So komprimieren Sie Bilder in Python

Bei der Bildkomprimierung wird die Größe eines Bildes minimiert, ohne die Bildqualität zu beeinträchtigen. Es gibt viele Online-Tools, die diesen Service anbieten; Die meisten von ihnen sind eine großartige Option, wenn Sie Ihre Bilder schnell und zuverlässig minimieren möchten. In diesem Tutorial lernen Sie jedoch, wie Sie die Bilddateigröße in Python mithilfe der Pillow-Bibliothek reduzieren .

Es steht Ihnen frei, den Code dieses Tutorials zu verwenden. Sie können beispielsweise eine API darum herum erstellen, um Bildgrößen in Stapeln zu reduzieren, anstatt eine API eines Drittanbieters zu verwenden, die Sie möglicherweise Geld kostet.

Ich habe den Code für dieses Tutorial so flexibel wie möglich gestaltet. Sie können das Bild komprimieren und die Größe mit einem Skalierungsfaktor oder exakter Breite und Höhe ändern. Sie können auch das Qualitätsverhältnis angeben.

In Ordnung, installieren wir zunächst Pillow:

$ pip install Pillow

Öffnen Sie eine neue Python-Datei und importieren Sie sie:

import os
from PIL import Image

Bevor wir uns mit dem Komprimieren von Bildern befassen, holen wir uns eine Funktion aus diesem Tutorial , um die Dateigröße in einem benutzerfreundlichen Format zu drucken:

def get_size_format(b, factor=1024, suffix="B"):
    """
    Scale bytes to its proper byte format
    e.g:
        1253656 => '1.20MB'
        1253656678 => '1.17GB'
    """
    for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]:
        if b < factor:
            return f"{b:.2f}{unit}{suffix}"
        b /= factor
    return f"{b:.2f}Y{suffix}"

Als nächstes machen wir unsere Kernfunktion zum Komprimieren von Bildern:

def compress_img(image_name, new_size_ratio=0.9, quality=90, width=None, height=None, to_jpg=True):
    # load the image to memory
    img = Image.open(image_name)
    # print the original image shape
    print("[*] Image shape:", img.size)
    # get the original image size in bytes
    image_size = os.path.getsize(image_name)
    # print the size before compression/resizing
    print("[*] Size before compression:", get_size_format(image_size))
    if new_size_ratio < 1.0:
        # if resizing ratio is below 1.0, then multiply width & height with this ratio to reduce image size
        img = img.resize((int(img.size[0] * new_size_ratio), int(img.size[1] * new_size_ratio)), Image.ANTIALIAS)
        # print new image shape
        print("[+] New Image shape:", img.size)
    elif width and height:
        # if width and height are set, resize with them instead
        img = img.resize((width, height), Image.ANTIALIAS)
        # print new image shape
        print("[+] New Image shape:", img.size)
    # split the filename and extension
    filename, ext = os.path.splitext(image_name)
    # make new filename appending _compressed to the original file name
    if to_jpg:
        # change the extension to JPEG
        new_filename = f"{filename}_compressed.jpg"
    else:
        # retain the same extension of the original image
        new_filename = f"{filename}_compressed{ext}"
    try:
        # save the image with the corresponding quality and optimize set to True
        img.save(new_filename, quality=quality, optimize=True)
    except OSError:
        # convert the image to RGB mode first
        img = img.convert("RGB")
        # save the image with the corresponding quality and optimize set to True
        img.save(new_filename, quality=quality, optimize=True)
    print("[+] New file saved:", new_filename)
    # get the new image size in bytes
    new_image_size = os.path.getsize(new_filename)
    # print the new size in a good format
    print("[+] Size after compression:", get_size_format(new_image_size))
    # calculate the saving bytes
    saving_diff = new_image_size - image_size
    # print the saving percentage
    print(f"[+] Image size change: {saving_diff/image_size*100:.2f}% of the original image size.")

Eine riesige Funktion, die eine Menge Dinge tut, lassen Sie uns sie genauer behandeln:

• Zuerst verwenden wir die Image.open()Methode, um das Bild in den Speicher zu laden, wir erhalten die Größe der Bilddatei, die verwendet wird, os.path.getsize()damit wir diese Größe später mit der Größe der neu generierten Datei vergleichen können.
• Wenn new_size_ratiounten eingestellt ist 1.0, ist eine Größenänderung erforderlich. widthDiese Zahl reicht von 0 bis 1 und wird mit dem und des Originalbildes multipliziert , um heightein Bild mit niedrigerer Auflösung zu erhalten. Dies ist ein geeigneter Parameter, wenn Sie die Bildgröße weiter reduzieren möchten. Sie können es auch auf 0.95oder einstellen 0.9, um die Bildgröße mit minimalen Änderungen an der Auflösung zu reduzieren.
• Wenn new_size_ratioist 1.0, aber widthund heightgesetzt sind, ändern wir die Größe auf diese neuen widthund height-Werte und stellen sicher, dass sie unter den ursprünglichen widthund liegen height.
• Wenn to_jpgauf eingestellt ist True, ändern wir die Erweiterung des Originalbildes in JPEG. Dadurch wird die Bildgröße erheblich reduziert, insbesondere bei PNG-Bildern. Wenn die Konvertierung ein OSErrorauslöst, löst das Konvertieren des Bildformats in RGB das Problem.
• Schließlich verwenden wir die save()Methode, um das optimierte Bild zu schreiben, und legen optimizezusammen Truemit der von der Funktion übergebenen Qualität fest. Wir erhalten dann die Größe des neuen Bildes und vergleichen sie mit der Größe des Originalbildes.

Nachdem wir nun unsere Kernfunktion haben, verwenden wir argparsemodule, um sie in die Befehlszeilenargumente zu integrieren:

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser(description="Simple Python script for compressing and resizing images")
    parser.add_argument("image", help="Target image to compress and/or resize")
    parser.add_argument("-j", "--to-jpg", action="store_true", help="Whether to convert the image to the JPEG format")
    parser.add_argument("-q", "--quality", type=int, help="Quality ranging from a minimum of 0 (worst) to a maximum of 95 (best). Default is 90", default=90)
    parser.add_argument("-r", "--resize-ratio", type=float, help="Resizing ratio from 0 to 1, setting to 0.5 will multiply width & height of the image by 0.5. Default is 1.0", default=1.0)
    parser.add_argument("-w", "--width", type=int, help="The new width image, make sure to set it with the `height` parameter")
    parser.add_argument("-hh", "--height", type=int, help="The new height for the image, make sure to set it with the `width` parameter")
    args = parser.parse_args()
    # print the passed arguments
    print("="*50)
    print("[*] Image:", args.image)
    print("[*] To JPEG:", args.to_jpg)
    print("[*] Quality:", args.quality)
    print("[*] Resizing ratio:", args.resize_ratio)
    if args.width and args.height:
        print("[*] Width:", args.width)
        print("[*] Height:", args.height)
    print("="*50)
    # compress the image
    compress_img(args.image, args.resize_ratio, args.quality, args.width, args.height, args.to_jpg)

Wir erstellen unseren Befehlszeilen-Argument-Parser im obigen Code und fügen die zuvor besprochenen Parameter hinzu.

Lassen Sie uns jetzt unser Skript verwenden. Das Beispielbild erhalten Sie hier . Lassen Sie uns zunächst unser Skript ohne Parameter verwenden:

$ python compress_image.py sample-satellite-images.png

Ausgabe:

==================================================
[*] Image: sample-satellite-images.png
[*] To JPEG: False
[*] Quality: 90
[*] Resizing ratio: 1.0
==================================================
[*] Image shape: (953, 496)
[*] Size before compression: 425.65KB
[+] New file saved: sample-satellite-images_compressed.png
[+] Size after compression: 379.25KB
[+] Image size change: -10.90% of the original image size.

Die Bildgröße wird von 425,65 KB auf 379,25 KB reduziert, das sind etwa 11 % der Reduzierung. Versuchen wir als Nächstes, die -jKonvertierung von PNG in JPEG durchzuführen:

$ python compress_image.py sample-satellite-images.png -j

Ausgabe:

==================================================
[*] Image: sample-satellite-images.png
[*] To JPEG: True
[*] Quality: 90
[*] Resizing ratio: 1.0
==================================================
[*] Image shape: (953, 496)
[*] Size before compression: 425.65KB
[+] New file saved: sample-satellite-images_compressed.jpg
[+] Size after compression: 100.07KB
[+] Image size change: -76.49% of the original image size.

Hinweis : Das Beispielbild erhalten Sie hier .

Das ist fantastisch, 76,5 % Verbesserung. Lassen Sie uns die Qualität etwas verringern:

$ python compress_image.py sample-satellite-images.png -j -q 75

Ausgabe:

==================================================
[*] Image: sample-satellite-images.png
[*] To JPEG: True
[*] Quality: 75
[*] Resizing ratio: 1.0
==================================================
[*] Image shape: (953, 496)
[*] Size before compression: 425.65KB
[+] New file saved: sample-satellite-images_compressed.jpg
[+] Size after compression: 64.95KB
[+] Image size change: -84.74% of the original image size.

Etwa 85% der Reduzierung, ohne die ursprüngliche Bildauflösung zu berühren. Versuchen wir, das widthund heightdes Bildes mit zu multiplizieren 0.9:

$ python compress_image.py sample-satellite-images.png -j -q 75 -r 0.9

Ausgabe:

==================================================
[*] Image: sample-satellite-images.png
[*] To JPEG: True
[*] Quality: 75
[*] Resizing ratio: 0.9
==================================================
[*] Image shape: (953, 496)
[*] Size before compression: 425.65KB
[+] New Image shape: (857, 446)
[+] New file saved: sample-satellite-images_compressed.jpg
[+] Size after compression: 56.94KB
[+] Image size change: -86.62% of the original image size.

Jetzt genaue widthund heightWerte einstellen:

$ python compress_image.py sample-satellite-images.png -j -q 75 -w 800 -hh 400 

Ausgabe:

==================================================
[*] Image: sample-satellite-images.png
[*] To JPEG: True
[*] Quality: 75
[*] Resizing ratio: 1.0
[*] Width: 800
[*] Height: 400
==================================================
[*] Image shape: (953, 496)
[*] Size before compression: 425.65KB
[+] New Image shape: (800, 400)
[+] New file saved: sample-satellite-images_compressed.jpg
[+] Size after compression: 49.73KB
[+] Image size change: -88.32% of the original image size.

Genial! Sie können versuchen, die Parameter an Ihre spezifischen Anforderungen anzupassen. Ich hoffe, dieses Skript war hilfreich für Sie, um die Entwicklung Ihrer Anwendung zu beschleunigen.

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

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

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python

Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object

a**=25+**85j

type**(a)**

output**:<class’complex’>**

b**={1:10,2:“Pinky”****}**

id**(b)**

output**:**238989244168

Built-in data types in Python

a**=str(“Hello python world”)****#str**

b**=int(18)****#int**

c**=float(20482.5)****#float**

d**=complex(5+85j)****#complex**

e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**

f**=tuple((“python”,“easy”,“learning”))****#tuple**

g**=range(10)****#range**

h**=dict(name=“Vidu”,age=36)****#dict**

i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**

j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**

k**=bool(18)****#bool**

l**=bytes(8)****#bytes**

m**=bytearray(8)****#bytearray**

n**=memoryview(bytes(18))****#memoryview**

Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger

age**=**18

print**(age)**

Output**:**18

Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).

String

The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.

“Hello”+“python”

output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type