Lesson 20 (#140)

* Adding content

* Update en.json

* Update README.md

* Update TRANSLATIONS.md

* Adding lesson tempolates

* Fixing code files with each others code in

* Update README.md

* Adding lesson 16

* Adding virtual camera

* Adding Wio Terminal camera capture

* Adding wio terminal code

* Adding SBC classification to lesson 16

* Adding challenge, review and assignment

* Adding images and using new Azure icons

* Update README.md

* Update iot-reference-architecture.png

* Adding structure for JulyOT links

* Removing icons

* Sketchnotes!

* Create lesson-1.png

* Starting on lesson 18

* Updated sketch

* Adding virtual distance sensor

* Adding Wio Terminal image classification

* Update README.md

* Adding structure for project 6 and wio terminal distance sensor

* Adding some of the smart timer stuff

* Updating sketchnotes

* Adding virtual device speech to text

* Adding chapter 21

* Language tweaks

* Lesson 22 stuff

* Update en.json

* Bumping seeed libraries

* Adding functions lab to lesson 22

* Almost done with LUIS

* Update README.md

* Reverting sunlight sensor change

Fixes #88

* Structure

* Adding speech to text lab for Pi

* Adding virtual device text to speech lab

* Finishing lesson 23

* Clarifying privacy

Fixes #99

* Update README.md

* Update hardware.md

* Update README.md

* Fixing some code samples that were wrong

* Adding more on translation

* Adding more on translator

* Update README.md

* Update README.md

* Adding public access to the container

* First part of retail object detection

* More on stock lesson

* Tweaks to maps lesson

* Update README.md

* Update pi-sensor.md

* IoT Edge install stuffs

* Notes on consumer groups and not running the event monitor at the same time

* Assignment for object detector

* Memory notes for speech to text

* Migrating LUIS to an HTTP trigger

* Adding Wio Terminal speech to text

* Changing smart timer to functions from hub

* Changing a param to body to avoid URL encoding

* Update README.md

* Tweaks before IoT Show

* Adding sketchnote links

* Adding object detection labs

* Adding more on object detection

* More on stock detection

* Finishing stock counting

3-transport/lessons/2-store-location-data/README.md

@@ -18,6 +18,7 @@ In this lesson we'll cover:
 
 * [Structured and unstructured data](#structured-and-unstructured-data)
 * [Send GPS data to an IoT Hub](#send-gps-data-to-an-iot-hub)
+* [Hot, warm, and cold paths](#hot-warm-and-cold-paths)
 * [Handle GPS events using serverless code](#handle-gps-events-using-serverless-code)
 * [Azure Storage Accounts](#azure-storage-accounts)
 * [Connect your serverless code to storage](#connect-your-serverless-code-to-storage)
@@ -44,6 +45,8 @@ Imagine you were adding IoT devices to a fleet of vehicles for a large commercia
 
 This data can change constantly. For example, if the IoT device is in a truck cab, then the data it sends may change as the trailer changes, for example only sending temperature data when a refrigerated trailer is used.
 
+✅ What other IoT data might be captured? Think about the kinds of loads trucks can carry, as well as maintenance data.
+
 This data varies from vehicle to vehicle, but it all gets sent to the same IoT service for processing. The IoT service needs to be able to process this unstructured data, storing it in a way that allows it to be searched or analyzed, but works with different structures to this data.
 
 ### SQL vs NoSQL storage
@@ -58,10 +61,14 @@ The first databases were Relational Database Management Systems (RDBMS), or rela
 
 For example, if you stored a users personal details in a table, you would have some kind of internal unique ID per user that is used in a row in a table that contains the users name and address. If you then wanted to store other details about that user, such as their purchases, in another table, you would have one column in the new table for that users ID. When you look up a user, you can use their ID to get their personal details from one table, and their purchases from another.
 
-SQL databases are ideal for storing structured data, and for when you want to ensure the data matches your schema. Some well known SQL databases are Microsoft SQL Server, MySQL, and PostgreSQL.
+SQL databases are ideal for storing structured data, and for when you want to ensure the data matches your schema.
 
 ✅ If you haven't used SQL before, take a moment to read up on it on the [SQL page on Wikipedia](https://wikipedia.org/wiki/SQL).
 
+Some well known SQL databases are Microsoft SQL Server, MySQL, and PostgreSQL.
+
+✅ Do some research: Read up on some of these SQL databases and their capabilities.
+
 #### NoSQL database
 
 NoSQL databases are called NoSQL because they don't have the same rigid structure of SQL databases. They are also known as document databases as they can store unstructured data such as documents.
@@ -74,6 +81,8 @@ NoSQL database do not have a pre-defined schema that limits how data is stored,
 
 Some well known NoSQL databases include Azure CosmosDB, MongoDB, and CouchDB.
 
+✅ Do some research: Read up on some of these NoSQL databases and their capabilities.
+
 In this lesson, you will be using NoSQL storage to store IoT data.
 
 ## Send GPS data to an IoT Hub
@@ -136,9 +145,33 @@ message = Message(json.dumps(message_json))
 
 Run your device code and ensure messages are flowing into IoT Hub using the `az iot hub monitor-events` CLI command.
 
+## Hot, warm, and cold paths
+
+Data that flows from an IoT device to the cloud is not always processed in real time. Some data needs real time processing, other data can be processed a short time later, and other data can be processed much later. The flow of data to different services that process the data at different times is referred to hot, warm and cold paths.
+
+### Hot path
+
+The hot path refers to data that needs to be processed in real time or near real time. You would use hot path data for alerts, such as getting alerts that a vehicle is approaching a depot, or that the temperature in a refrigerated truck is too high.
+
+To use hot path data, your code would respond to events as soon as they are received by your cloud services.
+
+### Warm path
+
+The warm path refers to data that can be processed a short while after being received, for example for reporting or short term analytics. You would use warm path data for daily reports on vehicle mileage, using data gathered the previous day.
+
+Warm path data is stored once it is received by the cloud service inside some kind of storage that can be quickly accessed.
+
+### Cold path
+
+THe cold path refers to historic data, storing data for the long term to be processed whenever needed. For example, you could use the cold path to get annual mileage reports for vehicles, or run analytics on routes to find the most optimal route to reduce fuel costs.
+
+Cold path data is stored in data warehouses - databases designed for storing large amounts of data that will never change and can be queried quickly and easily. You would normally have a regular job in your cloud application that would run at a regular time each day, week, or month to move data from warm path storage into the data warehouse.
+
+✅ Think about the data you have captured so far in these lessons. Is it hot, warm or cold path data?
+
 ## Handle GPS events using serverless code
 
-Once data is flowing into your IoT Hub, you can write some serverless code to listen for events published to the Event-Hub compatible endpoint.
+Once data is flowing into your IoT Hub, you can write some serverless code to listen for events published to the Event-Hub compatible endpoint. This is the warm path - this data will be stored and used in the next lesson for reporting on the journey.
 
 ![Sending GPS telemetry from an IoT device to IoT Hub, then to Azure Functions via an event hub trigger](../../../images/gps-telemetry-iot-hub-functions.png)
 

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

@@ -74,6 +74,8 @@ ML models don't give a binary answer, instead they give probabilities. For examp
 
 The ML model used to detect images like this is called an *image classifier* - it is given labelled images, and then classifies new images based off these labels.
 
+> 💁 This is an over-simplification, and there are many other ways to train models that don't always need labelled outputs, such as unsupervised learning. If you want to learn more about ML, check out [ML for beginners, a 24 lesson curriculum on Machine Learning](https://aka.ms/ML-beginners).
+
 ## Train an image classifier
 
 To successfully train an image classifier you need millions of images. As it turns out, once you have an image classifier trained on millions or billions of assorted images, you can re-use it and re-train it using a small set of images and get great results, using a process called *transfer learning*.

5-retail/lessons/1-train-stock-detector/README.md

@@ -56,7 +56,7 @@ Object detection involves training a model to recognize objects. Instead of givi
 
 When you then use it to predict images, instead of getting back a list of tags and percentages, you get back a list of detected objects, with their bounding box and the probability that the object matches the assigned tag.
 
-> 🎓 *Bounding boxes* are the boxes around an object. They are given using coordinates relative to the image as a whole on a scale of 0-1. For example, if the image is 800 pixels wide, by 600 tall and the object it detected between 400 and 600 pixels along, and 150 and 300 pixels down, the bounding box would have a top/left coordinate of 0.5,0.25, with a width of 0.25 and a height of 0.25. That way no matter what size the image is scaled to, the bounding box starts half way along, and a quarter of the way down, and is a quarter of the width and the height.
+> 🎓 *Bounding boxes* are the boxes around an object.
 
 ![Object detection of cashew nuts and tomato paste](../../../images/object-detector-cashews-tomato.png)
 

5-retail/lessons/2-check-stock-device/README.md

@@ -10,24 +10,164 @@ Add a sketchnote if possible/appropriate
 
 ## Introduction
 
-In this lesson you will learn about
+In the previous lesson you learned about the different uses of object detection in retail. You also learned how to train an object detector to identify stock. In this lesson you will learn how to use your object detector from your IoT device to count stock.
 
 In this lesson we'll cover:
 
-* [Thing 1](#thing-1)
+* [Stock counting](#stock-counting)
+* [Call your object detector from your IoT device](#call-your-object-detector-from-your-iot-device)
+* [Bounding boxes](#bounding-boxes)
+* [Retrain the model](#retrain-the-model)
+* [Count stock](#count-stock)
 
-## Thing 1
+## Stock counting
+
+Object detectors can be used for stock checking, either counting stock or ensuring stock is where it should be. IoT devices with cameras can be deployed all around the store to monitor stock, starting with hot spots where having items restocked is important, such as areas where small numbers of high value items are stocked.
+
+For example, if a camera is pointing at a set of shelves that can hold 8 cans of tomato paste, and an object detector only detects 7 cans, then one is missing and needs to be restocked.
+
+![7 cans of tomato paste on a shelf, 4 on the top row, 3 on top](../../../images/stock-7-cans-tomato-paste.png)
+
+In the above image, an object detector has detected 7 cans of tomato paste on a shelf that can hold 8 cans. Not only can the IoT device send a notification of the need to restock, but it can even give an indication of the location of the missing item, important data if you are using robots to restock shelves.
+
+> 💁 Depending on the store and popularity of the item, restocking probably wouldn't happen if only 1 can was missing. You would need to build an algorithm that determines when to restock based on your produce, customers and other criteria.
+
+✅ In what other scenarios could you combine object detection and robots?
+
+Sometimes the wrong stock can be on the shelves. This could be human error when restocking, or customers changing their mind on a purchase and putting an item back in the first available space. When this is a non-perishable item such as canned goods, this is an annoyance. If it is a perishable item such as frozen or chilled goods, this can mean that the product can no longer be sold as it might be impossible to tell how long the item was out of the freezer.
+
+Object detection can be used to detect unexpected items, again alerting a human or robot to return the item as soon as it is detected.
+
+![A rogue can of baby corn on the tomato paste shelf](../../../images/stock-rogue-corn.png)
+
+In the above image, a can of baby corn has been put on the shelf next to the tomato paste. The object detector has detected this, allowing the IoT device to notify a human or robot to return the can to it's correct location.
+
+## Call your object detector from your IoT device
+
+The object detector you trained in the last lesson can be called from your IoT device.
+
+### Task - publish an iteration of your object detector
+
+Iterations are published from the Custom Vision portal.
+
+1. Launch the Custom Vision portal at [CustomVision.ai](https://customvision.ai) and sign in if you don't have it open already. Then open your `stock-detector` project.
+
+1. Select the **Performance** tab from the options at the top
+
+1. Select the latest iteration from the *Iterations* list on the side
+
+1. Select the **Publish** button for the iteration
+
+    ![The publish button](../../../images/custom-vision-object-detector-publish-button.png)
+
+1. In the *Publish Model* dialog, set the *Prediction resource* to the `stock-detector-prediction` resource you created in the last lesson. Leave the name as `Iteration2`, and select the **Publish** button.
+
+1. Once published, select the **Prediction URL** button. This will show details of the prediction API, and you will need these to call the model from your IoT device. The lower section is labelled *If you have an image file*, and this is the details you want. Take a copy of the URL that is shown which will be something like:
+
+    ```output
+    https://<location>.api.cognitive.microsoft.com/customvision/v3.0/Prediction/<id>/detect/iterations/Iteration2/image
+    ```
+
+    Where `<location>` will be the location you used when creating your custom vision resource, and `<id>` will be a long ID made up of letters and numbers.
+
+    Also take a copy of the *Prediction-Key* value. This is a secure key that you have to pass when you call the model. Only applications that pass this key are allowed to use the model, any other applications are rejected.
+
+    ![The prediction API dialog showing the URL and key](../../../images/custom-vision-prediction-key-endpoint.png)
+
+✅ When a new iteration is published, it will have a different name. How do you think you would change the iteration an IoT device is using?
+
+### Task - call your object detector from your IoT device
+
+Follow the relevant guide below to use the object detector from your IoT device:
+
+* [Arduino - Wio Terminal](wio-terminal-object-detector.md)
+* [Single-board computer - Raspberry Pi/Virtual device](single-board-computer-object-detector.md)
+
+## Bounding boxes
+
+When you use the object detector, you not only get back the detected objects with their tags and probabilities, but you also get the bounding boxes of the objects. These define where the object detector detected the object with the given probability.
+
+> 💁 A bounding box is a box that defines the area that contains the object detected, a box that defines the boundary for the object.
+
+The results of a prediction in the **Predictions** tab in Custom Vision have the bounding boxes drawn on the image that was sent for prediction.
+
+![4 cans of tomato paste on a shelf with predictions for the 4 detections of 35.8%, 33.5%, 25.7% and 16.6%](../../../images/custom-vision-stock-prediction.png)
+
+In the image above, 4 cans of tomato paste were detected. In the results a red square is overlaid for each object that was detected in the image, indicating the bounding box for the image.
+
+✅ Open the predictions in Custom Vision and check out the bounding boxes.
+
+Bounding boxes are defined with 4 values - top, left, height and width. These values are on a scale of 0-1, representing the positions as a percentage of the size of the image. The origin (the 0,0 position) is the top left of the image, so the top value is the distance from the top, and the bottom of the bounding box is the top plus the height.
+
+![A bounding box around a can of tomato paste](../../../images/bounding-box.png)
+
+The above image is 600 pixels wide and 800 pixels tall. The bounding box starts at 320 pixels down, giving a top coordinate of 0.4 (800 x 0.4 = 320). From the left, the bounding box starts at 240 pixels across, giving a left coordinate of 0.4 (600 x 0.4 = 240). The height of the bounding box is 240 pixels, giving a height value of 0.3 (800 x 0.3 = 240). The width of the bounding box is 120 pixels, giving a width value of 0.2 (600 x 0.2 = 120).
+
+| Coordinate | Value |
+| ---------- | ----: |
+| Top        | 0.4   |
+| Left       | 0.4   |
+| Height     | 0.3   |
+| Width      | 0.2   |
+
+Using percentage values from 0-1 means no matter what size the image is scaled to, the bounding box starts 0.4 of the way along and down, and is a 0.3 of the height and 0.2 of the width.
+
+You can use bounding boxes combined with probabilities to evaluate how accurate a detection is. For example, an object detector can detect multiple objects that overlap, for example detecting one can inside another. Your code could look at the bounding boxes, understand that this is impossible, and ignore any objects that have a significant overlap with other objects.
+
+![Two bonding boxes overlapping a can of tomato paste](../../../images/overlap-object-detection.png)
+
+In the example above, one bounding box indicated a predicted can of tomato paste at 78.3%. A second bounding box is slightly smaller, and is inside the first bounding box with a probability of 64.3%. You code can check the bounding boxes, see they overlap completely, and ignore the lower probability as there is no way one can can be inside another.
+
+✅ Can you think of a situation where is it valid to detect one object inside another?
+
+## Retrain the model
+
+Like with the image classifier, you can retrain your model using data captured by your IoT device. Using this real-world data will ensure your model works well when used from your IoT device.
+
+Unlike with the image classifier, you can't just tag an image. Instead you need to review every bounding box detected by the model. If the box is around the wrong thing then it needs to be deleted, if it is in the wrong location it needs to be adjusted.
+
+### Task - retrain the model
+
+1. Make sure you have captured a range of images using your IoT device.
+
+1. From the **Predictions** tab, select an image. You will see red boxes indicating the bounding boxes of the detected objects.
+
+1. Work through each bounding box. Select it first and you will see a pop-up showing the tag. Use the handles on the corners of the bounding box to adjust the size if necessary. If the tag is wrong, remove it with the **X** button and add the correct tag. If the bounding box doesn't contain an object, delete it with the trashcan button.
+
+1. Close the editor when done and the image will move from the **Predictions** tab to the **Training Images** tab. Repeat the process for all the predictions.
+
+1. Use the **Train** button to re-train your model. Once it has trained, publish the iteration and update your IoT device to use the URL of the new iteration.
+
+1. Re-deploy your code and test your IoT device.
+
+## Count stock
+
+Using a combination of the number of objects detected and the bounding boxes, you can count the stock on a shelf.
+
+### Task - count stock
+
+Follow the relevant guide below to count stock using the results from the object detector from your IoT device:
+
+* [Arduino - Wio Terminal](wio-terminal-count-stock.md)
+* [Single-board computer - Raspberry Pi/Virtual device](single-board-computer-count-stock.md)
 
 ---
 
 ## 🚀 Challenge
 
+Can you detect incorrect stock? Train your model on multiple objects, then update your app to alert you if the wrong stock is detected.
+
+Maybe even take this further and detect stock side by side on the same shelf, and see if something has been put in the wrong place bu defining limits on the bounding boxes.
+
 ## Post-lecture quiz
 
 [Post-lecture quiz](https://brave-island-0b7c7f50f.azurestaticapps.net/quiz/40)
 
 ## Review & Self Study
 
+* Learn more about how to architect an end-to-end stock detection system from the [Out of stock detection at the edge pattern guide on Microsoft Docs](https://docs.microsoft.com/hybrid/app-solutions/pattern-out-of-stock-at-edge?WT.mc_id=academic-17441-jabenn)
+* Learn other ways to build end-to-end retail solutions combining a range of IoT and cloud services by watching this [Behind the scenes of a retail solution - Hands On! video on YouTube](https://www.youtube.com/watch?v=m3Pc300x2Mw).
+
 ## Assignment
 
-[](assignment.md)
+[Use your object detector on the edge](assignment.md)

5-retail/lessons/2-check-stock-device/assignment.md

@@ -1,9 +1,11 @@
-#
+# Use your object detector on the edge
 
 ## Instructions
 
+In the last project, you deployed your image classifier to the edge. Do the same with your object detector, exporting it as a compact model and running it on the edge, accessing the edge version from your IoT device.
+
 ## Rubric
 
 | Criteria | Exemplary | Adequate | Needs Improvement |
 | -------- | --------- | -------- | ----------------- |
-| |  |  |  |
+| Deploy your object detector to the edge | Was able to use the correct compact domain, export the object detector and run it on the edge | Was able to use the correct compact domain, and export the object detector, but was unable to run it on the edge | Was unable to use the correct compact domain, export the object detector, and run it on the edge  |

5-retail/lessons/2-check-stock-device/code-count/pi/fruit-quality-detector/app.py

@@ -0,0 +1,92 @@
+import io
+import time
+from picamera import PiCamera
+
+from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
+from msrest.authentication import ApiKeyCredentials
+
+from PIL import Image, ImageDraw, ImageColor
+from shapely.geometry import Polygon
+
+camera = PiCamera()
+camera.resolution = (640, 480)
+camera.rotation = 0
+
+time.sleep(2)
+
+image = io.BytesIO()
+camera.capture(image, 'jpeg')
+image.seek(0)
+
+with open('image.jpg', 'wb') as image_file:
+    image_file.write(image.read())
+
+prediction_url = '<prediction_url>'
+prediction_key = '<prediction key>'
+
+parts = prediction_url.split('/')
+endpoint = 'https://' + parts[2]
+project_id = parts[6]
+iteration_name = parts[9]
+
+prediction_credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key})
+predictor = CustomVisionPredictionClient(endpoint, prediction_credentials)
+
+image.seek(0)
+results = predictor.detect_image(project_id, iteration_name, image)
+
+threshold = 0.3
+
+predictions = list(prediction for prediction in results.predictions if prediction.probability > threshold)
+
+for prediction in predictions:
+    print(f'{prediction.tag_name}:\t{prediction.probability * 100:.2f}%')
+
+overlap_threshold = 0.002
+
+def create_polygon(prediction):
+    scale_left = prediction.bounding_box.left
+    scale_top = prediction.bounding_box.top
+    scale_right = prediction.bounding_box.left + prediction.bounding_box.width
+    scale_bottom = prediction.bounding_box.top + prediction.bounding_box.height
+
+    return Polygon([(scale_left, scale_top), (scale_right, scale_top), (scale_right, scale_bottom), (scale_left, scale_bottom)])
+
+to_delete = []
+
+for i in range(0, len(predictions)):
+    polygon_1 = create_polygon(predictions[i])
+
+    for j in range(i+1, len(predictions)):
+        polygon_2 = create_polygon(predictions[j])
+        overlap = polygon_1.intersection(polygon_2).area
+
+        smallest_area = min(polygon_1.area, polygon_2.area)
+
+        if overlap > (overlap_threshold * smallest_area):
+            to_delete.append(predictions[i])
+            break
+
+for d in to_delete:
+    predictions.remove(d)
+
+print(f'Counted {len(predictions)} stock items')
+
+
+with Image.open('image.jpg') as im:
+    draw = ImageDraw.Draw(im)
+
+    for prediction in predictions:
+        scale_left = prediction.bounding_box.left
+        scale_top = prediction.bounding_box.top
+        scale_right = prediction.bounding_box.left + prediction.bounding_box.width
+        scale_bottom = prediction.bounding_box.top + prediction.bounding_box.height
+        
+        left = scale_left * im.width
+        top = scale_top * im.height
+        right = scale_right * im.width
+        bottom = scale_bottom * im.height
+
+        draw.rectangle([left, top, right, bottom], outline=ImageColor.getrgb('red'), width=2)
+
+    im.save('image.jpg')

5-retail/lessons/2-check-stock-device/code-count/virtual-iot-device/fruit-quality-detector/app.py

@@ -0,0 +1,92 @@
+from counterfit_connection import CounterFitConnection
+CounterFitConnection.init('127.0.0.1', 5000)
+
+import io
+from counterfit_shims_picamera import PiCamera
+
+from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
+from msrest.authentication import ApiKeyCredentials
+
+from PIL import Image, ImageDraw, ImageColor
+from shapely.geometry import Polygon
+
+camera = PiCamera()
+camera.resolution = (640, 480)
+camera.rotation = 0
+
+image = io.BytesIO()
+camera.capture(image, 'jpeg')
+image.seek(0)
+
+with open('image.jpg', 'wb') as image_file:
+    image_file.write(image.read())
+
+prediction_url = '<prediction_url>'
+prediction_key = '<prediction key>'
+
+parts = prediction_url.split('/')
+endpoint = 'https://' + parts[2]
+project_id = parts[6]
+iteration_name = parts[9]
+
+prediction_credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key})
+predictor = CustomVisionPredictionClient(endpoint, prediction_credentials)
+
+image.seek(0)
+results = predictor.detect_image(project_id, iteration_name, image)
+
+threshold = 0.3
+
+predictions = list(prediction for prediction in results.predictions if prediction.probability > threshold)
+
+for prediction in predictions:
+    print(f'{prediction.tag_name}:\t{prediction.probability * 100:.2f}%')
+
+overlap_threshold = 0.002
+
+def create_polygon(prediction):
+    scale_left = prediction.bounding_box.left
+    scale_top = prediction.bounding_box.top
+    scale_right = prediction.bounding_box.left + prediction.bounding_box.width
+    scale_bottom = prediction.bounding_box.top + prediction.bounding_box.height
+
+    return Polygon([(scale_left, scale_top), (scale_right, scale_top), (scale_right, scale_bottom), (scale_left, scale_bottom)])
+
+to_delete = []
+
+for i in range(0, len(predictions)):
+    polygon_1 = create_polygon(predictions[i])
+
+    for j in range(i+1, len(predictions)):
+        polygon_2 = create_polygon(predictions[j])
+        overlap = polygon_1.intersection(polygon_2).area
+
+        smallest_area = min(polygon_1.area, polygon_2.area)
+
+        if overlap > (overlap_threshold * smallest_area):
+            to_delete.append(predictions[i])
+            break
+
+for d in to_delete:
+    predictions.remove(d)
+
+print(f'Counted {len(predictions)} stock items')
+
+
+with Image.open('image.jpg') as im:
+    draw = ImageDraw.Draw(im)
+
+    for prediction in predictions:
+        scale_left = prediction.bounding_box.left
+        scale_top = prediction.bounding_box.top
+        scale_right = prediction.bounding_box.left + prediction.bounding_box.width
+        scale_bottom = prediction.bounding_box.top + prediction.bounding_box.height
+        
+        left = scale_left * im.width
+        top = scale_top * im.height
+        right = scale_right * im.width
+        bottom = scale_bottom * im.height
+
+        draw.rectangle([left, top, right, bottom], outline=ImageColor.getrgb('red'), width=2)
+
+    im.save('image.jpg')

5-retail/lessons/2-check-stock-device/code-count/wio-terminal/fruit-quality-detector/.gitignore

@@ -0,0 +1,5 @@
+.pio
+.vscode/.browse.c_cpp.db*
+.vscode/c_cpp_properties.json
+.vscode/launch.json
+.vscode/ipch

5-retail/lessons/2-check-stock-device/code-count/wio-terminal/fruit-quality-detector/.vscode/extensions.json

@@ -0,0 +1,7 @@
+{
+    // See http://go.microsoft.com/fwlink/?LinkId=827846
+    // for the documentation about the extensions.json format
+    "recommendations": [
+        "platformio.platformio-ide"
+    ]
+}

5-retail/lessons/2-check-stock-device/code-count/wio-terminal/fruit-quality-detector/include/README

@@ -0,0 +1,39 @@
+
+This directory is intended for project header files.
+
+A header file is a file containing C declarations and macro definitions
+to be shared between several project source files. You request the use of a
+header file in your project source file (C, C++, etc) located in `src` folder
+by including it, with the C preprocessing directive `#include'.
+
+```src/main.c
+
+#include "header.h"
+
+int main (void)
+{
+ ...
+}
+```
+
+Including a header file produces the same results as copying the header file
+into each source file that needs it. Such copying would be time-consuming
+and error-prone. With a header file, the related declarations appear
+in only one place. If they need to be changed, they can be changed in one
+place, and programs that include the header file will automatically use the
+new version when next recompiled. The header file eliminates the labor of
+finding and changing all the copies as well as the risk that a failure to
+find one copy will result in inconsistencies within a program.
+
+In C, the usual convention is to give header files names that end with `.h'.
+It is most portable to use only letters, digits, dashes, and underscores in
+header file names, and at most one dot.
+
+Read more about using header files in official GCC documentation:
+
+* Include Syntax
+* Include Operation
+* Once-Only Headers
+* Computed Includes
+
+https://gcc.gnu.org/onlinedocs/cpp/Header-Files.html

5-retail/lessons/2-check-stock-device/code-count/wio-terminal/fruit-quality-detector/lib/README

@@ -0,0 +1,46 @@
+
+This directory is intended for project specific (private) libraries.
+PlatformIO will compile them to static libraries and link into executable file.
+
+The source code of each library should be placed in a an own separate directory
+("lib/your_library_name/[here are source files]").
+
+For example, see a structure of the following two libraries `Foo` and `Bar`:
+
+|--lib
+|  |
+|  |--Bar
+|  |  |--docs
+|  |  |--examples
+|  |  |--src
+|  |     |- Bar.c
+|  |     |- Bar.h
+|  |  |- library.json (optional, custom build options, etc) https://docs.platformio.org/page/librarymanager/config.html
+|  |
+|  |--Foo
+|  |  |- Foo.c
+|  |  |- Foo.h
+|  |
+|  |- README --> THIS FILE
+|
+|- platformio.ini
+|--src
+   |- main.c
+
+and a contents of `src/main.c`:
+```
+#include <Foo.h>
+#include <Bar.h>
+
+int main (void)
+{
+  ...
+}
+
+```
+
+PlatformIO Library Dependency Finder will find automatically dependent
+libraries scanning project source files.
+
+More information about PlatformIO Library Dependency Finder
+- https://docs.platformio.org/page/librarymanager/ldf.html

5-retail/lessons/2-check-stock-device/code-count/wio-terminal/fruit-quality-detector/platformio.ini

@@ -0,0 +1,26 @@
+; PlatformIO Project Configuration File
+;
+;   Build options: build flags, source filter
+;   Upload options: custom upload port, speed and extra flags
+;   Library options: dependencies, extra library storages
+;   Advanced options: extra scripting
+;
+; Please visit documentation for the other options and examples
+; https://docs.platformio.org/page/projectconf.html
+
+[env:seeed_wio_terminal]
+platform = atmelsam
+board = seeed_wio_terminal
+framework = arduino
+lib_deps =
+    seeed-studio/Seeed Arduino rpcWiFi @ 1.0.5
+    seeed-studio/Seeed Arduino FS @ 2.0.3
+    seeed-studio/Seeed Arduino SFUD @ 2.0.1
+    seeed-studio/Seeed Arduino rpcUnified @ 2.1.3
+    seeed-studio/Seeed_Arduino_mbedtls @ 3.0.1
+    seeed-studio/Seeed Arduino RTC @ 2.0.0
+    bblanchon/ArduinoJson @ 6.17.3
+build_flags = 
+    -w
+    -DARDUCAM_SHIELD_V2
+    -DOV2640_CAM

5-retail/lessons/2-check-stock-device/code-count/wio-terminal/fruit-quality-detector/src/camera.h

@@ -0,0 +1,160 @@
+#pragma once
+
+#include <ArduCAM.h>
+#include <Wire.h>
+
+class Camera
+{
+public:
+    Camera(int format, int image_size) : _arducam(OV2640, PIN_SPI_SS)
+    {
+        _format = format;
+        _image_size = image_size;
+    }
+
+    bool init()
+    {
+        // Reset the CPLD
+        _arducam.write_reg(0x07, 0x80);
+        delay(100);
+
+        _arducam.write_reg(0x07, 0x00);
+        delay(100);
+
+        // Check if the ArduCAM SPI bus is OK
+        _arducam.write_reg(ARDUCHIP_TEST1, 0x55);
+        if (_arducam.read_reg(ARDUCHIP_TEST1) != 0x55)
+        {
+            return false;
+        }
+            
+        // Change MCU mode
+        _arducam.set_mode(MCU2LCD_MODE);
+
+        uint8_t vid, pid;
+
+        // Check if the camera module type is OV2640
+        _arducam.wrSensorReg8_8(0xff, 0x01);
+        _arducam.rdSensorReg8_8(OV2640_CHIPID_HIGH, &vid);
+        _arducam.rdSensorReg8_8(OV2640_CHIPID_LOW, &pid);
+        if ((vid != 0x26) && ((pid != 0x41) || (pid != 0x42)))
+        {
+            return false;
+        }
+        
+        _arducam.set_format(_format);
+        _arducam.InitCAM();
+        _arducam.OV2640_set_JPEG_size(_image_size);
+        _arducam.OV2640_set_Light_Mode(Auto);
+        _arducam.OV2640_set_Special_effects(Normal);
+        delay(1000);
+
+        return true;
+    }
+
+    void startCapture()
+    {
+        _arducam.flush_fifo();
+        _arducam.clear_fifo_flag();
+        _arducam.start_capture();
+    }
+
+    bool captureReady()
+    {
+        return _arducam.get_bit(ARDUCHIP_TRIG, CAP_DONE_MASK);
+    }
+
+    bool readImageToBuffer(byte **buffer, uint32_t &buffer_length)
+    {
+        if (!captureReady()) return false;
+
+        // Get the image file length
+        uint32_t length = _arducam.read_fifo_length();
+        buffer_length = length;
+
+        if (length >= MAX_FIFO_SIZE)
+        {
+            return false;
+        }
+        if (length == 0)
+        {
+            return false;
+        }
+
+        // create the buffer
+        byte *buf = new byte[length];
+
+        uint8_t temp = 0, temp_last = 0;
+        int i = 0;
+        uint32_t buffer_pos = 0;
+        bool is_header = false;
+
+        _arducam.CS_LOW();
+        _arducam.set_fifo_burst();
+        
+        while (length--)
+        {
+            temp_last = temp;
+            temp = SPI.transfer(0x00);
+            //Read JPEG data from FIFO
+            if ((temp == 0xD9) && (temp_last == 0xFF)) //If find the end ,break while,
+            {
+                buf[buffer_pos] = temp;
+
+                buffer_pos++;
+                i++;
+                
+                _arducam.CS_HIGH();
+            }
+            if (is_header == true)
+            {
+                //Write image data to buffer if not full
+                if (i < 256)
+                {
+                    buf[buffer_pos] = temp;
+                    buffer_pos++;
+                    i++;
+                }
+                else
+                {
+                    _arducam.CS_HIGH();
+
+                    i = 0;
+                    buf[buffer_pos] = temp;
+
+                    buffer_pos++;
+                    i++;
+
+                    _arducam.CS_LOW();
+                    _arducam.set_fifo_burst();
+                }
+            }
+            else if ((temp == 0xD8) & (temp_last == 0xFF))
+            {
+                is_header = true;
+
+                buf[buffer_pos] = temp_last;
+                buffer_pos++;
+                i++;
+
+                buf[buffer_pos] = temp;
+                buffer_pos++;
+                i++;
+            }
+        }
+        
+        _arducam.clear_fifo_flag();
+
+        _arducam.set_format(_format);
+        _arducam.InitCAM();
+        _arducam.OV2640_set_JPEG_size(_image_size);
+
+        // return the buffer
+        *buffer = buf;
+    }
+
+private:
+    ArduCAM _arducam;
+    int _format;
+    int _image_size;
+};

5-retail/lessons/2-check-stock-device/code-count/wio-terminal/fruit-quality-detector/src/config.h

@@ -0,0 +1,49 @@
+#pragma once
+
+#include <string>
+
+using namespace std;
+
+// WiFi credentials
+const char *SSID = "<SSID>";
+const char *PASSWORD = "<PASSWORD>";
+
+const char *PREDICTION_URL = "<PREDICTION_URL>";
+const char *PREDICTION_KEY = "<PREDICTION_KEY>";
+
+// Microsoft Azure DigiCert Global Root G2 global certificate
+const char *CERTIFICATE =
+    "-----BEGIN CERTIFICATE-----\r\n"
+    "MIIF8zCCBNugAwIBAgIQAueRcfuAIek/4tmDg0xQwDANBgkqhkiG9w0BAQwFADBh\r\n"
+    "MQswCQYDVQQGEwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMRkwFwYDVQQLExB3\r\n"
+    "d3cuZGlnaWNlcnQuY29tMSAwHgYDVQQDExdEaWdpQ2VydCBHbG9iYWwgUm9vdCBH\r\n"
+    "MjAeFw0yMDA3MjkxMjMwMDBaFw0yNDA2MjcyMzU5NTlaMFkxCzAJBgNVBAYTAlVT\r\n"
+    "MR4wHAYDVQQKExVNaWNyb3NvZnQgQ29ycG9yYXRpb24xKjAoBgNVBAMTIU1pY3Jv\r\n"
+    "c29mdCBBenVyZSBUTFMgSXNzdWluZyBDQSAwNjCCAiIwDQYJKoZIhvcNAQEBBQAD\r\n"
+    "ggIPADCCAgoCggIBALVGARl56bx3KBUSGuPc4H5uoNFkFH4e7pvTCxRi4j/+z+Xb\r\n"
+    "wjEz+5CipDOqjx9/jWjskL5dk7PaQkzItidsAAnDCW1leZBOIi68Lff1bjTeZgMY\r\n"
+    "iwdRd3Y39b/lcGpiuP2d23W95YHkMMT8IlWosYIX0f4kYb62rphyfnAjYb/4Od99\r\n"
+    "ThnhlAxGtfvSbXcBVIKCYfZgqRvV+5lReUnd1aNjRYVzPOoifgSx2fRyy1+pO1Uz\r\n"
+    "aMMNnIOE71bVYW0A1hr19w7kOb0KkJXoALTDDj1ukUEDqQuBfBxReL5mXiu1O7WG\r\n"
+    "0vltg0VZ/SZzctBsdBlx1BkmWYBW261KZgBivrql5ELTKKd8qgtHcLQA5fl6JB0Q\r\n"
+    "gs5XDaWehN86Gps5JW8ArjGtjcWAIP+X8CQaWfaCnuRm6Bk/03PQWhgdi84qwA0s\r\n"
+    "sRfFJwHUPTNSnE8EiGVk2frt0u8PG1pwSQsFuNJfcYIHEv1vOzP7uEOuDydsmCjh\r\n"
+    "lxuoK2n5/2aVR3BMTu+p4+gl8alXoBycyLmj3J/PUgqD8SL5fTCUegGsdia/Sa60\r\n"
+    "N2oV7vQ17wjMN+LXa2rjj/b4ZlZgXVojDmAjDwIRdDUujQu0RVsJqFLMzSIHpp2C\r\n"
+    "Zp7mIoLrySay2YYBu7SiNwL95X6He2kS8eefBBHjzwW/9FxGqry57i71c2cDAgMB\r\n"
+    "AAGjggGtMIIBqTAdBgNVHQ4EFgQU1cFnOsKjnfR3UltZEjgp5lVou6UwHwYDVR0j\r\n"
+    "BBgwFoAUTiJUIBiV5uNu5g/6+rkS7QYXjzkwDgYDVR0PAQH/BAQDAgGGMB0GA1Ud\r\n"
+    "JQQWMBQGCCsGAQUFBwMBBggrBgEFBQcDAjASBgNVHRMBAf8ECDAGAQH/AgEAMHYG\r\n"
+    "CCsGAQUFBwEBBGowaDAkBggrBgEFBQcwAYYYaHR0cDovL29jc3AuZGlnaWNlcnQu\r\n"
+    "Y29tMEAGCCsGAQUFBzAChjRodHRwOi8vY2FjZXJ0cy5kaWdpY2VydC5jb20vRGln\r\n"
+    "aUNlcnRHbG9iYWxSb290RzIuY3J0MHsGA1UdHwR0MHIwN6A1oDOGMWh0dHA6Ly9j\r\n"
+    "cmwzLmRpZ2ljZXJ0LmNvbS9EaWdpQ2VydEdsb2JhbFJvb3RHMi5jcmwwN6A1oDOG\r\n"
+    "MWh0dHA6Ly9jcmw0LmRpZ2ljZXJ0LmNvbS9EaWdpQ2VydEdsb2JhbFJvb3RHMi5j\r\n"
+    "cmwwHQYDVR0gBBYwFDAIBgZngQwBAgEwCAYGZ4EMAQICMBAGCSsGAQQBgjcVAQQD\r\n"
+    "AgEAMA0GCSqGSIb3DQEBDAUAA4IBAQB2oWc93fB8esci/8esixj++N22meiGDjgF\r\n"
+    "+rA2LUK5IOQOgcUSTGKSqF9lYfAxPjrqPjDCUPHCURv+26ad5P/BYtXtbmtxJWu+\r\n"
+    "cS5BhMDPPeG3oPZwXRHBJFAkY4O4AF7RIAAUW6EzDflUoDHKv83zOiPfYGcpHc9s\r\n"
+    "kxAInCedk7QSgXvMARjjOqdakor21DTmNIUotxo8kHv5hwRlGhBJwps6fEVi1Bt0\r\n"
+    "trpM/3wYxlr473WSPUFZPgP1j519kLpWOJ8z09wxay+Br29irPcBYv0GMXlHqThy\r\n"
+    "8y4m/HyTQeI2IMvMrQnwqPpY+rLIXyviI2vLoI+4xKE4Rn38ZZ8m\r\n"
+    "-----END CERTIFICATE-----\r\n";

5-retail/lessons/2-check-stock-device/code-count/wio-terminal/fruit-quality-detector/src/main.cpp

@@ -0,0 +1,223 @@
+#include <Arduino.h>
+#include <ArduinoJson.h>
+#include <HTTPClient.h>
+#include <rpcWiFi.h>
+#include "SD/Seeed_SD.h"
+#include <Seeed_FS.h>
+#include <SPI.h>
+#include <vector>
+#include <WiFiClientSecure.h>
+
+#include "config.h"
+#include "camera.h"
+
+Camera camera = Camera(JPEG, OV2640_640x480);
+
+WiFiClientSecure client;
+
+void setupCamera()
+{
+    pinMode(PIN_SPI_SS, OUTPUT);
+    digitalWrite(PIN_SPI_SS, HIGH);
+
+    Wire.begin();
+    SPI.begin();
+
+    if (!camera.init())
+    {
+        Serial.println("Error setting up the camera!");
+    }
+}
+
+void connectWiFi()
+{
+    while (WiFi.status() != WL_CONNECTED)
+    {
+        Serial.println("Connecting to WiFi..");
+        WiFi.begin(SSID, PASSWORD);
+        delay(500);
+    }
+
+    client.setCACert(CERTIFICATE);
+    Serial.println("Connected!");
+}
+
+void setup()
+{
+    Serial.begin(9600);
+
+    while (!Serial)
+        ; // Wait for Serial to be ready
+
+    delay(1000);
+
+    connectWiFi();
+
+    setupCamera();
+
+    pinMode(WIO_KEY_C, INPUT_PULLUP);
+}
+
+const float threshold = 0.0f;
+const float overlap_threshold = 0.20f;
+
+struct Point {
+    float x, y;
+};
+
+struct Rect {
+    Point topLeft, bottomRight;
+};
+
+float area(Rect rect)
+{
+    return abs(rect.bottomRight.x - rect.topLeft.x) * abs(rect.bottomRight.y - rect.topLeft.y);
+}
+    
+float overlappingArea(Rect rect1, Rect rect2)
+{
+    float left = max(rect1.topLeft.x, rect2.topLeft.x);
+    float right = min(rect1.bottomRight.x, rect2.bottomRight.x);
+    float top = max(rect1.topLeft.y, rect2.topLeft.y);
+    float bottom = min(rect1.bottomRight.y, rect2.bottomRight.y);
+
+
+    if ( right > left && bottom > top )
+    {
+        return (right-left)*(bottom-top);
+    }
+    
+    return 0.0f;
+}
+
+Rect rectFromBoundingBox(JsonVariant prediction)
+{
+    JsonObject bounding_box = prediction["boundingBox"].as<JsonObject>();
+
+    float left = bounding_box["left"].as<float>();
+    float top = bounding_box["top"].as<float>();
+    float width = bounding_box["width"].as<float>();
+    float height = bounding_box["height"].as<float>();
+
+    Point topLeft = {left, top};
+    Point bottomRight = {left + width, top + height};
+
+    return {topLeft, bottomRight};
+}
+
+void processPredictions(std::vector<JsonVariant> &predictions)
+{
+    std::vector<JsonVariant> passed_predictions;
+
+    for (int i = 0; i < predictions.size(); ++i)
+    {
+        Rect prediction_1_rect = rectFromBoundingBox(predictions[i]);
+        float prediction_1_area = area(prediction_1_rect);
+        bool passed = true;
+
+        for (int j = i + 1; j < predictions.size(); ++j)
+        {
+            Rect prediction_2_rect = rectFromBoundingBox(predictions[j]);
+            float prediction_2_area = area(prediction_2_rect);
+
+            float overlap = overlappingArea(prediction_1_rect, prediction_2_rect);
+            float smallest_area = min(prediction_1_area, prediction_2_area);
+
+            if (overlap > (overlap_threshold * smallest_area))
+            {
+                passed = false;
+                break;
+            }
+        }
+
+        if (passed)
+        {
+            passed_predictions.push_back(predictions[i]);
+        }
+    }
+
+    for(JsonVariant prediction : passed_predictions)
+    {
+        String boundingBox = prediction["boundingBox"].as<String>();
+        String tag = prediction["tagName"].as<String>();
+        float probability = prediction["probability"].as<float>();
+
+        char buff[32];
+        sprintf(buff, "%s:\t%.2f%%\t%s", tag.c_str(), probability * 100.0, boundingBox.c_str());
+        Serial.println(buff);
+    }
+
+    Serial.print("Counted ");
+    Serial.print(passed_predictions.size());
+    Serial.println(" stock items.");
+}
+
+void detectStock(byte *buffer, uint32_t length)
+{
+    HTTPClient httpClient;
+    httpClient.begin(client, PREDICTION_URL);
+    httpClient.addHeader("Content-Type", "application/octet-stream");
+    httpClient.addHeader("Prediction-Key", PREDICTION_KEY);
+
+    int httpResponseCode = httpClient.POST(buffer, length);
+
+    if (httpResponseCode == 200)
+    {
+        String result = httpClient.getString();
+
+        DynamicJsonDocument doc(1024);
+        deserializeJson(doc, result.c_str());
+
+        JsonObject obj = doc.as<JsonObject>();
+        JsonArray predictions = obj["predictions"].as<JsonArray>();
+
+        std::vector<JsonVariant> passed_predictions;
+
+        for(JsonVariant prediction : predictions) 
+        {
+            float probability = prediction["probability"].as<float>();
+            if (probability > threshold)
+            {
+                passed_predictions.push_back(prediction);
+            }
+        }
+
+        processPredictions(passed_predictions);
+    }
+
+    httpClient.end();
+}
+
+void buttonPressed()
+{
+    camera.startCapture();
+
+    while (!camera.captureReady())
+        delay(100);
+
+    Serial.println("Image captured");
+
+    byte *buffer;
+    uint32_t length;
+
+    if (camera.readImageToBuffer(&buffer, length))
+    {
+        Serial.print("Image read to buffer with length ");
+        Serial.println(length);
+
+        detectStock(buffer, length);
+
+        delete (buffer);
+    }
+}
+
+void loop()
+{
+    if (digitalRead(WIO_KEY_C) == LOW)
+    {
+        buttonPressed();
+        delay(2000);
+    }
+
+    delay(200);
+}

5-retail/lessons/2-check-stock-device/code-count/wio-terminal/fruit-quality-detector/test/README

@@ -0,0 +1,11 @@
+
+This directory is intended for PlatformIO Unit Testing and project tests.
+
+Unit Testing is a software testing method by which individual units of
+source code, sets of one or more MCU program modules together with associated
+control data, usage procedures, and operating procedures, are tested to
+determine whether they are fit for use. Unit testing finds problems early
+in the development cycle.
+
+More information about PlatformIO Unit Testing:
+- https://docs.platformio.org/page/plus/unit-testing.html

5-retail/lessons/2-check-stock-device/code-detect/pi/fruit-quality-detector/app.py

@@ -0,0 +1,40 @@
+import io
+import time
+from picamera import PiCamera
+
+from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
+from msrest.authentication import ApiKeyCredentials
+
+camera = PiCamera()
+camera.resolution = (640, 480)
+camera.rotation = 0
+
+time.sleep(2)
+
+image = io.BytesIO()
+camera.capture(image, 'jpeg')
+image.seek(0)
+
+with open('image.jpg', 'wb') as image_file:
+    image_file.write(image.read())
+
+prediction_url = '<prediction_url>'
+prediction_key = '<prediction key>'
+
+parts = prediction_url.split('/')
+endpoint = 'https://' + parts[2]
+project_id = parts[6]
+iteration_name = parts[9]
+
+prediction_credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key})
+predictor = CustomVisionPredictionClient(endpoint, prediction_credentials)
+
+image.seek(0)
+results = predictor.detect_image(project_id, iteration_name, image)
+
+threshold = 0.3
+
+predictions = list(prediction for prediction in results.predictions if prediction.probability > threshold)
+
+for prediction in predictions:
+    print(f'{prediction.tag_name}:\t{prediction.probability * 100:.2f}%')

5-retail/lessons/2-check-stock-device/code-detect/virtual-iot-device/fruit-quality-detector/app.py

@@ -0,0 +1,40 @@
+from counterfit_connection import CounterFitConnection
+CounterFitConnection.init('127.0.0.1', 5000)
+
+import io
+from counterfit_shims_picamera import PiCamera
+
+from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
+from msrest.authentication import ApiKeyCredentials
+
+camera = PiCamera()
+camera.resolution = (640, 480)
+camera.rotation = 0
+
+image = io.BytesIO()
+camera.capture(image, 'jpeg')
+image.seek(0)
+
+with open('image.jpg', 'wb') as image_file:
+    image_file.write(image.read())
+
+prediction_url = '<prediction_url>'
+prediction_key = '<prediction key>'
+
+parts = prediction_url.split('/')
+endpoint = 'https://' + parts[2]
+project_id = parts[6]
+iteration_name = parts[9]
+
+prediction_credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key})
+predictor = CustomVisionPredictionClient(endpoint, prediction_credentials)
+
+image.seek(0)
+results = predictor.detect_image(project_id, iteration_name, image)
+
+threshold = 0.3
+
+predictions = list(prediction for prediction in results.predictions if prediction.probability > threshold)
+
+for prediction in predictions:
+    print(f'{prediction.tag_name}:\t{prediction.probability * 100:.2f}%')

5-retail/lessons/2-check-stock-device/code-detect/wio-terminal/fruit-quality-detector/.gitignore

@@ -0,0 +1,5 @@
+.pio
+.vscode/.browse.c_cpp.db*
+.vscode/c_cpp_properties.json
+.vscode/launch.json
+.vscode/ipch

5-retail/lessons/2-check-stock-device/code-detect/wio-terminal/fruit-quality-detector/.vscode/extensions.json

@@ -0,0 +1,7 @@
+{
+    // See http://go.microsoft.com/fwlink/?LinkId=827846
+    // for the documentation about the extensions.json format
+    "recommendations": [
+        "platformio.platformio-ide"
+    ]
+}

5-retail/lessons/2-check-stock-device/code-detect/wio-terminal/fruit-quality-detector/include/README

@@ -0,0 +1,39 @@
+
+This directory is intended for project header files.
+
+A header file is a file containing C declarations and macro definitions
+to be shared between several project source files. You request the use of a
+header file in your project source file (C, C++, etc) located in `src` folder
+by including it, with the C preprocessing directive `#include'.
+
+```src/main.c
+
+#include "header.h"
+
+int main (void)
+{
+ ...
+}
+```
+
+Including a header file produces the same results as copying the header file
+into each source file that needs it. Such copying would be time-consuming
+and error-prone. With a header file, the related declarations appear
+in only one place. If they need to be changed, they can be changed in one
+place, and programs that include the header file will automatically use the
+new version when next recompiled. The header file eliminates the labor of
+finding and changing all the copies as well as the risk that a failure to
+find one copy will result in inconsistencies within a program.
+
+In C, the usual convention is to give header files names that end with `.h'.
+It is most portable to use only letters, digits, dashes, and underscores in
+header file names, and at most one dot.
+
+Read more about using header files in official GCC documentation:
+
+* Include Syntax
+* Include Operation
+* Once-Only Headers
+* Computed Includes
+
+https://gcc.gnu.org/onlinedocs/cpp/Header-Files.html

5-retail/lessons/2-check-stock-device/code-detect/wio-terminal/fruit-quality-detector/lib/README

@@ -0,0 +1,46 @@
+
+This directory is intended for project specific (private) libraries.
+PlatformIO will compile them to static libraries and link into executable file.
+
+The source code of each library should be placed in a an own separate directory
+("lib/your_library_name/[here are source files]").
+
+For example, see a structure of the following two libraries `Foo` and `Bar`:
+
+|--lib
+|  |
+|  |--Bar
+|  |  |--docs
+|  |  |--examples
+|  |  |--src
+|  |     |- Bar.c
+|  |     |- Bar.h
+|  |  |- library.json (optional, custom build options, etc) https://docs.platformio.org/page/librarymanager/config.html
+|  |
+|  |--Foo
+|  |  |- Foo.c
+|  |  |- Foo.h
+|  |
+|  |- README --> THIS FILE
+|
+|- platformio.ini
+|--src
+   |- main.c
+
+and a contents of `src/main.c`:
+```
+#include <Foo.h>
+#include <Bar.h>
+
+int main (void)
+{
+  ...
+}
+
+```
+
+PlatformIO Library Dependency Finder will find automatically dependent
+libraries scanning project source files.
+
+More information about PlatformIO Library Dependency Finder
+- https://docs.platformio.org/page/librarymanager/ldf.html

5-retail/lessons/2-check-stock-device/code-detect/wio-terminal/fruit-quality-detector/platformio.ini

@@ -0,0 +1,26 @@
+; PlatformIO Project Configuration File
+;
+;   Build options: build flags, source filter
+;   Upload options: custom upload port, speed and extra flags
+;   Library options: dependencies, extra library storages
+;   Advanced options: extra scripting
+;
+; Please visit documentation for the other options and examples
+; https://docs.platformio.org/page/projectconf.html
+
+[env:seeed_wio_terminal]
+platform = atmelsam
+board = seeed_wio_terminal
+framework = arduino
+lib_deps =
+    seeed-studio/Seeed Arduino rpcWiFi @ 1.0.5
+    seeed-studio/Seeed Arduino FS @ 2.0.3
+    seeed-studio/Seeed Arduino SFUD @ 2.0.1
+    seeed-studio/Seeed Arduino rpcUnified @ 2.1.3
+    seeed-studio/Seeed_Arduino_mbedtls @ 3.0.1
+    seeed-studio/Seeed Arduino RTC @ 2.0.0
+    bblanchon/ArduinoJson @ 6.17.3
+build_flags = 
+    -w
+    -DARDUCAM_SHIELD_V2
+    -DOV2640_CAM

5-retail/lessons/2-check-stock-device/code-detect/wio-terminal/fruit-quality-detector/src/camera.h

@@ -0,0 +1,160 @@
+#pragma once
+
+#include <ArduCAM.h>
+#include <Wire.h>
+
+class Camera
+{
+public:
+    Camera(int format, int image_size) : _arducam(OV2640, PIN_SPI_SS)
+    {
+        _format = format;
+        _image_size = image_size;
+    }
+
+    bool init()
+    {
+        // Reset the CPLD
+        _arducam.write_reg(0x07, 0x80);
+        delay(100);
+
+        _arducam.write_reg(0x07, 0x00);
+        delay(100);
+
+        // Check if the ArduCAM SPI bus is OK
+        _arducam.write_reg(ARDUCHIP_TEST1, 0x55);
+        if (_arducam.read_reg(ARDUCHIP_TEST1) != 0x55)
+        {
+            return false;
+        }
+            
+        // Change MCU mode
+        _arducam.set_mode(MCU2LCD_MODE);
+
+        uint8_t vid, pid;
+
+        // Check if the camera module type is OV2640
+        _arducam.wrSensorReg8_8(0xff, 0x01);
+        _arducam.rdSensorReg8_8(OV2640_CHIPID_HIGH, &vid);
+        _arducam.rdSensorReg8_8(OV2640_CHIPID_LOW, &pid);
+        if ((vid != 0x26) && ((pid != 0x41) || (pid != 0x42)))
+        {
+            return false;
+        }
+        
+        _arducam.set_format(_format);
+        _arducam.InitCAM();
+        _arducam.OV2640_set_JPEG_size(_image_size);
+        _arducam.OV2640_set_Light_Mode(Auto);
+        _arducam.OV2640_set_Special_effects(Normal);
+        delay(1000);
+
+        return true;
+    }
+
+    void startCapture()
+    {
+        _arducam.flush_fifo();
+        _arducam.clear_fifo_flag();
+        _arducam.start_capture();
+    }
+
+    bool captureReady()
+    {
+        return _arducam.get_bit(ARDUCHIP_TRIG, CAP_DONE_MASK);
+    }
+
+    bool readImageToBuffer(byte **buffer, uint32_t &buffer_length)
+    {
+        if (!captureReady()) return false;
+
+        // Get the image file length
+        uint32_t length = _arducam.read_fifo_length();
+        buffer_length = length;
+
+        if (length >= MAX_FIFO_SIZE)
+        {
+            return false;
+        }
+        if (length == 0)
+        {
+            return false;
+        }
+
+        // create the buffer
+        byte *buf = new byte[length];
+
+        uint8_t temp = 0, temp_last = 0;
+        int i = 0;
+        uint32_t buffer_pos = 0;
+        bool is_header = false;
+
+        _arducam.CS_LOW();
+        _arducam.set_fifo_burst();
+        
+        while (length--)
+        {
+            temp_last = temp;
+            temp = SPI.transfer(0x00);
+            //Read JPEG data from FIFO
+            if ((temp == 0xD9) && (temp_last == 0xFF)) //If find the end ,break while,
+            {
+                buf[buffer_pos] = temp;
+
+                buffer_pos++;
+                i++;
+                
+                _arducam.CS_HIGH();
+            }
+            if (is_header == true)
+            {
+                //Write image data to buffer if not full
+                if (i < 256)
+                {
+                    buf[buffer_pos] = temp;
+                    buffer_pos++;
+                    i++;
+                }
+                else
+                {
+                    _arducam.CS_HIGH();
+
+                    i = 0;
+                    buf[buffer_pos] = temp;
+
+                    buffer_pos++;
+                    i++;
+
+                    _arducam.CS_LOW();
+                    _arducam.set_fifo_burst();
+                }
+            }
+            else if ((temp == 0xD8) & (temp_last == 0xFF))
+            {
+                is_header = true;
+
+                buf[buffer_pos] = temp_last;
+                buffer_pos++;
+                i++;
+
+                buf[buffer_pos] = temp;
+                buffer_pos++;
+                i++;
+            }
+        }
+        
+        _arducam.clear_fifo_flag();
+
+        _arducam.set_format(_format);
+        _arducam.InitCAM();
+        _arducam.OV2640_set_JPEG_size(_image_size);
+
+        // return the buffer
+        *buffer = buf;
+    }
+
+private:
+    ArduCAM _arducam;
+    int _format;
+    int _image_size;
+};

5-retail/lessons/2-check-stock-device/code-detect/wio-terminal/fruit-quality-detector/src/config.h

@@ -0,0 +1,49 @@
+#pragma once
+
+#include <string>
+
+using namespace std;
+
+// WiFi credentials
+const char *SSID = "<SSID>";
+const char *PASSWORD = "<PASSWORD>";
+
+const char *PREDICTION_URL = "<PREDICTION_URL>";
+const char *PREDICTION_KEY = "<PREDICTION_KEY>";
+
+// Microsoft Azure DigiCert Global Root G2 global certificate
+const char *CERTIFICATE =
+    "-----BEGIN CERTIFICATE-----\r\n"
+    "MIIF8zCCBNugAwIBAgIQAueRcfuAIek/4tmDg0xQwDANBgkqhkiG9w0BAQwFADBh\r\n"
+    "MQswCQYDVQQGEwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMRkwFwYDVQQLExB3\r\n"
+    "d3cuZGlnaWNlcnQuY29tMSAwHgYDVQQDExdEaWdpQ2VydCBHbG9iYWwgUm9vdCBH\r\n"
+    "MjAeFw0yMDA3MjkxMjMwMDBaFw0yNDA2MjcyMzU5NTlaMFkxCzAJBgNVBAYTAlVT\r\n"
+    "MR4wHAYDVQQKExVNaWNyb3NvZnQgQ29ycG9yYXRpb24xKjAoBgNVBAMTIU1pY3Jv\r\n"
+    "c29mdCBBenVyZSBUTFMgSXNzdWluZyBDQSAwNjCCAiIwDQYJKoZIhvcNAQEBBQAD\r\n"
+    "ggIPADCCAgoCggIBALVGARl56bx3KBUSGuPc4H5uoNFkFH4e7pvTCxRi4j/+z+Xb\r\n"
+    "wjEz+5CipDOqjx9/jWjskL5dk7PaQkzItidsAAnDCW1leZBOIi68Lff1bjTeZgMY\r\n"
+    "iwdRd3Y39b/lcGpiuP2d23W95YHkMMT8IlWosYIX0f4kYb62rphyfnAjYb/4Od99\r\n"
+    "ThnhlAxGtfvSbXcBVIKCYfZgqRvV+5lReUnd1aNjRYVzPOoifgSx2fRyy1+pO1Uz\r\n"
+    "aMMNnIOE71bVYW0A1hr19w7kOb0KkJXoALTDDj1ukUEDqQuBfBxReL5mXiu1O7WG\r\n"
+    "0vltg0VZ/SZzctBsdBlx1BkmWYBW261KZgBivrql5ELTKKd8qgtHcLQA5fl6JB0Q\r\n"
+    "gs5XDaWehN86Gps5JW8ArjGtjcWAIP+X8CQaWfaCnuRm6Bk/03PQWhgdi84qwA0s\r\n"
+    "sRfFJwHUPTNSnE8EiGVk2frt0u8PG1pwSQsFuNJfcYIHEv1vOzP7uEOuDydsmCjh\r\n"
+    "lxuoK2n5/2aVR3BMTu+p4+gl8alXoBycyLmj3J/PUgqD8SL5fTCUegGsdia/Sa60\r\n"
+    "N2oV7vQ17wjMN+LXa2rjj/b4ZlZgXVojDmAjDwIRdDUujQu0RVsJqFLMzSIHpp2C\r\n"
+    "Zp7mIoLrySay2YYBu7SiNwL95X6He2kS8eefBBHjzwW/9FxGqry57i71c2cDAgMB\r\n"
+    "AAGjggGtMIIBqTAdBgNVHQ4EFgQU1cFnOsKjnfR3UltZEjgp5lVou6UwHwYDVR0j\r\n"
+    "BBgwFoAUTiJUIBiV5uNu5g/6+rkS7QYXjzkwDgYDVR0PAQH/BAQDAgGGMB0GA1Ud\r\n"
+    "JQQWMBQGCCsGAQUFBwMBBggrBgEFBQcDAjASBgNVHRMBAf8ECDAGAQH/AgEAMHYG\r\n"
+    "CCsGAQUFBwEBBGowaDAkBggrBgEFBQcwAYYYaHR0cDovL29jc3AuZGlnaWNlcnQu\r\n"
+    "Y29tMEAGCCsGAQUFBzAChjRodHRwOi8vY2FjZXJ0cy5kaWdpY2VydC5jb20vRGln\r\n"
+    "aUNlcnRHbG9iYWxSb290RzIuY3J0MHsGA1UdHwR0MHIwN6A1oDOGMWh0dHA6Ly9j\r\n"
+    "cmwzLmRpZ2ljZXJ0LmNvbS9EaWdpQ2VydEdsb2JhbFJvb3RHMi5jcmwwN6A1oDOG\r\n"
+    "MWh0dHA6Ly9jcmw0LmRpZ2ljZXJ0LmNvbS9EaWdpQ2VydEdsb2JhbFJvb3RHMi5j\r\n"
+    "cmwwHQYDVR0gBBYwFDAIBgZngQwBAgEwCAYGZ4EMAQICMBAGCSsGAQQBgjcVAQQD\r\n"
+    "AgEAMA0GCSqGSIb3DQEBDAUAA4IBAQB2oWc93fB8esci/8esixj++N22meiGDjgF\r\n"
+    "+rA2LUK5IOQOgcUSTGKSqF9lYfAxPjrqPjDCUPHCURv+26ad5P/BYtXtbmtxJWu+\r\n"
+    "cS5BhMDPPeG3oPZwXRHBJFAkY4O4AF7RIAAUW6EzDflUoDHKv83zOiPfYGcpHc9s\r\n"
+    "kxAInCedk7QSgXvMARjjOqdakor21DTmNIUotxo8kHv5hwRlGhBJwps6fEVi1Bt0\r\n"
+    "trpM/3wYxlr473WSPUFZPgP1j519kLpWOJ8z09wxay+Br29irPcBYv0GMXlHqThy\r\n"
+    "8y4m/HyTQeI2IMvMrQnwqPpY+rLIXyviI2vLoI+4xKE4Rn38ZZ8m\r\n"
+    "-----END CERTIFICATE-----\r\n";

5-retail/lessons/2-check-stock-device/code-detect/wio-terminal/fruit-quality-detector/src/main.cpp

@@ -0,0 +1,145 @@
+#include <Arduino.h>
+#include <ArduinoJson.h>
+#include <HTTPClient.h>
+#include <list>
+#include <rpcWiFi.h>
+#include "SD/Seeed_SD.h"
+#include <Seeed_FS.h>
+#include <SPI.h>
+#include <vector>
+#include <WiFiClientSecure.h>
+
+#include "config.h"
+#include "camera.h"
+
+Camera camera = Camera(JPEG, OV2640_640x480);
+
+WiFiClientSecure client;
+
+void setupCamera()
+{
+    pinMode(PIN_SPI_SS, OUTPUT);
+    digitalWrite(PIN_SPI_SS, HIGH);
+
+    Wire.begin();
+    SPI.begin();
+
+    if (!camera.init())
+    {
+        Serial.println("Error setting up the camera!");
+    }
+}
+
+void connectWiFi()
+{
+    while (WiFi.status() != WL_CONNECTED)
+    {
+        Serial.println("Connecting to WiFi..");
+        WiFi.begin(SSID, PASSWORD);
+        delay(500);
+    }
+
+    client.setCACert(CERTIFICATE);
+    Serial.println("Connected!");
+}
+
+void setup()
+{
+    Serial.begin(9600);
+
+    while (!Serial)
+        ; // Wait for Serial to be ready
+
+    delay(1000);
+
+    connectWiFi();
+
+    setupCamera();
+
+    pinMode(WIO_KEY_C, INPUT_PULLUP);
+}
+
+const float threshold = 0.3f;
+
+void processPredictions(std::vector<JsonVariant> &predictions)
+{
+    for(JsonVariant prediction : predictions)
+    {
+        String tag = prediction["tagName"].as<String>();
+        float probability = prediction["probability"].as<float>();
+
+        char buff[32];
+        sprintf(buff, "%s:\t%.2f%%", tag.c_str(), probability * 100.0);
+        Serial.println(buff);
+    }
+}
+
+void detectStock(byte *buffer, uint32_t length)
+{
+    HTTPClient httpClient;
+    httpClient.begin(client, PREDICTION_URL);
+    httpClient.addHeader("Content-Type", "application/octet-stream");
+    httpClient.addHeader("Prediction-Key", PREDICTION_KEY);
+
+    int httpResponseCode = httpClient.POST(buffer, length);
+
+    if (httpResponseCode == 200)
+    {
+        String result = httpClient.getString();
+
+        DynamicJsonDocument doc(1024);
+        deserializeJson(doc, result.c_str());
+
+        JsonObject obj = doc.as<JsonObject>();
+        JsonArray predictions = obj["predictions"].as<JsonArray>();
+
+        std::vector<JsonVariant> passed_predictions;
+
+        for(JsonVariant prediction : predictions) 
+        {
+            float probability = prediction["probability"].as<float>();
+            if (probability > threshold)
+            {
+                passed_predictions.push_back(prediction);
+            }
+        }
+
+        processPredictions(passed_predictions);
+    }
+
+    httpClient.end();
+}
+
+void buttonPressed()
+{
+    camera.startCapture();
+
+    while (!camera.captureReady())
+        delay(100);
+
+    Serial.println("Image captured");
+
+    byte *buffer;
+    uint32_t length;
+
+    if (camera.readImageToBuffer(&buffer, length))
+    {
+        Serial.print("Image read to buffer with length ");
+        Serial.println(length);
+
+        detectStock(buffer, length);
+
+        delete (buffer);
+    }
+}
+
+void loop()
+{
+    if (digitalRead(WIO_KEY_C) == LOW)
+    {
+        buttonPressed();
+        delay(2000);
+    }
+
+    delay(200);
+}

5-retail/lessons/2-check-stock-device/code-detect/wio-terminal/fruit-quality-detector/test/README

@@ -0,0 +1,11 @@
+
+This directory is intended for PlatformIO Unit Testing and project tests.
+
+Unit Testing is a software testing method by which individual units of
+source code, sets of one or more MCU program modules together with associated
+control data, usage procedures, and operating procedures, are tested to
+determine whether they are fit for use. Unit testing finds problems early
+in the development cycle.
+
+More information about PlatformIO Unit Testing:
+- https://docs.platformio.org/page/plus/unit-testing.html

5-retail/lessons/2-check-stock-device/single-board-computer-count-stock.md

@@ -0,0 +1,163 @@
+# Count stock from your IoT device - Virtual IoT Hardware and Raspberry Pi
+
+A combination of the predictions and their bounding boxes can be used to count stock in an image
+
+## Show bounding boxes
+
+As a helpful debugging step you can not only print out the bounding boxes, but you can also draw them on the image that was written to disk when an image was captured.
+
+### Task - print the bounding boxes
+
+1. Ensure the `stock-counter` project is open in VS Code, and the virtual environment is activated if you are using a virtual IoT device.
+
+1. Change the `print` statement in the `for` loop to the following to print the bounding boxes to the console:
+
+    ```python
+    print(f'{prediction.tag_name}:\t{prediction.probability * 100:.2f}%\t{prediction.bounding_box}')
+    ```
+
+1. Run the app with the camera pointing at some stock on a shelf. The bounding boxes will be printed to the console, with left, top, width and height values from 0-1.
+
+    ```output
+    pi@raspberrypi:~/stock-counter $ python3 app.py 
+    tomato paste:   33.42%  {'additional_properties': {}, 'left': 0.3455171, 'top': 0.09916268, 'width': 0.14175442, 'height': 0.29405564}
+    tomato paste:   34.41%  {'additional_properties': {}, 'left': 0.48283678, 'top': 0.10242918, 'width': 0.11782813, 'height': 0.27467814}
+    tomato paste:   31.25%  {'additional_properties': {}, 'left': 0.4923783, 'top': 0.35007596, 'width': 0.13668466, 'height': 0.28304994}
+    tomato paste:   31.05%  {'additional_properties': {}, 'left': 0.36416405, 'top': 0.37494493, 'width': 0.14024884, 'height': 0.26880276}
+    ```
+
+### Task - draw bounding boxes on the image
+
+1. The Pip package [Pillow](https://pypi.org/project/Pillow/) can be used to draw on images. Install this with the following command:
+
+    ```sh
+    pip3 install pillow
+    ```
+
+    If you are using a virtual IoT device, make sure to run this from inside the activated virtual environment.
+
+1. Add the following import statement to the top of the `app.py` file:
+
+    ```python
+    from PIL import Image, ImageDraw, ImageColor
+    ```
+
+    This imports code needed to edit the image.
+
+1. Add the following code to the end of the `app.py` file:
+
+    ```python
+    with Image.open('image.jpg') as im:
+        draw = ImageDraw.Draw(im)
+    
+        for prediction in predictions:
+            scale_left = prediction.bounding_box.left
+            scale_top = prediction.bounding_box.top
+            scale_right = prediction.bounding_box.left + prediction.bounding_box.width
+            scale_bottom = prediction.bounding_box.top + prediction.bounding_box.height
+            
+            left = scale_left * im.width
+            top = scale_top * im.height
+            right = scale_right * im.width
+            bottom = scale_bottom * im.height
+    
+            draw.rectangle([left, top, right, bottom], outline=ImageColor.getrgb('red'), width=2)
+    
+        im.save('image.jpg')
+    ```
+
+    This code opens the image that was saved earlier for editing. It then loops through the predictions getting the bounding boxes, and calculates the bottom right coordinate using the bounding box values from 0-1. These are then converted to image coordinates by multiplying by the relevant dimension of the image. For example, if the left value was 0.5 on an image that was 600 pixels wide, this would convert it to 300 (0.5 x 600 = 300).
+
+    Each bounding box is drawn on the image using a red line. Finally the edited image is saved, overwriting the original image.
+
+1. Run the app with the camera pointing at some stock on a shelf. You will see the `image.jpg` file in the VS Code explorer, and you will be able to select it to see the bounding boxes.
+
+    ![4 cans of tomato paste with bounding boxes around each can](../../../images/rpi-stock-with-bounding-boxes.jpg)
+
+## Count stock
+
+In the image shown above, the bounding boxes have a small overlap. If this overlap was much larger, then the bounding boxes may indicate the same object. To count the objects correctly, you need to ignore boxes with a significant overlap.
+
+### Task - count stock ignoring overlap
+
+1. The Pip package [Shapely](https://pypi.org/project/Shapely/) can be used to calculate the intersection. If you are using a Raspberry Pi, you will need to instal a library dependency first:
+
+    ```sh
+    sudo apt install libgeos-dev
+    ```
+
+1. Install the Shapely Pip package:
+
+    ```sh
+    pip3 install shapely
+    ```
+
+    If you are using a virtual IoT device, make sure to run this from inside the activated virtual environment.
+
+1. Add the following import statement to the top of the `app.py` file:
+
+    ```python
+    from shapely.geometry import Polygon
+    ```
+
+    This imports code needed to create polygons to calculate overlap.
+
+1. Above the code that draws the bounding boxes, add the following code:
+
+    ```python
+    overlap_threshold = 0.20
+    ```
+
+    This defines the percentage overlap allowed before the bounding boxes are considered to be the same object. 0.20 defines a 20% overlap.
+
+1. To calculate overlap using Shapely, the bounding boxes need to be converted into Shapely polygons. Add the following function to do this:
+
+    ```python
+    def create_polygon(prediction):
+        scale_left = prediction.bounding_box.left
+        scale_top = prediction.bounding_box.top
+        scale_right = prediction.bounding_box.left + prediction.bounding_box.width
+        scale_bottom = prediction.bounding_box.top + prediction.bounding_box.height
+    
+        return Polygon([(scale_left, scale_top), (scale_right, scale_top), (scale_right, scale_bottom), (scale_left, scale_bottom)])
+    ```
+
+    This creates a polygon using the bounding box of a prediction.
+
+1. The logic for removing overlapping objects involves comparing all bounding boxes and if any pairs of predictions have bounding boxes that overlap more than the threshold, delete one of the predictions. To compare all the predictions, you compare prediction 1 with 2, 3, 4, etc., then 2 with 3, 4, etc. The following code does this:
+
+    ```python
+    to_delete = []
+
+    for i in range(0, len(predictions)):
+        polygon_1 = create_polygon(predictions[i])
+    
+        for j in range(i+1, len(predictions)):
+            polygon_2 = create_polygon(predictions[j])
+            overlap = polygon_1.intersection(polygon_2).area
+
+            smallest_area = min(polygon_1.area, polygon_2.area)
+    
+            if overlap > (overlap_threshold * smallest_area):
+                to_delete.append(predictions[i])
+                break
+    
+    for d in to_delete:
+        predictions.remove(d)
+
+    print(f'Counted {len(predictions)} stock items')
+    ```
+
+    The overlap is calculated using the Shapely `Polygon.intersection` method that returns a polygon that has the overlap. The area is then calculated from this polygon. This overlap threshold is not an absolute value, but needs to be a percentage of the bounding box, so the smallest bounding box is found, and the overlap threshold is used to calculate what area the overlap can be to not exceed the percentage overlap threshold of the smallest bounding box. If the overlap exceeds this, the prediction is marked for deletion.
+
+    Once a prediction has been marked for deletion it doesn't need to be checked again, so the inner loop breaks out to check the next prediction. You can't delete items from a list whilst iterating through it, so the bounding boxes that overlap more than the threshold are added to the `to_delete` list, then deleted at the end.
+
+    Finally the stock count is printed to the console. This could then be sent to an IoT service to alert if the stock levels are low. All of this code is before the bounding boxes are drawn, so you will see the stock predictions without overlaps on the generated images.
+
+    > 💁 This is very simplistic way to remove overlaps, just removing the first one in an overlapping pair. For production code, you would want to put more logic in here, such as considering the overlaps between multiple objects, or if one bounding box is contained by another.
+
+1. Run the app with the camera pointing at some stock on a shelf. The output will indicate the number of bounding boxes without overlaps that exceed the threshold. Try adjusting the `overlap_threshold` value to see predictions being ignored.
+
+> 💁 You can find this code in the [code-count/pi](code-count/pi) or [code-count/virtual-device](code-count/virtual-device) folder.
+
+😀 Your stock counter program was a success!

5-retail/lessons/2-check-stock-device/single-board-computer-object-detector.md

@@ -0,0 +1,74 @@
+# Call your object detector from your IoT device - Virtual IoT Hardware and Raspberry Pi
+
+Once your object detector has been published, it can be used from your IoT device.
+
+## Copy the image classifier project
+
+The majority of your stock detector is the same as the image classifier you created in a previous lesson.
+
+### Task - copy the image classifier project
+
+1. Create a folder called `stock-counter` either on your computer if you are using a virtual IoT device, or on your Raspberry Pi. If you are using a virtual IoT device make sure you set up a virtual environment.
+
+1. Set up the camera hardware.
+
+    * If you are using a Raspberry Pi you will need to fit the PiCamera. You might also want to fix the camera in a single position, for example, by hanging the cable over a box or can, or fixing the camera to a box with double-sided tape.
+    * If you are using a virtual IoT device then you will need to install CounterFit and the CounterFit PyCamera shim. If you are going to use still images, then capture some images that your object detector hasn't seen yet, if you are going to use your web cam make sure it is positioned in a way that can see the stock you are detecting.
+
+1. Replicate the steps from [lesson 2 of the manufacturing project](../../../4-manufacturing/lessons/2-check-fruit-from-device/README.md#task---capture-an-image-using-an-iot-device) to capture images from the camera.
+
+1. Replicate the steps from [lesson 2 of the manufacturing project](../../../4-manufacturing/lessons/2-check-fruit-from-device/README.md#task---classify-images-from-your-iot-device) to call the image classifier. The majority of this code will be re-used to detect objects.
+
+## Change the code from a classifier to an image detector
+
+The code you used to classify images is very similar to the code to detect objects. The main difference is the method called on the Custom Vision SDK, and the results of the call.
+
+### Task - change the code from a classifier to an image detector
+
+1. Delete the three lines of code that classifies the image and processes the predictions:
+
+    ```python
+    results = predictor.classify_image(project_id, iteration_name, image)
+    
+    for prediction in results.predictions:
+        print(f'{prediction.tag_name}:\t{prediction.probability * 100:.2f}%')
+    ```
+
+    Remove these three lines.
+
+1. Add the following code to detect objects in the image:
+
+    ```python
+    results = predictor.detect_image(project_id, iteration_name, image)
+
+    threshold = 0.3
+    
+    predictions = list(prediction for prediction in results.predictions if prediction.probability > threshold)
+    
+    for prediction in predictions:
+        print(f'{prediction.tag_name}:\t{prediction.probability * 100:.2f}%')
+    ```
+
+    This code calls the `detect_image` method on the predictor to run the object detector. It then gathers all the predictions with a probability above a threshold, printing them to the console.
+
+    Unlike an image classifier that only returns one result per tag, the object detector will return multiple results, so any with a low probability need to be filtered out.
+
+1. Run this code and it will capture an image, sending it to the object detector, and print out the detected objects. If you are using a virtual IoT device ensure you have an appropriate image set in CounterFit, or our web cam is selected. If you are using a Raspberry Pi, make sure your camera is pointing to objects on a shelf.
+
+    ```output
+    pi@raspberrypi:~/stock-counter $ python3 app.py 
+    tomato paste:   34.13%
+    tomato paste:   33.95%
+    tomato paste:   35.05%
+    tomato paste:   32.80%
+    ```
+
+    > 💁 You may need to adjust the `threshold` to an appropriate value for your images.
+
+    You will be able to see the image that was taken, and these values in the **Predictions** tab in Custom Vision.
+
+    ![4 cans of tomato paste on a shelf with predictions for the 4 detections of 35.8%, 33.5%, 25.7% and 16.6%](../../../images/custom-vision-stock-prediction.png)
+
+> 💁 You can find this code in the [code-detect/pi](code-detect/pi) or [code-detect/virtual-device](code-detect/virtual-device) folder.
+
+😀 Your stock counter program was a success!

5-retail/lessons/2-check-stock-device/wio-terminal-count-stock.md

@@ -0,0 +1,167 @@
+# Count stock from your IoT device - Wio Terminal
+
+A combination of the predictions and their bounding boxes can be used to count stock in an image.
+
+## Count stock
+
+![4 cans of tomato paste with bounding boxes around each can](../../../images/rpi-stock-with-bounding-boxes.jpg)
+
+In the image shown above, the bounding boxes have a small overlap. If this overlap was much larger, then the bounding boxes may indicate the same object. To count the objects correctly, you need to ignore boxes with a significant overlap.
+
+### Task - count stock ignoring overlap
+
+1. Open your `stock-counter` project if it is not already open.
+
+1. Above the `processPredictions` function, add the following code:
+
+    ```cpp
+    const float overlap_threshold = 0.20f;
+    ```
+
+    This defines the percentage overlap allowed before the bounding boxes are considered to be the same object. 0.20 defines a 20% overlap.
+
+1. Below this, and above the `processPredictions` function, add the following code to calculate the overlap between two rectangles:
+
+    ```cpp
+    struct Point {
+        float x, y;
+    };
+
+    struct Rect {
+        Point topLeft, bottomRight;
+    };
+
+    float area(Rect rect)
+    {
+        return abs(rect.bottomRight.x - rect.topLeft.x) * abs(rect.bottomRight.y - rect.topLeft.y);
+    }
+     
+    float overlappingArea(Rect rect1, Rect rect2)
+    {
+        float left = max(rect1.topLeft.x, rect2.topLeft.x);
+        float right = min(rect1.bottomRight.x, rect2.bottomRight.x);
+        float top = max(rect1.topLeft.y, rect2.topLeft.y);
+        float bottom = min(rect1.bottomRight.y, rect2.bottomRight.y);
+    
+    
+        if ( right > left && bottom > top )
+        {
+            return (right-left)*(bottom-top);
+        }
+        
+        return 0.0f;
+    }
+    ```
+
+    This code defines a `Point` struct to store points on the image, and a `Rect` struct to define a rectangle using a top left and bottom right coordinate. It then defines an `area` function that calculates the area of a rectangle from a top left and bottom right coordinate.
+
+    Next it defines a `overlappingArea` function that calculates the overlapping area of 2 rectangles. If they don't overlap, it returns 0.
+
+1. Below the `overlappingArea` function, declare a function to convert a bounding box to a `Rect`:
+
+    ```cpp
+    Rect rectFromBoundingBox(JsonVariant prediction)
+    {
+        JsonObject bounding_box = prediction["boundingBox"].as<JsonObject>();
+    
+        float left = bounding_box["left"].as<float>();
+        float top = bounding_box["top"].as<float>();
+        float width = bounding_box["width"].as<float>();
+        float height = bounding_box["height"].as<float>();
+    
+        Point topLeft = {left, top};
+        Point bottomRight = {left + width, top + height};
+    
+        return {topLeft, bottomRight};
+    }
+    ```
+
+    This takes a prediction from the object detector, extracts the bounding box and uses the values on the bounding box to define a rectangle. The right side is calculated from the left plus the width. The bottom is calculated as the top plus the height.
+
+1. The predictions need to be compared to each other, and if 2 predictions have an overlap of more that the threshold, one of them needs to be deleted. The overlap threshold is a percentage, so needs to be multiplied by the size of the smallest bounding box to check that the overlap exceeds the given percentage of the bounding box, not the given percentage of the whole image. Start by deleting the content of the `processPredictions` function.
+
+1. Add the following to the empty `processPredictions` function:
+
+    ```cpp
+    std::vector<JsonVariant> passed_predictions;
+
+    for (int i = 0; i < predictions.size(); ++i)
+    {
+        Rect prediction_1_rect = rectFromBoundingBox(predictions[i]);
+        float prediction_1_area = area(prediction_1_rect);
+        bool passed = true;
+
+        for (int j = i + 1; j < predictions.size(); ++j)
+        {
+            Rect prediction_2_rect = rectFromBoundingBox(predictions[j]);
+            float prediction_2_area = area(prediction_2_rect);
+
+            float overlap = overlappingArea(prediction_1_rect, prediction_2_rect);
+            float smallest_area = min(prediction_1_area, prediction_2_area);
+
+            if (overlap > (overlap_threshold * smallest_area))
+            {
+                passed = false;
+                break;
+            }
+        }
+
+        if (passed)
+        {
+            passed_predictions.push_back(predictions[i]);
+        }
+    }
+    ```
+
+    This code declares a vector to store the predictions that don't overlap. It then loops through all the predictions, creating a `Rect` from the bounding box.
+
+    Next this code loops through the remaining predictions, starting at the one after the current prediction. This stops predictions being compared more than once - once 1 and 2 have been compared, there's no need to compare 2 with 1, only with 3, 4, etc.
+
+    For each pair of predictions the overlapping area is calculated. This is then compared to the area of the smallest bounding box - if the overlap exceeds the threshold percentage of the smallest bounding box, the prediction is marked as not passed. If after comparing all the overlap, the prediction passes the checks it is added to the `passed_predictions` collection.
+
+    > 💁 This is very simplistic way to remove overlaps, just removing the first one in an overlapping pair. For production code, you would want to put more logic in here, such as considering the overlaps between multiple objects, or if one bounding box is contained by another.
+
+1. After this, add the following code to send details of the passed predictions to the serial monitor:
+
+    ```cpp
+    for(JsonVariant prediction : passed_predictions)
+    {
+        String boundingBox = prediction["boundingBox"].as<String>();
+        String tag = prediction["tagName"].as<String>();
+        float probability = prediction["probability"].as<float>();
+
+        char buff[32];
+        sprintf(buff, "%s:\t%.2f%%\t%s", tag.c_str(), probability * 100.0, boundingBox.c_str());
+        Serial.println(buff);
+    }
+    ```
+
+    This code loops through the passed predictions and prints their details to the serial monitor.
+
+1. Below this, add code to print the number of counted items to the serial monitor:
+
+    ```cpp
+    Serial.print("Counted ");
+    Serial.print(passed_predictions.size());
+    Serial.println(" stock items.");
+    ```
+
+    This could then be sent to an IoT service to alert if the stock levels are low.
+
+1. Upload and run your code. Point the camera at objects on a shelf and press the C button. Try adjusting the `overlap_threshold` value to see predictions being ignored.
+
+    ```output
+    Connecting to WiFi..
+    Connected!
+    Image captured
+    Image read to buffer with length 17416
+    tomato paste:   35.84%  {"left":0.395631,"top":0.215897,"width":0.180768,"height":0.359364}
+    tomato paste:   35.87%  {"left":0.378554,"top":0.583012,"width":0.14824,"height":0.359382}
+    tomato paste:   34.11%  {"left":0.699024,"top":0.592617,"width":0.124411,"height":0.350456}
+    tomato paste:   35.16%  {"left":0.513006,"top":0.647853,"width":0.187472,"height":0.325817}
+    Counted 4 stock items.
+    ```
+
+> 💁 You can find this code in the [code-count/wio-terminal](code-count/wio-terminal) folder.
+
+😀 Your stock counter program was a success!

5-retail/lessons/2-check-stock-device/wio-terminal-object-detector.md

@@ -0,0 +1,102 @@
+# Call your object detector from your IoT device - Wio Terminal
+
+Once your object detector has been published, it can be used from your IoT device.
+
+## Copy the image classifier project
+
+The majority of your stock detector is the same as the image classifier you created in a previous lesson.
+
+### Task - copy the image classifier project
+
+1. Connect your ArduCam your Wio Terminal, following the steps from [lesson 2 of the manufacturing project](../../../4-manufacturing/lessons/2-check-fruit-from-device/wio-terminal-camera.md#task---connect-the-camera).
+
+    You might also want to fix the camera in a single position, for example, by hanging the cable over a box or can, or fixing the camera to a box with double-sided tape.
+
+1. Create a brand new Wio Terminal project using PlatformIO. Call this project `stock-counter`.
+
+1. Replicate the steps from [lesson 2 of the manufacturing project](../../../4-manufacturing/lessons/2-check-fruit-from-device/README.md#task---capture-an-image-using-an-iot-device) to capture images from the camera.
+
+1. Replicate the steps from [lesson 2 of the manufacturing project](../../../4-manufacturing/lessons/2-check-fruit-from-device/README.md#task---classify-images-from-your-iot-device) to call the image classifier. The majority of this code will be re-used to detect objects.
+
+## Change the code from a classifier to an image detector
+
+The code you used to classify images is very similar to the code to detect objects. The main difference is the URL that is called that you obtained from Custom Vision, and the results of the call.
+
+### Task - change the code from a classifier to an image detector
+
+1. Add the following include directive to the top of the `main.cpp` file:
+
+    ```cpp
+    #include <vector>
+    ```
+
+1. Rename the `classifyImage` function to `detectStock`, both the name of the function and the call in the `buttonPressed` function.
+
+1. Above the `detectStock` function, declare a threshold to filter out any detections that have a low probability:
+
+    ```cpp
+    const float threshold = 0.3f;
+    ```
+
+    Unlike an image classifier that only returns one result per tag, the object detector will return multiple results, so any with a low probability need to be filtered out.
+
+1. Above the `detectStock` function, declare a function to process the predictions:
+
+    ```cpp
+    void processPredictions(std::vector<JsonVariant> &predictions)
+    {
+        for(JsonVariant prediction : predictions)
+        {
+            String tag = prediction["tagName"].as<String>();
+            float probability = prediction["probability"].as<float>();
+    
+            char buff[32];
+            sprintf(buff, "%s:\t%.2f%%", tag.c_str(), probability * 100.0);
+            Serial.println(buff);
+        }
+    }
+    ```
+
+    This takes a list of predictions and prints them to the serial monitor.
+
+1. In the `detectStock` function, replace the contents of the `for` loop that loops through the predictions with the following:
+
+    ```cpp
+    std::vector<JsonVariant> passed_predictions;
+
+    for(JsonVariant prediction : predictions) 
+    {
+        float probability = prediction["probability"].as<float>();
+        if (probability > threshold)
+        {
+            passed_predictions.push_back(prediction);
+        }
+    }
+
+    processPredictions(passed_predictions);
+    ```
+
+    This loops through the predictions, comparing the probability to the threshold. All predictions that have a probability higher than the threshold are added to a `list` and passed to the `processPredictions` function.
+
+1. Upload and run your code. Point the camera at objects on a shelf and press the C button. You will see the output in the serial monitor:
+
+    ```output
+    Connecting to WiFi..
+    Connected!
+    Image captured
+    Image read to buffer with length 17416
+    tomato paste:   35.84%
+    tomato paste:   35.87%
+    tomato paste:   34.11%
+    tomato paste:   35.16%
+    ```
+
+    > 💁 You may need to adjust the `threshold` to an appropriate value for your images.
+
+    You will be able to see the image that was taken, and these values in the **Predictions** tab in Custom Vision.
+
+    ![4 cans of tomato paste on a shelf with predictions for the 4 detections of 35.8%, 33.5%, 25.7% and 16.6%](../../../images/custom-vision-stock-prediction.png)
+
+> 💁 You can find this code in the [code-detect/wio-terminal](code-detect/wio-terminal) folder.
+
+😀 Your stock counter program was a success!

README.md

@@ -64,7 +64,7 @@ We have two choices of IoT hardware to use for the projects depending on persona
 |     | Project Name | Concepts Taught | Learning Objectives | Linked Lesson |
 | :-: | :----------: | :-------------: | ------------------- | :-----------: |
 | 01 | [Getting started](./1-getting-started) | Introduction to IoT | Learn the basic principles of IoT and the basic building blocks of IoT solutions such as sensors and cloud services whilst you are setting up your first IoT device | [Introduction to IoT](./1-getting-started/lessons/1-introduction-to-iot/README.md) |
-| 02 | [Getting started](./1-getting-started) | A deeper dive into IoT| Learn more about the components of an IoT system, as well as microcontrollers and single-board computers | [A deeper dive into IoT](./1-getting-started/lessons/2-deeper-dive/README.md) |
+| 02 | [Getting started](./1-getting-started) | A deeper dive into IoT | Learn more about the components of an IoT system, as well as microcontrollers and single-board computers | [A deeper dive into IoT](./1-getting-started/lessons/2-deeper-dive/README.md) |
 | 03 | [Getting started](./1-getting-started) | Interact with the physical world with sensors and actuators | Learn about sensors to gather data from the physical world, and actuators to send feedback, whilst you build a nightlight | [Interact with the physical world with sensors and actuators](./1-getting-started/lessons/3-sensors-and-actuators/README.md) |
 | 04 | [Getting started](./1-getting-started) | Connect your device to the Internet | Learn about how to connect an IoT device to the Internet to send and receive messages by connecting your nightlight to an MQTT broker | [Connect your device to the Internet](./1-getting-started/lessons/4-connect-internet/README.md) |
 | 05 | [Farm](./2-farm) | Predict plant growth | Learn how to predict plant growth using temperature data captured by an IoT device | [Predict plant growth](./2-farm/lessons/1-predict-plant-growth/README.md) |

quiz-app/src/assets/translations/en.json

@@ -104,7 +104,7 @@
 			},
 			{
 				"id": 3,
-				"title": "Lesson 2 - Introduction to IoT devices: Pre-Lecture Quiz",
+				"title": "Lesson 2 - A deeper dive into IoT: Pre-Lecture Quiz",
 				"quiz": [
 					{
 						"questionText": "The T in IoT stands for:",
@@ -157,7 +157,7 @@
 			},
 			{
 				"id": 4,
-				"title": "Lesson 2 - Introduction to IoT devices: Post-Lecture Quiz",
+				"title": "Lesson 2 - A deeper dive into IoT: Post-Lecture Quiz",
 				"quiz": [
 					{
 						"questionText": "The three steps in a CPU instruction cycle are:",