Como detectar Harris Corner com Python OpenCV

O algoritmo Harris Corner Detector é comumente usado em visão computacional para extrair cantos e inferir características de uma imagem. Foi introduzido pela primeira vez por Chris Harris e Mike Stephens em 1988. O canto é o ponto onde duas arestas se juntam. Este canto é denominado como pontos de interesse que são invariantes à translação, rotação e iluminação. Existem muitos algoritmos de detecção de cantos que capturam os cantos da imagem. Mas o algoritmo Harris Corner Detector é mais simples, eficiente e confiável para uso na detecção de cantos. É rápido o suficiente para funcionar em computadores.

cv2.cornerHarris ()

A biblioteca OpenCV fornece a função cv2.cornerHarris () para essa finalidade. A sintaxe é-

cv2.cornerHarris(image, blockSize, ksize, k, borderType)

image- a imagem de entrada, imagem de um canal de 8 bits ou de ponto flutuante,
blockSize- o tamanho da vizinhança considerada para detecção de canto,
ksize- Parâmetro de abertura da derivada de Sobel usada,
k- parâmetro livre do detector de Harris na equação,
borderType- método de extrapolação de pixel.

Explicação do código de detecção de Harris Corner

Primeiro, carregaremos a imagem de entrada usando a função imread () do OpenCV.

image = cv2.imread('nature.jpg')

A seguir, converteremos a imagem importada em tons de cinza. O método cv2.cvtColor () é usado para converter a imagem de um espaço de cor para outro. Ele especifica o tipo de conversão, ou seja, cv2.COLOR_BGR2GRAY no segundo parâmetro.

img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 

Modificaremos o tipo de dados da imagem de entrada para 32 bits flutuantes. É um único ponto flutuante de precisão de expoente de 8 bits e mantissa de 23 bits.

img_gray = np.float32(img_gray) 

Em seguida, aplique o método Harris Corner Detector para detectar os cantos com os parâmetros de entrada apropriados -

hcd_img = cv2.cornerHarris(img_gray, 3, 5, 0.08) 

usaremos o método cv2.dilate () para marcar os cantos da imagem retornada. Ele adiciona pixels aos cantos dos objetos em uma imagem.

hcd_img = cv2.dilate(hcd_img, None) 

Por fim, reverteremos a imagem original com o valor limite ideal e especificaremos a cor do canto.

image[hcd_img > 0.01 * hcd_img.max()]=[0, 0, 255]

Código Completo

Esperamos que a explicação do código acima ajude você a entender o fluxo do código. Aqui, mesclamos os blocos de código acima.

import cv2 
import numpy as np 
  
# loading image
image = cv2.imread('house.jpg') 
  
# convert the input image into grayscale
img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
  
# convert the data type 
img_gray = np.float32(img_gray) 
  
# implementing cv2.cornerHarris method 
hcd_img = cv2.cornerHarris(img_gray, 5, 5, 0.08) 
  
# marking dilated corners 
hcd_img = cv2.dilate(hcd_img, None) 
  
# reverting back to the original image
image[hcd_img > 0.01 * hcd_img.max()]=[0, 97, 38] 
  
# show the image
cv2.imshow('Image with corners', image) 
  
cv2.waitKey(0);
cv2.destroyAllWindows();
cv2.waitKey(1)

A imagem mais à esquerda é a imagem original e a imagem mais à direita é a saída do código acima.

Da mesma forma, quando fornecemos uma imagem de cubo na entrada, obtemos a seguinte saída -

What is GEEK

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Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

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How to Remove all Duplicate Files on your Drive via Python

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Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

• md5
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• sha224, sha256, sha384 and sha512

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A Simple HDR Implementation on OpenCV Python

Learn how to create a high dynamic range (HDR) image using Python and OpenCV

HDR images encompass the information of multiple pictures with different exposures. In a scene which the source of light is uneven, a single shot may overexpose certain areas of the image and details will be lost due to elevated brightness. Conversely, this picture may also present underexposed areas which will also lead to information loss.

To create an HDR image you will need:

1. Take pictures with different exposures. Minimum of 2, generally 3, you can use more than 3 images but it will take a lot of CPU resources.
2. Align the images. Even if you use a tripod you will need to perform this step (we are talking about pixel level alignment). Not properly aligning your image will lead to artifacts and ‘ghosts’ in your HDR image.
3. Merge the aligned images into one.
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