DOCS-2482: Quickstart detect people with a webcam (#3067)

docs/get-started/quickstarts/detect-people.md

@@ -0,0 +1,125 @@
+---
+title: "Detect People with a Webcam"
+linkTitle: "Detect people with a webcam"
+weight: 50
+type: "docs"
+tags: ["vision", "data", "services", "quickstart", "ml", "camera", "webcam"]
+no_list: true
+description: "Use Viam's machine learning capabilities to deploy a person detector ML model to your machine."
+images: ["/icons/components/camera.svg"]
+imageAlt: "Machine Learning"
+---
+
+Follow this guide to detect people with a webcam on the Viam platform, using a [pre trained ML model from the registry](https://app.viam.com/ml-model/ml-models-scuttle/people):
+
+{{<imgproc src="/get-started/quickstarts/vision-card.png" resize="x1100" declaredimensions=true alt="Positive person detection on the vision card." >}}
+
+## Requirements
+
+- A computer: a development machine such as your laptop and optionally a single-board computer such as the Raspberry Pi
+- A webcam: this could be the webcam on your laptop or any other webcam you can connect to your computer
+- An account on the [Viam app](https://app.viam.com)
+
+Follow these instructions to configure your machine and test detections:
+
+{{%expand "Step 1: Create a new machine" %}}
+
+Add a new machine in the [Viam app](https://app.viam.com) by providing a name in the **New machine** field and clicking **Add machine**.
+
+![The 'First Location' page on the Viam app with a new machine name in the New machine field and the Add Machine button next to the field highlighted.](/fleet/app-usage/create-machine.png)
+
+Click the name of a machine to go to that machine's page, where you'll find a variety of tools for working with your machine.
+
+{{% /expand%}}
+{{%expand "Step 2: Install viam-server" %}}
+
+Navigate to the **CONFIGURE** tab of your machine's page in [the Viam app](https://app.viam.com).
+Follow the {{< glossary_tooltip term_id="setup" text="setup instructions" >}} that appear on your new machine's **CONFIGURE** page to install `viam-server` on your computer and connect it to the Viam app.
+
+{{% /expand%}}
+{{%expand "Step 3: Configure your webcam" %}}
+
+Navigate to the **CONFIGURE** tab of your machine's page in [the Viam app](https://app.viam.com).
+Click the **+** icon next to your machine part in the left-hand menu and select **Component**.
+Select the `camera` type, then select the `webcam` model.
+Enter a name or use the suggested name for your camera and click **Create**.
+
+Click the **Save** button in the top right corner of the page to save your configuration.
+Go to the **CONTROL** tab and expand the camera's remote control card to test the camera stream.
+If you don't see an image stream, you need to [configure the `video_path` attribute](/components/camera/webcam/#using-video_path).
+
+For more detailed information, including optional attribute configuration, see the [`webcam` docs](/components/camera/webcam/).
+
+{{% /expand%}}
+{{%expand "Step 4: Deploy a person detection model" %}}
+
+Navigate to the **CONFIGURE** tab of your machine's page in [the Viam app](https://app.viam.com).
+Click the **+** icon next to your machine part in the left-hand menu and select **Service**.
+Select the `ML model` type, then select the `TFLite CPU` model.
+Enter a name or use the suggested name for your service and click **Create**.
+
+In the resulting ML model service configuration pane, ensure that **Deploy model on machine** is selected for the **Deployment** field.
+
+Click on **Select models** to open a dropdown with all of the ML models available to you.
+Select the `ml-models-scuttle:people` model (the **people** model by **ml-models-scuttle**) to deploy an object detection TFLite model that has been trained to be able to detect a person.
+
+Click the **Save** button in the top right corner of the page to save your configuration.
+
+For more detailed information, including optional attribute configuration, see the [`tflite_cpu` docs](/services/ml/deploy/tflite_cpu/).
+
+{{% /expand%}}
+{{%expand "Step 5: Configure your detector" %}}
+
+Navigate to the **CONFIGURE** tab of your machine's page in the [Viam app](https://app.viam.com).
+Click the **+** icon next to your machine part in the left-hand menu and select **Service**.
+Select the `vision` type, then select the `ML model` model.
+Enter a name or use the suggested name for your service and click **Create**.
+
+Select the ML model service your person detector model is deployed on (which you created in step 4) from the **ML Model** dropdown.
+Then, click **{}** (Switch to advanced) in the top right corner of the configuration pane.
+Add the following JSON to the JSON configuration to set the `default_minimum_confidence` of the detector:
+
+```json
+"default_minimum_confidence": 0.82
+```
+
+This optional attribute simplifies your detections output (which you'll view in step 6) by filtering out detections below the threshold of 82% confidence.
+You can adjust this attribute as necessary.
+
+Click the **Save** button in the top right corner of the page to save your configuration.
+
+For more detailed information, including optional attribute configuration, see the [`mlmodel` docs](/services/vision/mlmodel/).
+
+{{% /expand%}}
+{{%expand "Step 6: Control person detection" %}}
+
+Now, test your person detection with the computer vision service's remote control card.
+
+Navigate to the **CONTROL** tab in the [Viam app](https://app.viam.com).
+Click on the `vision` service's card to expand it.
+
+Click **Refresh** to refresh the image stream and see detections as labeled boxes on the images along with position, dimension, class and confidence data.
+Detections in class `Person` with a high confidence score show positive person detections, but the ML model can also detect other objects:
+
+{{<imgproc src="/get-started/quickstarts/vision-card-more-detections.png" resize="x1100" declaredimensions=true alt="Positive person detection on the vision card with a lower default minimum confidence threshold." >}}
+
+You can view detections with a lower confidence score from the vision control card, like in the above screenshot, by lowering the `"default_minimum_confidence"` attribute.
+
+{{% /expand%}}
+
+## Next steps
+
+You can continue to test your [`mlmodel` detector](/services/vision/mlmodel/#test-your-detector-or-classifier) with [existing images in the Viam app](/services/vision/mlmodel/#existing-images-in-the-cloud) or [existing images on a computer](/services/vision/mlmodel/#existing-images-on-your-machine).
+
+You can use one of [Viam's SDKs](/sdks/) to write a script that captures detections and triggers an alarm when a person is detected, or even sends you photos of the person detected.
+See [A Person Detection Security Robot That Sends You Photos](/tutorials/projects/send-security-photo/#use-the-viam-python-sdk-to-control-your-security-robot) for an example of this.
+
+You can also use the Viam platform to [train your own detector or classifier](/use-cases/deploy-ml/).
+
+You can also explore our [tutorials](/tutorials/) for more machine learning ideas:
+
+{{< cards >}}
+{{% card link="/tutorials/services/data-mlmodel-tutorial/" %}}
+{{% card link="/tutorials/projects/pet-treat-dispenser/" customTitle="Smart Pet Feeder" %}}
+{{% card link="/registry/examples/tflite-module/" %}}
+{{< /cards >}}

docs/services/ml/deploy/tflite_cpu.md

@@ -10,6 +10,7 @@ icon: true
 images: ["/services/icons/ml.svg"]
 aliases:
   - /ml/deploy/tflite_cpu/
+  - /services/ml/deploy/tflite_cpu/
 # SME: Khari
 ---
 
@@ -38,7 +39,7 @@ You can choose to configure your service with an existing model on the machine o
 {{< tabs >}}
 {{% tab name="Deploy Model on Machine" %}}
 
-1. To configure your service and deploy a model onto your machine, select **Deploy model on machine** for the **Deployment** field in the resulting vision service configuration pane,.
+1. To configure your service and deploy a model onto your machine, select **Deploy model on machine** for the **Deployment** field in the resulting ML model service configuration pane.
 
 2. Click on **Select models** to open a dropdown with all of the ML models available to you privately, as well as all of the ML models available in [the registry](https://app.viam.com), which are shared by users.
    Models that your organization has trained that are not uploaded to the registry will appear first in the dropdown.