chapter 4 (lesson 2&4) (#144)

* Update README.md

* chapter 4 (lesson one)

* chapter 4 (lesson 2&4)

* Update pi-camera.md

* Update pi-proximity.md

Co-authored-by: Jim Bennett <jim.bennett@microsoft.com>

4-manufacturing/lessons/1-train-fruit-detector/README.md

@@ -144,7 +144,7 @@ To use Custom Vision, you first need to create two cognitive services resources
 
 1. Launch the Custom Vision portal at [CustomVision.ai](https://customvision.ai), and sign in with the Microsoft account you used for your Azure account.
 
-1. Follow the [Create a new Project section of the Build a classifier quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier?WT.mc_id=academic-17441-jabenn#create-a-new-project) to create a new Custom Vision project. The UI may change and these docs are always the most up to date reference.
+1. Follow the [create a new Project section of the build a classifier quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier?WT.mc_id=academic-17441-jabenn#create-a-new-project) to create a new Custom Vision project. The UI may change and these docs are always the most up to date reference.
 
     Call your project `fruit-quality-detector`.
 
@@ -178,11 +178,11 @@ Image classifiers run at very low resolution. For example Custom Vision can take
 
     If you don't have both ripe and unripe fruit, you can use different fruits, or any two objects you have available. You can also find some example images in the [images](./images) folder of ripe and unripe bananas that you can use.
 
-1. Follow the [Upload and tag images section of the Build a classifier quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier?WT.mc_id=academic-17441-jabenn#upload-and-tag-images) to upload your training images. Tag the ripe fruit as `ripe`, and the unripe fruit as `unripe`.
+1. Follow the [upload and tag images section of the build a classifier quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier?WT.mc_id=academic-17441-jabenn#upload-and-tag-images) to upload your training images. Tag the ripe fruit as `ripe`, and the unripe fruit as `unripe`.
 
     ![The upload dialogs showing the upload of ripe and unripe banana pictures](../../../images/image-upload-bananas.png)
 
-1. Follow the [Train the classifier section of the Build a classifier quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier?WT.mc_id=academic-17441-jabenn#train-the-classifier) to train the image classifier on your uploaded images.
+1. Follow the [train the classifier section of the build a classifier quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier?WT.mc_id=academic-17441-jabenn#train-the-classifier) to train the image classifier on your uploaded images.
 
     You will be given a choice of training type. Select **Quick Training**.
 
@@ -196,7 +196,7 @@ Once your classifier is trained, you can test it by giving it a new image to cla
 
 ### Task - test your image classifier
 
-1. Follow the [Test your model documentation on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/test-your-model?WT.mc_id=academic-17441-jabenn#test-your-model) to test your image classifier. Use the testing images you created earlier, not any of the images you used for training.
+1. Follow the [test your model documentation on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/test-your-model?WT.mc_id=academic-17441-jabenn#test-your-model) to test your image classifier. Use the testing images you created earlier, not any of the images you used for training.
 
     ![A unripe banana predicted as unripe with a 98.9% probability, ripe with a 1.1% probability](../../../images/banana-unripe-quick-test-prediction.png)
 
@@ -210,7 +210,7 @@ Every time you make a prediction using the quick test option, the image and resu
 
 ### Task - retrain your image classifier
 
-1. Follow the [Use the predicted image for training documentation on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/test-your-model?WT.mc_id=academic-17441-jabenn#use-the-predicted-image-for-training) to retrain your model, using the correct tag for each image.
+1. Follow the [use the predicted image for training documentation on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/test-your-model?WT.mc_id=academic-17441-jabenn#use-the-predicted-image-for-training) to retrain your model, using the correct tag for each image.
 
 1. Once you model has been retrained, test on new images.
 
@@ -228,8 +228,8 @@ Try it out and see what the predictions are. You can find images to try with usi
 
 ## Review & Self Study
 
-* When you trained your classifier, you would have seen values for *Precision*, *Recall*, and *AP* that rate the model that was created. Read up on what these values are using [the Evaluate the classifier section of the Build a classifier quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier?WT.mc_id=academic-17441-jabenn#evaluate-the-classifier)
+* When you trained your classifier, you would have seen values for *Precision*, *Recall*, and *AP* that rate the model that was created. Read up on what these values are using [the evaluate the classifier section of the build a classifier quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier?WT.mc_id=academic-17441-jabenn#evaluate-the-classifier)
-* Read up on how to improve your classifier from the [How to improve your Custom Vision model on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-improving-your-classifier?WT.mc_id=academic-17441-jabenn)
+* Read up on how to improve your classifier from the [how to improve your Custom Vision model on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-improving-your-classifier?WT.mc_id=academic-17441-jabenn)
 
 ## Assignment
 

4-manufacturing/lessons/2-check-fruit-from-device/README.md

@@ -149,7 +149,7 @@ If you were to create a production device to sell to farms or factories, how wou
 
 You trained your custom vision model using the portal. This relies on having images available - and in the real world you may not be able to get training data that matches what the camera on your device captures. You can work round this by training directly from your device using the training API, to train a model using images captured from your IoT device.
 
-* Read up on the training API in the [Using the Custom Vision SDK quick start](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/quickstarts/image-classification?tabs=visual-studio&pivots=programming-language-python&WT.mc_id=academic-17441-jabenn)
+* Read up on the training API in the [using the Custom Vision SDK quick start](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/quickstarts/image-classification?tabs=visual-studio&pivots=programming-language-python&WT.mc_id=academic-17441-jabenn)
 
 ## Assignment
 

4-manufacturing/lessons/2-check-fruit-from-device/single-board-computer-classify-image.md

@@ -1,6 +1,6 @@
 # Classify an image - Virtual IoT Hardware and Raspberry Pi
 
-In this part of the lesson, you will add send the image captured by the camera to the Custom Vision service to classify it.
+In this part of the lesson, you will send the image captured by the camera to the Custom Vision service to classify it.
 
 ## Send images to Custom Vision
 
@@ -25,7 +25,7 @@ The Custom Vision service has a Python SDK you can use to classify images.
 
     This brings in some modules from the Custom Vision libraries, one to authenticate with the prediction key, and one to provide a prediction client class that can call Custom Vision.
 
-1. Add the following code to to the end of the file:
+1. Add the following code to the end of the file:
 
     ```python
     prediction_url = '<prediction_url>'
@@ -86,6 +86,6 @@ The Custom Vision service has a Python SDK you can use to classify images.
 
     ![A banana in custom vision predicted ripe at 56.8% and unripe at 43.1%](../../../images/custom-vision-banana-prediction.png)
 
-> 💁 You can find this code in the [code-classify/pi](code-classify/pi) or [code-classify/virtual-device](code-classify/virtual-device) folder.
+> 💁 You can find this code in the [code-classify/pi](code-classify/pi) or [code-classify/virtual-iot-device](code-classify/virtual-iot-device) folder.
 
 😀 Your fruit quality classifier program was a success!

4-manufacturing/lessons/2-check-fruit-from-device/virtual-device-camera.md

@@ -101,7 +101,7 @@ Program the device.
 
     > 💁 You can capture the image directly to a file instead of a `BytesIO` object by passing the file name to the `camera.capture` call. The reason for using the `BytesIO` object is so that later in this lesson you can send the image to your image classifier.
 
-1. Configure the image that the camera in CounterFit will capture. You can either set the *Source* to *File*, then upload an image file, or set the *Source* to *WebCam*, and images will be captures from your web cam. Make sure you select the **Set** button after selecting a picture or selecting your webcam.
+1. Configure the image that the camera in CounterFit will capture. You can either set the *Source* to *File*, then upload an image file, or set the *Source* to *WebCam*, and images will be captured from your web cam. Make sure you select the **Set** button after selecting a picture or selecting your webcam.
 
     ![CounterFit with a file set as the image source, and a web cam set showing a person holding a banana in a preview of the webcam](../../../images/counterfit-camera-options.png)
 

4-manufacturing/lessons/2-check-fruit-from-device/wio-terminal-camera.md

@@ -10,7 +10,7 @@ The camera you'll use is an [ArduCam Mini 2MP Plus](https://www.arducam.com/prod
 
 ## Connect the camera
 
-The ArduCam doesn't have a Grove socket, instead it connects to both the SPI and I<sup>2</sup>C busses via the GPIO pins on the Wio Terminal.
+The ArduCam doesn't have a Grove socket, instead it connects to both the SPI and I<sup>2</sup>C buses via the GPIO pins on the Wio Terminal.
 
 ### Task - connect the camera
 

4-manufacturing/lessons/2-check-fruit-from-device/wio-terminal-classify-image.md

@@ -1,10 +1,10 @@
 # Classify an image - Wio Terminal
 
-In this part of the lesson, you will add send the image captured by the camera to the Custom Vision service to classify it.
+In this part of the lesson, you will send the image captured by the camera to the Custom Vision service to classify it.
 
 ## Classify an image
 
-The Custom Vision service has a REST API you can call from the Wio Terminal use to classify images. THis REST API is accessed over an HTTPS connection - a secure HTTP connection.
+The Custom Vision service has a REST API you can call from the Wio Terminal use to classify images. This REST API is accessed over an HTTPS connection - a secure HTTP connection.
 
 When interacting with HTTPS endpoints, the client code needs to request the public key certificate from the server being accessed, and use that to encrypt the traffic it sends. Your web browser does this automatically, but microcontrollers do not. You will need to request this certificate manually and use it to create a secure connection to the REST API. These certificates don't change, so once you have a certificate, it can be hard coded in your application.
 

4-manufacturing/lessons/4-trigger-fruit-detector/README.md

@@ -74,7 +74,7 @@ As you define the architecture of your system, you need to constantly consider d
 
 ## Design a fruit quality control system
 
-Lets now take this idea of things, insights, and actions and apply it to our fruit quality detector to design a larger end-to-end application.
+Let's now take this idea of things, insights, and actions and apply it to our fruit quality detector to design a larger end-to-end application.
 
 Imagine you have been given the task of building a fruit quality detector to be used in a processing plant. Fruit travels on a conveyer belt system where currently employees spend time checking the fruit by hand and removing any unripe fruit as it arrives. To reduce costs, the plant owner wants an automated system.
 
@@ -205,7 +205,7 @@ The prototype will form the basis of a final production system. Some of the diff
 
 ## 🚀 Challenge
 
-In this lesson you have learned some of the concepts you need to know to architect an IoT system. Think back to the previous projects. How would do they fit into the reference architecture shown above?
+In this lesson you have learned some of the concepts you need to know on how to architect an IoT system. Think back to the previous projects. How would do they fit into the reference architecture shown above?
 
 Pick one of the projects so far and think of the design of a more complicated solution bringing together multiple capabilities beyond what was covered in the projects. Draw the architecture and think of all the devices and services you would need.
 
@@ -218,7 +218,7 @@ For example - a vehicle tracking device that combines GPS with sensors to monito
 ## Review & Self Study
 
 * Read more about IoT architecture on the [Azure IoT reference architecture documentation on Microsoft docs](https://docs.microsoft.com/azure/architecture/reference-architectures/iot?WT.mc_id=academic-17441-jabenn)
-* Read more about device twins in the [Understand and use device twins in IoT Hub documentation on Microsoft docs](https://docs.microsoft.com/azure/iot-hub/iot-hub-devguide-device-twins?WT.mc_id=academic-17441-jabenn)
+* Read more about device twins in the [understand and use device twins in IoT Hub documentation on Microsoft docs](https://docs.microsoft.com/azure/iot-hub/iot-hub-devguide-device-twins?WT.mc_id=academic-17441-jabenn)
 * Read about OPC-UA, a machine to machine communication protocol used in industrial automation on the [OPC-UA page on Wikipedia](https://wikipedia.org/wiki/OPC_Unified_Architecture)
 
 ## Assignment

4-manufacturing/lessons/4-trigger-fruit-detector/pi-proximity.md

@@ -40,6 +40,12 @@ Program the device.
 
 1. Open the `fruit-quality-detector` code in VS Code, either directly on the Pi, or connect via the Remote SSH extension.
 
+1. Install the rpi-vl53l0x Pip package, a Python package that interacts with a VL53L0X time-of-flight distance sensor. Install it using this pip command
+
+    ```sh
+        pip install rpi-vl53l0x
+    ```
+
 1. Create a new file in this project called `distance-sensor.py`.
 
     > 💁 An easy way to simulate multiple IoT devices is to do each in a different Python file, then run them at the same time.
@@ -95,4 +101,4 @@ Program the device.
 
 > 💁 You can find this code in the [code-proximity/pi](code-proximity/pi) folder.
 
-😀 Your proximity sensor program was a success!
+😀 Your proximity sensor program was a success!