The Importance of Testing Your Python Code

Why test at all?

A commonly overlooked aspect of data science is properly testing your code. This generally means making sure it works as intended and is free of major bugs. When working on smaller, isolated coding projects and analyses, writing tests may not be as important and at times, may be skipped entirely. As a project grows in size and complexity, more users start to interact with it. Everything will still work fine… until it doesn’t.

This is where testing comes in. Writing tests is essential to be able to maintain clean and usable code. It may not be the most exciting or glamorous side of data science, but it certainly is necessary for preventing problems with your code later on. A sufficiently large and complex code base will inevitably contain bugs and issues. Keeping these bugs and issues to a minimum will largely depend on the comprehensiveness of your testing. It will also help when adding new functionality or refactoring your code, making sure you haven’t broken anything you didn’t intend to.

So where do we start? For those of us who do not come from a traditional programming background, writing testing may seem like a daunting task. In this post, I’ll break down the basics of testing Python code and hopefully eliminate those bug-induced headaches later on.

Making assertions

The most basic way to add tests in your code is through assert statements. This built-in keyword allows you to pass in a given condition and check if it is Trueor False.

assert 1 == 1
>> True

We can add an error message after the initial condition to be printed when the assertion is False. The moment an assert condition is evaluated as False, an error is raised and the error message is shown.

assert 1 == 2, "The assertion is False"
>> AssertionError: The assertion is False

#programming #data-science #unittest #python

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How to Use Hotwire Rails

Introduction

We are back with another exciting and much-talked-about Rails tutorial on how to use Hotwire with the Rails application. This Hotwire Rails tutorial is an alternate method for building modern web applications that consume a pinch of JavaScript.

Rails 7 Hotwire is the default front-end framework shipped with Rails 7 after it was launched. It is used to represent HTML over the wire in the Rails application. Previously, we used to add a hotwire-rails gem in our gem file and then run rails hotwire: install. However, with the introduction of Rails 7, the gem got deprecated. Now, we use turbo-rails and stimulus rails directly, which work as Hotwire’s SPA-like page accelerator and Hotwire’s modest JavaScript framework.

What is Hotwire?

Hotwire is a package of different frameworks that help to build applications. It simplifies the developer’s work for writing web pages without the need to write JavaScript, and instead sending HTML code over the wire.

Introduction to The Hotwire Framework:

1. Turbo:

It uses simplified techniques to build web applications while decreasing the usage of JavaScript in the application. Turbo offers numerous handling methods for the HTML data sent over the wire and displaying the application’s data without actually loading the entire page. It helps to maintain the simplicity of web applications without destroying the single-page application experience by using the below techniques:

Turbo Frames: Turbo Frames help to load the different sections of our markup without any dependency as it divides the page into different contexts separately called frames and updates these frames individually.
Turbo Drive: Every link doesn’t have to make the entire page reload when clicked. Only the HTML contained within the tag will be displayed.
Turbo Streams: To add real-time features to the application, this technique is used. It helps to bring real-time data to the application using CRUD actions.

2. Stimulus

It represents the JavaScript framework, which is required when JS is a requirement in the application. The interaction with the HTML is possible with the help of a stimulus, as the controllers that help those interactions are written by a stimulus.

3. Strada

Not much information is available about Strada as it has not been officially released yet. However, it works with native applications, and by using HTML bridge attributes, interaction is made possible between web applications and native apps.

Simple diagrammatic representation of Hotwire Stack:

Prerequisites For Hotwire Rails Tutorial

As we are implementing the Ruby on Rails Hotwire tutorial, make sure about the following installations before you can get started.

• Ruby on Rails
• Hotwire gem
• PostgreSQL/SQLite (choose any one database)
• Turbo Rails
• Stimulus.js

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Create a new Rails Project

Find the following commands to create a rails application.

mkdir ~/projects/railshotwire
cd ~/projects/railshotwire
echo "source 'https://rubygems.org'" > Gemfile
echo "gem 'rails', '~> 7.0.0'" >> Gemfile
bundle install  
bundle exec rails new . --force -d=postgresql

Now create some files for the project, up till now no usage of Rails Hotwire can be seen.
Fire the following command in your terminal.

• For creating a default controller for the application

echo "class HomeController < ApplicationController" > app/controllers/home_controller.rb
echo "end" >> app/controllers/home_controller.rb

• For creating another controller for the application

echo "class OtherController < ApplicationController" > app/controllers/other_controller.rb
echo "end" >> app/controllers/home_controller.rb

• For creating routes for the application

echo "Rails.application.routes.draw do" > config/routes.rb
echo '  get "home/index"' >> config/routes.rb
echo '  get "other/index"' >> config/routes.rb
echo '  root to: "home#index"' >> config/routes.rb
echo 'end' >> config/routes.rb

• For creating a default view for the application

mkdir app/views/home
echo '<h1>This is Rails Hotwire homepage</h1>' > app/views/home/index.html.erb
echo '<div><%= link_to "Enter to other page", other_index_path %></div>' >> app/views/home/index.html.erb

• For creating another view for the application

mkdir app/views/other
echo '<h1>This is Another page</h1>' > app/views/other/index.html.erb
echo '<div><%= link_to "Enter to home page", root_path %></div>' >> app/views/other/index.html.erb

• For creating a database and schema.rb file for the application

bin/rails db:create
bin/rails db:migrate

• For checking the application run bin/rails s and open your browser, your running application will have the below view.

Additionally, you can clone the code and browse through the project. Here’s the source code of the repository: Rails 7 Hotwire application

Now, let’s see how Hotwire Rails can work its magic with various Turbo techniques.

Hotwire Rails: Turbo Drive

Go to your localhost:3000 on your web browser and right-click on the Inspect and open a Network tab of the DevTools of the browser.

Now click on go to another page link that appears on the home page to redirect from the home page to another page. In our Network tab, we can see that this action of navigation is achieved via XHR. It appears only the part inside HTML is reloaded, here neither the CSS is reloaded nor the JS is reloaded when the navigation action is performed.

By performing this action we can see that Turbo Drive helps to represent the HTML response without loading the full page and only follows redirect and reindeer HTML responses which helps to make the application faster to access.

Hotwire Rails: Turbo Frame

This technique helps to divide the current page into different sections called frames that can be updated separately independently when new data is added from the server.
Below we discuss the different use cases of Turbo frame like inline edition, sorting, searching, and filtering of data.

Let’s perform some practical actions to see the example of these use cases.

Make changes in the app/controllers/home_controller.rb file

#CODE

class HomeController < ApplicationController
   def turbo_frame_form
   end
   
   def turbo_frame submit
      extracted_anynumber = params[:any][:anynumber]
      render :turbo_frame_form, status: :ok, locals: {anynumber: extracted_anynumber,      comment: 'turbo_frame_submit ok' }
   end
end

Add app/views/home/turbo_frame_form.html.erb file to the application and add this content inside the file.

#CODE

<section>

    <%= turbo_frame_tag 'anyframe' do %>
            
      <div>
          <h2>Frame view</h2>
          <%= form_with scope: :any, url: turbo_frame_submit_path, local: true do |form| %>
              <%= form.label :anynumber, 'Type an integer (odd or even)', 'class' => 'my-0  d-inline'  %>
              <%= form.text_field :anynumber, type: 'number', 'required' => 'true', 'value' => "#{local_assigns[:anynumber] || 0}",  'aria-describedby' => 'anynumber' %>
              <%= form.submit 'Submit this number', 'id' => 'submit-number' %>
          <% end %>
      </div>
      <div>
        <h2>Data of the view</h2>
        <pre style="font-size: .7rem;"><%= JSON.pretty_generate(local_assigns) %></pre> 
      </div>
      
    <% end %>

</section>

Make some adjustments in routes.rb

#CODE

Rails.application.routes.draw do
  get 'home/index'
  get 'other/index'

  get '/home/turbo_frame_form' => 'home#turbo_frame_form', as: 'turbo_frame_form'
  post '/home/turbo_frame_submit' => 'home#turbo_frame_submit', as: 'turbo_frame_submit'


  root to: "home#index"
end

• Next step is to change homepage view in app/views/home/index.html.erb

#CODE

<h1>This is Rails Hotwire home page</h1>
<div><%= link_to "Enter to other page", other_index_path %></div>

<%= turbo_frame_tag 'anyframe' do %>        
  <div>
      <h2>Home view</h2>
      <%= form_with scope: :any, url: turbo_frame_submit_path, local: true do |form| %>
          <%= form.label :anynumber, 'Type an integer (odd or even)', 'class' => 'my-0  d-inline'  %>
          <%= form.text_field :anynumber, type: 'number', 'required' => 'true', 'value' => "#{local_assigns[:anynumber] || 0}",  'aria-describedby' => 'anynumber' %>
          <%= form.submit 'Submit this number', 'id' => 'submit-number' %>
      <% end %>
  <div>
<% end %>

After making all the changes, restart the rails server and refresh the browser, the default view will appear on the browser.

Now in the field enter any digit, after entering the digit click on submit button, and as the submit button is clicked we can see the Turbo Frame in action in the below screen, we can observe that the frame part changed, the first title and first link didn’t move.

Hotwire Rails: Turbo Streams

Turbo Streams deliver page updates over WebSocket, SSE or in response to form submissions by only using HTML and a series of CRUD-like operations, you are free to say that either

• Update the piece of HTML while responding to all the other actions like the post, put, patch, and delete except the GET action.
• Transmit a change to all users, without reloading the browser page.

This transmit can be represented by a simple example.

• Make changes in app/controllers/other_controller.rb file of rails application

#CODE

class OtherController < ApplicationController

  def post_something
    respond_to do |format|
      format.turbo_stream {  }
    end
  end

   end

Add the below line in routes.rb file of the application

#CODE

post '/other/post_something' => 'other#post_something', as: 'post_something'

Superb! Rails will now attempt to locate the app/views/other/post_something.turbo_stream.erb template at any moment the ‘/other/post_something’ endpoint is reached.

For this, we need to add app/views/other/post_something.turbo_stream.erb template in the rails application.

#CODE

<turbo-stream action="append" target="messages">
  <template>
    <div id="message_1">This changes the existing message!</div>
  </template>
</turbo-stream>

This states that the response will try to append the template of the turbo frame with ID “messages”.

Now change the index.html.erb file in app/views/other paths with the below content.

#CODE

<h1>This is Another page</h1>
<div><%= link_to "Enter to home page", root_path %></div>

<div style="margin-top: 3rem;">
  <%= form_with scope: :any, url: post_something_path do |form| %>
      <%= form.submit 'Post any message %>
  <% end %>
  <turbo-frame id="messages">
    <div>An empty message</div>
  </turbo-frame>
</div>

• After making all the changes, restart the rails server and refresh the browser, and go to the other page.

• Once the above screen appears, click on the Post any message button

This action shows that after submitting the response, the Turbo Streams help the developer to append the message, without reloading the page.

Another use case we can test is that rather than appending the message, the developer replaces the message. For that, we need to change the content of app/views/other/post_something.turbo_stream.erb template file and change the value of the action attribute from append to replace and check the changes in the browser.

#CODE

<turbo-stream action="replace" target="messages">
  <template>
    <div id="message_1">This changes the existing message!</div>
  </template>
</turbo-stream>

When we click on Post any message button, the message that appear below that button will get replaced with the message that is mentioned in the app/views/other/post_something.turbo_stream.erb template

Stimulus

There are some cases in an application where JS is needed, therefore to cover those scenarios we require Hotwire JS tool. Hotwire has a JS tool because in some scenarios Turbo-* tools are not sufficient. But as we know that Hotwire is used to reduce the usage of JS in an application, Stimulus considers HTML as the single source of truth. Consider the case where we have to give elements on a page some JavaScript attributes, such as data controller, data-action, and data target. For that, a stimulus controller that can access elements and receive events based on those characteristics will be created.

Make a change in app/views/other/index.html.erb template file in rails application

#CODE

<h1>This is Another page</h1>
<div><%= link_to "Enter to home page", root_path %></div>

<div style="margin-top: 2rem;">
  <%= form_with scope: :any, url: post_something_path do |form| %>
      <%= form.submit 'Post something' %>
  <% end %>
  <turbo-frame id="messages">
    <div>An empty message</div>
  </turbo-frame>
</div>

<div style="margin-top: 2rem;">
  <h2>Stimulus</h2>  
  <div data-controller="hello">
    <input data-hello-target="name" type="text">
    <button data-action="click->hello#greet">
      Greet
    </button>
    <span data-hello-target="output">
    </span>
  </div>
</div>

Make changes in the hello_controller.js in path app/JavaScript/controllers and add a stimulus controller in the file, which helps to bring the HTML into life.

#CODE

import { Controller } from "@hotwired/stimulus"

export default class extends Controller {
  static targets = [ "name", "output" ]

  greet() {
    this.outputTarget.textContent =
      `Hello, ${this.nameTarget.value}!`
  }
}

Go to your browser after making the changes in the code and click on Enter to other page link which will navigate to the localhost:3000/other/index page there you can see the changes implemented by the stimulus controller that is designed to augment your HTML with just enough behavior to make it more responsive.

With just a little bit of work, Turbo and Stimulus together offer a complete answer for applications that are quick and compelling.

Using Rails 7 Hotwire helps to load the pages at a faster speed and allows you to render templates on the server, where you have access to your whole domain model. It is a productive development experience in ROR, without compromising any of the speed or responsiveness associated with SPA.

Conclusion

We hope you were satisfied with our Rails Hotwire tutorial. Write to us at service@bacancy.com for any query that you want to resolve, or if you want us to share a tutorial on your query.

For more such solutions on RoR, check out our Ruby on Rails Tutorials. We will always strive to amaze you and cater to your needs.

Original article source at: https://www.bacancytechnology.com/

#rails #ruby 

Complete Tutorial on OpenCV in Python

In this article, let's learn together about the Complete Tutorial of OpenCV in Python. OpenCV is a Python library that allows you to perform image processing and computer vision tasks. It provides a wide range of features, including object detection, face recognition, and tracking. In this OpenCV Tutorial in Python, we’ll be learning more about the library.

What is OpenCV?

OpenCV is an open-source software library for computer vision and machine learning. The OpenCV full form is Open Source Computer Vision Library. It was created to provide a shared infrastructure for applications for computer vision and to speed up the use of machine perception in consumer products. OpenCV, as a BSD-licensed software, makes it simple for companies to use and change the code. There are some predefined packages and libraries that make our life simple and OpenCV is one of them.

Gary Bradsky invented OpenCV in 1999 and soon the first release came in 2000. This library is based on optimised C / C++ and supports Java and Python along with C++ through an interface. The library has more than 2500 optimised algorithms, including an extensive collection of computer vision and machine learning algorithms, both classic and state-of-the-art.Using OpenCV it becomes easy to do complex tasks such as identify and recognise faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D object models, generate 3D point clouds from stereo cameras, stitch images together to generate an entire scene with a high resolution image and many more.

Python is a user friendly language and easy to work with but this advantage comes with a cost of speed, as Python is slower to languages such as C or C++. So we extend Python with C/C++, which allows us to write computationally intensive code in C/C++ and create Python wrappers that can be used as Python modules. Doing this, the code is fast, as it is written in original C/C++ code (since it is the actual C++ code working in the background) and also, it is easier to code in Python than C/C++. OpenCV-Python is a Python wrapper for the original OpenCV C++ implementation.

Let’s get started!

What is Computer Vision?

The term Computer Vision (CV) is used and heard very often in artificial intelligence (AI) and deep learning (DL) applications. The term essentially means giving a computer the ability to see the world as we humans do.

Computer Vision is a field of study which enables computers to replicate the human visual system. As already mentioned above, It’s a subset of artificial intelligence which collects information from digital images or videos and processes them to define the attributes. The entire process involves image acquiring, screening, analysing, identifying and extracting information. This extensive processing helps computers to understand any visual content and act on it accordingly. 

Computer vision projects translate digital visual content into explicit descriptions to gather multi-dimensional data. This data is then turned into a computer-readable language to aid the decision-making process. The main objective of this branch of artificial intelligence is to teach machines to collect information from pixels. 

How does a computer read an image?

How does a human mind apprehend an image? When you see the image below, what do you actually see and how do you say what is in the Image?

You  most probably look for different shapes and colours in the Image and that might help you decide that this is an image of a dog. But does a computer also see it in the same way? The answer is no.
 

A digital image is an image composed of picture elements, also known as pixels, each with finite, discrete quantities of numeric representation for its intensity or grey level. So the computer sees an image as numerical values of these pixels and in order to recognise a certain image, it has to recognise the patterns and regularities in this numerical data.

Here is a hypothetical example of how pixels form an image. The darker pixels are represented by a number closer to the zero and lighter pixels are represented by numbers approaching one. All other colours are represented by the numbers between 0 and 1. 

But usually, you will find that for any colour image, there are 3 primary channels – Red, green and blue and the value of each channel varies from 0-255. In more simpler terms we can say that a digital image is actually formed by the combination of three basic colour channels  Red, green, and blue whereas for a grayscale image we have only one channel whose values also vary from 0-255.

OpenCV installation

There are many ways in which you can install OpenCV on your computer. Here are some:

Install using Anaconda

Anaconda is a conditional free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. You can download it from here and install it.
 

After successfully installing anaconda, just go to the anaconda prompt and use this command to install OpenCV:
 

conda install -c conda-forge opencv  

After this command is successfully executed, OpenCV will be available on your computer.Now let us see some other ways to install OpenCV

For Windows

You can use pip to install OpenCV on windows. Pip is a de facto standard package-management system used to install and manage software packages written in Python and it usually comes in installed when you install Python. If you do not have Python installed, I would suggest download it from here. Use this command in the command prompt to install OpenCV:

pip install opencv-python  

After installing it,do check if it is installed successfully.For that just go to the command prompt and type ‘python’ and hit enter.You should see some message like this:
 

If this is not the message you see, I suggest reinstalling python into your system. Next type import cv2 and if there is no error then it is installed successfully.

For Mac

You can use homebrew to install OpenCV as it makes it really easy and you just have to use this command for installing:
 

brew install opencv

Now that you have installed the OpenCV onto your system, let’s see how it works.

Read & Save Images

Now for OpenCV to work on any image, it must be able to read it. Here we will see how to read a file and save it after we are done with it. Let’s see how to do it:

Imread function in OpenCV

We use the imread function to read images. Here is the syntax of this function:

1	cv2.imread(path, flag)

The path parameter takes a string representing the path of the image to be read.The file should be in the working directory or we must give the full path to the image.The other parameter is the flag which is used to specify how our image should be read. Here are possible values that it takes and their working:

cv2.IMREAD_COLOR: It specifies to convert the image to the 3 channel BGR colour image. Any transparency of image will be neglected. It is the default flag. Alternatively, we can passinteger value 1 for this flag.cv2.IMREAD_GRAYSCALE: It specifies to convert an image to thesingle channel grayscale image. Alternatively, we can pass integer value 0 for this flag.cv2.IMREAD_UNCHANGED: It specifies to load an image as such including alpha channel.Alternatively, we can pass integer value -1 for this flag.

Usually the method imread() returns an image that is loaded from the specified file but in case the image cannot be read because of unsupported file format, missing file, unsupported or invalid format, it just returns a matrix. Here is a example in which we read an image from my storage.

#importing the opencv module  
import cv2  
# using imread('path') and 1 denotes read as  color image  
img = cv2.imread('dog.jpg',1)  
#This is using for display the image  
cv2.imshow('image',img)  
cv2.waitKey() # This is necessary to be required so that the image doesn't close immediately.  
#It will run continuously until the key press.  
cv2.destroyAllWindows() 

Imwrite function in OpenCV

We can use OpenCV’s imwrite() function to save an image in a storage device and the file extension defines the image format as shown in the example below. The syntax is the following:

1	cv2.imwrite(filename, image)

Parameters:

filename: A string representing the file name. The filename must include image format.

image: It is the image that is to be saved.

Here is an example in which we use this function:

import cv2  
# read image  
img = cv2.imread(r'C:\Users\Mirza\dog.jpeg', 1)  
# save image  
status = cv2.imwrite(r'C:\Users\Mirza\dog.jpeg',img)  
print("Image written sucess? : ", status)  

If the file is successfully written then this function returns True and thus it is important to store the outcome of this function.In the example above,we have done the same and used the ‘status’ variable to know if the file is written successfully.

Basic Operation On images

In this section,we are going to discuss some of the basic operations that we can do on the images once we have successfully read them.The operations we are going to do here ae:

• Access pixel values and modify them
• Access image properties
• Set a Region of Interest (ROI)
• Split and merge image channels

Access pixel values and modify them

So there are basically two ways to access a pixel value in an Image and modify them. First let us see how we can access a particular pixel value of an image.

import numpy as np
import cv2 as cv
img = cv.imread(r'C:\Users\Mirza\dog.jpeg')
px = img[100,100]
print( px )

Output:

[157 166 200]

Now as you can see we got a list containing 3 values.As we know OpenCV stores the color image as BGR color image,so the first value in the list is the value of the blue channel of this particular pixel, and the rest are values for green and red channels.

We can also access only one of the channels as shown below:

# accessing only blue pixel
blue = img[100,100,0]
print( blue )

Output:

157

To modify the values, we just need to access the pixel and then overwrite it with a value as shown below:

img[100,100] = [255,255,255]
print( img[100,100] )

Output:

[255 255 255]

This method to access and modify the pixel values is slow so you should make use of NumPy library as it is  optimized for fast array calculations. For accessing individual pixel values, the Numpy array methods, array.item() and array.itemset() are considered better as  they always return a scalar. However, if you want to access all the B,G,R values, you will need to call array.item() separately for each value as shown below:

# accessing RED value
img.item(10,10,2)
>>59
# modifying RED value
img.itemset((10,10,2),100)
img.item(10,10,2)
>>100

Access Image properties
 

What do we mean by image properties here? Often it is important to know the size(total number of pixels in the image), number of rows, columns, and channels.We can access the later three by using the shape() method as shown below:

print( img.shape )
>>(342, 548, 3)
print( img.size )
>>562248

So here we have three numbers in the returned tuple, these are number of rows, number of columns and number of channels respectively. Incase an image is grayscale, the tuple returned contains only the number of rows and columns.

Often a large number of errors in OpenCV-Python code are caused by invalid datatype so img.dtype which returns the image datatype is very important while debugging.

Here is an example:

print( img.dtype )
>>uint8

Image ROI(Region of interest)

Often you may come across some images where you are only interested in a specific region. Say you want to detect eyes in an image, will you search the entire image, possibly not as that may not fetch accurate results. But we know that eyes are a part of face, so it is better to detect a face first ,thus here the face is our ROI. You may want to have a look at the article Face detection using Viola-Jones algorithm where we detect the faces and then find eyes in the area we found faces.

Splitting and Merging Image Channels

We can also split the channels from an image and then work on each channel separately. Or sometimes you may need to merge them back together, here is how we do it:

But this method is painfully slow, so we can also use the Numpy to do the same, here is how:

b,g,r = cv.split(img)
img = cv.merge((b,g,r))
b = img[:,:,0]
g = img[:,:,1]
r = img[:,:,2]

Now suppose you want to just set all the values in the red channel to zero, here is how to do that:

#sets all values in red channel as zero
img[:,:,2] = 0

OpenCV Resize Image

Usually when working on images, we often need to resize the images according to certain requirements. Mostly you will do such operation in Machine learning and deep learning as it reduces the time of training of a neural network. As the number of pixels in an image increases, the more is the number of input nodes that in turn increases the complexity of the model. We use an inbuilt resize() method to resize an image.

Syntax:

1	cv2.resize(s, size,fx,fy,interpolation)

Parameters:

s – input image (required).

size – desired size for the output image after resizing (required)

fx – Scale factor along the horizontal axis.(optional)

fy – Scale factor along the vertical axis.

Interpolation(optional) – This flag uses following methods:

Interpolation(optional) – This flag uses following methods:
INTER_NEAREST – a nearest-neighbor interpolation
INTER_LINEAR – a bilinear interpolation (used by default) 
INTER_AREA – resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire’-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method.
INTER_CUBIC – a bicubic interpolation over 4×4 pixel neighborhood 
INTER_LANCZOS4 – a Lanczos interpolation over 8×8 pixel neighborhood

Here is an example of how we can use this method:

import cv2
import numpy as np

#importing the opencv module  
import cv2  
# using imread('path') and 1 denotes read as  color image  
img = cv2.imread('dog.jpg',1)  
print(img.shape)
img_resized=cv2.resize(img, (780, 540),  
               interpolation = cv2.INTER_NEAREST) 
cv2.imshow("Resized",img_resized)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output:

OpenCV Tutorial: A Guide to Learn OpenCV in Python

By Great Learning Team Updated on Oct 24, 2022 38979

 

OpenCV is a Python library that allows you to perform image processing and computer vision tasks. It provides a wide range of features, including object detection, face recognition, and tracking. In this OpenCV Tutorial in Python, we’ll be learning more about the library.

What is OpenCV?

OpenCV is an open-source software library for computer vision and machine learning. The OpenCV full form is Open Source Computer Vision Library. It was created to provide a shared infrastructure for applications for computer vision and to speed up the use of machine perception in consumer products. OpenCV, as a BSD-licensed software, makes it simple for companies to use and change the code. There are some predefined packages and libraries that make our life simple and OpenCV is one of them.

Gary Bradsky invented OpenCV in 1999 and soon the first release came in 2000. This library is based on optimised C / C++ and supports Java and Python along with C++ through an interface. The library has more than 2500 optimised algorithms, including an extensive collection of computer vision and machine learning algorithms, both classic and state-of-the-art.Using OpenCV it becomes easy to do complex tasks such as identify and recognise faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D object models, generate 3D point clouds from stereo cameras, stitch images together to generate an entire scene with a high resolution image and many more.

Python is a user friendly language and easy to work with but this advantage comes with a cost of speed, as Python is slower to languages such as C or C++. So we extend Python with C/C++, which allows us to write computationally intensive code in C/C++ and create Python wrappers that can be used as Python modules. Doing this, the code is fast, as it is written in original C/C++ code (since it is the actual C++ code working in the background) and also, it is easier to code in Python than C/C++. OpenCV-Python is a Python wrapper for the original OpenCV C++ implementation.

Let’s get started!

What is Computer Vision?

The term Computer Vision (CV) is used and heard very often in artificial intelligence (AI) and deep learning (DL) applications. The term essentially means giving a computer the ability to see the world as we humans do.

Computer Vision is a field of study which enables computers to replicate the human visual system. As already mentioned above, It’s a subset of artificial intelligence which collects information from digital images or videos and processes them to define the attributes. The entire process involves image acquiring, screening, analysing, identifying and extracting information. This extensive processing helps computers to understand any visual content and act on it accordingly. 

Computer vision projects translate digital visual content into explicit descriptions to gather multi-dimensional data. This data is then turned into a computer-readable language to aid the decision-making process. The main objective of this branch of artificial intelligence is to teach machines to collect information from pixels. 

How does a computer read an image?

How does a human mind apprehend an image? When you see the image below, what do you actually see and how do you say what is in the Image?

You  most probably look for different shapes and colours in the Image and that might help you decide that this is an image of a dog. But does a computer also see it in the same way? The answer is no.
 

A digital image is an image composed of picture elements, also known as pixels, each with finite, discrete quantities of numeric representation for its intensity or grey level. So the computer sees an image as numerical values of these pixels and in order to recognise a certain image, it has to recognise the patterns and regularities in this numerical data.

Here is a hypothetical example of how pixels form an image. The darker pixels are represented by a number closer to the zero and lighter pixels are represented by numbers approaching one. All other colours are represented by the numbers between 0 and 1. 

But usually, you will find that for any colour image, there are 3 primary channels – Red, green and blue and the value of each channel varies from 0-255. In more simpler terms we can say that a digital image is actually formed by the combination of three basic colour channels  Red, green, and blue whereas for a grayscale image we have only one channel whose values also vary from 0-255.

OpenCV installation

There are many ways in which you can install OpenCV on your computer. Here are some:

Install using Anaconda

Anaconda is a conditional free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. You can download it from here and install it.
 

After successfully installing anaconda, just go to the anaconda prompt and use this command to install OpenCV:
 

conda install -c conda-forge opencv  

After this command is successfully executed, OpenCV will be available on your computer.Now let us see some other ways to install OpenCV

For Windows

You can use pip to install OpenCV on windows. Pip is a de facto standard package-management system used to install and manage software packages written in Python and it usually comes in installed when you install Python. If you do not have Python installed, I would suggest download it from here. Use this command in the command prompt to install OpenCV:

pip install opencv-python  

After installing it,do check if it is installed successfully.For that just go to the command prompt and type ‘python’ and hit enter.You should see some message like this:
 

If this is not the message you see, I suggest reinstalling python into your system. Next type import cv2 and if there is no error then it is installed successfully.

For Mac

You can use homebrew to install OpenCV as it makes it really easy and you just have to use this command for installing:
 

brew install opencv

Now that you have installed the OpenCV onto your system, let’s see how it works.

Read & Save Images

Now for OpenCV to work on any image, it must be able to read it. Here we will see how to read a file and save it after we are done with it. Let’s see how to do it:

Imread function in OpenCV

We use the imread function to read images. Here is the syntax of this function:

1	cv2.imread(path, flag)

The path parameter takes a string representing the path of the image to be read.The file should be in the working directory or we must give the full path to the image.The other parameter is the flag which is used to specify how our image should be read. Here are possible values that it takes and their working:

cv2.IMREAD_COLOR: It specifies to convert the image to the 3 channel BGR colour image. Any transparency of image will be neglected. It is the default flag. Alternatively, we can passinteger value 1 for this flag.cv2.IMREAD_GRAYSCALE: It specifies to convert an image to thesingle channel grayscale image. Alternatively, we can pass integer value 0 for this flag.cv2.IMREAD_UNCHANGED: It specifies to load an image as such including alpha channel.Alternatively, we can pass integer value -1 for this flag.

Usually the method imread() returns an image that is loaded from the specified file but in case the image cannot be read because of unsupported file format, missing file, unsupported or invalid format, it just returns a matrix. Here is a example in which we read an image from my storage.

#importing the opencv module  
import cv2  
# using imread('path') and 1 denotes read as  color image  
img = cv2.imread('dog.jpg',1)  
#This is using for display the image  
cv2.imshow('image',img)  
cv2.waitKey() # This is necessary to be required so that the image doesn't close immediately.  
#It will run continuously until the key press.  
cv2.destroyAllWindows() 

Imwrite function in OpenCV

We can use OpenCV’s imwrite() function to save an image in a storage device and the file extension defines the image format as shown in the example below. The syntax is the following:

1	cv2.imwrite(filename, image)

Parameters:

filename: A string representing the file name. The filename must include image format.

image: It is the image that is to be saved.

Here is an example in which we use this function:

import cv2  
# read image  
img = cv2.imread(r'C:\Users\Mirza\dog.jpeg', 1)  
# save image  
status = cv2.imwrite(r'C:\Users\Mirza\dog.jpeg',img)  
print("Image written sucess? : ", status)  

If the file is successfully written then this function returns True and thus it is important to store the outcome of this function.In the example above,we have done the same and used the ‘status’ variable to know if the file is written successfully.

Basic Operation On images

In this section,we are going to discuss some of the basic operations that we can do on the images once we have successfully read them.The operations we are going to do here ae:

• Access pixel values and modify them
• Access image properties
• Set a Region of Interest (ROI)
• Split and merge image channels

Access pixel values and modify them

So there are basically two ways to access a pixel value in an Image and modify them. First let us see how we can access a particular pixel value of an image.

import numpy as np
import cv2 as cv
img = cv.imread(r'C:\Users\Mirza\dog.jpeg')
px = img[100,100]
print( px )

Output:

[157 166 200]

Now as you can see we got a list containing 3 values.As we know OpenCV stores the color image as BGR color image,so the first value in the list is the value of the blue channel of this particular pixel, and the rest are values for green and red channels.

We can also access only one of the channels as shown below:

# accessing only blue pixel
blue = img[100,100,0]
print( blue )

Output:

157

To modify the values, we just need to access the pixel and then overwrite it with a value as shown below:

img[100,100] = [255,255,255]
print( img[100,100] )

Output:

[255 255 255]

This method to access and modify the pixel values is slow so you should make use of NumPy library as it is  optimized for fast array calculations. For accessing individual pixel values, the Numpy array methods, array.item() and array.itemset() are considered better as  they always return a scalar. However, if you want to access all the B,G,R values, you will need to call array.item() separately for each value as shown below:

# accessing RED value
img.item(10,10,2)
>>59
# modifying RED value
img.itemset((10,10,2),100)
img.item(10,10,2)
>>100

Access Image properties
 

What do we mean by image properties here? Often it is important to know the size(total number of pixels in the image), number of rows, columns, and channels.We can access the later three by using the shape() method as shown below:

print( img.shape )
>>(342, 548, 3)
print( img.size )
>>562248

So here we have three numbers in the returned tuple, these are number of rows, number of columns and number of channels respectively. Incase an image is grayscale, the tuple returned contains only the number of rows and columns.

Often a large number of errors in OpenCV-Python code are caused by invalid datatype so img.dtype which returns the image datatype is very important while debugging.

Here is an example:

print( img.dtype )
>>uint8

Image ROI(Region of interest)

Often you may come across some images where you are only interested in a specific region. Say you want to detect eyes in an image, will you search the entire image, possibly not as that may not fetch accurate results. But we know that eyes are a part of face, so it is better to detect a face first ,thus here the face is our ROI. You may want to have a look at the article Face detection using Viola-Jones algorithm where we detect the faces and then find eyes in the area we found faces.

Splitting and Merging Image Channels

We can also split the channels from an image and then work on each channel separately. Or sometimes you may need to merge them back together, here is how we do it:

But this method is painfully slow, so we can also use the Numpy to do the same, here is how:

b,g,r = cv.split(img)
img = cv.merge((b,g,r))
b = img[:,:,0]
g = img[:,:,1]
r = img[:,:,2]

Now suppose you want to just set all the values in the red channel to zero, here is how to do that:

#sets all values in red channel as zero
img[:,:,2] = 0

OpenCV Resize Image

Usually when working on images, we often need to resize the images according to certain requirements. Mostly you will do such operation in Machine learning and deep learning as it reduces the time of training of a neural network. As the number of pixels in an image increases, the more is the number of input nodes that in turn increases the complexity of the model. We use an inbuilt resize() method to resize an image.

Syntax:

1	cv2.resize(s, size,fx,fy,interpolation)

Parameters:

s – input image (required).

size – desired size for the output image after resizing (required)

fx – Scale factor along the horizontal axis.(optional)

fy – Scale factor along the vertical axis.

Interpolation(optional) – This flag uses following methods:

Interpolation(optional) – This flag uses following methods:
INTER_NEAREST – a nearest-neighbor interpolation
INTER_LINEAR – a bilinear interpolation (used by default) 
INTER_AREA – resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire’-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method.
INTER_CUBIC – a bicubic interpolation over 4×4 pixel neighborhood 
INTER_LANCZOS4 – a Lanczos interpolation over 8×8 pixel neighborhood

Here is an example of how we can use this method:

import cv2
import numpy as np

#importing the opencv module  
import cv2  
# using imread('path') and 1 denotes read as  color image  
img = cv2.imread('dog.jpg',1)  
print(img.shape)
img_resized=cv2.resize(img, (780, 540),  
               interpolation = cv2.INTER_NEAREST) 
cv2.imshow("Resized",img_resized)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output:

original

resized

OpenCV Image Rotation

We may need to rotate an image in some of the cases and we can do it easily by using OpenCV .We use cv2.rotate() method to rotate a 2D array in multiples of 90 degrees. Here is the syntax:

1	cv2.rotate( src, rotateCode[, dst] )

Parameters:
src: It is the image to be rotated.
rotateCode: It is an enum to specify how to rotate the array.Here are some of the possible values :
cv2.cv2.ROTATE_90_CLOCKWISE
cv2.ROTATE_180
cv2.ROTATE_90_COUNTERCLOCKWISE

Here is an example using this function.

import cv2
import numpy as np

#importing the opencv module  
import cv2  
# using imread('path') and 1 denotes read as  color image  
img = cv2.imread('dog.jpg',1)  
print(img.shape)
image = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE) 
cv2.imshow("Rotated",image)
cv2.waitKey()
cv2.destroyAllWindows()

Output:

rotated image

Now what if we want to rotate the image by a certain angle.We can use another method for that.First calculate the affine matrix that does the affine transformation (linear mapping of pixels) by using the getRotationMatrix2D method,next we warp the input image with the affine matrix using warpAffine method.

Here is the syntax of these functions:

1 2	cv2.getRotationMatrix2D(center, angle, scale) cv2.warpAffine(Img, M, (W, H))

center: center of the image (the point about which rotation has to happen)
angle: angle by which image has to be rotated in the anti-clockwise direction.
scale: scales the image by the value provided,1.0 means the shape is preserved.
H:height of image
W: width of the image.
M: affine matrix returned by cv2.getRotationMatrix2D
Img: image to be rotated.

Here is an example in which we rotate an image by various angles.

import cv2
import numpy as np

#importing the opencv module  
import cv2  
# using imread('path') and 1 denotes read as  color image  
img = cv2.imread('dog.jpg',1)  
# get image height, width
(h, w) = img.shape[:2]
# calculate the center of the image
center = (w / 2, h / 2)
 
scale = 1.0
 
# Perform the counter clockwise rotation holding at the center
# 45 degrees
M = cv2.getRotationMatrix2D(center, 45, scale)
print(M)
rotated45 = cv2.warpAffine(img, M, (h, w))
 
# 110 degrees
M = cv2.getRotationMatrix2D(center,110, scale)
rotated110 = cv2.warpAffine(img, M, (w, h))
 
# 150 degrees
M = cv2.getRotationMatrix2D(center, 150, scale)
rotated150 = cv2.warpAffine(img, M, (h, w))
 
 
cv2.imshow('Original Image',img)
cv2.waitKey(0) # waits until a key is pressed
cv2.destroyAllWindows() # destroys the window showing image
 
cv2.imshow('Image rotated by 45 degrees',rotated45)
cv2.waitKey(0) # waits until a key is pressed
cv2.destroyAllWindows() # destroys the window showing image
 
cv2.imshow('Image rotated by 110 degrees',rotated110)
cv2.waitKey(0) # waits until a key is pressed
cv2.destroyAllWindows() # destroys the window showing image
 
cv2.imshow('Image rotated by 150 degrees',rotated150)
cv2.waitKey(0) # waits until a key is pressed
cv2.destroyAllWindows() # destroys the window showing image

Output

OpenCV Tutorial: A Guide to Learn OpenCV in Python

By Great Learning Team Updated on Oct 24, 2022 38979

 

OpenCV is a Python library that allows you to perform image processing and computer vision tasks. It provides a wide range of features, including object detection, face recognition, and tracking. In this OpenCV Tutorial in Python, we’ll be learning more about the library.

What is OpenCV?

OpenCV is an open-source software library for computer vision and machine learning. The OpenCV full form is Open Source Computer Vision Library. It was created to provide a shared infrastructure for applications for computer vision and to speed up the use of machine perception in consumer products. OpenCV, as a BSD-licensed software, makes it simple for companies to use and change the code. There are some predefined packages and libraries that make our life simple and OpenCV is one of them.

Gary Bradsky invented OpenCV in 1999 and soon the first release came in 2000. This library is based on optimised C / C++ and supports Java and Python along with C++ through an interface. The library has more than 2500 optimised algorithms, including an extensive collection of computer vision and machine learning algorithms, both classic and state-of-the-art.Using OpenCV it becomes easy to do complex tasks such as identify and recognise faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D object models, generate 3D point clouds from stereo cameras, stitch images together to generate an entire scene with a high resolution image and many more.

Python is a user friendly language and easy to work with but this advantage comes with a cost of speed, as Python is slower to languages such as C or C++. So we extend Python with C/C++, which allows us to write computationally intensive code in C/C++ and create Python wrappers that can be used as Python modules. Doing this, the code is fast, as it is written in original C/C++ code (since it is the actual C++ code working in the background) and also, it is easier to code in Python than C/C++. OpenCV-Python is a Python wrapper for the original OpenCV C++ implementation.

Let’s get started!

What is Computer Vision?

The term Computer Vision (CV) is used and heard very often in artificial intelligence (AI) and deep learning (DL) applications. The term essentially means giving a computer the ability to see the world as we humans do.

Computer Vision is a field of study which enables computers to replicate the human visual system. As already mentioned above, It’s a subset of artificial intelligence which collects information from digital images or videos and processes them to define the attributes. The entire process involves image acquiring, screening, analysing, identifying and extracting information. This extensive processing helps computers to understand any visual content and act on it accordingly. 

Computer vision projects translate digital visual content into explicit descriptions to gather multi-dimensional data. This data is then turned into a computer-readable language to aid the decision-making process. The main objective of this branch of artificial intelligence is to teach machines to collect information from pixels. 

How does a computer read an image?

How does a human mind apprehend an image? When you see the image below, what do you actually see and how do you say what is in the Image?

You  most probably look for different shapes and colours in the Image and that might help you decide that this is an image of a dog. But does a computer also see it in the same way? The answer is no.
 

A digital image is an image composed of picture elements, also known as pixels, each with finite, discrete quantities of numeric representation for its intensity or grey level. So the computer sees an image as numerical values of these pixels and in order to recognise a certain image, it has to recognise the patterns and regularities in this numerical data.

Here is a hypothetical example of how pixels form an image. The darker pixels are represented by a number closer to the zero and lighter pixels are represented by numbers approaching one. All other colours are represented by the numbers between 0 and 1. 

But usually, you will find that for any colour image, there are 3 primary channels – Red, green and blue and the value of each channel varies from 0-255. In more simpler terms we can say that a digital image is actually formed by the combination of three basic colour channels  Red, green, and blue whereas for a grayscale image we have only one channel whose values also vary from 0-255.

OpenCV installation

There are many ways in which you can install OpenCV on your computer. Here are some:

Install using Anaconda

Anaconda is a conditional free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. You can download it from here and install it.
 

After successfully installing anaconda, just go to the anaconda prompt and use this command to install OpenCV:
 

conda install -c conda-forge opencv  

After this command is successfully executed, OpenCV will be available on your computer.Now let us see some other ways to install OpenCV

For Windows

You can use pip to install OpenCV on windows. Pip is a de facto standard package-management system used to install and manage software packages written in Python and it usually comes in installed when you install Python. If you do not have Python installed, I would suggest download it from here. Use this command in the command prompt to install OpenCV:

pip install opencv-python  

After installing it,do check if it is installed successfully.For that just go to the command prompt and type ‘python’ and hit enter.You should see some message like this:
 

If this is not the message you see, I suggest reinstalling python into your system. Next type import cv2 and if there is no error then it is installed successfully.

For Mac

You can use homebrew to install OpenCV as it makes it really easy and you just have to use this command for installing:
 

brew install opencv

Now that you have installed the OpenCV onto your system, let’s see how it works.

Read & Save Images

Now for OpenCV to work on any image, it must be able to read it. Here we will see how to read a file and save it after we are done with it. Let’s see how to do it:

Imread function in OpenCV

We use the imread function to read images. Here is the syntax of this function:

1	cv2.imread(path, flag)

The path parameter takes a string representing the path of the image to be read.The file should be in the working directory or we must give the full path to the image.The other parameter is the flag which is used to specify how our image should be read. Here are possible values that it takes and their working:

cv2.IMREAD_COLOR: It specifies to convert the image to the 3 channel BGR colour image. Any transparency of image will be neglected. It is the default flag. Alternatively, we can passinteger value 1 for this flag.cv2.IMREAD_GRAYSCALE: It specifies to convert an image to thesingle channel grayscale image. Alternatively, we can pass integer value 0 for this flag.cv2.IMREAD_UNCHANGED: It specifies to load an image as such including alpha channel.Alternatively, we can pass integer value -1 for this flag.

Usually the method imread() returns an image that is loaded from the specified file but in case the image cannot be read because of unsupported file format, missing file, unsupported or invalid format, it just returns a matrix. Here is a example in which we read an image from my storage.

#importing the opencv module  
import cv2  
# using imread('path') and 1 denotes read as  color image  
img = cv2.imread('dog.jpg',1)  
#This is using for display the image  
cv2.imshow('image',img)  
cv2.waitKey() # This is necessary to be required so that the image doesn't close immediately.  
#It will run continuously until the key press.  
cv2.destroyAllWindows() 

Imwrite function in OpenCV

We can use OpenCV’s imwrite() function to save an image in a storage device and the file extension defines the image format as shown in the example below. The syntax is the following:

1	cv2.imwrite(filename, image)

Parameters:

filename: A string representing the file name. The filename must include image format.

image: It is the image that is to be saved.

Here is an example in which we use this function:

import cv2  
# read image  
img = cv2.imread(r'C:\Users\Mirza\dog.jpeg', 1)  
# save image  
status = cv2.imwrite(r'C:\Users\Mirza\dog.jpeg',img)  
print("Image written sucess? : ", status)  

If the file is successfully written then this function returns True and thus it is important to store the outcome of this function.In the example above,we have done the same and used the ‘status’ variable to know if the file is written successfully.

Basic Operation On images

In this section,we are going to discuss some of the basic operations that we can do on the images once we have successfully read them.The operations we are going to do here ae:

• Access pixel values and modify them
• Access image properties
• Set a Region of Interest (ROI)
• Split and merge image channels

Access pixel values and modify them

So there are basically two ways to access a pixel value in an Image and modify them. First let us see how we can access a particular pixel value of an image.

import numpy as np
import cv2 as cv
img = cv.imread(r'C:\Users\Mirza\dog.jpeg')
px = img[100,100]
print( px )

Output:

[157 166 200]

Now as you can see we got a list containing 3 values.As we know OpenCV stores the color image as BGR color image,so the first value in the list is the value of the blue channel of this particular pixel, and the rest are values for green and red channels.

We can also access only one of the channels as shown below:

# accessing only blue pixel
blue = img[100,100,0]
print( blue )

Output:

157

To modify the values, we just need to access the pixel and then overwrite it with a value as shown below:

img[100,100] = [255,255,255]
print( img[100,100] )

Output:

[255 255 255]

This method to access and modify the pixel values is slow so you should make use of NumPy library as it is  optimized for fast array calculations. For accessing individual pixel values, the Numpy array methods, array.item() and array.itemset() are considered better as  they always return a scalar. However, if you want to access all the B,G,R values, you will need to call array.item() separately for each value as shown below:

# accessing RED value
img.item(10,10,2)
>>59
# modifying RED value
img.itemset((10,10,2),100)
img.item(10,10,2)
>>100

Access Image properties
 

What do we mean by image properties here? Often it is important to know the size(total number of pixels in the image), number of rows, columns, and channels.We can access the later three by using the shape() method as shown below:

print( img.shape )
>>(342, 548, 3)
print( img.size )
>>562248

So here we have three numbers in the returned tuple, these are number of rows, number of columns and number of channels respectively. Incase an image is grayscale, the tuple returned contains only the number of rows and columns.

Often a large number of errors in OpenCV-Python code are caused by invalid datatype so img.dtype which returns the image datatype is very important while debugging.

Here is an example:

print( img.dtype )
>>uint8

Image ROI(Region of interest)

Often you may come across some images where you are only interested in a specific region. Say you want to detect eyes in an image, will you search the entire image, possibly not as that may not fetch accurate results. But we know that eyes are a part of face, so it is better to detect a face first ,thus here the face is our ROI. You may want to have a look at the article Face detection using Viola-Jones algorithm where we detect the faces and then find eyes in the area we found faces.

Splitting and Merging Image Channels

We can also split the channels from an image and then work on each channel separately. Or sometimes you may need to merge them back together, here is how we do it:

But this method is painfully slow, so we can also use the Numpy to do the same, here is how:

b,g,r = cv.split(img)
img = cv.merge((b,g,r))
b = img[:,:,0]
g = img[:,:,1]
r = img[:,:,2]

Now suppose you want to just set all the values in the red channel to zero, here is how to do that:

#sets all values in red channel as zero
img[:,:,2] = 0

OpenCV Resize Image

Usually when working on images, we often need to resize the images according to certain requirements. Mostly you will do such operation in Machine learning and deep learning as it reduces the time of training of a neural network. As the number of pixels in an image increases, the more is the number of input nodes that in turn increases the complexity of the model. We use an inbuilt resize() method to resize an image.

Syntax:

1	cv2.resize(s, size,fx,fy,interpolation)

Parameters:

s – input image (required).

size – desired size for the output image after resizing (required)

fx – Scale factor along the horizontal axis.(optional)

fy – Scale factor along the vertical axis.

Interpolation(optional) – This flag uses following methods:

Interpolation(optional) – This flag uses following methods:
INTER_NEAREST – a nearest-neighbor interpolation
INTER_LINEAR – a bilinear interpolation (used by default) 
INTER_AREA – resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire’-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method.
INTER_CUBIC – a bicubic interpolation over 4×4 pixel neighborhood 
INTER_LANCZOS4 – a Lanczos interpolation over 8×8 pixel neighborhood

Here is an example of how we can use this method:

import cv2
import numpy as np

#importing the opencv module  
import cv2  
# using imread('path') and 1 denotes read as  color image  
img = cv2.imread('dog.jpg',1)  
print(img.shape)
img_resized=cv2.resize(img, (780, 540),  
               interpolation = cv2.INTER_NEAREST) 
cv2.imshow("Resized",img_resized)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output:

originalresized

OpenCV Image Rotation

We may need to rotate an image in some of the cases and we can do it easily by using OpenCV .We use cv2.rotate() method to rotate a 2D array in multiples of 90 degrees. Here is the syntax:

1	cv2.rotate( src, rotateCode[, dst] )

Parameters:
src: It is the image to be rotated.
rotateCode: It is an enum to specify how to rotate the array.Here are some of the possible values :
cv2.cv2.ROTATE_90_CLOCKWISE
cv2.ROTATE_180
cv2.ROTATE_90_COUNTERCLOCKWISE

Here is an example using this function.

import cv2
import numpy as np

#importing the opencv module  
import cv2  
# using imread('path') and 1 denotes read as  color image  
img = cv2.imread('dog.jpg',1)  
print(img.shape)
image = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE) 
cv2.imshow("Rotated",image)
cv2.waitKey()
cv2.destroyAllWindows()

Output:

rotated image

Now what if we want to rotate the image by a certain angle.We can use another method for that.First calculate the affine matrix that does the affine transformation (linear mapping of pixels) by using the getRotationMatrix2D method,next we warp the input image with the affine matrix using warpAffine method.

Here is the syntax of these functions:

1 2	cv2.getRotationMatrix2D(center, angle, scale) cv2.warpAffine(Img, M, (W, H))

center: center of the image (the point about which rotation has to happen)
angle: angle by which image has to be rotated in the anti-clockwise direction.
scale: scales the image by the value provided,1.0 means the shape is preserved.
H:height of image
W: width of the image.
M: affine matrix returned by cv2.getRotationMatrix2D
Img: image to be rotated.

Here is an example in which we rotate an image by various angles.

import cv2
import numpy as np

#importing the opencv module  
import cv2  
# using imread('path') and 1 denotes read as  color image  
img = cv2.imread('dog.jpg',1)  
# get image height, width
(h, w) = img.shape[:2]
# calculate the center of the image
center = (w / 2, h / 2)
 
scale = 1.0
 
# Perform the counter clockwise rotation holding at the center
# 45 degrees
M = cv2.getRotationMatrix2D(center, 45, scale)
print(M)
rotated45 = cv2.warpAffine(img, M, (h, w))
 
# 110 degrees
M = cv2.getRotationMatrix2D(center,110, scale)
rotated110 = cv2.warpAffine(img, M, (w, h))
 
# 150 degrees
M = cv2.getRotationMatrix2D(center, 150, scale)
rotated150 = cv2.warpAffine(img, M, (h, w))
 
 
cv2.imshow('Original Image',img)
cv2.waitKey(0) # waits until a key is pressed
cv2.destroyAllWindows() # destroys the window showing image
 
cv2.imshow('Image rotated by 45 degrees',rotated45)
cv2.waitKey(0) # waits until a key is pressed
cv2.destroyAllWindows() # destroys the window showing image
 
cv2.imshow('Image rotated by 110 degrees',rotated110)
cv2.waitKey(0) # waits until a key is pressed
cv2.destroyAllWindows() # destroys the window showing image
 
cv2.imshow('Image rotated by 150 degrees',rotated150)
cv2.waitKey(0) # waits until a key is pressed
cv2.destroyAllWindows() # destroys the window showing image

Output

rotated images

OpenCV Drawing Functions
 

We may require to draw certain shapes on an image such as circle, rectangle, ellipse, polylines, convex, etc. and we can easily do this using OpenCV. It is often used when we want to highlight any object in the input image for example in case of face detection, we might want to highlight the face with a rectangle. Here we will learn about the drawing functions such as circle, rectangle, lines, polylines and also see how to write text on an image.

Drawing circle:

We use the method to circle to draw a circle in an image. Here is the syntax and parameters:

1	cv2.circle(image, center_coordinates, radius, color, thickness)

Parameters: 
image: It is the input image on which a circle is to be drawn. 
center_coordinates: It is the center coordinates of the circle. The coordinates are represented as tuples of two values i.e. (X coordinate value, Y coordinate value). 
radius: It is the radius of the circle. 
color: It is the color of the border line of the circle to be drawn. We can pass a tuple For in BGR,  eg: (255, 0, 0) for blue color. 
thickness: It is the thickness of the circle border line in px. Thickness of -1 px will fill the circle shape by the specified color.
Return Value: It returns an image.

Here are the few of the examples:

import numpy as np  
import cv2  
img = cv2.imread(r'C:\Users\Mirza\dog.jpeg', 1)  
cv2.circle(img,(80,80), 55, (255,0,0), -1)  
cv2.imshow('image',img)  
cv2.waitKey(0)  
cv2.destroyAllWindows() 

Drawing Rectangle

In a similar we can draw a rectangle. Here is the the syntax for this function:

1	cv2.rectangle(image, start_point, end_point, color, thickness)

Parameters:

image: It is the input image on which rectangle is to be drawn.
start_point: It is the starting coordinates(top left vertex) of the rectangle. The coordinates are represented as tuples of two values i.e. (X coordinate value, Y coordinate value).
end_point: It is the ending coordinates(bottom right) of the rectangle. The coordinates are represented as tuples of two values i.e. (X coordinate value, Y coordinate value).
color: It is the color of the border line of the rectangle to be drawn. We can pass a tuple For in BGR,  eg: (255, 0, 0) for blue color. 
thickness: It is the thickness of the rectangle border line in px. Thickness of -1 px will fill the rectangle shape by the specified color.

Return Value: It returns an image.

Here is an example of this function:

import numpy as np  
import cv2  
img = cv2.imread(r'C:\Users\Mirza\dog.jpeg', 1)  
cv2.rectangle(img,(15,25),(200,150),(0,255,255),15)  
cv2.imshow('image',img)  
cv2.waitKey(0)  
cv2.destroyAllWindows()  

Drawing Lines

Here is the syntax of the line method using which we can make lines on an image.

1	cv2.line(image, start_point, end_point, color, thickness)

Parameters:
image: It is the input image on which line is to be drawn.
start_point: It is the starting coordinates of the line. The coordinates are represented as tuples of two values i.e. (X coordinate value, Y coordinate value).
end_point: It is the ending coordinates of the line. The coordinates are represented as tuples of two values i.e. (X coordinate value, Y coordinate value).
color: It is the color of the line to be drawn. We can pass a tuple For in BGR,  eg: (255, 0, 0) for blue color. 
thickness: It is the thickness of the line in px.

Return Value: It returns an image.

Here is an example:

import numpy as np  
import cv2  
img = cv2.imread(r'C:\Users\Mirza\dog.jpeg', 1)  
#defining points for polylines  
pts = np.array([[100,50],[200,300],[700,200],[500,100]], np.int32)  
# pts = pts.reshape((-1,1,2))  
cv2.polylines(img, [pts], True, (0,255,255), 3)  
cv2.imshow('image',img)  
cv2.waitKey(0)  
cv2.destroyAllWindows() 

Drawing Polylines

We can draw the polylines using the polylines() method on the image. And these can be used to draw polygonal curves on the image. The syntax is given below:

1	cv2.polyLine(image, arr, is_closed, color, thickness)

Parameters:

img – It represents an image.
arr -represents the coordinates of vertices into an array of shape nx1x2 where n is number of vertices and it should be of type int32.
is_Closed – It is a flag that indicates whether the drawn polylines are closed or not.
color – Color of polylines. We can pass a tuple For in BGR,  eg: (255, 0, 0) for blue color. 
thickness – It represents the Thickness of the polyline’s edges.

Here is an example:

import numpy as np  
import cv2  
img = cv2.imread(r'C:\Users\Mirza\dog.jpeg', 1)  
#defining points for polylines  
pts = np.array([[100,50],[200,300],[700,200],[500,100]], np.int32)  
# pts = pts.reshape((-1,1,2))  
cv2.polylines(img, [pts], True, (0,255,255), 3)  
cv2.imshow('image',img)  
cv2.waitKey(0)  
cv2.destroyAllWindows() 

Write text on an image

We can write text on the image by using the putText() method. The syntax is given below.

1	cv2.putText(img, text, org, font,fontScale color)

Parameters:
img: It represents the input image on which we have to write text
text: The text which we want to write on the image.
org: It denotes the Bottom-left corner of the text string on the image. So it is used to set the location of text on the image
font: the font of text. Here is the list of supported fonts.
fontScale: The scale of the font by which you can increase or decrease size
color: Represents the color. We can pass a tuple For in BGR,  eg: (255, 0, 0) for blue color. 

Here is an example:

import numpy as np  
import cv2  
font = cv2.FONT_HERSHEY_SIMPLEX  
mg = cv2.imread(r'C:\Users\Mirza\dog.jpeg', 1)  
cv2.putText(img,'Dog',(10,500), font, 1,(255,255,255),2)  
#Display the image  
cv2.imshow("image",img)  
cv2.waitKey(0) 

OpenCV Blob Detection

Blob stands for Binary Large Object where the term “Large” focuses on the object of a specific size, and that other “small” binary objects are usually considered as noise.

In simpler terms, a Blob is a group of connected pixels which we can find in an image and all of these pixels have some common property. In the image below, the coloured connected regions are blobs, and the goal of blob detection is to identify and mark these regions( marked by red circle).

Using OpenCV’s SimpleBlobDetector method, we can  easily find blobs in our images.But how does this method work?Let us see this in detail:
 

1. Thresholding :First the algorithm converts the source images to several binary images by applying thresholding with various thresholds.We define two threshold values,viz- minThreshold (inclusive) to maxThreshold (exclusive)  and start from threshold value equal to minThreshold.Then it is incremented by thresholdStep until we reach maxThreshold,so the first threshold is minThreshold, the second is minThreshold + thresholdStep and so on.
2. Grouping : In each binary image, we have a curve joining all the continuous points (along the boundary), having the same color or intensity.
3. Merging  : The centers of the binary blobs in the binary images are computed, and  blobs located closer than minDistBetweenBlobs(minimum distance between two blobs) are merged.
4. Center & Radius Calculation :  The centers and radii of the new merged blobs are computed and returned.

This class can perform several filtrations of returned blobs by setting filterBy* to True to turn on corresponding filtration. Available filtrations are as following:

• By color. We define a parameter blobColor to filter the blobs of colours we are interested in. Set blobColor equal to zero to extract dark blobs and to extract light blobs,set it to 255. This filter compares the intensity of a binary image at the center of a blob to blobColor and filters accordingly.
• By area. By using this filter the extracted blobs have an area between minArea (inclusive) and maxArea (exclusive).
• By circularity. By using this filter the extracted blobs have circularity between minCircularity (inclusive) and maxCircularity (exclusive).
• By ratio of the minimum inertia to maximum inertia.By using this filter the extracted blobs have this ratio between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive).
• By convexity.By using this filter the extracted blobs have convexity (area / area of blob convex hull) between minConvexity (inclusive) and maxConvexity (exclusive).

By default, the values of  these parameters are tuned to extract dark circular blobs.

Here is an example of how to use simple SimpleBlobDetector()

import cv2  
import numpy as np;  
  
img = cv2.imread(r"pic1.jpeg", cv2.IMREAD_GRAYSCALE)  
# Set up the detector with default parameters.  
detector = cv2.SimpleBlobDetector()  
  
# Detecting blobs.  
keypoints = detector.detect(img)  
# Draw detected blobs as red circles.  
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob  
im_with_keypoints = cv2.drawKeypoints(img, keypoints, np.array([]), (0, 0, 255),  
                                      cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)  
# Show keypoints  
cv2.imshow("Keypoints", im_with_keypoints)  
cv2.waitKey(0)  

Now here is an example in which we use the filters mentioned above:

import cv2
import numpy as np;

# Read image
im = cv2.imread("blob.jpg")

# Setup SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()

# Change thresholds
params.minThreshold = 10
params.maxThreshold = 200


# Filter by Area.
params.filterByArea = True
params.minArea = 1500

# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0.1

# Filter by Convexity
params.filterByConvexity = True
params.minConvexity = 0.87

# Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = 0.01

# Create a detector with the parameters
detector = cv2.SimpleBlobDetector(params)


# Detect blobs.
keypoints = detector.detect(im)

# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures
# the size of the circle corresponds to the size of blob

im_with_keypoints = cv2.drawKeypoints(im, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

# Show blobs
cv2.imshow("Keypoints", im_with_keypoints)
cv2.waitKey(0)

Canny Edge Detection

Edge detection is an image processing technique used for finding the boundaries of objects within images. Here we will use a popular edge detection algorithm Canny Edge Detection, developed by John F. Canny. In OpenCV, we have Canny() method to implement this algorithm. Here is the syntax:

1	edges = cv2.Canny(img, minVal, maxVal, apertureSize, L2gradient)

Parameters –

img: input image whose edges we want to detect.
minVal: Minimum intensity gradient (required)
maxVal: Maximum intensity gradient (required)
L2gradient: is a flag with default value =False, indicating the default L1 norm is enough to calculate the image gradient magnitude, if its is set as True a more accurate L2 norm is used to calculate the image gradient magnitude but it is computationally more expensive.
aperture: aperture size for the Sobel operator.

As we can see we have two threshold values, minVal and maxVal. Any edges with intensity gradient more than maxVal are sure to be edges.also those edges with intensity gradient less than minVal are sure to be non-edges and are discarded. The edges which lie between these two thresholds are classified edges or non-edges based on their connectivity with the ‘sure edges’. If they are connected to “sure-edge” pixels, they are considered to be part of edges. Otherwise, they are also discarded as non-edges.

Here is an example:

import cv2
img = cv2.imread('dog.jpg')
edges = cv2.Canny(img,200,300,True)
cv2.imshow("Edge Detected Image", edges)  
cv2.imshow("Original Image", img)  
cv2.waitKey(0)  # waits until a key is pressed  
cv2.destroyAllWindows()  # destroys the window showing image

Now we can also do this in real-time, here is how:

# import libraries of python OpenCV    
import cv2  
  
# import Numpy by alias name np  
import numpy as np  
  
# capture frames from a camera   
cap = cv2.VideoCapture(0)  
  
# loop runs if capturing has been initialized   
while (1):  
  
    # reads frames from a camera   
    ret, frame = cap.read()  
  
    # Display an original image   
    cv2.imshow('Original', frame)  
  
    # discovers edges in the input image image and   
    # marks them in the output map edges   
    edges = cv2.Canny(frame, 100, 200,True)  
  
    # Display edges in a frame   
    cv2.imshow('Edges', edges)  
  
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
  
# Close the window   
cap.release()  
  
# De-allocate any associated memory usage   
cv2.destroyAllWindows()  

OpenCV Image Smoothing

Image smoothing is an image processing technique used for removing the noise in an image.Blurring(smoothing) removes low-intensity edges and is also beneficial in hiding the details; for example, blurring is required in many cases, such as hiding any confidential information in an image.OpenCV provides mainly the following type of blurring techniques.

Here are a few of the methods that we are going to use for smoothing an image:

• OpenCV averaging
• OpenCV median Blur
• OpenCV Gaussian Blur
• OpenCV Bilateral Filter

OpenCV averaging

In this technique, we normalize the image with a box filter. It calculates the average of all the pixels which are under the kernel area(box filter) and replaces the value of the pixel at the center of the box filter with the calculated average. OpenCV provides the cv2.blur() to perform this operation. The syntax of cv2.blur() function is as follows.

1	cv2.blur(src, ksize,anchor, borderType)

Parameters:

src: It is the image which is to be blurred.
ksize: A tuple representing the blurring kernel size.
anchor: It is a variable of type integer representing anchor point and it’s default value Point is (-1, -1) which means that the anchor is at the kernel center.
borderType: It represents the type of border to be used for the output.

Here is an example:

import cv2
img = cv2.imread('dog.jpg')
cv2.imshow('Original Image',img)  
cv2.imshow('cv2.blur output', cv2.blur(img, (3,3)))  
cv2.waitKey(0)  
cv2.destroyAllWindows()  

OpenCV median Blur 

In this technique, the median of all the pixels under the kernel window is computed and the central pixel is replaced with this median value. It has one advantage over the Gaussian and box filters, that being the filtered value for the central element is always replaced by some pixel value in the image which is not the case in case of either Gaussian or box filters. OpenCV provides a function medianBlur() that can be used to easily implement this kind of smoothing. Here is the syntax:

1	cv2.medianBlur(src, dst, ksize)

Parameters:

src- It represents the source (input image).
dst – It represents the destination (output image).
ksize – It represents the size of the kernel.

Consider the following example:

import cv2
img = cv2.imread('dog.jpg')
cv2.imshow('Original Image',img)  
cv2.imshow('cv2.medianBlur output', cv2.medianBlur(img,5))  
cv2.waitKey(0)  
cv2.destroyAllWindows()  

OpenCV Gaussian Blur

In this technique, a Gaussian function(kernel) instead of a box filter to blur the image. The width and height of the kernel needs to be specified and they should be positive and odd. We also have to specify the standard deviation in the directions X and Y and are represented by sigmaX and sigmaY respectively. If both sigmaX and sigmaY are given as zeros, they are calculated from the kernel size and if we only specify sigmaX, sigmaY is set to the same value. Gaussian blurring is highly effective when removing Gaussian noise from an image. In OpenCV we have a function GaussianBlur() to implement this technique easily. Here is the syntax:

1	GaussianBlur(src, dst, ksize, sigmaX,sigmaY)

Parameters:

src − Input image which is to be blurred
dst − output image of the same size and type as src.
ksize − A Size object representing the size of the kernel.
sigmaX − A variable of the type double representing the Gaussian kernel standard deviation in X direction.
sigmaY − A variable of the type double representing the Gaussian kernel standard deviation in Y direction.

Here is an example:

import cv2
img = cv2.imread('dog.jpg')
cv2.imshow('Original Image',img)  
cv2.imshow('cv2.GaussianBlur output', cv2.GaussianBlur(img, (5, 5), cv2.BORDER_DEFAULT))     
cv2.waitKey(0)  
cv2.destroyAllWindows() 

OpenCV Bilateral Filter

This method of noise removal is highly effective but is slower compared to other filters. The Gaussian filter blurred the edges too and that is not what we want, but this filter makes sure that only those pixels with similar intensities to the central pixel are considered for blurring, thus preserving the edges since pixels at edges will have large intensity variation. In OpenCV we have cv.bilateralFilter() method that can implement this filter. Here is the syntax:

1	cv2.bilateralFilter(src, dst, d, sigmaColor,sigmaSpace, borderType)

Parameters:
src Source 8-bit or floating-point, 1-channel or 3-channel image.
dst Destination image of the same size and type as src .
d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace.
sigmaColor Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting in larger areas of semi-equal color.
sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is proportional to sigmaSpace.
borderType border mode used to extrapolate pixels outside of the image, see the BorderTypes available here.

Here is an example:

import cv2
img = cv2.imread('dog.jpg')
cv2.imshow('Original Image',img)  
cv2.imshow('bilateral Image', cv2.bilateralFilter(img,9,75,75))  
cv2.waitKey(0)  
cv2.destroyAllWindows() 

OpenCV Image Threshold

Thresholding is a popular segmentation technique, used for separating an object considered as a foreground from its background.In this technique we assign pixel values in relation to the threshold value provided.This technique of thresholding is done on grayscale images,so initially, the image has to be converted in grayscale color space.Here we will discuss two different approaches taken when performing thresholding on an image:

• Simple Thresholding
• Adaptive Thresholding

Simple Thresholding:

In this basic Thresholding technique, for every pixel, the same threshold value is applied. If the pixel value is smaller than the threshold, it is set to a certain value(usually zero) , otherwise, it is set to another value(usually maximum value) .There are various variations of this technique as shown below.
 

In OpenCV, we use cv2.threshold function to implement it. Here is the syntax:

1	cv2.threshold(source, thresholdValue, maxVal, thresholdingTechnique)

Parameters:

-> source: Input Image array (must be in Grayscale).
-> thresholdValue: Value of Threshold below and above which pixel values will change accordingly.
-> maxVal: Maximum value that can be assigned to a pixel.
-> thresholdingTechnique: The type of thresholding to be applied.Here are various types of thresholding we can use

cv2.THRESH_BINARY: If  the pixel intensity is greater than the threshold, the pixel value is set to 255(white), else it is set to 0 (black).
cv2.THRESH_BINARY_INV: Inverted or Opposite case of cv2.THRESH_BINARY.If  the pixel intensity is greater than the threshold, the pixel value is set to 0(black), else it is set to 255 (white).
cv.THRESH_TRUNC: If  the pixel intensity is greater than the threshold,the pixel values are set to be the same as the threshold. All other values remain the same.
cv.THRESH_TOZERO: Pixel intensity is set to 0, for all the pixels intensity, less than the threshold value.All other pixel values remain same
cv.THRESH_TOZERO_INV: Inverted or Opposite case of cv2.THRESH_TOZERO.

Here is an example:

import cv2  
import numpy as np  
  
# path to input image is specified and   
# image is loaded with imread command  
image = cv2.imread('gl.png')  
  

# to convert the image in grayscale  
img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
  
threshold=160
ret, thresh1 = cv2.threshold(img, threshold, 255, cv2.THRESH_BINARY) 
ret, thresh2 = cv2.threshold(img, threshold, 255, cv2.THRESH_BINARY_INV) 
ret, thresh3 = cv2.threshold(img, threshold, 255, cv2.THRESH_TRUNC) 
ret, thresh4 = cv2.threshold(img, threshold, 255, cv2.THRESH_TOZERO) 
ret, thresh5 = cv2.threshold(img, threshold, 255, cv2.THRESH_TOZERO_INV) 
  
# the window showing output images 
# with the corresponding thresholding  
# techniques applied to the input images 
cv2.imshow('Original',image)
cv2.imshow('Binary Threshold', thresh1) 
cv2.imshow('Binary Threshold Inverted', thresh2) 
cv2.imshow('Truncated Threshold', thresh3) 
cv2.imshow('Zero Threshold', thresh4) 
cv2.imshow('Zero Inverted', thresh5) 
    
# De-allocate any associated memory usage   
cv2.waitKey(0)
cv2.destroyAllWindows() 

Adaptive Thresholding:

In simple thresholding, the threshold value was global which means it was same for all the pixels in the image. But this may not be the best approach for thresholding as the different image sections can have different lightings. Thus we need Adaptive thresholding, which is the method where the threshold value is calculated for smaller regions and therefore, there will be different threshold values for different regions. In OpenCV we have adaptiveThreshold() function to implement this type of thresholding. Here is the syntax of this function:

1	adaptiveThreshold(src, dst, maxValue, adaptiveMethod, thresholdType, blockSize, C)

This method accepts the following parameters −

src − An object of the class Mat representing the source (input) image.
dst − An object of the class Mat representing the destination (output) image.
maxValue − A variable of double type representing the value that is to be given if pixel value is more than the threshold value.
adaptiveMethod − A variable of integer the type representing the adaptive method to be used. This will be either of the following two values:
cv.ADAPTIVE_THRESH_MEAN_C: The threshold value is the mean of the neighbourhood area minus the constant C.
cv.ADAPTIVE_THRESH_GAUSSIAN_C: The threshold value is a gaussian-weighted sum of the neighbourhood values minus the constant C.

thresholdType − A variable of integer type representing the type of threshold to be used.
blockSize − A variable of the integer type representing size of the pixelneighborhood used to calculate the threshold value.
C − A variable of double type representing the constant used in the both methods (subtracted from the mean or weighted mean).

Here is an example:

import cv2  
import numpy as np  
  
# path to input image is specified and   
# image is loaded with imread command  
image = cv2.imread('lamp.jpg')  
  

# to convert the image in grayscale  
img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

ret, th1 = cv2.threshold(img,160, 255, cv2.THRESH_BINARY) 
  
th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,
            cv2.THRESH_BINARY,11,2)
th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
            cv2.THRESH_BINARY,11,2)

cv2.imshow('Original',image)
cv2.imshow('Binary Threshold', th1) 
cv2.imshow('Adaptive Threshold', th2) 
cv2.imshow('Gaussain Adaptive Threshold', th3) 
    
# De-allocate any associated memory usage   
cv2.waitKey(0)
cv2.destroyAllWindows() 

OpenCV Contours

So what are contours? A Contour is a curve joining all the continuous points having the same color or intensity (along the boundary).So the counters are useful especially when we want to find out a shape of some object or incase of object detection and recognition. Finding contours is like finding white object from black background, so remember, the object to be found should be white and background should be black. Thus, for better accuracy, we should use binary images and before finding contours, apply thresholding as we discussed in the last section.
In OpenCV, we use two functions, one to find contours and other to draw contours. We use findContours() function to find contours and drawCounter() to draw one. Here is the syntax of these functions.

1	image, contours, hierarchy=cv.findContours(img, mode, method,contours, hierarchy,offset)

This function returns three objects:

Img:The input image in which we have to find contours
Contours: It contains detected contours and  contour is stored as a vector of points
Hierarchy:Optional output vector, containing information about the image topology. It has as many elements as the number of contours. For each i-th contour contours[i], the elements hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices in contours of the next and previous contours at the same hierarchical level, the first child contour and the parent contour, respectively. If for the contour i there are no next, previous, parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
Parameters of this function:

mode: Contour retrieval mode, see RetrievalModes
method:Contour approximation method, see ContourApproximationModes
offset : Optional offset by which every contour point is shifted. This is useful if the contours are extracted from the image ROI and then they should be analyzed in the whole image context.

Here is the syntax of drawCounter():

1	cv.drawContours(image, contours, contourIdx, color, thickness, lineType, hierarchy, maxLevel, offset)

Parameters

Image: Input image.
contours: All the input contours. Each contour is stored as a point vector.
contourIdx: Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color: Color of the contours.
thickness: Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType: Line connectivity. See LineTypes
hierarchy: Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel: Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset: Optional contour shift parameter. Shift all the drawn contours by the specified offset=(dx,dy).

Here is an example using these functions:

import cv2 
import numpy as np 
  
# Let's load a simple image with 3 black squares 
image = cv2.imread('contor.png',1) 
# Find Canny edges 
edged = cv2.Canny(image, 30, 200) 
cv2.waitKey(0) 
  
# Finding Contours 
# Use a copy of the image e.g. edged.copy() 
# since findContours alters the image 
contours, hierarchy = cv2.findContours(edged,  
    cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) 
cv2.imshow('Original', image) 
cv2.imshow('Canny Edges After Contouring', edged)  
  
cv2.drawContours(image, contours, -1, (0, 255, 0), 3) 
  
cv2.imshow('Contours', image) 
cv2.waitKey(0) 
cv2.destroyAllWindows() 

OpenCV Mouse Event

OpenCV also provides the functionality to control and manage different types of mouse events and gives us the flexibility to manage them. As we know there can be different types of mouse events such as double_click, left button click, right button click, etc. For managing these events, we need to design callback functions for each of these mouse click events while the window or frame is opened by OpenCV.The callback function gives us flexibility to implement what type of functionality you want with a particular mouse click event.

Mouse as a Paint Brush

Using OpenCV, we have an option to use the mouse as a paint brush or a drawing tool. Whenever any mouse event occurs on the window screen, it gives us the coordinates (x,y) for that particular mouse event. Now that we have the coordinates of the point we are interested in, we can draw anything we desire, be it a circle or a rectangle or a simple line. First let us see the available mouse events, to get the list of all these events, run the code below:

import cv2  
mouse_events = [j for j in dir(cv2) if 'EVENT' in j]  
print(mouse_events)  

Output:

[‘EVENT_FLAG_ALTKEY’, ‘EVENT_FLAG_CTRLKEY’, ‘EVENT_FLAG_LBUTTON’, ‘EVENT_FLAG_MBUTTON’, ‘EVENT_FLAG_RBUTTON’, ‘EVENT_FLAG_SHIFTKEY’, ‘EVENT_LBUTTONDBLCLK’, ‘EVENT_LBUTTONDOWN’, ‘EVENT_LBUTTONUP’, ‘EVENT_MBUTTONDBLCLK’, ‘EVENT_MBUTTONDOWN’, ‘EVENT_MBUTTONUP’, ‘EVENT_MOUSEHWHEEL’, ‘EVENT_MOUSEMOVE’, ‘EVENT_MOUSEWHEEL’, ‘EVENT_RBUTTONDBLCLK’, ‘EVENT_RBUTTONDOWN’, ‘EVENT_RBUTTONUP’]

Draw Circle

To draw anything on the window screen, we first need to create a mouse callback function by using the cv2.setMouseCallback() function. It has a specific format that remains the same everywhere. Our mouse callback function is facilitated by drawing a circle using double-click. Here is the code:

import cv2  
import numpy as np  
# Creating mouse callback function  
def draw_circle(event,x,y,flags,param):  
    if(event == cv2.EVENT_LBUTTONDBLCLK):  
            cv2.circle(img,(x,y),50,(123,125, 200),-1)  
# Creating a black image, a window and bind the function to window  
img = np.zeros((512,512,3), np.uint8)  
cv2.namedWindow('image')  
cv2.setMouseCallback('image',draw_circle)  
while(1):  
    cv2.imshow('image',img)  
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
  
cv2.destroyAllWindows()  

OpenCV Template Matching

Template Matching is a method used for finding the location of a template image in a larger image. In OpenCV, we use a function cv.matchTemplate() for template matching. It simply slides the template image over the larger input image (as in 2D convolution) and compares the template image with the patch of input image under the template image. It returns a grayscale image, where each pixel denotes how much does the neighbourhood of that pixel match with the template. There are several comparison methods that can be implemented in OpenCV. 

If input image is of size (WxH) and template image is of size (wxh), output image will have a size of (W-w+1, H-h+1).Upon getting results, the best matches can be found as global minimums (when TM_SQDIFF was used) or maximums (when TM_CCORR or TM_CCOEFF was used) using the minMaxLoc function. Take it as the top-left corner of the rectangle and take (w,h) as width and height of the rectangle. That rectangle is your region of template.

Here is the syntax of  cv.matchTemplate():

1	cv.matchTemplate(image, templ, method,mask)

Parameters:

image: Image where the search is running. It must be 8-bit or 32-bit floating-point.
templ: Searched template. It must be not greater than the source image and have the same data type.
result Map of comparison results. It must be single-channel 32-bit floating-point. If image is W×H and templ is w×h , then result is (W−w+1)×(H−h+1) .
method: Parameter specifying the comparison method, see TemplateMatchModes
mask: Optional

Here is an example in which we take this image as the template image:

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
img = cv.imread('headphone1.jpeg',0)
img2 = img.copy()
template = cv.imread('logo1.jpeg',0)
w, h = template.shape[::-1]
# All the 6 methods for comparison in a list
# Apply template Matching
res = cv.matchTemplate(img,template,eval('cv.TM_CCOEFF'))
min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)
# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
top_left = max_loc
bottom_right = (top_left[0] + w, top_left[1] + h)
cv.rectangle(img,top_left, bottom_right, 255, 2)
plt.subplot(121),plt.imshow(res,cmap = 'gray')
plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(img,cmap = 'gray')
plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
plt.show()

Template Matching with Multiple Objects

In the above example, we searched for template images that occurred only once in the image. Suppose a particular object occurs multiple times in a particular image. In this scenario, we will use the thresholding as cv2.minMaxLoc() just gives the location of one template image and it won’t give all locations of the template images. Consider the following example.

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
img2=cv.imread('headohone2.jpeg',1)
img_gray = cv.imread('headohone2.jpeg',0)
template = cv.imread('logo1.jpeg',0)
w, h = template.shape[::-1]
res = cv.matchTemplate(img_gray,template,eval('cv.TM_CCOEFF_NORMED'))
print(res)
threshold = 0.52
loc = np.where( res >= threshold)
for pt in zip(*loc[::-1]):
    cv.rectangle(img2, pt, (pt[0] + w, pt[1] + h), (255,0,0), 1)
plt.subplot(121),plt.imshow(res,cmap = 'gray')
plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(img2,cmap = 'gray')
plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
plt.show()

OpenCV Video Capture

OpenCV can also be used for video processing. With OpenCV, we can capture a video from the camera and it also lets us create a video capture object which is helpful to capture videos through webcam and then you may perform desired operations on that video. Besides this you can also play and perform operation on a video file and save them.

Capture Video from Camera

Often, we have to capture a live stream with a camera. Using OpenCV’s very simple interface, we can easily do it. Here is a simple task to get started. In this task we will capture a video from the camera ( in-built webcam of my laptop) and display it as a grayscale video.

In OpenCV we need to create a VideoCapture object to capture a video. We pass either the device index or the name of a video file as its arguments. Device index is just the number to specify the camera in case we have multiple webcams available. Normally one has only a single camera connected (as in my case), so simply pass 0.After this we start to capture each frame using a loop and process it accordingly. At the end, we just break from the loop and release the capture.

import numpy as np
import cv2

capture = cv2.VideoCapture(0)

while(True):
    # Capture frame-by-frame
    ret, frame = cap.read()

    # Our operations on the frame come here
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Display the resulting frame
    cv2.imshow('frame',gray)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything done, release the capture
capture.release()
cv2.destroyAllWindows()

capture.read() returns a bool (True/False) and the frame which webcam is currently reading. If the frame is read correctly, it will be True. So you can check the end of the video by checking this return value.

Playing Video from file

Playing a video using OpenCV is very similar to capturing live feed from a webcam as we saw in the last section.We just have to change the camera index with the video file name. But sometimes the video file may be corrupt or couldn’t be read properly,so we use isOpened() method of VideoCapture object to make sure that the video is read successfully. Also, while displaying the frame, we should use appropriate time for cv2.waitKey(),as for too less, video will be very fast and for too high values, video will be slow.

import numpy as np
import cv2

cap = cv2.VideoCapture('vtest.avi')

while(cap.isOpened()):
    ret, frame = cap.read()

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    cv2.imshow('frame',gray)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Saving a Video

Saving an image after processing it using OpenCV is quite simple and we saw how to do it using cv2.imwrite() function. But for a video, after processing it frame-by-frame a little more work is required to save it.

Here to save a video we create a VideoWriter object in addition to VideoCapture Object. The syntax of VideoWriter is given below:

1	cv2.VideoWriter(filename,fourcc,fps,frameSize,isColor)

Parameters:

filename: The output file name (eg: bday.avi).
fourcc: specify the FourCC code. FourCC is a 4-byte code used to specify the video codec. The list of available codes can be found in fourcc.org. It is platform dependent. Following codecs works fine for me.

In Fedora: DIVX, XVID, MJPG, X264, WMV1, WMV2. (XVID is more preferable. MJPG results in high size video. X264 gives very small size video)
In Windows: DIVX (More to be tested and added)
In OSX : (I don’t have access to OSX. Can some one fill this?)
FourCC code is passed as cv2.VideoWriter_fourcc(‘M’,’J’,’P’,’G’) or cv2.VideoWriter_fourcc(*’MJPG) for MJPG.

fps: number of frames per second (fps) 
frameSize: size of frame. 
isColor: It is a flag value. If it is True, encoders expect a color frame, otherwise it works with grayscale frames.

Here is a code that captures frames from a Camera, flip each one in a vertical direction and save it.

import numpy as np
import cv2

cap = cv2.VideoCapture(0)

# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi',fourcc, 20.0, (640,480))

while(cap.isOpened()):
    ret, frame = cap.read()
    if ret==True:
        frame = cv2.flip(frame,0)

        # write the flipped frame
        out.write(frame)

        cv2.imshow('frame',frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    else:
        break

# Release everything if job is finished
cap.release()
out.release()
cv2.destroyAllWindows()

Face Detection Using OpenCV

Using OpenCV, complex tasks such as face detection becomes easy to implement and since pre-trained models that are capable of detecting faces, noses, and eyes are included in the OpenCV package, we don’t need to train any classifier. Here is an article on Face detection using Viola-Jones algorithm that explains how we can detect faces using OpenCV.You will also go through the concept of cascading classifier in this article that is also used in our next section i.e. car detection using OpenCV
 

https://youtu.be/6myMFJl8CbA

Car detection with OpenCV

You may have seen in the article Face detection using Face Recognition Using Python and OpenCV Viola-Jones algorithm that we used pre-trained models to detect faces and eyes. Similarly, we also have a pre-trained model that can detect cars. All we have to do is to use this model to erect cars and then mark the cars with rectangles.

# OpenCV Python program to detect cars in video frame 
# import libraries of python OpenCV  
import cv2 
  
# capture frames from a video 
cap = cv2.VideoCapture('video.avi') 
  
# Trained XML classifiers describes some features of some object we want to detect 
car_cascade = cv2.CascadeClassifier('cars.xml') 
  
# loop runs if capturing has been initialized. 
while True: 
    # reads frames from a video 
    ret, frames = cap.read() 
      
    # convert to gray scale of each frames 
    gray = cv2.cvtColor(frames, cv2.COLOR_BGR2GRAY) 
      
  
    # Detects cars of different sizes in the input image 
    cars = car_cascade.detectMultiScale(gray, 1.1, 1) 
      
    # To draw a rectangle in each cars 
    for (x,y,w,h) in cars: 
        cv2.rectangle(frames,(x,y),(x+w,y+h),(0,0,255),2) 
  
   # Display frames in a window  
   cv2.imshow('video2', frames) 
      
    # Wait for Esc key to stop 
    if cv2.waitKey(33) == 27: 
        break
  
# De-allocate any associated memory usage 
cv2.destroyAllWindows() 

Face Recognition Using OpenCV

Face recognition, as the names suggest includes detecting faces and then label them with the name of the person. It is a bit more complex than the last two examples. Here we have to use some libraries outside of OpenCV. I would highly recommend going through the article Face Recognition Using Python and OpenCV to understand face recognition works and how to implement it using OpenCV.

FAQs of OpenCV in Python

Q: What is the use of OpenCV in Python? 

A: OpenCV-Python uses Numpy, which is considered an effectively optimized library for numerical operations with a MATLAB-style syntax. Each of the OpenCV array structures is changed into as well as from Numpy arrays. This also helps in incorporating other libraries that use Numpy, which include SciPy and Matplotlib.

Q: How do I install OpenCV 3.0 and Python 3.4+ on Ubuntu?

A: To install OpenCV 3.0 and Python 3.4+ on Ubuntu, you need to follow the steps mentioned below:

• Start with installing prerequisites
• Now Setup Python (Part 1) 
• Setup Python (Part 2) 
• Now you need to build and install OpenCV 3.0 with Python 3.4+ bindings
• Sym-link OpenCV 3.0
• The last step includes testing out the OpenCV 3.0 and Python 3.4+ install.

Q: How do I start learning OpenCV? 

A: To start learning OpenCV, you can refer to the tutorials offered by Great Learning. You will not only learn the basics and also get a good idea of the overall OpenCV.

Q: What is the full form of OpenCV? 

A: The full form for OpenCV is Open Source Computer Vision Library.

Q: Why is OpenCV used? 

A: OpenCV is a vast open-source library that is used for machine learning, computer vision, and image processing. At present, it plays a key role in real-time. Using OpenCV helps in processing images as well as videos to classify faces, objects, or even handwriting of humans.

Q: Is OpenCV easy? 

A: Earlier OpenCV was not one of the easiest things to learn. However, these days it has been simplified. You can go through the easy-to-learn tutorials to understand OpenCV.

Q: Is OpenCV better in Python or C++? 

A: It totally depends on the stage of a project. If you are prototyping, Python is more useful. If it is for the purpose of production, C++ is better. However, you also need to know that Python is slower than C++.

Q: Is OpenCV worth learning? 

A: Learning OpenCV is certainly worth it, and you should start learning OpenCV with Python. This programming language is easier to learn and faster to prototype the Computer Vision algorithms.

Q: What should I learn before OpenCV?

A: It is a library; therefore you first need to know how to use a library. The next thing is learning the fundamentals of image processing. You also need to have in-depth knowledge of classes and inheritance in C++.
Q: How do I know if Opencv is installed?

A: First fire up your Python and follow the commands mentioned below:

• Importing cv2 # import the opencv library, and
• cv2. __version__ #. This will help in printing the version of your opencv3.

This brings us to the end of this article on OpenCV Tutorial where we learned about OpenCV. We hope that you found this helpful and are now better equipped in OpenCV.

---

Original article source at: https://www.mygreatlearning.com

#python #opencv 

Comment Exporter Une Fonction De C++ Vers React Native

Aujourd'hui, je continue à partager mon expérience avec le module natif et C++.

Comme nous verrons beaucoup de bibliothèques C/C++ écrire pour les plates-formes mobiles, nous devons les implémenter dans notre application iOS ou React Native. C'est pourquoi je souhaite écrire un article sur la façon d'exporter une fonction de C++ vers React Native, ce qui est facile à comprendre et fait gagner du temps aux débutants. Je vais commencer avec une nouvelle application native réactive

1. Créez une nouvelle application native React, ouvrez votre terminal et exécutez

npx react-native init NativeModules

2. Ouvrez Xcode et accédez à NativeModules/ios/NativeModule.xcworkspace

3. Travailler du côté C++

Créez un nouveau fichier C++ et nommez-leCpp_to_RN.cpp

Lorsque nous créons un nouveau fichier C++, Xcode créera un fichier d'en-tête Cpp_to_RN.hpp pour nous

Tout d'abord, ouvrez le fichier " Cpp_to_RN.hpp" et créez une classe qui inclut une fonction sans le corps.

#ifndef Cpp_to_RN_hpp
#define Cpp_to_RN_hpp#include <stdio.h>
#include <string>class Cpp_to_RN {
public:
    std::string sayHello();
};#endif /* Cpp_to_RN_hpp */

Ouvrez ensuite le Cpp_to_RN.cppfichier et écrivez une fonction simple " sayHello()"

#include "Cpp_to_RN.hpp"
std::string Cpp_to_RN::sayHello(){
    return "Hello from CPP";
}

4. Travail sur l'encapsulation du fichier C++.

Pour envelopper les fichiers C++ et les exporter vers le côté IOS (swift)

un. Créez un fichier Objective C et nommez-leCpp_to_RN.m

Renommez le Cpp_to_RN.m en Cpp_to_RN.mm

b. Ouvrez le WrapCpp_to_RN.mm fichier et écrivez le contenu du corps qui encapsulera la fonction sayHelloà partir du fichier C++.

#import <Foundation/Foundation.h>
#import "WrapCpp_to_RN.h"
#import "Cpp_to_RN.hpp"@implementation WrapCpp_to_RN- (NSString *) sayHello {
  Cpp_to_RN fromCPP;
    std::string helloWorldMessage = fromCPP.sayHello();
    return [NSString
            stringWithCString:helloWorldMessage.c_str()
            encoding:NSUTF8StringEncoding];
}
@end

c. Créez un fichier d'en-tête et nommez-leWrapCpp_to_RN.h

Exporter la wrapSayHellofonction vers le fichier Swift

#import <Foundation/Foundation.h>
@interface WrapCpp_to_RN : NSObject
- (NSString *) wrapSayHello;
@end

5. Travailler du côté iOS (Swift)

Pour exporter la fonction C++ vers React Native

un. Créez un fichier Swift et nommez-leSendCpp_to_RN.swift

Remarque : Xcode nous demandera de créer un NativeModules-Bridging-Header.hfichier pour nous.

Créez une classe SendCpp_to_RNet déclarez-la commeNSObject

#import <Foundation/Foundation.h>
@interface WrapCpp_to_RN : NSObject
- (NSString *) wrapSayHello;
@end

Écrire une fonction requiresMainQueueSetup()pour empêcher l'avertissement lorsque nous exécutons l'application

#import <Foundation/Foundation.h>
@interface WrapCpp_to_RN : NSObject
- (NSString *) wrapSayHello;
@end

Ecrire une fonction pour envelopper le WrapCpp_to_RN()fromWrapCpp_to_RN.mm

import Foundation@objc(SendCpp_to_RN)
class SendCpp_to_RN : NSObject {
    
  @objc static func requiresMainQueueSetup() -> Bool {
        return false
    }
  
  @objc func fromCpp(_ successCallback: RCTResponseSenderBlock) -> Void {
    successCallback([NSNull(), WrapCpp_to_RN().wrapSayHello() as Any])
    }}

b. Exporter une fonction wrap dans un fichier Swift vers React Native

Créez un fichier Objective C pour exporter la classe Swift et sa fonction à l'aide deCallback

#import <React/RCTBridgeModule.h>
#import <Foundation/Foundation.h>
#import "UIKit/UIKit.h"
@interface RCT_EXTERN_MODULE(SendCpp_to_RN, NSObject)RCT_EXTERN_METHOD(fromCpp:(RCTResponseSenderBlock)successCallback)@end

c. Connectez Swift à React Native, ouvrez le NativeModules-Bridging-Header.h fichier

#import <React/RCTBridgeModule.h>#import <React/RCTViewManager.h>#import "WrapCpp_to_RN.h"

6. Travailler du côté React Native

Appelez la classe Swift et ses fonctions

import React from 'react';
import {StyleSheet, Text, View, NativeModules, Button} from 'react-native';const App = () => {
  const onPress = () => {
    const {SendCpp_to_RN} = NativeModules;
    SendCpp_to_RN.fromCpp((_err, res) => console.log(res));
  };
  return (
    <View style={styles.container}>
      <Text> Practice !</Text>
      <Button title="C++ to React Native" color="#841584" onPress={onPress} />
    </View>
  );
};
const styles = StyleSheet.create({
  container: {
    flex: 1,
    justifyContent: 'center',
    alignItems: 'center',
  },
});
export default App;

Et nous avons terminé, il suffit de lancer l'application

react-native run-ios

Ou cliquez simplement sur le bouton "exécuter" sur Xcode et voyez ce que nous avons fait.

J'espère que mon article vous sera utile, merci pour le temps de lecture.

 Source : https://betterprogramming.pub/native-modules-export-c-function-to-react-native-for-beginners-77e89934b210

#cpp #cplusplus #react 

如何将函数从 C++ 导出到 React Native

今天，我继续分享我在 Native Module 和 C++ 方面的经验。

由于我们将看到很多为移动平台编写的 C/C++ 库，因此我们需要将它们实现到我们的 iOS 或 React Native 应用程序中。这就是为什么我想写一篇关于如何将一个函数从 C++ 导出到 React Native 的文章，它易于理解并且为初学者节省了时间。我将从一个新的 react native 应用程序开始

1.新建一个react native app，打开你的终端运行

npx react-native init NativeModules

2. 打开 Xcode 并导航到 NativeModules/ios/NativeModule.xcworkspace

3. 在 C++ 端工作

创建一个新的 C++ 文件并命名Cpp_to_RN.cpp

当我们创建一个新的 C++ 文件时，Xcode 会Cpp_to_RN.hpp 为我们创建一个头文件

首先，打开“ Cpp_to_RN.hpp”文件，并创建一个包含没有主体的函数的类。

#ifndef Cpp_to_RN_hpp
#define Cpp_to_RN_hpp#include <stdio.h>
#include <string>class Cpp_to_RN {
public:
    std::string sayHello();
};#endif /* Cpp_to_RN_hpp */

然后打开Cpp_to_RN.cpp文件，写一个简单的函数“ sayHello()”

#include "Cpp_to_RN.hpp"
std::string Cpp_to_RN::sayHello(){
    return "Hello from CPP";
}

4. 处理包装 C++ 文件。

包装 C++ 文件并将它们导出到 IOS (swift) 端

一个。创建一个Objective C文件并命名Cpp_to_RN.m

重命名Cpp_to_RN.m 为 Cpp_to_RN.mm

湾。打开WrapCpp_to_RN.mm 文件并编写将包装sayHelloC++ 文件中的函数的正文内容。

#import <Foundation/Foundation.h>
#import "WrapCpp_to_RN.h"
#import "Cpp_to_RN.hpp"@implementation WrapCpp_to_RN- (NSString *) sayHello {
  Cpp_to_RN fromCPP;
    std::string helloWorldMessage = fromCPP.sayHello();
    return [NSString
            stringWithCString:helloWorldMessage.c_str()
            encoding:NSUTF8StringEncoding];
}
@end

C。创建头文件并命名WrapCpp_to_RN.h

将函数导出wrapSayHello到 Swift 文件

#import <Foundation/Foundation.h>
@interface WrapCpp_to_RN : NSObject
- (NSString *) wrapSayHello;
@end

5. 在 iOS (Swift) 端工作

将 C++ 函数导出到 React Native

一个。创建一个 Swift 文件并命名SendCpp_to_RN.swift

注意：Xcode 会要求我们为我们创建一个NativeModules-Bridging-Header.h文件。

创建一个类SendCpp_to_RN并将其声明为NSObject

#import <Foundation/Foundation.h>
@interface WrapCpp_to_RN : NSObject
- (NSString *) wrapSayHello;
@end

编写一个函数requiresMainQueueSetup()来防止我们运行应用程序时出现警告

#import <Foundation/Foundation.h>
@interface WrapCpp_to_RN : NSObject
- (NSString *) wrapSayHello;
@end

编写一个函数来包装WrapCpp_to_RN()fromWrapCpp_to_RN.mm

import Foundation@objc(SendCpp_to_RN)
class SendCpp_to_RN : NSObject {
    
  @objc static func requiresMainQueueSetup() -> Bool {
        return false
    }
  
  @objc func fromCpp(_ successCallback: RCTResponseSenderBlock) -> Void {
    successCallback([NSNull(), WrapCpp_to_RN().wrapSayHello() as Any])
    }}

湾。将 Swift 文件中的包装函数导出到 React Native

创建一个 Objective C 文件以导出 Swift 类及其函数，使用Callback

#import <React/RCTBridgeModule.h>
#import <Foundation/Foundation.h>
#import "UIKit/UIKit.h"
@interface RCT_EXTERN_MODULE(SendCpp_to_RN, NSObject)RCT_EXTERN_METHOD(fromCpp:(RCTResponseSenderBlock)successCallback)@end

C。将 Swift 连接到 React Native，打开NativeModules-Bridging-Header.h 文件

#import <React/RCTBridgeModule.h>#import <React/RCTViewManager.h>#import "WrapCpp_to_RN.h"

6. 在 React Native 方面工作

调用 Swift 类及其函数

import React from 'react';
import {StyleSheet, Text, View, NativeModules, Button} from 'react-native';const App = () => {
  const onPress = () => {
    const {SendCpp_to_RN} = NativeModules;
    SendCpp_to_RN.fromCpp((_err, res) => console.log(res));
  };
  return (
    <View style={styles.container}>
      <Text> Practice !</Text>
      <Button title="C++ to React Native" color="#841584" onPress={onPress} />
    </View>
  );
};
const styles = StyleSheet.create({
  container: {
    flex: 1,
    justifyContent: 'center',
    alignItems: 'center',
  },
});
export default App;

我们完成了，只需运行应用程序

react-native run-ios

或者只需单击 Xcode 上的“运行”按钮，看看我们做了什么。

希望我的文章对您有所帮助，感谢您的阅读时间。

 来源：https ://betterprogramming.pub/native-modules-export-c-function-to-react-native-for-beginners-77e89934b210

#cpp #cplusplus #react 

Cómo Exportar Una Función De C++ A React Native

Hoy sigo compartiendo mi experiencia con el Módulo Nativo y C++.

Dado que veremos muchas bibliotecas C/C++ escribiendo para las plataformas móviles, debemos implementarlas en nuestra aplicación iOS o React Native. Por eso quiero escribir un artículo sobre cómo exportar una función de C++ a React Native, que es fácil de entender y ahorra tiempo a los principiantes. Comenzaré con una nueva aplicación nativa de reacción.

1. Cree una nueva aplicación nativa de reacción, abra su terminal y ejecute

npx react-native init NativeModules

2. Abra Xcode y vaya a NativeModules/ios/NativeModule.xcworkspace

3. Trabajando en el lado de C++

Cree un nuevo archivo C++ y asígnele un nombreCpp_to_RN.cpp

Cuando creamos un nuevo archivo C++, Xcode creará un archivo de encabezado Cpp_to_RN.hpp para nosotros

Primero, abra el archivo " Cpp_to_RN.hpp" y cree una clase que incluya una función sin el cuerpo.

#ifndef Cpp_to_RN_hpp
#define Cpp_to_RN_hpp#include <stdio.h>
#include <string>class Cpp_to_RN {
public:
    std::string sayHello();
};#endif /* Cpp_to_RN_hpp */

Luego abre el Cpp_to_RN.cpparchivo y escribe una función simple “ sayHello()”

#include "Cpp_to_RN.hpp"
std::string Cpp_to_RN::sayHello(){
    return "Hello from CPP";
}

4. Trabajando en el archivo Wrapping C++.

Para envolver los archivos C++ y exportarlos al lado IOS (swift)

una. Cree un archivo Objective C y asígnele un nombreCpp_to_RN.m

Renombrar el Cpp_to_RN.m a Cpp_to_RN.mm

b. Abra el WrapCpp_to_RN.mm archivo y escriba el contenido del cuerpo que envolverá la función sayHellodel archivo C++.

#import <Foundation/Foundation.h>
#import "WrapCpp_to_RN.h"
#import "Cpp_to_RN.hpp"@implementation WrapCpp_to_RN- (NSString *) sayHello {
  Cpp_to_RN fromCPP;
    std::string helloWorldMessage = fromCPP.sayHello();
    return [NSString
            stringWithCString:helloWorldMessage.c_str()
            encoding:NSUTF8StringEncoding];
}
@end

C. Cree un archivo de encabezado y asígnele un nombreWrapCpp_to_RN.h

Exportar la wrapSayHellofunción al archivo Swift

#import <Foundation/Foundation.h>
@interface WrapCpp_to_RN : NSObject
- (NSString *) wrapSayHello;
@end

5. Trabajando en el lado de iOS (Swift)

Para exportar la función C++ a React Native

una. Cree un archivo Swift y asígnele un nombreSendCpp_to_RN.swift

Nota: Xcode nos pedirá que creemos un NativeModules-Bridging-Header.harchivo para nosotros.

Crear una clase SendCpp_to_RNy declararla comoNSObject

#import <Foundation/Foundation.h>
@interface WrapCpp_to_RN : NSObject
- (NSString *) wrapSayHello;
@end

Escribir una función requiresMainQueueSetup()para evitar advertencias cuando ejecutamos la aplicación.

#import <Foundation/Foundation.h>
@interface WrapCpp_to_RN : NSObject
- (NSString *) wrapSayHello;
@end

Escriba una función para envolver el WrapCpp_to_RN()fromWrapCpp_to_RN.mm

import Foundation@objc(SendCpp_to_RN)
class SendCpp_to_RN : NSObject {
    
  @objc static func requiresMainQueueSetup() -> Bool {
        return false
    }
  
  @objc func fromCpp(_ successCallback: RCTResponseSenderBlock) -> Void {
    successCallback([NSNull(), WrapCpp_to_RN().wrapSayHello() as Any])
    }}

b. Exporte una función de ajuste en un archivo Swift a React Native

Cree un archivo Objective C para exportar la clase Swift y su función usandoCallback

#import <React/RCTBridgeModule.h>
#import <Foundation/Foundation.h>
#import "UIKit/UIKit.h"
@interface RCT_EXTERN_MODULE(SendCpp_to_RN, NSObject)RCT_EXTERN_METHOD(fromCpp:(RCTResponseSenderBlock)successCallback)@end

C. Conecte Swift a React Native, abra el NativeModules-Bridging-Header.h archivo

#import <React/RCTBridgeModule.h>#import <React/RCTViewManager.h>#import "WrapCpp_to_RN.h"

6. Trabajando en el lado de React Native

Llame a la clase Swift y sus funciones

import React from 'react';
import {StyleSheet, Text, View, NativeModules, Button} from 'react-native';const App = () => {
  const onPress = () => {
    const {SendCpp_to_RN} = NativeModules;
    SendCpp_to_RN.fromCpp((_err, res) => console.log(res));
  };
  return (
    <View style={styles.container}>
      <Text> Practice !</Text>
      <Button title="C++ to React Native" color="#841584" onPress={onPress} />
    </View>
  );
};
const styles = StyleSheet.create({
  container: {
    flex: 1,
    justifyContent: 'center',
    alignItems: 'center',
  },
});
export default App;

Y listo, solo ejecuta la aplicación

react-native run-ios

O simplemente haga clic en el botón "ejecutar" en Xcode y vea lo que hemos hecho.

Espero que mi artículo te sea útil, gracias por tu tiempo de lectura.

 Fuente: https://betterprogramming.pub/native-modules-export-c-function-to-react-native-for-beginners-77e89934b210

#cpp #cplusplus #react