Table of Contents
Number Classifier and Regression (ML)
PictoBlox Block

is identified class is ()?

Description

The block checks if the currently recognized class from the analyse numbers block is the selected class or not.

Example

Gesture Controlled Robotic Arm With Machine Learning

Discover how gesture-controlled robotic arms revolutionize robotics with intuitive control. Learn about their applications in manufacturing, healthcare, and virtual reality.
Download File

Introduction

A gesture-controlled robotic arm is a robotic arm that can be controlled using hand or body movements instead of traditional buttons or joysticks. It uses sensors and algorithms to interpret the gestures made by a user and translates them into commands for the robotic arm.

The user wears or holds a device with sensors, such as a glove or wristband, that captures their hand movements or body gestures. These movements are processed by a computer or microcontroller, which analyzes them and recognizes specific gestures using algorithms and machine learning techniques.

Once the gestures are recognized, the system generates commands for the robotic arm to move accordingly. The arm can have multiple joints and degrees of freedom to perform complex movements. The user’s gestures are mimicked by the robotic arm, allowing them to control its actions.

Gesture-controlled robotic arms are used in various fields, including manufacturing, healthcare, and virtual reality. They provide a more intuitive and natural way of controlling robotic systems, eliminating the need for complex input devices and extensive training.

Hand Gesture Classifier Workflow

Follow the steps below:

  1. Open PictoBlox and create a new file.
  2. Select the coding environment as appropriate Coding Environment. you can click on “Machine Learning Environment” to open it.
  3. Select the “Open ML Environment” option under the “Files” tab to access the ML Environment.
  4. Click on “Create New Project“.
  5. 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 Hand Pose Classifier window.
  6. 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.

Class in Hand Gesture Classifier

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. Hand Pose Data: This data can be taken from the webcam or uploaded from local storage.

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 Webcam or by Uploading the files from the local folder.
    1. Webcam:
Note: You must add at least 20 samples to each of your classes for your model to train. More samples will lead to better results.
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.

Logic

The robotic arm will move according to the following logic:

  1. When the left gesture is detected – the robotic arm will move in the anti-clockwise direction.
  2. When the right gesture is detected – the robotic arm will move in a Clockwise direction.

Code

Logic

  1. First we Initialize Robitc Arm extension.
  2. Then, we open the recognition window, which will identify different poses, and turn on the camera with a certain level of transparency to identify images from the stage.
  3. If the identified class is “left,” the Robotic arm will move in anti-clockwise direction using move in () circle of center X() Z(),radius() & along Y() in ()ms block.
  4. If the identified class is “right,” the Robotic Arm will move right using move in () circle of center X() Z(),radius() & along Y() in ()ms block
  5. Press Run to run the code.

Output

Read More

Hand Pose Controlled Mecanum

This project demonstrates how to use Machine Learning Environment to make a machine–learning model that identifies the hand gestures and makes the Mecanum robot move accordingly.
Download File

This project demonstrates how to use Machine Learning Environment to make a machine–learning model that identifies the hand gestures and makes the Mecanum move accordingly.

We are going to use the Hand Classifier of the Machine Learning Environment. The model works by analyzing your hand position with the help of 21 data points. We will add in total 8 different classes to operate the different motions of the Mecanum Robot with the help of the ML Environment of the Pictoblox Software.

Hand Gesture Classifier Workflow

Follow the steps below:

  1. Open PictoBlox and create a new file.
  2. Select the coding environment as appropriate Coding Environment.
  3. Select the “Open ML Environment” option under the “Files” tab to access the ML Environment.
  4. Click on “Create New Project“.
  5. 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 Hand Pose Classifier window.
  6. 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.

Class in Hand Gesture Classifier

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

  1. Class Name: It’s the name to which the class will be referred as.
  2. Hand Pose Data: This data can either be taken from the webcam or by uploading from local storage.

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 Webcam or by Uploading the files from the local folder.
    1. Webcam:
Note: You must add at least 20 samples to each of your classes for your model to train. More samples will lead to better results.
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.

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 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.

Logic

The mecanum will move according to the following logic:

  1. When the forward gesture is detected – Mecanum will move forward.
  2. When the backward gesture is detected – Mecanum will move backwards.
  3. When the Lateral Left gesture is detected – Mecanum will move towards the left direction laterally with the help of its omnidirectional wheels.
  4. When the Lateral Right gesture is detected – Mecanum will move towards the right direction laterally with the help of its omnidirectional wheels.
  5. When the Stop gesture is detected – Mecanum will stop moving.
  6. When the Normal Left gesture is detected – Mecanum will rotate in the left direction.
  7. When the Normal Right gesture is detected – Mecanum will rotate in the right direction.
  8. When the Circular Motion gesture is detected – Mecanum will move in a lateral arc.

Code

Initialization

Main Code

Output

Forward-Backward Motions:

Lateral Right-Left Motions:

Circular Right-Left Motions:

Lateral Arc Motion:

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

Sound-Based Mars Control

Learn how to create custom sounds to control Mars Rover with the Audio Classifier of the Machine Learning Environment in PictoBlox. Start building your Sound Based Controlled Mars Rover now!
Download File

In this activity, we will use the Machine Learning Environment of the Pictoblox Software. We will use the Audio Classifier of the Machine Learning Environment and create our custom sounds to control the Mars Rover.

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 appropriate Coding Environment.
  3. Select the “Open ML Environment” option under the “Files” tab to access the ML Environment.
  4. Click on “Create New Project“.
  5. 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.
  6. 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.
  7. As you can observe in the above image, we will add two classes for audio. We will be able to add audio samples with the help of the microphone. Rename the class1 as “Clap” and class2 as “Snap”.

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 atleast 20 samples for the model to run with good accuracy.
  4. Add the first class as “clap”  and record the audio for clap noises through the microphone.
  5. Add the second class as “snap” and record the audio for snap noises through the microphone.

Note: You will only be able to change the class name in the starting before adding any audio samples. You will not be able to change the class name after adding the audio samples in the respective class.

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.

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 the 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.

 

Logic

The Mars Rover will move according to the following logic:

  1. When the audio is identified as “clap”- Mars Rover will move forward.
  2. When the “snap” sound is detected –Mars Rover will move backward.

Note: You can add even more classes with different types of differentiating sounds to customize your control. This is just a small example from which you can build your own Sound Based Controlled Mars Rover in a very easy stepwise procedure.

 

Code

 

Logic

  1. First  we will initialize different Audio classes.
  2. Then, we will open the recognition window, which will identify different audio and turn on the microphone to identify and record the audio from the microphone.
  3. If the identified class from the analyzed audio is “clap,” the Mars Rover will move forward at a specific speed.
  4. If the identified class is “snap,” the Mars Rover will move backward.

Output

Read More

Sound Based Quadruped with Machine Learning

Learn how to create custom sounds to control Quadruped with the Audio Classifier of the Machine Learning Environment in PictoBlox.
Download File

Introduction

A Sound-Based Quadruped with Machine Learning refers to a Quadruped robot that can perceive and interact with its environment through sound-based sensing and uses machine-learning techniques to process and analyze the auditory data it receives.
Quadruped robots with machine learning have the potential to greatly enhance the way we interact with machines and each other, making communication more natural and intuitive while also enabling new applications in fields such as healthcare, education, and entertainment.
In this activity, we will use the Machine Learning Environment of the Pictoblox Software. We will use the Audio Classifier of the Machine Learning Environment and create our custom sounds to control the Quadruped.

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 two classes for audio. We will be able to add audio samples with the help of the microphone. Rename class 1 as “Clap” and class 2 as “Snap”.

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.
  4. Add the first class as “clap”  and record the audio for clap noises through the microphone.
  5. Add the second class as “snap” and record the audio for snap noises through the microphone.

Note: You will only be able to change the class name in the starting before adding any audio samples. You will not be able to change the class name after adding the audio samples in the respective class.

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.

 

The Quadruped will move according to the following logic:

  1. When the audio is identified as “clap” sound– Quadruped will move forward.
  2. When the “snap” sound is detected –Quadruped will move backward.


Note: You can add even more classes with different types of differentiating sounds to customize your control. This is just a small example from which you can build your own Sound Based Controlled Quadruped in a very easy stepwise procedure.

Code

Logic

  1. First, initialize the Quadruped extension.
  2. Then, initialize a forever loop to continuously loop and analyze the camera from the stage.
  3. If the program detects a clap sound, the Quadruped will move forward at a specific speed.
  4. Similarly, if it identifies a snap sound, the Quadruped will move backward at a specific speed.
  5. Otherwise, the Quadruped will remain in its initial position (home position).

Output

Read More
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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

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