Image Manipulation

Most image processing and manipulation techniques can be carried outeffectively using two libraries: Python Imaging Library (PIL) and Open SourceComputer Vision (OpenCV).

A brief description of both is given below.

Python Imaging Library

The Python Imaging Library, or PILfor short, is one of the core libraries for image manipulation in Python. Unfortunately,its development has stagnated, with its last release in 2009.

Luckily for you, there’s an actively-developed fork of PIL calledPillow – it’s easier to install, runs onall major operating systems, and supports Python 3.

Installation

Before installing Pillow, you’ll have to install Pillow’s prerequisites. Findthe instructions for your platform in thePillow installation instructions.

After that, it’s straightforward:

$ pip install Pillow

Example

from PIL import Image, ImageFilter
#Read image
im = Image.open( 'image.jpg' )
#Display image
im.show()
 
#Applying a filter to the image
im_sharp = im.filter( ImageFilter.SHARPEN )
#Saving the filtered image to a new file
im_sharp.save( 'image_sharpened.jpg', 'JPEG' )
 
#Splitting the image into its respective bands, i.e. Red, Green,
#and Blue for RGB
r,g,b = im_sharp.split()
 
#Viewing EXIF data embedded in image
exif_data = im._getexif()
exif_data

There are more examples of the Pillow library in thePillow tutorial.

Open Source Computer Vision

Open Source Computer Vision, more commonly known as OpenCV, is a more advancedimage manipulation and processing software than PIL. It has been implementedin several languages and is widely used.

Installation

In Python, image processing using OpenCV is implemented using the cv2 andNumPy modules. The installation instructions for OpenCVshould guide you through configuring the project for yourself.

NumPy can be downloaded from the Python Package Index(PyPI):

$ pip install numpy

Example

import cv2
#Read Image
img = cv2.imread('testimg.jpg')
#Display Image
cv2.imshow('image',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
 
#Applying Grayscale filter to image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
#Saving filtered image to new file
cv2.imwrite('graytest.jpg',gray)

There are more Python-implemented examples of OpenCV in this collection oftutorials.

原文: https://docs.python-guide.org/scenarios/imaging/