Table of Contents
Pose Classifier (ML)
PictoBlox Block

open recognition window

Description

The block opens the recognition window and shows the machine learning analysis on the camera feed. Very good for visualization of the model in PictoBlox.

Example

Gesture Controlled Gripper Mecanum

In this activity, learn how to create a new Machine Learning model that will be able to identify and detect different types of hand poses and that can help us to control the Mecanum Gripper Robot.
Download File

In this activity, we will try to create a new Machine Learning model that will be able to identify and detect different types of hand poses and that can help us to control the Mecanum Gripper Robot. This activity can be quite fun and by knowing the process, you can develop your own customized hand pose classifier model easily!

We will use the same model that we have created in the previous Hand Pose Controlled Mecanum model to avoid any misdirection and confusion.

Note: You can always create your own model and use it to perform any type of functions as per your choice. This example proves the same point and helps you understand well the concept of Machine Learning models and environment.

Hand Gesture Classifier Workflow

Follow the steps below:

  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 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. If the detected class is “forward”, we will make the Mecanum 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 Normal Right gesture is detected – Mecanum will rotate on a single point towards the right direction.
  6. When the Normal Left gesture is detected – Mecanum will rotate on a single point towards the left direction.
  7. When the “Stop” class gesture is detected, we will use the gripper functions of the Mecanum and Pick the object.
  8. When the “Circular Motion ” class is detected, we will use the gripper functions of the Mecanum and Drop the object by opening the arms of the gripper robot.

Code

Initialization

Main Code

 

Output

Forward-Backward Motion:

Circular Right-Left Motion:

Lateral Right-Left Motions:

Gripper Mechanism with Hand Gestures:

Read More

Hand Pose Controlled Mecanum Pick and Place Robot

In this activity, learn how to create a new Machine Learning model that will be able to identify and detect different types of hand poses and that can help us to control the Mecanum Pick and Place Robot.
Download File

In this activity, we will try to create a new Machine Learning model that will be able to identify and detect different types of hand poses and that can help us to control the Mecanum Pick and Place Robot. This activity can be quite fun and by knowing the process, you can develop your own customized hand pose classifier model easily!

We will use the same model that we have created in the previous Hand Pose Controlled Mecanum model to avoid any misdirection and confusion.

Note: You can always create your own model and use it to perform any type of functions as per your choice. This example proves the same point and helps you understand well the concept of Machine Learning models and environment.

Hand Gesture Classifier Workflow

Follow the steps below:

  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 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. If the detected class is “forward”, we will make the Mecanum 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 Normal Right gesture is detected – Mecanum will rotate on a single point towards the right direction.
  6. When the Normal Left gesture is detected – Mecanum will rotate on a single point towards the left direction.
  7. When the Stop gesture is detected – Mecanum will stop and initiate the Pick Mechanism using the Pick function.
  8. When the Circular Motion gesture is detected – Mecanum will initiate the Place Mechanism using the Place function.

Code

Initialization

Main Code

Output

Forward-Backward Motion:

Circular Right-Left Motion:

Lateral Right-Left Motion:

Pick and Place Mechanism with Hand Pose:

Read More

Gesture Controlled Humanoid With Machine Learning

Learn how to use Machine Learning Environment to make a model that identifies the hand gestures and makes the Humanoid move accordingly.
Download File

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

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 Humanoid will move according to the following logic:

  1. When the forward gesture is detected – the Humanoid will move forward.
  2. When the backward gesture is detected – the Humanoid will move backward.
  3. When the left gesture is detected – the Humanoid will turn left.
  4. When the right gesture is detected – the Humanoid will turn right.

Code

Logic

  1. First we Initialize Humanoid classes.
  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 from the analyzed image is “forward,” the Humanoid will move forward at a specific speed.
  4. If the identified class is “backward,” the Humanoid will move backward using do () action () times at () speed block.
  5. If the identified class is “left,” the Humanoid will move left using do () action () times at () speed block.
  6. If the identified class is “right,” the Humanoid will move right using do () action () times at () speed block.
  7. Otherwise, the Humanoid will be in the home position.

Output

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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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