OpenCV: Complete Beginners Guide To Master the Basics Of Computer Vision With Code!

Computer Vision is perhaps the most intriguing and fascinating concept in artificial intelligence. Computer Vision is an interdisciplinary field that deals with how computers or any software can learn a high-level understanding of the visualizations in the surroundings. After obtaining this conceptual perspective, it can be useful to automate tasks or perform the desired action.

The tasks that are obvious to the human brain are not so intuitive to the computers as they need to be trained specifically on these jobs to produce effective results. This process involves complicated steps like acquiring the data from the real world, processing the acquired data in a suitable format, analyzing the processed images, and finally teaching and training the model to perform the complex task with very high accuracy.

To understand computer vision more intuitively, let us consider an example. Assume you have to teach a computer to differentiate between the various colors. Consider, you have three objects with the following colors, namely red, blue, and green, and you want to differentiate these colors accordingly. This job is an extremely simple task for the human brain to perform, but it is quite a complicated task for the computer to perform.

The task mentioned above is one of the most basic actions that can be performed by using computer vision. We will learn about how images in the digital world work and also try to understand the image and how these stacked layers exactly work. . We will also learn in-depth about the basics of the open-cv module. Finally, we will also be implementing some hands on basic level projects with this library. So, without further ado let us dive into understanding all the aspects required for mastering the basic computer vision skills.

Dealing With Images:

Image for post

Screenshot By Author

The composition of these three colors, namely red, green, and blue can be used to compose almost any other color. Mixing them in the right proportion allows us to frame any other desired color. This concept has existed since the cathode ray televisions a few decades ago. So how does this exactly work?

Each of these colors has an 8 bit integer value. This means a matrix of these could range from 0 to 255. The reasoning for this is because 2⁸ is 256 and 0–255 consist of 256 values. Each of these colors will have a value of this range and since we have a 3-Dimensional image, we can stack each of these upon each other. This might be a slightly more complex example, so let us switch over to the grayscale images which only consists of black and white and that would be easier to understand. Below is the grayscale representation.

Image for post

Screenshot By Author

This grayscale representation shown above should be a good starting point to understand the concept of how images work in computer vision better. The below figure shows how the level change starts to happen as we move from the 0th mark to the 255th mark. After 256 levels of changing we go from a completely black shade to a fully white shade.

#programming #machine-learning #computer-vision #data-science #opencv

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OpenCV: Complete Beginners Guide To Master the Basics Of Computer Vision With Code!

OpenCV: Complete Beginners Guide To Master the Basics Of Computer Vision With Code!

Computer Vision is perhaps the most intriguing and fascinating concept in artificial intelligence. Computer Vision is an interdisciplinary field that deals with how computers or any software can learn a high-level understanding of the visualizations in the surroundings. After obtaining this conceptual perspective, it can be useful to automate tasks or perform the desired action.

The tasks that are obvious to the human brain are not so intuitive to the computers as they need to be trained specifically on these jobs to produce effective results. This process involves complicated steps like acquiring the data from the real world, processing the acquired data in a suitable format, analyzing the processed images, and finally teaching and training the model to perform the complex task with very high accuracy.

To understand computer vision more intuitively, let us consider an example. Assume you have to teach a computer to differentiate between the various colors. Consider, you have three objects with the following colors, namely red, blue, and green, and you want to differentiate these colors accordingly. This job is an extremely simple task for the human brain to perform, but it is quite a complicated task for the computer to perform.

The task mentioned above is one of the most basic actions that can be performed by using computer vision. We will learn about how images in the digital world work and also try to understand the image and how these stacked layers exactly work. . We will also learn in-depth about the basics of the open-cv module. Finally, we will also be implementing some hands on basic level projects with this library. So, without further ado let us dive into understanding all the aspects required for mastering the basic computer vision skills.

Dealing With Images:

Image for post

Screenshot By Author

The composition of these three colors, namely red, green, and blue can be used to compose almost any other color. Mixing them in the right proportion allows us to frame any other desired color. This concept has existed since the cathode ray televisions a few decades ago. So how does this exactly work?

Each of these colors has an 8 bit integer value. This means a matrix of these could range from 0 to 255. The reasoning for this is because 2⁸ is 256 and 0–255 consist of 256 values. Each of these colors will have a value of this range and since we have a 3-Dimensional image, we can stack each of these upon each other. This might be a slightly more complex example, so let us switch over to the grayscale images which only consists of black and white and that would be easier to understand. Below is the grayscale representation.

Image for post

Screenshot By Author

This grayscale representation shown above should be a good starting point to understand the concept of how images work in computer vision better. The below figure shows how the level change starts to happen as we move from the 0th mark to the 255th mark. After 256 levels of changing we go from a completely black shade to a fully white shade.

#programming #machine-learning #computer-vision #data-science #opencv

Samanta  Moore

Samanta Moore

1621137960

Guidelines for Java Code Reviews

Get a jump-start on your next code review session with this list.

Having another pair of eyes scan your code is always useful and helps you spot mistakes before you break production. You need not be an expert to review someone’s code. Some experience with the programming language and a review checklist should help you get started. We’ve put together a list of things you should keep in mind when you’re reviewing Java code. Read on!

1. Follow Java Code Conventions

2. Replace Imperative Code With Lambdas and Streams

3. Beware of the NullPointerException

4. Directly Assigning References From Client Code to a Field

5. Handle Exceptions With Care

#java #code quality #java tutorial #code analysis #code reviews #code review tips #code analysis tools #java tutorial for beginners #java code review

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

Morse Code Translator Detect Blinks — Python, OpenCV, MediaPipe

Hello everyone,

It has been a while since the last time I posted a tutorial, or something in general. Basically life happened and I decided not to share rather than sharing low quality content. Today, I’ll walk you through a computer vision project that takes your live video input and translates your blinks into Morse Alphabet so you can blink short and long to write messages.

The source code for the project is here, I also used this awesome tutorial as a boiler plate to start with, if you want to learn more about Computer Vision applications you can check the channel owner’s channel from the link I posted. So without further ado let’s dive right into it.

As for the beginning I want to explain MediaPipe library a little bit, “MediaPipe offers open source cross-platform, customizable ML solutions for live and streaming media.”, this definition is from their own website and explains what you can do with that library shortly and cleanly, they offer several other solutions that can run on different platforms and I’ll explain all of them in a different post in the future. The feature that we’ll use today is called “Face Mesh”, this solution provides us a face landmark map with the most important 468 landmarks that can be seen in a human’s face. Using that map we’ll calculate the ratio between some particular points in the face and with that information we’ll detect if the person on the camera blinked or not.

#python #opencv #mediapipe #computer-vision #morse code translator detect blinks — python, opencv, mediapipe #morse code translator detect blinks

Joseph  Murray

Joseph Murray

1621511340

Minimum Java Knowledge Requirements for Your First Coding Job

What does a potential Java junior need to know to get their first job or even qualify for a trainee position in a good company? What tools will help a Java programmer reach the next level? Which technologies should you study, and which ones are better to hold off on?

There is no standard answer to these questions, just as there is no single action plan that would suit absolutely everyone. Some companies are striving for development, constantly introducing new technologies and testing the capabilities of new versions of the language, while others stubbornly cling to old ones. There are also middle options, and perhaps these are most of them.

I get asked this question so often that I decided to write an article that I can then refer to in order to answer it. In addition, it will be useful not only to those who ask me personally but also to everyone who has already decided (or did not decide in any way) to connect their lives with Java programming.

#java #java-development-resources #java-development #learn-to-code #learning-to-code #beginners #tutorial-for-beginners #beginners-to-coding