Object Detection

Extension Description

AI to identify objects from images. It also tells the location and size of the objects identified.

Available in: Block Coding, Python Coding
Mode: Stage Mode
WiFi Required: No
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
Compatible Hardware in Python: Quarky, None
Object Declaration in Python: od = ObjectDetection()
Extension Catergory: Artificial Intelligence

Introduction

Object detection is used to locate and identify multiple objects in digital photographs. It is a computer vision technique that helps to detect objects as well as classify them. The object class may appear once or several times in the image. For example, in the following image, object detection assists us to locate the objects and classify them accordingly to the known set of objects.

Object Detection

One of the applications of Object detection includes Self-driving vehicles which detect objects in real-time and act accordingly.

The object detection extension in PictoBlox allows you to detect the following 90 objects:

ID	OBJECT (PAPER)	SUPER CATEGORY
1	person	person
2	bicycle	vehicle
3	car	vehicle
4	motorcycle	vehicle
5	airplane	vehicle
6	bus	vehicle
7	train	vehicle
8	truck	vehicle
9	boat	vehicle
10	traffic light	outdoor
11	fire hydrant	outdoor
12	street sign	outdoor
13	stop sign	outdoor
14	parking meter	outdoor
15	bench	outdoor
16	bird	animal
17	cat	animal
18	dog	animal
19	horse	animal
20	sheep	animal
21	cow	animal
22	elephant	animal
23	bear	animal
24	zebra	animal
25	giraffe	animal
26	hat	accessory
27	backpack	accessory
28	umbrella	accessory
29	shoe	accessory
30	eye glasses	accessory
31	handbag	accessory
32	tie	accessory
33	suitcase	accessory
34	frisbee	sports
35	skis	sports
36	snowboard	sports
37	sports ball	sports
38	kite	sports
39	baseball bat	sports
40	baseball glove	sports
41	skateboard	sports
42	surfboard	sports
43	tennis racket	sports
44	bottle	kitchen
45	plate	kitchen
46	wine glass	kitchen
47	cup	kitchen
48	fork	kitchen
49	knife	kitchen
50	spoon	kitchen
51	bowl	kitchen
52	banana	food
53	apple	food
54	sandwich	food
55	orange	food
56	broccoli	food
57	carrot	food
58	hot dog	food
59	pizza	food
60	donut	food
61	cake	food
62	chair	furniture
63	couch	furniture
64	potted plant	furniture
65	bed	furniture
66	mirror	furniture
67	dining table	furniture
68	window	furniture
69	desk	furniture
70	toilet	furniture
71	door	furniture
72	tv	electronic
73	laptop	electronic
74	mouse	electronic
75	remote	electronic
76	keyboard	electronic
77	cell phone	electronic
78	microwave	appliance
79	oven	appliance
80	toaster	appliance
81	sink	appliance
82	refrigerator	appliance
83	blender	appliance
84	book	indoor
85	clock	indoor
86	vase	indoor
87	scissors	indoor
88	teddy bear	indoor
89	hair drier	indoor
90	toothbrush	indoor
91	hair brush	indoor

Accessing Object Detection in Block Coding

Following is the process to add Object Detection capability to the PictoBlox Project.

Open PictoBlox and create a new file.
Select the coding environment as appropriate Coding Environment.
Next, click on the Add Extension button and add the Object Detection extension.
The object detection models will be downloaded, which may take a few seconds to load.
Once downloaded you will get a message saying “Model loaded successfully”.
You can find the object detection blocks available in the project.

Accessing Object Detection in Python Coding

Following is the process to add Object Detection capability to the PictoBlox Project.

Open PictoBlox and create a new file.
Select the coding environment as Block Coding.
Next, click on the Add Modules/Libraries button and add the Face Detection extension.
To access the library functions, you have to add the object declaration.
```
od = ObjectDetection()
```

PictoBlox Blocks

turn () video on stage with () % transparency block controls the control the camera feed on the stage.

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.

Once you have analyzed the images, you can use the get # of objects 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 is () detected? block checks whether the selected class has been detected or not. If the class is detected, then the block returns true; else, false.

The block returns the number of objects identified with the specified object class.

All articles loaded

No more articles to load

Block Coding Examples

All articles loaded

No more articles to load

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()

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)

classname()

This function is used to get the class name of the analyzed object.

Syntax: classname(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)

isdetected()

The function returns whether the specified object is detected in the analysis or not.

Syntax: isdetected(object_name = “person”)

detectedcount()

The function returns whether the number of objects identified with the specified object class.

Syntax: detectedcount(object_name = “person”)

All articles loaded

No more articles to load

Object Detection

Introduction

Accessing Object Detection in Block Coding

Accessing Object Detection in Python Coding

PictoBlox Blocks

Block Coding Examples

Biodegradable vs Non Biodegradable Waste

Object Detection on Stage

Threshold Demonstration – Person Detection

Python Functions

Python Coding Examples

Object Detection on Stage – Python

Person Detector – Python

Waste Classifier – Python

Company

Community

Products

School Programs

Impact Programs

Learning Resources

Product Documentation

Get in Touch

Follow Us

PictoBlox - Block & Python Coding for Kids

Dabble App - One App. Infinite Control.