Table of Contents

switch costume to ()

Description

The block changes its sprite’s costume to a specified one.

Example

The example demonstrates the costume change in PictoBlox.

Script

Output

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The example demonstrates the wall bouncing of the sprite and rotation style.

Script

 

Output

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The example demonstrates how to run different actions with the Quarky touch sensor to make a disco party in PictoBlox.

Script

Output

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The examples show how to use pose recognition in PictoBlox to maintain a yoga pose for a particular time interval.

Script

The idea is simple, we’ll add one image of  each class in the “costume” column by making one new sprite which will we display on the stage according to input from user. we’ll also change name of the image according to pose.

  1. Add testing images to the backdrop and delete the default backdrop.
  2. Now, come back to the coding tab and select the Tobi sprite.
  3. We’ll start by adding a when flag clicked block from the Events palette.
  4. We made the new variable “count” by choosing the “Make a Variable” option from the Variables palette.
  5. Add the “hide variable () block from the Variables palette. Select count.
  6. Add the “turn () video on stage with () transparency” block from the Machine Learning palette. Select the off option at the first empty place, and for the second, write a 0 value.
  7. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  8. Add the “if () then” block from the control palette for checking the user’s input.
  9. In the empty place of the “if () then” block, add a condition checking block from the operators palette block. At the first empty place, put the answer block from the sensing palette, and at the second place, write an appropriate statement.
  10. Inside the “if () then” block, add a “broadcast ()” block from the Events palette block. Select the “New message” option and write an appropriate statement for broadcasting a message to another sprite.
  11. Add the “turn () video on stage with () transparency” block from the Machine Learning palette. Select the option at the first empty place, and for the second, write a 0 value.
  12. Add the “() key points” block from the Machine Learning palette. Select the show option.
  13. Add the “Set () to ()” block from the Variables palette. Select the count option at the first empty place, and for the second, write a 30 value.
  14. Add the Show variable () block from the Variables palette. Select count.
  15. Add “forever” from the Control palette.
  16. Inside the “forever” block, add an “analysis image from ()” block from the Machine Learning palette. Select the Web camera option.
  17. Inside the “forever” block, add an “if () then” block from the Control palette.
  18. In the empty place of the “if () then” block, add an “is identified class ()” block from the Machine Learning palette. Select the appropriate class from the options.
  19. Inside the “if () then” block, add an “say ()” block from the Looks palette block. Write an appropriate statement in an empty place.
  20. Add “change () by ()” from the Variables palette. Select the count option in the first empty place, and for the second, write a -1 value.

  21. Add the “if () then” block from the control palette for checking the user’s input.
  22. In the empty place of the “if () then” block, add a condition checking block from the operators palette block. In the first empty place, put the “count” block from the sensing palette, and in the second place, write 0.
  23. Add the “Set () to ()” block from the Variables palette. Select the count option at the first empty place, and for the second, write a 30 value.
  24. Add the “turn () video on stage with () transparency” block from the Machine Learning palette. Select the off option at the first empty place, and for the second, write a 0 value.
  25. Inside the “if () then” block, add an “say ()” block from the Looks palette block. Write an appropriate statement in an empty place.
  26. Add the “() key points” block from the Machine Learning palette. Select the hide option
  27. Add the “stop ()” block to the control pallet. Select all options.
  28. Repeat “if () then” block code for other classes, make appropriate changes in copying block code according to other classes, and add code just below it.
  29. The final block code looks like
  30. Now click on another sprite and write code.
  31. We’ll start writing code for this sprite by adding a when flag is clicked block from the Events palette.
  32. Add the “hide” block from the Looks pallet.
  33. Write a new code in the same sprite according to class and add the “when I receive ()” block from the Events palette. Select the appropriate class from the options.
  34. Add the “show” block from the Looks pallet.
  35. Add the “switch costume to ()” block from the Looks palette. Select the appropriate class from the options.
  36. Repeat the same code for other classes and make changes according to the class.

    Final Result

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The example shows how to use a hand pose classifier in PictoBlox to make the Sign Classifier Bot.

Introduction

In this example project, we are going to create a machine learning model that can classify different sign messages from the camera feed or image.

Hand Gesture Classifier in Machine Learning Environment

The Hand Gesture Classifier is the extension of the ML Environment used for classifying different hand poses into different classes.

 

Hand Gesture Classifier Workflow

  1. Open PictoBlox and create a new file.
  2. You can click on “Machine Learning Environment” to open it.
  3. Click on “Create New Project“.
  4. A window will open. Type in a project name of your choice and select the Hand Gesture Classifier” extension. Click the “Create Project” button to open the Pose Classifier window.
  5. You shall see the Gesture Pose Classifier workflow with two classes already made for you. Your environment is all set. Now it’s time to upload the data.

Class in Pose Classifier

Class is the category in which the Machine Learning model classifies the poses. Similar posts are put in one class.

Class in Hand Pose Classifier

Class is the category in which the Machine Learning model classifies the hand poses. Similar hand poses are put in one class.

Adding Data to Class

You can perform the following operations to manipulate the data into a class.

  1. Naming the Class: You can rename the class by clicking on the edit button.
  2. Adding Data to the Class: You can add the data using the Webcam or by Uploading the files from the local folder.
    1. Webcam:

      Note: You can edit the capture setting in the camera with the following. Hold to Record allows you to capture images with pose till the time button is pressed. Whereas when it is off you can set the start delay and duration of the sample collection.

      If you want to change your camera feed, you can do it from the webcam selector in the top right corner.

Training the Model

After data is added, it’s fit to be used in model training. In order to do this, we have to train the model. By training the model, we extract meaningful information from the hand pose, and that in turn updates the weights. Once these weights are saved, we can use our model to make predictions on data previously unseen.

However, before training the model, there are a few hyperparameters that you should be aware of. Click on the “Advanced” tab to view them.

Note: These hyperparameters can affect the accuracy of your model to a great extent. Experiment with them to find what works best for your data.

Note: Hover your mouse over the question mark next to the hyperparameters to see their description.

It’s a good idea to train a numeric classification model for a high number of epochs. The model can be trained in both JavaScript and Python. In order to choose between the two, click on the switch on top of the Training panel.

Alert: Dependencies must be downloaded to train the model in Python, JavaScript will be chosen by default.

The accuracy of the model should increase over time. The x-axis of the graph shows the epochs, and the y-axis represents the accuracy at the corresponding epoch. Remember, the higher the reading in the accuracy graph, the better the model. The x-axis of the graph shows the epochs, and the y-axis represents the corresponding accuracy. The range of the accuracy is 0 to 1.

Testing the Model

To test the model, simply enter the input values in the “Testing” panel and click on the “Predict” button.

The model will return the probability of the input belonging to the classes.

Export in Block Coding

Click on the “Export Model” button on the top right of the Testing box, and PictoBlox will load your model into the Block Coding Environment if you have opened the ML Environment in the Block Coding.

Script

The idea is simple, we’ll add one image of  each class in the “costume” column by making one new sprite which will we display on the stage according to input from user. we’ll also change name of the image according to sign class type.

  1. Add one sign image as another sprite and upload at-least one image of all sign classes on costume.
  2. Now, come back to the coding tab and select the Tobi sprite.
  3. We’ll start by adding a when flag clicked block from the Events palette.
  4. Add the “open recognition window” block from the Machine Learning palette.
  5. Add an “forever” block from the Control palette.
  6. Add the “if () then” block from the control palette for checking the user’s input.
  7. In the empty place of the “if () then” block, add an “is identified class ()” block from the Machine Learning palette. Select the appropriate class from the options.
  8. Inside the “if () then” block, add an “say ()” block from the Looks palette block. Write an appropriate statement in an empty place.
  9. Inside the “if () then” block, add a “broadcast ()” block from the Events palette block. Select the “New message” option and write an appropriate statement for broadcasting a message to another sprite.
  10. Repeat “if () then” block code for other classes, make appropriate changes in copying block code according to other classes, and add code just below it.
  11. Final code of “Tobi” sprite is
  12. Now click on another sprite and write code.
  13. We’ll start writing code for this sprite by adding a when flag is clicked block from the Events palette.
  14. Add the “hide” block from the Looks pallet.
  15. Write a new code in the same sprite according to class and add the “when I receive ()” block from the Events palette. Select the appropriate class from the options.
  16. Add the “show” block from the Looks pallet.
  17. Add the “switch costume to ()” block from the Looks palette. Select the appropriate class from the options.
  18. Repeat the same code for other classes and make changes according to the class.

Final Output

 

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The example shows how to use a audio classifier in PictoBlox to make the Bird Audio Classifier Bot.

Introduction

In this example project, we are going to create a machine learning model that can classify different audio messages of birds from the microphone feed of computer.

Audio Classifier in Machine Learning Environment

The Audio Classifier is the extension of the ML Environment used for classifying different birds voice.

Audio Classifier Workflow

Follow the steps below to create your own Audio Classifier Model:

  1. Open PictoBlox and create a new file.
  2. Select the Block coding environment as the appropriate Coding Environment.
  3. Select the “Open ML Environment” option under the “Files” tab to access the ML Environment.
  4. A new window will open. Type in an appropriate project name of your choice and select the “Audio Classifier” extension. Click the “Create Project” button to open the Audio Classifier Window.
  5. You shall see the Classifier workflow with two classes already made for you. Your environment is all set. Now it’s time to upload the data.
  6. As you can observe in the above image, we will add many classes for audio. We will be able to add audio samples with the help of the microphone.

Note: You can add more classes to the projects using the Add Class button.

Adding Data to Class

You can perform the following operations to manipulate the data into a class.

  1. Naming the Class: You can rename the class by clicking on the edit button.
  2. Adding Data to the Class: You can add the data using the Microphone.
  3. You will be able to add the audio sample in each class and make sure you add at least 20 samples for the model to run with good accuracy.

Training the Model

After data is added, it’s fit to be used in model training. To do this, we have to train the model. By training the model, we extract meaningful information from the hand pose, and that in turn updates the weights. Once these weights are saved, we can use our model to make predictions on data previously unseen.

The accuracy of the model should increase over time. The x-axis of the graph shows the epochs, and the y-axis represents the accuracy at the corresponding epoch. Remember, the higher the reading in the accuracy graph, the better the model. The range of accuracy is 0 to 1.

Testing the Model

To test the model simply, use the microphone directly and check the classes as shown in the below image:

You will be able to test the difference in audio samples recorded from the microphone as shown below:

Export in Block Coding

Click on the “Export Model” button on the top right of the Testing box, and PictoBlox will load your model into the Block Coding Environment if you have opened the ML Environment in the Block Coding.

Script

The idea is simple, we’ll add one image of  each class in the “costume” column by making one new sprite which will we display on the stage according to input from user. we’ll also change name of the image according to bird class type.

  1. Add one bird image as another sprite and upload at-least one image of all bird classes on costume.
  2. Now, come back to the coding tab and select the Tobi sprite.
  3. We’ll start by adding a when flag clicked block from the Events palette.
  4. Add the “open recognition window” block from the Machine Learning palette.
  5. Add an “when () is predicted” block from the Machine Learning palette. Select the appropriate class from the options.
  6. Add an “say () for () seconds” block from the Looks palette block. Write an appropriate statement in an empty place.
  7. Repeat the same code for other classes and make changes according to the class.
  8. For “BackNoise” class, don’t add any statement at place of empty space of “say () for () seconds” block
  9. Final code of “Tobi” sprite is
  10. Now click on another sprite and write code.
  11. We’ll start writing code for this sprite by adding a “when () is predicted” block from the Machine Learning palette.
  12. Add the “switch costume to ()” block from the Looks palette. Select the appropriate class from the options.
  13. Repeat the same code for other classes and make changes according to the class.
  14. Final code of another sprite is

Final Output

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