5 Best Ways to Flip an Image in Python

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💡 Problem Formulation: In image processing tasks with Python, developers are often required to transform images, including flipping them horizontally or vertically. This article explores how to achieve these modifications. If an image displays a sunrise on the right edge, we might want to flip it to display the sunrise on the left for a different perspective.

Method 1: Using Python’s Pillow Library

The Pillow library is an open-source Python Imaging Library that adds image processing capabilities to your Python interpreter. This library provides the transpose() method which can be used to flip an image. Using Image.FLIP_LEFT_RIGHT will flip the image horizontally, and using Image.FLIP_TOP_BOTTOM will flip it vertically.

Here’s an example:

from PIL import Image

def flip_image_horizontally(image_path, output_path):
    with Image.open(image_path) as img:
        flipped_img = img.transpose(Image.FLIP_LEFT_RIGHT)
        flipped_img.save(output_path)

flip_image_horizontally('sunrise.jpg', 'flipped_sunrise.jpg')

The output will be an image saved as ‘flipped_sunrise.jpg’ which is a horizontally flipped version of the original ‘sunrise.jpg’.

This code defines a function flip_image_horizontally() that takes an input image file path and an output file path, then opens the image, flips it horizontally, and saves the new image to the specified output path.

Method 2: Using OpenCV

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. OpenCV provides a function called cv2.flip() where you can specify the flip direction: 0 for vertical, 1 for horizontal, or -1 for both.

Here’s an example:

import cv2

img = cv2.imread('sunrise.jpg')
flipped_img = cv2.flip(img, 1)
cv2.imwrite('flipped_sunrise.jpg', flipped_img)

The result is a new image ‘flipped_sunrise.jpg’ with horizontal mirroring of the original ‘sunrise.jpg’.

In this snippet, we load an image using OpenCV, call the cv2.flip() function with the flip code 1 to flip the image horizontally, and finally, we save the flipped image to disk using cv2.imwrite().

Method 3: Using NumPy

NumPy is a fundamental package for scientific computing in Python. It provides a simple way to flip an array, which means you can also flip image data since images are represented as arrays of pixels. The numpy.flip() function can flip arrays along the specified axis.

Here’s an example:

import numpy as np
from imageio import imread, imwrite

img = imread('sunrise.jpg')
flipped_img = np.flip(img, 1)
imwrite('flipped_sunrise.jpg', flipped_img)

This will create a horizontally flipped image of ‘sunrise.jpg’, saved as ‘flipped_sunrise.jpg’.

We read an image into a NumPy array, flip the array along the horizontal axis (axis 1), and write the output image. This method directly manipulates the pixel array, offering low-level control over the flipping process.

Method 4: Using Matplotlib

Matplotlib is a plotting library for the Python programming language. It can also be used to manipulate images. The imshow() function can display an image, and by altering the array with array slicing, you can flip an image.

Here’s an example:

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

img = mpimg.imread('sunrise.jpg')
flipped_img = img[:, ::-1]
plt.imsave('flipped_sunrise.jpg', flipped_img)

Once executed, a new image called ‘flipped_sunrise.jpg’ will be created, which is a left-to-right flipped version of the original image.

This code loads an image using Matplotlib’s image module and utilizes slicing to reverse the direction of the pixel rows, effectively flipping the image horizontally. The output is then saved using plt.imsave().

Bonus One-Liner Method 5: Using Imageio

Imageio is a Python library that provides an easy interface to read and write a wide range of image data, including animated images, video, and volumetric data. For a simple flip, one line is sufficient.

Here’s an example:

from imageio import imread, imsave

imsave('flipped_sunrise.jpg', imread('sunrise.jpg')[:, ::-1])

The result is a flipped image ‘flipped_sunrise.jpg’ with the horizontal flip of ‘sunrise.jpg’.

This concise line reads an image with Imageio, immediately flips it using slicing, and writes the flipped image back to the disk in one go.

Summary/Discussion

  • Method 1: Pillow: Offers extended image processing capabilities beyond flipping. Straightforward API. Slower compared to array manipulations for large images.
  • Method 2: OpenCV: Suited for computer vision tasks and handles flipping speedily. Rich in image processing functions. Installation can be complex for some environments.
  • Method 3: NumPy: Very fast and efficient for large images. Provides maximum control over image data. Requires understanding of array manipulation.
  • Method 4: Matplotlib: Good for integrating image processing with plotting tasks. Not primarily designed for image manipulation, so limited functionality compared to others.
  • Method 5: Imageio: One-liner solution for flipping images. Very concise, but might have less flexibility for more complex image processing needs.