Lesson 16 (#62)

* 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

Author: Jim Bennett

.vscode/settings.json

@@ -15,6 +15,8 @@
         "geofencing",
         "microcontrollers",
         "mosquitto",
+        "photodiode",
+        "photodiodes",
         "sketchnote"
     ]
 }

1-getting-started/lessons/4-connect-internet/wio-terminal-mqtt.md

@@ -14,7 +14,7 @@ Libraries can be installed globally and compiled in if needed, or into a specifi
 
 ✅ You can learn more about library management and how to find and install libraries in the [PlatformIO library documentation](https://docs.platformio.org/en/latest/librarymanager/index.html).
 
-### Task
+### Task - install the WiFi and MQTT Arduino libraries
 
 Install the Arduino libraries.
 
@@ -49,7 +49,7 @@ Install the Arduino libraries.
 
 The Wio Terminal can now be connected to WiFi.
 
-### Task
+### Task - connect to WiFi
 
 Connect the Wio Terminal to WiFi.
 
@@ -85,7 +85,7 @@ Connect the Wio Terminal to WiFi.
     #include "config.h"
     ```
 
-    This includes header files for the libraries you added earlier, as well as the config header file.
+    This includes header files for the libraries you added earlier, as well as the config header file. These header files are needed to tell PlatformIO to bring in the code from the libraries. Without explicitly including these header files, some code won't be compiled in and you will get compiler errors.
 
 1. Add the following code above the `setup` function:
 
@@ -128,7 +128,7 @@ Connect the Wio Terminal to WiFi.
 
 Once the Wio Terminal is connected to WiFi, it can connect to the MQTT broker.
 
-### Task
+### Task - connect to MQTT
 
 Connect to the MQTT broker.
 

3-transport/lessons/1-location-tracking/code-gps-decode/pi/gps-sensor/app.py

@@ -16,7 +16,7 @@
 device_client.connect()
 print("Connected")
 
-def print_gps_data(line):
+def printGPSData(line):
     msg = pynmea2.parse(line)
     if msg.sentence_type == 'GGA':
         lat = pynmea2.dm_to_sd(msg.lat)
@@ -37,7 +37,7 @@ def print_gps_data(line):
     line = serial.readline().decode('utf-8')
 
     while len(line) > 0:
-        print_gps_data(line)
+        printGPSData(line)
         line = serial.readline().decode('utf-8')
 
     time.sleep(1)

3-transport/lessons/1-location-tracking/code-gps-decode/wio-terminal/gps-sensor/src/main.cpp

@@ -24,7 +24,7 @@ void setup()
 	pinPeripheral(PIN_WIRE_SCL, PIO_SERCOM_ALT);
 }
 
-void print_gps_data()
+void printGPSData()
 {
 	if (gps.encode(Serial3.read()))
 	{
@@ -44,7 +44,7 @@ void loop()
 {
 	while (Serial3.available() > 0)
 	{
-		print_gps_data();
+		printGPSData();
 	}
 
 	delay(1000);

3-transport/lessons/1-location-tracking/code-gps/pi/gps-sensor/app.py

@@ -5,14 +5,14 @@
 serial.reset_input_buffer()
 serial.flush()
 
-def print_gps_data():
+def printGPSData():
     print(line.rstrip())
 
 while True:
     line = serial.readline().decode('utf-8')
 
     while len(line) > 0:
-        print_gps_data()
+        printGPSData()
         line = serial.readline().decode('utf-8')
 
     time.sleep(1)

3-transport/lessons/1-location-tracking/code-gps/virtual-device/gps-sensor/app.py

@@ -6,14 +6,14 @@
 
 serial = counterfit_shims_serial.Serial('/dev/ttyAMA0')
 
-def print_gps_data(line):
+def printGPSData(line):
     print(line.rstrip())
 
 while True:
     line = serial.readline().decode('utf-8')
 
     while len(line) > 0:
-        print_gps_data(line)
+        printGPSData(line)
         line = serial.readline().decode('utf-8')
 
     time.sleep(1)

3-transport/lessons/1-location-tracking/code-gps/wio-terminal/gps-sensor/src/main.cpp

@@ -22,7 +22,7 @@ void setup()
 	pinPeripheral(PIN_WIRE_SCL, PIO_SERCOM_ALT);
 }
 
-void print_gps_data()
+void printGPSData()
 {
 	Serial.println(Serial3.readStringUntil('\n'));
 }
@@ -31,7 +31,7 @@ void loop()
 {
 	while (Serial3.available() > 0)
 	{
-		print_gps_data();
+		printGPSData();
 	}
 
 	delay(1000);

3-transport/lessons/1-location-tracking/pi-gps-sensor.md

@@ -10,11 +10,11 @@ The sensor you'll use is a [Grove GPS Air530 sensor](https://www.seeedstudio.com
 
 This is a UART sensor, so sends GPS data over UART.
 
-### Connect the GPS sensor
+## Connect the GPS sensor
 
 The Grove GPS sensor can be connected to the Raspberry Pi.
 
-#### Task - connect the GPS sensor
+### Task - connect the GPS sensor
 
 Connect the GPS sensor.
 
@@ -98,14 +98,14 @@ Program the device.
 
     1. Reboot your Pi, then reconnect in VS Code once the Pi has rebooted.
 
-1. From the terminal, create a new folder in the `pi` users home directory called `gps-sensor`. Create a file in this folder called `app.py`:
+1. From the terminal, create a new folder in the `pi` users home directory called `gps-sensor`. Create a file in this folder called `app.py`.
 
 1. Open this folder in VS Code
 
 1. The GPS module sends UART data over a serial port. Install the `pyserial` Pip package to communicate with the serial port from your Python code:
 
     ```sh
-    pip3 install pip install pyserial
+    pip3 install pyserial
     ```
 
 1. Add the following code to your `app.py` file:
@@ -118,24 +118,24 @@ Program the device.
     serial.reset_input_buffer()
     serial.flush()
     
-    def print_gps_data(line):
+    def printGPSData(line):
         print(line.rstrip())
     
     while True:
         line = serial.readline().decode('utf-8')
     
         while len(line) > 0:
-            print_gps_data(line)
+            printGPSData(line)
             line = serial.readline().decode('utf-8')
     
         time.sleep(1)
     ```
 
     This code imports the `serial` module from the `pyserial` Pip package. It then connects to the `/dev/ttyAMA0` serial port - this is the address of the serial port that the Grove Pi Base Hat uses for its UART port. It then clears any existing data from this serial connection.
 
-    Next a function called `print_gps_data` is defined that prints out the line passed to it to the console.
+    Next a function called `printGPSData` is defined that prints out the line passed to it to the console.
 
-    Next the code loops forever, reading as many lines of text as it can from the serial port in each loop. It calls the `print_gps_data` function for each line.
+    Next the code loops forever, reading as many lines of text as it can from the serial port in each loop. It calls the `printGPSData` function for each line.
 
     After all the data has been read, the loop sleeps for 1 second, then tries again.
 

3-transport/lessons/1-location-tracking/single-board-computer-gps-decode.md

@@ -24,7 +24,7 @@ Program the device to decode the GPS data.
     import pynmea2
     ```
 
-1. Replace the contents of the `print_gps_data` function with the following:
+1. Replace the contents of the `printGPSData` function with the following:
 
     ```python
     msg = pynmea2.parse(line)

3-transport/lessons/1-location-tracking/virtual-device-gps-sensor.md

@@ -14,7 +14,7 @@ A physical GPS sensor will have an antenna to pick up radio waves from GPS satel
 
 To use a virtual GPS sensor, you need to add one to the CounterFit app
 
-#### Task
+#### Task - add the sensor to CounterFit
 
 Add the GPS sensor to the CounterFit app.
 
@@ -36,7 +36,7 @@ Add the GPS sensor to the CounterFit app.
 
     1. Leave the *Port* set to */dev/ttyAMA0*
 
-    1. Select the **Add** button to create the humidity sensor on port `/dev/ttyAMA0`
+    1. Select the **Add** button to create the GPS sensor on port `/dev/ttyAMA0`
 
     ![The GPS sensor settings](../../../images/counterfit-create-gps-sensor.png)
 
@@ -77,22 +77,22 @@ Program the GPS sensor app.
 1. Add the following code below this to read from the serial port and print the values to the console:
 
     ```python
-    def print_gps_data(line):
+    def printGPSData(line):
         print(line.rstrip())
     
     while True:
         line = serial.readline().decode('utf-8')
     
         while len(line) > 0:
-            print_gps_data(line)
+            printGPSData(line)
             line = serial.readline().decode('utf-8')
     
         time.sleep(1)
     ```
 
-    A function called `print_gps_data` is defined that prints out the line passed to it to the console.
+    A function called `printGPSData` is defined that prints out the line passed to it to the console.
 
-    Next the code loops forever, reading as many lines of text as it can from the serial port in each loop. It calls the `print_gps_data` function for each line.
+    Next the code loops forever, reading as many lines of text as it can from the serial port in each loop. It calls the `printGPSData` function for each line.
 
     After all the data has been read, the loop sleeps for 1 second, then tries again.
 

3-transport/lessons/1-location-tracking/wio-terminal-gps-decode.md

@@ -31,7 +31,7 @@ Program the device to decode the GPS data.
     TinyGPSPlus gps;
     ```
 
-1. Change the contents of the `print_gps_data` function to be the following:
+1. Change the contents of the `printGPSData` function to be the following:
 
     ```cpp
     if (gps.encode(Serial3.read()))

3-transport/lessons/1-location-tracking/wio-terminal-gps-sensor.md

@@ -98,7 +98,7 @@ Program the device.
 1. Add the following function before the `loop` function to send the GPS data to the serial monitor:
 
     ```cpp
-    void print_gps_data()
+    void printGPSData()
     {
         Serial.println(Serial3.readStringUntil('\n'));
     }
@@ -109,13 +109,13 @@ Program the device.
     ```cpp
     while (Serial3.available() > 0)
     {
-        print_gps_data();
+        printGPSData();
     }
 
     delay(1000);
     ```
 
-    This code reads from the UART serial port. The `readStringUntil` function reads up until a terminator character, in this case a new line. This will read a whole NMEA sentence (NMEA sentences are terminated with a new line character). All the while data can be read from the UART serial port, it is read and sent to the serial monitor via the `print_gps_data` function. Once no more data can be read, the `loop` delays for 1 second (1,000ms).
+    This code reads from the UART serial port. The `readStringUntil` function reads up until a terminator character, in this case a new line. This will read a whole NMEA sentence (NMEA sentences are terminated with a new line character). All the while data can be read from the UART serial port, it is read and sent to the serial monitor via the `printGPSData` function. Once no more data can be read, the `loop` delays for 1 second (1,000ms).
 
 1. Build and upload the code to the Wio Terminal.
 

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

@@ -22,6 +22,7 @@ In this lesson we'll cover:
 * [Image classification via Machine Learning](#image-classification-via-machine-learning)
 * [Train an image classifier](#train-an-image-classifier)
 * [Test your image classifier](#test-your-image-classifier)
+* [Retrain your image classifier](#retrain-your-image-classifier)
 
 ## Using AI and ML to sort food
 
@@ -133,6 +134,8 @@ To use Custom Vision, you first need to create two cognitive services resources
 
 ### Task - create an image classifier project
 
+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.
 
     Call your project `fruit-quality-detector`.
@@ -151,6 +154,8 @@ Ideally each picture should be just the fruit, with either a consistent backgrou
 
 > 💁 It's important not to have specific backgrounds, or specific items that are not related to the thing being classified for each tag, otherwise the classifier may just classify based on the background. There was a classifier for skin cancer that was trained on moles both normal and cancerous, and the cancerous ones all had rulers against them to measure the size. It turned out the classifier was almost 100% accurate at identifying rulers in pictures, not cancerous moles.
 
+Image classifiers run at very low resolution. For example Custom Vision can take training and prediction images up to 10240x10240, but trains and runs the model on images at 227x227. Larger images are shrunk to this size, so ensure the thing you are classifying takes up a large part of the image otherwise it may be too small in the smaller image used by the classifier.
+
 1. Gather pictures for your classifier. You will need at least 5 pictures for each label to train the classifier, but the more the better. You will also need a few additional images to test the classifier. These images should all be different images of the same thing. For example:
 
     * Using 2 ripe bananas, take some pictures of each one from a few different angles, taking at least 7 pictures (5 to train, 2 to test), but ideally more.
@@ -181,18 +186,28 @@ 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 and retrain a model with Custom Vision Service 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)
 
 1. Try all the testing images you have access to and observe the probabilities.
 
+## Retrain your image classifier
+
+When you test you classifier, it may not give the results you expect. Image classifiers use machine learning to make predictions about what is in an image, based of probabilities that particular features of an image mean that it matches a particular label. It doesn't understand what is in the image - it doesn't know what a banana is or understand what makes a banana a banana instead of a boat. You can improve your classifier by retraining it with images it gets wrong.
+
+Every time you make a prediction using the quick test option, the image and results are stored. You can use these images to retrain your model.
+
+### 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. Once you model has been retrained, test on new images.
+
 ---
 
 ## 🚀 Challenge
 
-Image classifiers use machine learning to make predictions about what is in an image, based of probabilities that particular features of an image mean that it matches a particular label. It doesn't understand what is in the image - it doesn't know what a banana is or understand what makes a banana a banana instead of a boat.
-
 What do you think would happen if you used a picture of a strawberry with a model trained on bananas, or a picture of an inflatable banana, or a person in a banana suit, or even a yellow cartoon character like someone from the Simpsons?
 
 Try it out and see what the predictions are. You can find images to try with using [Bing Image search](https://www.bing.com/images/trending).