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
PictoBlox Extension

Recognition Cards

Pre-Trained-Models (1)
Extension Description
AI to identify pretrained recognition cards like signs, numbers, and other objects.
  • STEMpedia-Rocket-800x800 Available in: Block Coding, Python Coding
  • STEMpedia-Rocket-800x800 Mode: Stage Mode
  • STEMpedia-Rocket-800x800 WiFi Required: No
  • STEMpedia-Rocket-800x800 Compatible Hardware in Block Coding: evive, Quarky, Arduino Uno, Arduino Mega, Arduino Nano, ESP32, T-Watch, Boffin, micro:bit, TECbits, LEGO EV3, LEGO Boost, LEGO WeDo 2.0, Go DFA, None
  • STEMpedia-Rocket-800x800 Compatible Hardware in Python: Quarky, None
  • STEMpedia-Rocket-800x800 Object Declaration in Python: cards = RecognitionCards()
  • STEMpedia-Rocket-800x800 Extension Catergory:

Introduction

Recognition Cards is a special pre-trained machine learning model created to identify the following recognition cards (Download – Recognition Card – A4 Signs Pictoblox):

Signals

Go

Stop

GoStraight

TurnLeft

TurnRight

UTurn

PedestrianCrossing

CrossRoad

Numbers

Objects

Spades

Diamonds

Clubs

Hearts

Pizza

Hospital

Bomb

Home

Hazel

PizzaSlice

Tree

Tree

John

Accessing Recognition Card in Block Coding

Following is the process to add Recognition Card capability to the PictoBlox Project.

  1. Open PictoBlox and create a new file.
  2. Select the coding environment as Block Coding.
  3. Next, click on the Add Extension button and add the Recognition Card extension.
  4. The Recognition Card models will be downloaded, which may take a few seconds to load.
    Downloading Models
  5. Once downloaded you will get a message saying “Model loaded successfully”.
  6. You can find the Recognition Card blocks available in the project.

Accessing Recognition Card in Python Coding

Following is the process to add Recognition Card capability to the PictoBlox Project.

  1. Open PictoBlox and create a new file.
  2. Select the coding environment as Python Coding.
  3. Next, click on the Add Modules/Libraries button and add the Recognition Card extension.
  4. To access the library functions, you have to add the object declaration. 
    cards = RecognitionCards()
Read More

PictoBlox Blocks

The turn () video on stage with () % transparency block controls the control the camera feed on the stage.
The block enables or disables the automatic display of the box on object detection on the stage. This is useful when you want to see if the object detection happens during the analysis or not.
This block is used to set the threshold for the confidence (accuracy) of object detection, 0 being low confidence and 1 being high confidence. With the threshold value, you can set the level of confidence required for object detection.
The block analyses the image and saves the face information locally, which can be accessed using other blocks.
Once you have analyzed the images, you can use the get # of objects detected block to get the number of objects recognized in the image.
The () of object () block reports the following attributes of the object: class, x position, y position, width, height, and confidence.
The block returns the class label name.
The block returns whether the specified signal is detected in the analysis or not.
The block returns the specified parameter for the specified signal detected: class, x position, y position, width, height, and confidence.
The block returns whether the specified number card (0-9) is detected in the analysis or not.
The block returns the specified parameter for the specified number card detected.
The block returns whether the specified object card is detected in the analysis or not.
The block returns the specified parameter for the specified object detected.
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Block Coding Examples

AI Delivery Robot

The example demonstrates how to make a delivery robot that follows the line and stops when it reaches checkpoint 1.

Multiple Sign Detection

The example demonstrates how to find the closest sign from multiple sign detection and make the decision accordingly.

Object Tracking Robot

The example demonstrates how to make an object-tracking robot.

Sign Detection – Tobi

The example demonstrates how to implement sign detection in PictoBlox.

Sign Detection Controlled Mecanum

Learn how to code logic for video input detection with this example block code. You will be able to direct your own Mecanum easily by just showing signs through the camera input.

Sign Detection With Humanoid

Learn how to code logic for video input detection .set the bounding box threshold, and detect signals to control Humanoid movements.

Sign Detection with Mars Rover

Learn how to code logic for video input detection with this example block code. You will be able to direct your own Mars Rover easily by just showing signs through the camera input.

Sign Detection with Quadruped

Learn how to set the bounding box threshold, and detect signals such as 'Go', 'TurnRight', 'TurnLeft', and 'Stop' to control quadruped movements.

Sign Detection with Quarky

The example demonstrates how to use sign detection and make the Quarky show the detection on the LED.
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Python Functions

video()
The function is used to control the state of the camera.
Syntax: video(video_state = “on”, transparency = 1)
enablebox()
The function enables the automatic display of the box on object detection on the stage.
Syntax: enablebox()
disablebox()
The function disables the automatic display of the box on object detection on the stage.
Syntax: disablebox()
setthreshold()
This function is used to set the threshold for the confidence (accuracy) of object detection, 0 being low confidence and 1 being high confidence.
Syntax: setthreshold(threshold = 0.5)
analysecamera()
This function is used to analyze the image received as input from the camera, for objects.
Syntax: analysecamera()
analysestage()
This function is used to analyze the image received as input from the stage, for objects.
Syntax: analysestage()
count()
This function returns the total number of objects detected in the camera feed or the stage.
Syntax: count()
classname()
This function is used to get the class name of the analyzed object.
Syntax: classname(object = 1)
x()
This function returns the x position of the object detected. You can specify the object for which the value is needed. The position is mapped with the stage coordinates.
Syntax: x(object = 1)
y()
This function returns the y position of the object detected. You can specify the object for which the value is needed. The position is mapped with the stage coordinates.
Syntax: y(object = 1)
width()
This function returns the width of the object detected. You can specify the object for which the value is needed. The position is mapped with the stage coordinates.
Syntax: width(object = 1)
height()
This function returns the height of the object detected. You can specify the object for which the value is needed. The position is mapped with the stage coordinates.
Syntax: height(object = 1)
confidence()
This function is used to get the confidence (accuracy) of object detection, 0 being low confidence and 1 being high confidence.
Syntax: confidence(object = 1)
issignaldetected()
The function returns whether the specified signal is detected in the analysis or not.
Syntax: issignaldetected(signal_name = “Go”)
getsignaldetail()
The function returns the specified parameter for the specified signal detected.
Syntax: getsignaldetail(signal_name = “Go”, parameter_value = 1)
isnumberdetected()
The function returns whether the specified number card (0-9) is detected in the analysis or not.
Syntax: isnumberdetected(card_number = 1)
getnumberdetail()
The function returns the specified parameter for the specified number card detected.
Syntax: getnumberdetail(card_number = 1, parameter_value = 1)
isobjectdetected()
The function returns whether the specified object card is detected in the analysis or not.
Syntax: isobjectdetected(object_name = “Pizza”)
getobjectdetail()
The function returns the specified parameter for the specified object detected.
Syntax: getobjectdetail(object_name = “Pizza”, parameter_value = 1)
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Python Coding Examples

AI Delivery Robot – Python

The example demonstrates how to make a delivery robot that follows the line and stops when it reaches checkpoint 1 in the Python Coding Mode.

Python Based Sign Detection Controlled Mecanum

Learn how to code logic for video input detection with this example code. You will be able to direct your own Mecanum easily by just showing signs through the camera input.

Python Based Sign Detection with Mars Rover

Learn how to code logic for video input detection with this example code. You will be able to direct your own Mars Rover easily by just showing signs through the camera input.

Sign Detection with Humanoid

Learn how to set the bounding box threshold, and detect signals such as 'Go', 'TurnRight', 'TurnLeft', and 'Stop' to control humanoid movements.

Sign Detection with Quadruped

Learn how to set the bounding box threshold, and detect signals such as 'Go', 'TurnRight', 'TurnLeft', and 'Stop' to control quadruped movements.
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