sinceDigital Image processing means processing the image digitally with the help of a computer. Using image processing we can perform operations like enhancing the image, blurring the image, extracting text from images, and many more operations. There are various ways to process images digitally. Here we will discuss the Pillow module of Python. Python Pillow is built on the top of PIL (Python Image Library) and is considered as the fork for the same as PIL has been discontinued since 2011. Pillow supports many image file formats including BMP, PNG, JPEG, and TIFF. The library encourages adding support for newer formats in the library by creating new file decoders.
This article aims at providing information about Python Pillow from basics to advance with the help of well-explained concepts and examples. So, let's not waste any of the time and dive deep into the Pillow.
Installation
Python Pillow does not come in-built with Python. To install it type the below command in the terminal.
pip install pillow
After installation let's get started using the pillow module.
Opening and Displaying the image
The Pillow module provides the open() and show() function to read and display the image respectively. For displaying the image Pillow first converts the image to a .png format (on Windows OS) and stores it in a temporary buffer and then displays it. Therefore, due to the conversion of the image format to .png some properties of the original image file format might be lost (like animation). Therefore, it is advised to use this method only for test purposes.
Example:
Image Used for all the below Examples:
Python
from PIL import Image
# Location of the image
img = Image.open("geek.jpg")
img.show()
Output:
Refer to the below articles to get detailed information about opening and displaying images.
Getting information about the opened image
Getting the Size, and format of the Image
- size attribute provides the size of the image. It returns a tuple that contains width and height.
- format attribute returns the format of the image file.
Example:
Python
from PIL import Image
# Location of the image
img = Image.open("geek.jpg")
# size of the image
print(img.size)
# format of the image
print(img.format)
Output:
(287, 70)
JPEG
Refer to the below article to get detailed information about Getting the Size, and format of the Image
Getting Color mode of the image
The mode attribute of the image tells the type and depth of the pixel in the image. A 1-bit pixel has a range of 0-1, and an 8-bit pixel has a range of 0-255. There are different modes provided by this module. A few of them are:
| Mode | Description |
|---|
| 1 | 1-bit pixels, black and white |
| L | 8-bit pixels, Grayscale |
| P | 8-bit pixels, mapped to any other mode using a color palette |
| RGB | 3×8-bit pixels, true color |
| RGBA | 4×8-bit pixels, true color with transparency mask |
Example:
Python
from PIL import Image
# Location of the image
img = Image.open("geek.jpg")
# mode of the image
print(img.mode)
Output:
RGB
Rotating the Image
rotate() method of the Image class is used to rotate the image by a particular angle counterclockwise around its center. After rotating the image, the sections of the image having no pixel values are filled with black (for non-alpha images) and with completely transparent pixels (for images supporting transparency).
Syntax:
new_object = PIL.Image.Image.rotate(image_object, angle, resample=0, expand=0)
OR
new_object = image_object.rotate(angle, resample=0, expand=0)
Example:
Python
# Importing Image module from
# PIL package
from PIL import Image
import PIL
# creating a image object (main image)
im1 = Image.open(r"geek.jpg")
# rotating a image 90 deg counter clockwise
im1 = im1.rotate(90, PIL.Image.NEAREST, expand = 1)
# to show specified image
im1.show()
Output:
Refer to the below articles to get detailed information about rotating the image.
Flipping the Image
Image.transpose() is used to transpose the image (flip or rotate in 90 degree steps).
Syntax:
transpose(degree)
Keywords FLIP_TOP_BOTTOM and FLIP_LEFT_RIGHT will be passed to transpose method to flip it.
- FLIP_TOP_BOTTOM - returns an original image flipped Vertically
- FLIP_LEFT_RIGHT - returns an original image flipped Horizontally
Example:
Python
# importing PIL Module
from PIL import Image
# open the original image
original_img = Image.open("geek.jpg")
# Flip the original image vertically
vertical_img = original_img.transpose(method=Image.FLIP_TOP_BOTTOM)
vertical_img.save("vertical.png")
vertical_img.show()
# close all our files object
original_img.close()
vertical_img.close()
Output:
Refer to the below articles to get detailed information about flipping images.
Resizing the image
Image.resize() returns a resized copy of the image. Interpolation happens during the resize process, due to which the quality of image changes whether it is being upscaled (resized to a higher dimension than original) or downscaled (resized to a lower Image then original). Therefore resize() should be used cautiously and while providing suitable value for resampling argument.
Syntax:
Image.resize(size, resample=0)
Example:
Python
# Importing Image class from PIL module
from PIL import Image
# Opens a image in RGB mode
im = Image.open(r"geek.jpg")
# Size of the image in pixels
# (size of original image)
# (This is not mandatory)
width, height = im.size
# Setting the points for cropped image
left = 4
top = height / 5
right = 154
bottom = 3 * height / 5
# Cropped image of above dimension
# (It will not change original image)
im1 = im.crop((left, top, right, bottom))
newsize = (300, 300)
im1 = im1.resize(newsize)
# Shows the image in image viewer
im1.show()
Output:
Refer to the below article to get detailed information about resizing images.
Saving the Image
Image.save() saves the image under the given filename. If no format is specified, the format to use is determined from the filename extension, if possible. You can use a file object instead of a filename. In this case, you must always specify the format. The file object must implement the seek, tell, and write methods, and be opened in binary mode.
Syntax:
Image.save(fp, format=None, **params)
Example:
Python
from PIL import Image
size = (40, 40)
img = Image.open(r"geek.jpg")
print("Original size of the image")
print(img.size)
# resizing the image
r_img = img.resize(size, resample = Image.BILINEAR)
# resized_test.png => Destination_path
r_img.save("resized_test.jpg")
# Opening the new image
img = Image.open(r"resized_test.jpg")
print("\nNew size of the image")
print(img.size)
Output:
Original size of the image
(287, 70)
New size of the image
(40, 40)
Till now, we have learned the basics of pillow now let's start with some complex operations like blurring the image of merging two images or even creating a thumbnail. So let's get started by merging images.
Merging Images
Image.merge() is used to merge a set of single band images into a new multiband image.
Syntax:
PIL.Image.merge(mode, bands)
Parameters:
mode – The mode to use for the output image. See: Modes.
bands – A sequence containing one single-band image for each band in the output image. All bands must have the same size.
Returns: An Image object.
Note: We will be using Image.split() method to split the image into individual bands.
Example:
Python
# importing Image class from PIL package
from PIL import Image
# creating a object
image = Image.open(r"geek.jpg")
image.load()
# Splitting the image into individual
# bands
r, g, b, = image.split()
# merge function used
im1 = Image.merge('RGB', (g, b, r))
im1.show()
Output:
Merging Two or More Images
Using the merge() method we can also merge two or more images. We have to select two images of the same size or we can resize the image. Then using the new() function we will create a new image and will paste all the images there. See the below example for a better understanding.
Example:
Images Used:
Python
from PIL import Image
img_01 = Image.open("digit-number-img-0.jpg")
img_02 = Image.open("digit-number-img-1.jpg")
img_03 = Image.open("digit-number-img-2.jpg")
img_04 = Image.open("digit-number-img-3.jpg")
img_01_size = img_01.size
img_02_size = img_02.size
img_03_size = img_02.size
img_02_size = img_02.size
print('img 1 size: ', img_01_size)
print('img 2 size: ', img_02_size)
print('img 3 size: ', img_03_size)
print('img 4 size: ', img_03_size)
new_im = Image.new('RGB', (2*img_01_size[0],2*img_01_size[1]), (250,250,250))
new_im.paste(img_01, (0,0))
new_im.paste(img_02, (img_01_size[0],0))
new_im.paste(img_03, (0,img_01_size[1]))
new_im.paste(img_04, (img_01_size[0],img_01_size[1]))
new_im.save("merged_images.png", "PNG")
new_im.show()
Output:
Refer to the below articles to get detailed information about merging images.
Creating a Thumbnail
Image.thumbnail() convert the image into a thumbnail. This method modifies the image to contain a thumbnail version of itself, no larger than the given size. This method calculates an appropriate thumbnail size to preserve the aspect of the image, calls the draft() method to configure the file reader (where applicable), and finally resizes the image.
Note: This function modifies the Image object in place. If you need to use the full resolution image as well, apply this method to a copy() of the original image.
Example:
Image Used:
Python
# importing Image class from PIL package
from PIL import Image
# creating a object
image = Image.open(r"image.jpg")
MAX_SIZE = (100, 100)
# Creating the thumbnail
image.thumbnail(MAX_SIZE)
image.show()
Output:
Refer to the below articles to get detailed information about creating thumbnails.
Cropping the Image
Cropping is the process of selecting only a part of the image. The crop() method is used to crop a rectangular portion of any image.
Syntax:
PIL.Image.crop(box = None)
Parameters:
box: a 4-tuple defining the left, upper, right, and lower pixel coordinate.
Example:
Python
# Importing Image class from PIL module
from PIL import Image
# Opens a image in RGB mode
im = Image.open(r"geek.jpg")
# Size of the image in pixels
# (size of original image)
# (This is not mandatory)
width, height = im.size
# Setting the points for cropped image
left = 5
top = height / 4
right = 164
bottom = 3 * height / 4
# Cropped image of above dimension
# (It will not change original image)
im1 = im.crop((left, top, right, bottom))
# Shows the image in image viewer
im1.show()
Output:
Refer to the below articles to get detailed information about Cropping images.
Blurring the Image
If a blurred image is observed carefully then a common thing to notice is that image is smooth meaning edges are not observed. A filter used for blurring is also called a low pass filter because it allows the low frequency to enter and stop high frequency. The ImageFilter class in the pillow library provides various filters that can be applied using the filter() method. Let's see some of the blurring filters provided by the pillow.
Simple Blur
This method blurs the image using the kernel matrix or through the convolution matrix. It can be applied using the BLUR parameter.
Syntax:
filter(ImageFilter.BLUR)
Note: For more information refer, What is Image Blurring
Example:
Python
# Importing Image class from PIL module
from PIL import Image, ImageFilter
# Opens a image in RGB mode
im = Image.open(r"geek.jpg")
# Blurring the image
im1 = im.filter(ImageFilter.BLUR)
# Shows the image in image viewer
im1.show()
Output:
Gaussian Blur
The Gaussian filter is implemented as an Odd sized Symmetric Kernel (DIP version of a Matrix) which is passed through each pixel of the Region of Interest to get the desired effect. The kernel is not hard towards drastic color changed (edges) due to the pixels towards the center of the kernel having more weightage towards the final value than the periphery. A Gaussian filter could be considered as an approximation of the Gaussian Function (mathematics). The Pillow module provides the predefined gaussianblur kernel that does the underlying maths for us.
Syntax:
ImageFilter.GaussianBlur(radius=2)
Example:
Python
# Importing Image class from PIL module
from PIL import Image, ImageFilter
# Opens a image in RGB mode
im = Image.open(r"geek.jpg")
# Blurring the image
im1 = im.filter(ImageFilter.GaussianBlur(4))
# Shows the image in image viewer
im1.show()
Output:
Box blur
Box blur is also known as box linear filter. Box blurs are frequently used to approximate Gaussian blur. A box blur is generally implemented as an image effect that affects the whole screen. The blurred color of the current pixel is the average of the current pixel’s color and its 8 neighboring pixels. Pillow provides the BoxBlur() method to do the same.
Syntax:
ImageFilter.BoxBlur(radius)
Example:
Python
# Importing Image class from PIL module
from PIL import Image, ImageFilter
# Opens a image in RGB mode
im = Image.open(r"geek.jpg")
# Blurring the image
im1 = im.filter(ImageFilter.BoxBlur(4))
# Shows the image in image viewer
im1.show()
Output:
Refer to the below articles to get detailed information about blurring images.
Drawing on Images
Pillow provides the ImageDraw module that provides simple 2D graphics for Image objects. You can use this module to create new images, annotate or retouch existing images, and generate graphics on the fly for web use. Let's see various figures or texts that we can draw on the image.
Adding Text
Adding text to an image can sometimes be very necessary as it can be used to provide some useful information to the image or can also be used to add a digital signature to the image. With pillow, we can easily add a text to any image. Let's see the below example.
Python
# import all the libraries
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
# image opening
image = Image.open("image.jpg")
# creating a copy of original image
watermark_image = image.copy()
# Image is converted into editable form using
# Draw function and assigned to draw
draw = ImageDraw.Draw(watermark_image)
# ("font type",font size)
font = ImageFont.truetype("DroidSans.ttf", 50)
# Decide the text location, color and font
# (255,255,255)-White color text
draw.text((0, 0), "GeeksforGeeks", (255, 255, 255), font=font)
watermark_image.show()
Output:
- Make the image editable using ImageDraw.
- Use ImageFont to specify font and font size. This step is optional. It is for those who want their text to look cool or stylish because someone won’t select any font style then the system takes the default font style.
- Create a Font using ImageFont module function truetype() as it needs two parameters that are ("font type", size)
- Then used text() function of draw object and pass the four-parameters (Point of starting for text, "sample text", Color, ImageFont object).
Refer to the below articles to get detailed information about adding texts to the image.
Adding Multiline Texts
ImageDraw.Draw.multiline_text() is used to draws the string at the given position.
Syntax:
ImageDraw.Draw.multiline_text(xy, text, fill=None, font=None, anchor=None, spacing=0, align=”left”)
Example:
Python
# Importing Image and ImageFont, ImageDraw
# module from PIL package
from PIL import Image, ImageFont, ImageDraw
# creating a image object
image = Image.open(r'geek.jpg')
draw = ImageDraw.Draw(image)
# specified font size
font = ImageFont.truetype(r'DroidSans.ttf', 15)
text = u"""\
Geeks
FOR \n Geeks"""
# drawing text size
draw.text((6, 8), text, fill ="red", font = font, align ="right")
image.show()
Output:
Drawing Line
ImageDraw.Draw.line() is used to draws a line between the coordinates in the xy list.
Syntax:
ImageDraw.Draw.line(xy, fill=None, width=0)
Example:
Python
# importing image object from PIL
import math
from PIL import Image, ImageDraw
w, h = 220, 190
shape = [(40, 40), (w - 10, h - 10)]
# creating new Image object
img = Image.new("RGB", (w, h))
# create line image
img1 = ImageDraw.Draw(img)
img1.line(shape, fill="none", width=0)
img.show()
Output:
Drawing Rectangle
ImageDraw.Draw.rectangle() is used to draw a rectangle.
Syntax:
ImageDraw.Draw.rectangle(xy, fill=None, outline=None)
Example:
Python
# importing image object from PIL
import math
from PIL import Image, ImageDraw
w, h = 220, 190
shape = [(40, 40), (w - 10, h - 10)]
# creating new Image object
img = Image.new("RGB", (w, h))
# create rectangle image
img1 = ImageDraw.Draw(img)
img1.rectangle(shape, fill="# ffff33", outline="red")
img.show()
Output:
Drawing Polygon
ImageDraw.Draw.polygon() is used to draw a polygon. The polygon outline consists of straight lines between the given coordinates, plus a straight line between the last and the first coordinate.
Syntax:
ImageDraw.Draw.polygon(xy, fill=None, outline=None)
Example:
Python
import math
from PIL import Image, ImageDraw
from PIL import ImagePath
side = 8
xy = [
((math.cos(th) + 1) * 90,
(math.sin(th) + 1) * 60)
for th in [i * (2 * math.pi) / side for i in range(side)]
]
image = ImagePath.Path(xy).getbbox()
size = list(map(int, map(math.ceil, image[2:])))
img = Image.new("RGB", size, "# f9f9f9")
img1 = ImageDraw.Draw(img)
img1.polygon(xy, fill="# eeeeff", outline="blue")
img.show()
Output:
Enhancing Image
Python Pillow provides the ImageEnhance module to adjust the color, brightness, contrast, and sharpness of the image.
Adjusting Color and Contrast
ImageEnhance.Color() and ImageEnhance.Contrast() methods are used to adjust the color and contrast of the image respectively.
- ImageEnhance.Color() is used to adjust the color balance of an image, in a manner similar to the controls on a color TV set. An enhancement factor of 0.0 gives a black and white image. A factor of 1.0 gives the original image.
Syntax:
ImageEnhance.Color(image)
Example:
Python
# This will import Image and ImageEnhance modules
from PIL import Image, ImageEnhance
# Opening Image
im = Image.open(r"geek.jpg")
# Creating object of Color class
im3 = ImageEnhance.Color(im)
# showing resultant image
im3.enhance(5.0).show()
Output:
- ImageEnhance.Contrast() is used to control the contrast of an image, similar to the contrast control on a TV set. An enhancement factor of 0.0 gives a solid grey image. A factor of 1.0 gives the original image.
Syntax:
obj = ImageEnhance.Contrast(image)
obj.enhance(factor)
Example:
Python
# This will import Image and ImageEnhance modules
from PIL import Image, ImageEnhance
# Opening Image
im = Image.open(r"geek.jpg")
# Creating object of Contrast class
im3 = ImageEnhance.Contrast(im)
# showing resultant image
im3.enhance(5.0).show()
Output:
Refer to the below articles to get detailed information about enhancing color and contrast.
Adjusting Brightness and Sharpness
ImageEnhance.Brightness() and ImageEnhance.Sharpness() methods are used to adjust the brightness and sharpness of the image.
- ImageEnhance.Brightness() is used to control the brightness of an image. An enhancement factor of 0.0 gives a black image. A factor of 1.0 gives the original image.
Syntax:
obj = ImageEnhance.Brightness(image)
obj.enhance(factor)
Example:
Python
# This will import Image and ImageEnhance modules
from PIL import Image, ImageEnhance
# Opening Image
im = Image.open(r"geek.jpg")
# Creating object of Brightness class
im3 = ImageEnhance.Brightness(im)
# showing resultant image
im3.enhance(1.5).show()
Output:
- ImageEnhance.Sharpness() is used to adjust the sharpness of an image. An enhancement factor of 0.0 gives a blurred image, a factor of 1.0 gives the original image, and a factor of 2.0 gives a sharpened image.
Syntax:
obj = ImageEnhance.Sharpness(image)
obj.enhance(factor)
Example:
Python
# This will import Image and ImageChops modules
from PIL import Image, ImageEnhance
# Opening Image
im = Image.open(r"geek.jpg")
# Creating object of Sharpness class
im3 = ImageEnhance.Sharpness(im)
# showing resultant image
im3.enhance(5.0).show()
Output:
Refer to the below articles to get detailed information about enhancing the brightness and sharpness of the image.
Convert images between various Formats
Advance Operations on Image using Pillow
Python Pillow Exercises and Applications
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NumPy Tutorial - Python LibraryNumPy (short for Numerical Python ) is one of the most fundamental libraries in Python for scientific computing. It provides support for large, multi-dimensional arrays and matrices along with a collection of mathematical functions to operate on arrays.At its core it introduces the ndarray (n-dimens
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Pandas TutorialPandas is an open-source software library designed for data manipulation and analysis. It provides data structures like series and DataFrames to easily clean, transform and analyze large datasets and integrates with other Python libraries, such as NumPy and Matplotlib. It offers functions for data t
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Matplotlib TutorialMatplotlib is an open-source visualization library for the Python programming language, widely used for creating static, animated and interactive plots. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, Qt, GTK and wxPython. It
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Python Seaborn TutorialSeaborn is a library mostly used for statistical plotting in Python. It is built on top of Matplotlib and provides beautiful default styles and color palettes to make statistical plots more attractive.In this tutorial, we will learn about Python Seaborn from basics to advance using a huge dataset of
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StatsModel Library- TutorialStatsmodels is a useful Python library for doing statistics and hypothesis testing. It provides tools for fitting various statistical models, performing tests and analyzing data. It is especially used for tasks in data science ,economics and other fields where understanding data is important. It is
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Learning Model Building in Scikit-learnBuilding machine learning models from scratch can be complex and time-consuming. Scikit-learn which is an open-source Python library which helps in making machine learning more accessible. It provides a straightforward, consistent interface for a variety of tasks like classification, regression, clu
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TensorFlow TutorialTensorFlow is an open-source machine-learning framework developed by Google. It is written in Python, making it accessible and easy to understand. It is designed to build and train machine learning (ML) and deep learning models. It is highly scalable for both research and production.It supports CPUs
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PyTorch TutorialPyTorch is an open-source deep learning framework designed to simplify the process of building neural networks and machine learning models. With its dynamic computation graph, PyTorch allows developers to modify the network’s behavior in real-time, making it an excellent choice for both beginners an
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Web Development with Python
Flask TutorialFlask is a lightweight and powerful web framework for Python. It’s often called a "micro-framework" because it provides the essentials for web development without unnecessary complexity. Unlike Django, which comes with built-in features like authentication and an admin panel, Flask keeps things mini
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Django Tutorial | Learn Django FrameworkDjango is a Python framework that simplifies web development by handling complex tasks for you. It follows the "Don't Repeat Yourself" (DRY) principle, promoting reusable components and making development faster. With built-in features like user authentication, database connections, and CRUD operati
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Django ORM - Inserting, Updating & Deleting DataDjango's Object-Relational Mapping (ORM) is one of the key features that simplifies interaction with the database. It allows developers to define their database schema in Python classes and manage data without writing raw SQL queries. The Django ORM bridges the gap between Python objects and databas
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Templating With Jinja2 in FlaskFlask is a lightweight WSGI framework that is built on Python programming. WSGI simply means Web Server Gateway Interface. Flask is widely used as a backend to develop a fully-fledged Website. And to make a sure website, templating is very important. Flask is supported by inbuilt template support na
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Django TemplatesTemplates are the third and most important part of Django's MVT Structure. A Django template is basically an HTML file that can also include CSS and JavaScript. The Django framework uses these templates to dynamically generate web pages that users interact with. Since Django primarily handles the ba
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Python | Build a REST API using FlaskPrerequisite: Introduction to Rest API REST stands for REpresentational State Transfer and is an architectural style used in modern web development. It defines a set or rules/constraints for a web application to send and receive data. In this article, we will build a REST API in Python using the Fla
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How to Create a basic API using Django Rest Framework ?Django REST Framework (DRF) is a powerful extension of Django that helps you build APIs quickly and easily. It simplifies exposing your Django models as RESTfulAPIs, which can be consumed by frontend apps, mobile clients or other services.Before creating an API, there are three main steps to underst
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Python Practice
Python QuizThese Python quiz questions are designed to help you become more familiar with Python and test your knowledge across various topics. From Python basics to advanced concepts, these topic-specific quizzes offer a comprehensive way to practice and assess your understanding of Python concepts. These Pyt
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Python Coding Practice ProblemsThis collection of Python coding practice problems is designed to help you improve your overall programming skills in Python.The links below lead to different topic pages, each containing coding problems, and this page also includes links to quizzes. You need to log in first to write your code. Your
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Python Interview Questions and AnswersPython is the most used language in top companies such as Intel, IBM, NASA, Pixar, Netflix, Facebook, JP Morgan Chase, Spotify and many more because of its simplicity and powerful libraries. To crack their Online Assessment and Interview Rounds as a Python developer, we need to master important Pyth
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