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PictoBlox Extension

Computer Vision

Computer Vision_v1
Extension Description
AI to identify celebrities, brands, landmarks, and objects in images.
  • STEMpedia-Rocket-800x800 Available in: Block Coding, Python Coding
  • STEMpedia-Rocket-800x800 Mode: Stage Mode
  • STEMpedia-Rocket-800x800 WiFi Required: Yes
  • 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: cv = ComputerVision()
  • STEMpedia-Rocket-800x800 Extension Catergory:

Introduction

PictoBlox’s Computer Vision extension allows you to make AI projects in PictoBlox. You can create projects to identify celebrities, brands, landmarks, and objects in images.

By analyzing the images for image features, you can recognize the following things:

  1. Brands: Brand detection uses a database of thousands of global logos to identify commercial brands in images. The Computer Vision service detects if there are brand logos in a given image; if so, it returns the brand name; else, it returns NULL.
    Brands
  2. Celebrity: Celebrity detection uses a database to identify celebrities in images. The Computer Vision service detects if there is a celebrity in a given image; if so, it returns their name; else, it returns NULL.
    Celebrity
  3. Objects: Computer vision detects if there are objects in a given image; if so, it returns their name; else, it returns NULL.
    Object Detection
  4. Landmarks: Landmark detection uses a database of thousands of global landmarks to identify them in images, e.g., the Taj Mahal.
    Taj Mahal 2
  5. Image Tags: Computer vision returns the taxonomy-based categories detected in an image. Computer Vision can categorize an image broadly or specifically according to the 86 categories given in the following diagram:
    Category Tags
  6. Image Description: Human-readable sentence that describes the contents of the image.
Read More

PictoBlox Blocks

turn () video on stage with () % transparency block controls the control the camera feed on the stage.
The recognize () in image after () seconds block starts the camera and takes an image after the specified time and analyzes it. It then saves the image features in PictoBlox.
The recognize () in image from () block extracts the image from Stage, Costume, or Backdrop in PictoBlox, analyzes it, and saves information in PictoBlox.
The recognize () in image from URL () block extracts the image from the specified URL, analyzes it, and saves information in PictoBlox.
The recognized () count block reports the number of selected things to count in the image. Multiple celebrities, brands, objects, and image tags in a single image can be seen.
The recognized () () name block reports the name of the recognized celebrity/brand/object/image tag.
In computer vision, you can get the location and other parameters of the recognized celebrity, brand, and Object using the recognized () () () block.
The () recognition result block reports the landmark and the image description identified in the image.
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Python Functions

video()
This function helps turn the video on/off on the stage with a defined level of transparency.
Syntax: video(video_state = “on”, transparency = 1)
analysecamera()
This function is used to analyze the image received as input from the camera, for the feature.
Syntax: analysecamera()
analysestage()
This function is used to analyze the image received as input from the stage, for the feature.
Syntax: analysestage()
analysebackdrop()
This function is used to analyze the image received as input from the current backdrop image, for the feature.
Syntax: analysebackdrop()
analysecostume()
This function is used to analyze the image received as input from the current costume, for the feature.
Syntax: analysecostume()
analyseURL()
This function is used to analyze the image received as input from the specified URL of the image, for the feature.
Syntax: analyseURL(url = “https://ai.thestempedia.com/wp-content/uploads/2022/02/Robert-and-Chris.jpg”)
imagefeaturecount()
The function reports the number of specified features to count in the image. The options are celebrities, brands, objects, and image tags in a single image can be reported.
Syntax: imagefeaturecount(feature_type = “object”)
imagefeatureinfo()
The function reports the name/x position/y position/width/height or confidence of the recognized celebrity/brand/object/image tag. If the number is out of range, then the function will return NULL.
Syntax: imagefeatureinfo(feature_type = “object”, number = 1, information = “name”)
landmark()
The function reports the landmark identified in the image on analysis.
Syntax: landmark()
imagedescription()
The function reports the image description identified in the image on analysis – a Human-readable sentence that describes the contents of the image.
Syntax: imagedescription()
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