helmet!', + html_content='Hello!

    Please remember to keep hard hats on where required. \ + Thank you!

    You can view captured images in \ +
    the DATA tab.' + ) + + # Get a JSON-ready representation of the Mail object + mail_json = message.get() + + # Send an HTTP POST request to /mail/send + response = sg.client.mail.send.post(request_body=mail_json) + ``` + +2. Edit the **Entry point** field from `hello_world` to `email` to match the function name. + +3. Select requirements.txt (below main.py on the **SOURCE** console tab) and add the required dependencies: + + ```text {class="line-numbers linkable-line-numbers"} + # Function dependencies, for example: + # package>=version + sendgrid + functions-framework + ``` + +#### Register an HTTP function + +The webhook you will configure with Viam invokes the function via an HTTP request. +You can learn more about HTTP functions in [Google's Write HTTP functions guide](https://cloud.google.com/functions/docs/writing/write-http-functions). + +You need to add `@functions_framework.http` at the top of your function to register the HTTP function, and you need to add a `return` statement at the bottom to return an HTTP response. +Your final code should look like this: + +```python {class="line-numbers linkable-line-numbers" data-line="8,28"} +import functions_framework +import os +import sendgrid +from sendgrid import SendGridAPIClient +from sendgrid.helpers.mail import Mail, Email, To, Content +from python_http_client.exceptions import HTTPError + +@functions_framework.http +def email(request): + + sg = sendgrid.SendGridAPIClient(api_key=os.environ.get('EMAIL_API_KEY')) + + message = Mail( + from_email='youremailaddresstosendfrom@example.com', # Change to the email you created the API key for + to_emails='youremailaddresstosendto@example.com', # Change to whichever email should receive the notification + subject='Put on a helmet!', + html_content='Hello!

    Please remember to keep hard hats on where required. \ + Thank you!

    You can view captured images in \ + the DATA tab.' + ) + + # Get a JSON-ready representation of the Mail object + mail_json = message.get() + + # Send an HTTP POST request to /mail/send + response = sg.client.mail.send.post(request_body=mail_json) + + return 'Notification sent.' +``` + +Click **DEPLOY**. + +For simplicity while developing and testing your code, you can [allow unauthenticated HTTP function invocation](https://cloud.google.com/functions/docs/securing/managing-access-iam#allowing_unauthenticated_http_function_invocation), though we recommend that you ultimately set up proper authentication for better security. + +#### Test the cloud function + +Now you can test the script: + +1. Navigate to the **TRIGGER** tab in the Google Cloud console. + +2. Click the copy button to copy the trigger URL. + Paste it into a new browser window and hit your **Enter** key. + The browser should display "Notification sent." + If it displays an error, double-check your code, and double-check your [access authorization](https://cloud.google.com/functions/docs/securing/managing-access-iam). + +3. Check your email inbox. + + {{}} + + If you don't see an email, check your spam folder. + If you still don't see an email, make sure your SendGrid account is fully set up (2FA enabled, email confirmed) and that your email API key is [correctly configured](#configure-email-credentials). + +### Configure a webhook on your machine + +Now it's time to configure a [webhook](/build/configure/#webhooks) on your machine to trigger the email cloud function when a person is not wearing a hard hat. +Since you configured data to sync only when an image of a person without a hard hat is captured, configuring the webhook to trigger each time an image is synced to the cloud will produce the desired result. + +Configure a webhook as follows: + +1. Navigate to the **Config** tab of your machine. + Toggle the **Mode** to **Raw JSON**. + +2. Paste the following JSON template into your raw JSON config. + `"webhooks"` is a top-level section like `"components"`, `"services"`, or any of the other config sections. + + ```json {class="line-numbers linkable-line-numbers"} + "webhooks": [ + { + "url": "", + "event": { + "attributes": { + "data_types": ["binary"] + }, + "type": "part_data_ingested" + } + } + ] + ``` + +3. Replace the `url` value with your cloud function URL. + You can get this URL by copying it from the **TRIGGER** tab in the cloud function console. + Once you've done this, the `url` line should resemble, for example, `"url": "https://us-east1-blah-blah-123456.cloudfunctions.net/hat-email"`. + +4. Click **Save config**. + +## Test the whole system + +You've built all the pieces of the system and connected them together. +Now it's time to test the whole thing. + +Make sure `viam-server` is running on your machine. +Position yourself, without a hard hat, in front of your camera. +Wait a couple of minutes for the email to arrive in your inbox, then celebrate your success when it does! +Great work! + +## Next steps + +Here are some ways you could expand on this project: + +- Mount a camera on a rover and either drive it manually using remote control, or use the motion and navigation services to plan paths for the rover. + Or, mount cameras in multiple places. + +- Change your cloud function to send a different kind of notification, or trigger some other action. + For an example demonstrating how to configure text notifications, see the [Detect a Person and Send a Photo tutorial](/tutorials/projects/send-security-photo/). + +- Use a different existing model or [train your own](/ml/train-model/), to detect and send notifications about something else such as [forklifts](https://huggingface.co/keremberke/yolov8m-forklift-detection). + +{{< cards >}} +{{% card link="/tutorials/projects/send-security-photo/" %}} +{{% card link="/ml/train-model/" %}} +{{% card link="/tutorials/configure/scuttlebot/" %}} +{{% card link="/tutorials/services/navigate-with-rover-base/" %}} +{{< /cards >}} diff --git a/static/tutorials/helmet/hardhat.mp4 b/static/tutorials/helmet/hardhat.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..23a822882690ae3e27d848161a6308c3b632fdd9 GIT binary patch literal 595779 zcmY(q1z21$(>A$vrcfb+iWnAOZiL;2*oGhdGqP3fw6F&=! zr>7^g2gm~GXlLrc?C4^}@~;+VYd3p4Fpr~?8_3bYm7mny)Xdaeh>g?*Xeq=&Y5_E} zb2PUVV&iA!XC*atFtzh`1q!ixaqzQxv9WQG+5?5GfnKDp?q*<$ht$c{8!QUG8@pHt zu`#oPg}^sbdyp5`&@2XX~dyk5K( z=5AoZ+}U1;72E_eb#VEgO3Xp_rf&Zl0&;Kzy4ab5 znZW60cJ3~w-p1yR_D-g5;CFMdj@(>KK@Q*|z>F@Y|9C82OznZLU~QQhJ9&d?kcALC 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