Как обнаружить человеческое тело в Python OpenCV

В предыдущей статье мы рассказывали о простой программе распознавания лиц с использованием Python OpenCV. В этой статье вы узнаете, как определять человеческое тело полностью с помощью библиотеки Python OpenCV .

Существует множество компьютерных приложений, которые идентифицируют человеческое тело на цифровых изображениях, например, пешеходный переход, идентификация преступников, здравоохранение и т. Д. Программа обнаружения позволяет нам идентифицировать и определять местонахождение объектов. Это очень важно в той области исследований, где обнаруженный объект можно точно подсчитать, определить. Функции библиотеки Python OpenCV в основном предназначены для компьютерного зрения в реальном времени. В этой статье мы собираемся разработать программу обнаружения пешеходов. Библиотека OpenCV имеет встроенные методы обнаружения пешеходов.

Требуемый модуль

Для программы обнаружения пешеходов или людей нам понадобятся следующие модули:

• OpenCV (cv2)
• imutils

Установить модуль OpenCV

OpenCV расшифровывается как Open Source Computer Vision Library. Это бесплатная библиотека с открытым исходным кодом, которая используется для компьютерного зрения. Он обеспечивает хорошую поддержку в машинном обучении, распознавании лиц, глубоком обучении и т. Д.

Данная команда устанавливает модуль OpenCV с помощью инструмента pip . Эта команда также устанавливает некоторые другие вспомогательные модули OpenCV.

pip install opencv-contrib-python

Следующий код импортирует модуль OpenCV -

import cv2 

Установить модуль imutils

В Imutils используются для основной обработки изображений, например, перемещения, вращения, изменения размеров, скелетизации и отображение изображений Matplotlib проще с OpenCV. Он предоставляет ряд удобных функций для выполнения операций.

pip install imutils

При успешной установке он возвращает следующее -

Successfully built imutils
Installing collected packages: imutils
Successfully installed imutils-0.5.3

Используйте следующее, чтобы импортировать эту библиотеку -

import imutils 

Прочитать и изменить размер изображения

После установки модуля нам сначала нужно прочитать и изменить размер изображения. Мы будем использовать функцию imread () для чтения изображения.

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

Загруженное изображение может быть любого размера. Итак, нам нужно будет изменить его размер, используя метод resize () модуля imutils. Если вы не хотите изменять размер изображения, вы можете пропустить этот код -

image = imutils.resize(image, 
                       width=min(500, image.shape[1])) 

OpenCV HOGDescriptor

HOG (гистограмма ориентированных градиентов) - это детектор объектов, используемый для обнаружения объектов в компьютерном зрении и обработке изображений. Этот метод учитывает наличие градиентной ориентации в локализованных частях изображения. Метод cv.HOGDescriptor () создает дескриптор HOG. Метод hog.setSVMDetector () устанавливает коэффициенты для линейного классификатора SVM.

hog = cv2.HOGDescriptor()  
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) 

Затем определите все области, в которых находится человеческое тело. Метод hog.detectMultiScale () обнаруживает объекты разных размеров во входном изображении. Обнаруженные объекты возвращаются в виде списка прямоугольников.

(humans, _) = hog.detectMultiScale(image,  
                                    winStride=(5, 5), 
                                    padding=(3, 3), 
                                    scale=1.21)

Затем мы нарисуем прямоугольную область вокруг каждого человеческого тела изображения -

for (x, y, w, h) in humans: 
    cv2.rectangle(image, (x, y),  
                  (x + w, y + h),  
                  (0, 0, 255), 2) 

Полный код: Исходный код обнаружения человеческого тела

Мы надеемся, что приведенное выше объяснение кода поможет вам понять поток кода. Здесь мы объединили указанные выше фрагменты кода.

import cv2 
import imutils 
   
# Initializing the HOG person 
hog = cv2.HOGDescriptor() 
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) 
   
# Reading the Image 
image = cv2.imread('zebracrossing.jpg') 
   
# Resizing the Image 
image = imutils.resize(image, 
                       width=min(500, image.shape[1])) 
   
# Detecting all humans 
(humans, _) = hog.detectMultiScale(image,  
                                    winStride=(5, 5), 
                                    padding=(3, 3), 
                                    scale=1.21)
# getting no. of human detected
print('Human Detected : ', len(humans))
   
# Drawing the rectangle regions
for (x, y, w, h) in humans: 
    cv2.rectangle(image, (x, y),  
                  (x + w, y + h),  
                  (0, 0, 255), 2) 
  
# Displaying the output Image 
cv2.imshow("Image", image) 
cv2.waitKey(0) 
   
cv2.destroyAllWindows() 

Приведенный выше код возвращает вывод примерно так:

(env) c:\python37\Scripts\projects>pedestrain.py
Humans Detected :  3

What is GEEK

Buddha Community

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.

#python development services #python development company #python app development #python development #python in web development #python software development

Create a Virtual Pen and Eraser with Python OpenCV - Genial Code

Learn Free how to create a virtual pen and eraser with python and OpenCV with source code and complete guide. This entire application is built fundamentally on contour detection. It can be thought of as something like closed color curves on compromises that have the same color or intensity, it’s like a blob. In this project we use color masking to get the binary mask of our target color pen, then we use the counter detection to find the location of this pen and the contour to find it.

#python #create virtual pen and eraser with opencv #create virtual pen and eraser with python opencv #programming #opencv #python opencv

Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

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')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

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
• sha1
• sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

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.
4. Perform tone mapping on the merged image. In nature the minimum possible brightness is zero but the maximum is not limited to 255, in fact there is no limit to it, it can be infinity. For this reason we need to map the image obtained in the third step to a (0, 255) range. This can be achieved with tone mapping.

#hdr #opencv #computer-vision #python #opencv #opencv python