Table of Contents
Control
PictoBlox Block

if () then

  • PictoBlox Extension: Control
  • Type: C Block
  • Mode: Stage Mode, Upload Mode

Description

The block will check its Boolean condition. If the condition is true, the blocks held inside it will run, and then the script involved will continue. If the condition is false, the code inside the block will be ignored and the script will move on (unlike in the If () Then, Else block). The condition is only checked once; if the condition turns to false while the script inside the block is running, it will keep running until it has finished.

Example

Stamping Tobi

The example demonstrates how to use stamping and the mouse location sensing in Block coding.
Download File

Script

Output

Read More

Beetle Movement

The example demonstrates how to use keys sensing to control the movement of the sprite.
Download File

Script

Output

Read More

Bouncing Ball with Gravity

The example demonstrates how to add gravity into the project on a bouncing ball.
Download File

Script

  1. Main Script to change the speed and position parameters of the ball.
  2. Custom function to initialize the ball position and speed with random variables.
  3. Custom function to check the boundary conditions and set the rules.

Output

Read More

Yoga Pose Timer Bot using Pose Classifier in ML Environment

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

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

Read More

Jumping Jack Counter Bot using Pose Classifier in ML Environment

The examples show how to use pose recognition in PictoBlox to make jumping jack counter.
Download File

Introduction

In this example project, we are going to create a machine learning model that can count the number of jumping jack activities from the camera feed.

Pose Classifier in Machine Learning Environment

The pose Classifier is the extension of the ML Environment used for classifying different body poses into different classes.

The model works by analyzing your body position with the help of 17 data points.

Pose 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 “Pose Classifier” extension. Click the “Create Project” button to open the Pose Classifier window.
  5. You shall see the 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.

There are 2 things that you have to provide in a class:

  1. Class Name: The name to which the class will be referred.
  2. Pose Data: This data can be taken from the webcam or uploaded from local storage.

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:

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 predict previously unseen data.

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 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, after running code we will do jumping jack activity in front of camera and tobi sprite will say counting of jumping jack.

  1. Select the Tobi sprite.
  2. We’ll start by adding a when flag clicked block from the Events palette.
  3. We made the new variable “count” by choosing the “Make a Variable” option from the Variables palette.
  4. Also we made the new variable “temp” by choosing the “Make a Variable” option from the Variables palette.
  5. Add “forever” from the Control palette.
  6. Inside the “forever” block, add an “analysis image from ()” block from the Machine Learning palette. Select the Web camera option.
  7. Inside the “forever” block, add an “if () then” block from the Control palette.
  8. In the empty place of the “if () then” block, add an “key () pressed?” block from the Sensing palette. Select the ‘q’ key from the options.
  9. Inside the “if () then” block, add the “Set () to ()” block from the Variables palette. Select the count option at the first empty place, and for the second, write a 0 value.
  10. Also add the “Set () to ()” block from the Variables palette. Select the temp option at the first empty place, and for the second, write a 0 value.
  11. Inside the “forever” block, add an new “if () then” block from the Control palette.
  12. In the empty place of the “if () then” block, add an “is identified class ()” block from the Machine Learning palette. Select the ‘Upper hand‘ option from the options.
  13. Inside the “if () then” block, add the “Set () to ()” block from the Variables palette. Select the temp option at the first empty place, and for the second, write a 1 value.
  14. Inside the “forever” block, add an new “if () then” block from the Control palette.
  15. In the empty place of the “if () then” block, add an “is identified class ()” block from the Machine Learning palette. Select the ‘Down hand‘ option from the options.
  16. Inside the “if () then” block, add the another “if () then” block from the Control palette.
  17. 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 temp variable from the variables palette, and at the second place, write a 1 value.
  18. Inside the “if () then” block, add the “Set () to ()” block from the Variables palette. Select the count option at the first empty place, and for the second, write a 1 value.
  19. Also add the “Set () to ()” block from the Variables palette. Select the temp option at the first empty place, and for the second, write a 0 value
  20. Inside the “if () then” block, add an “say () for () seconds” block from the Looks palette block. At the first empty place, add the “join () ()” block from operator palette and at the second place, write a 2 value.
  21. Inside “join () ()” block at the first empty place, write the appropriate statement and at the second place, add count variable from Variables palette.

    Final Output

     

Read More

Iris Classifier Bot in Block Coding using Number Classifier

The example shows how to use a number classifier in PictoBlox to make the Iris Classifier Bot.
Download File

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 iris type.

  1. Add iris image as another sprite and upload one image of all iris 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 an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  5. Add the “set () as ()” block from the Machine Learning palette. Select the SepalLengthCm option at the first empty place, and for the second select an “answer” block from the Sensing palette.
  6. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  7. Add the “set () as ()” block from the Machine Learning palette. Select the SepalWidthCm option at the first empty place, and for the second select an “answer” block from the Sensing palette.
  8. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  9. Add the “set () as ()” block from the Machine Learning palette. Select the PetalLengthCm option at the first empty place, and for the second select an “answer” block from the Sensing palette.
  10. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  11. Add the “set () as ()” block from the Machine Learning palette. Select the PetalWidthCm option at the first empty place, and for the second select an “answer” block from the Sensing palette.
  12. Add an “analyse numbers” block from the Machine Learning palette.
  13. Add the “if () then” block from the control palette for checking the user’s input.
  14. 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.
  15. Inside the “if () then” block, add an “say ()” block from the Looks palette block. Write an appropriate statement in an empty place.
  16. 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.
  17. Repeat “if () then” block code for other iris classes, make appropriate changes in copying block code according to other classes, and add code just below it.
  18. Final code of “Tobi” sprite is
  19. Now click on another sprite and write code.
  20. We’ll start writing code for this sprite by adding a when flag is clicked block from the Events palette.
  21. Add the “hide” block from the Looks pallet.
  22. 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.
  23. Add the “show” block from the Looks pallet.
  24. Add the “switch costume to ()” block from the Looks palette. Select the appropriate class from the options.
  25. Repeat the same code for other classes and make changes according to the class.

    Final Result

Read More

Hand Sign Classifier Bot in Block Coding

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

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

 

Read More

Customer Spending Money Classifier Bot in Block Coding

The example shows how to use a number classifier in PictoBlox to make the customer spending money classifier bot.
Download File

Introduction

In this example project, we are going to create a Machine Learning Model that can predict the amount of money a customer will spend based on the input details added by the user.

Numbers(C/R) in Machine Learning Environment

Datasets on the internet are hardly ever fit to directly train on. Programmers often have to take care of unnecessary columns, text data, target columns, correlations, etc. Thankfully, PictoBlox’s ML Environment is packed with features to help us pre-process the data as per our liking.

Let’s create the ML model.

Opening Image Classifier Workflow

Alert: The Machine Learning Environment for model creation is available in the only desktop version of PictoBlox for Windows, macOS, or Linux. It is not available in Web, Android, and iOS versions.

Follow the steps below:

  1. Open PictoBlox and create a new file.
  2. Select the coding environment as Block Coding Environment.
  3. Select the “Open ML Environment” option under the “Files” tab to access the ML Environment.
  4. You’ll be greeted with the following screen.
    Click on “Create New Project“.
  5. You shall see the Numbers C/R workflow with an option to either “Upload Dataset” or “Create Dataset”.

    Uploading/Creating Dataset

    Datasets can either be uploaded or created on the ML Environment. Lets see how it is done.

    Uploading a dataset
    1. To upload a dataset, click on the Upload Dataset button and the Choose CSV from your files button.
      Note: An uploaded dataset must be a “.csv” file.
    2. Once uploaded the first 50 rows of the uploaded CSV document will show up in the window.
    Creating a Dataset
    1. To create a dataset, click on the Create Dataset button.
    2. Select the number of rows and columns that are to be added and click on the Create button. More rows and columns can be added as and when needed.

    Notes:

    1. Each column represents a feature. These are the values used by the model to train itself.
    2. The “Output” column contains the target values. These are the values that we expect the model to return when features are passed.
    3. The window only shows the first 50 rows of the dataset.
    4. Un-check the “Select All” checkbox to un-select all the columns.

    Training the Model

    After data is pre-processed and optimized, it’s fit to be used in model training. To train the model, simply click the “Train Model” button found in the “Training” panel.

    By training the model, meaningful information is extracted from the numbers, and that in turn updates the weights. Once these weights are saved, the model can be used to make predictions on data previously unseen.

    The model’s function is to use the input data and predict the output. The target column must always contain numbers.

    However, before training the model, there are a few hyperparameters that need to be understood. Click on the “Advanced” tab to view them.

    There are three hyperparameters that can be altered in the Numbers(C/R) Extension:

    1. Epochs– The total number of times the data will be fed through the training model. Therefore, in 10 epochs, the dataset will be fed through the training model 10 times. Increasing the number of epochs can often lead to better performance.
    2. Batch Size– The size of the set of samples that will be used in one step. For example, if there are 160 data samples in the dataset, and the batch size is set to 16, each epoch will be completed in 160/16=10 steps. This hyperparameter rarely needs any altering.
    3. Learning Rate– It dictates the speed at which the model updates the weights after iterating through a step. Even small changes in this parameter can have a huge impact on the model performance. The usual range lies between 0.001 and 0.0001.
    Note: Hover the mouse pointer 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.

    A window will open. Type in a project name of your choice and select the “Numbers(C/R)” extension. Click the “Create Project” button to open the Numbers(C/R) window.

    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

  1. Select the “Tobi” sprite.
  2. We’ll start by adding a “when flag clicked” block from the Events palette.
  3. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  4. Add the “if () then” block from the control palette for checking the user’s input.
  5. In the empty place of the “if () then” block, add an “()=()” block from the Operator palette. At the first empty place select an “answer” block from the Sensing palette and for the second place write an appropriate statement in an empty place.
  6. Add the “set gender as ()” block from the Machine Learning palette. Select the Male option at the empty place.
  7. Repeat “if () then” block code for other variables, make appropriate changes in copying block code according to other variables, and add code just below it.
  8. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  9. Add the “if () then” block from the control palette for checking the user’s input.
  10. In the empty place of the “if () then” block, add an “()=()” block from the Operator palette. At the first empty place select an “answer” block from the Sensing palette and for the second place write an appropriate statement in an empty place.
  11. Add the “set education as ()” block from the Machine Learning palette. Select the High School option at the empty place.
  12. Repeat “if () then” block code for other variables, make appropriate changes in copying block code according to other variables, and add code
  13. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  14. Add the “set () as ()” block from the Machine Learning palette. Select the age option at the first empty place, and for the second select an “answer” block from the Sensing palette.
  15. Repeat “ask () and wait” block code for other variables, make appropriate changes in copying block code according to other variables, and add code.
  16. Repeat “ask () and wait” block code for other variables, make appropriate changes in copying block code according to other variables, and add code.
  17. Add the “say ()” block from the Looks palette.
  18. Add an “join () ()” block from the Operator palette. Write an appropriate statement in an first empty place and at second empty place add “analyse numbers” block from the Machine Learning palette. 

    Final Result

Read More
All articles loaded
No more articles to load
PictoBlox Extensions
  1. Scratch Based Default Extensions
    1. Motion
    2. Looks
    3. Sound
    4. Control
    5. Events
    6. Sensing
    7. Operators
    8. My Blocks
    9. Variables
  2. Artificial Intelligence
    1. Face Detection
    2. Object Detection
    3. Human Body Detection
    4. Computer Vision
    5. Text Recognition
    6. Speech Recognition
    7. ChatGPT
    8. Natural Language Processing
    9. Recognition Cards
    10. Text to Speech
    11. Translate
    12. Open CV
  3. Innovative Extensions
    1. QR Code Scanner
    2. Weather Data
    3. Physics Engine
    4. IFTTT Webhooks
    5. Internet of Things (IoT)
    6. Data Logger
    7. Music
    8. Video Sensing
    9. Pen
  4. Machine Learning Environment
    1. Image Classifier (ML)
    2. Object Detection (ML)
    3. Pose Classifier (ML)
    4. Hand Pose Classifier (ML)
    5. Audio Classifier (ML)
    6. Number Classifier and Regression (ML)
    7. Text Classifier (ML)
  5. Quarky
    1. Quarky (Main)
    2. Display (Quarky)
    3. Robot (Quarky)
    4. Sensors (Quarky)
    5. Speaker (Quarky)
    6. Quarky Ultimate Robots
    7. Quarky Expansion Board
    8. Mars Rover
    9. Humanoid (Quarky)
    10. Quadruped (Quarky)
    11. IoT House (Quarky)
    12. Quarky Mecanum
    13. Quarky Robotic Arm
    14. Dabble
    15. Quarky Advance Line Following

Company

Community

Products

School Programs

Impact Programs

Learning Resources

Product Documentation

Get in Touch

Follow Us

Facebook Twitter Youtube Instagram Linkedin

PictoBlox - Block & Python Coding for Kids

Dabble App - One App. Infinite Control.

Copyright 2024 – Agilo Research Pvt. Ltd. All rights reserved – Terms & Condition | Privacy Policy