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Shiven Mian
107 lines
3.0 KiB
ReStructuredText
107 lines
3.0 KiB
ReStructuredText
==================
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Image Manipulation
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==================
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.. todo::
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Add introduction about image manipulation and its Python libraries.
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Most image processing and manipulation techniques can be carried out effectively using
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two libraries: Python Imaging Library (PIL) and OpenSource Computer Vision (OpenCV).
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A brief description of both is given below.
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Python Imaging Library
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----------------------
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The `Python Imaging Library <http://www.pythonware.com/products/pil/>`_, or PIL
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for short, is one of the core libraries for image manipulation in Python. Unfortunately,
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its development has stagnated, with its last release in 2009.
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Luckily for you, there's an actively-developed fork of PIL called
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`Pillow <http://python-pillow.github.io/>`_ - it's easier to install, runs on
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all operating systems, and supports Python 3.
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Installation
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~~~~~~~~~~~~
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Before installing Pillow, you'll have to install Pillow's prerequisites. Find
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the instructions for your platform in the
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`Pillow installation instructions <https://pillow.readthedocs.org/en/3.0.0/installation.html>`_.
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After that, it's straightforward:
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.. code-block:: console
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$ pip install Pillow
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Example
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~~~~~~~
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.. code-block:: python
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from PIL import Image, ImageFilter
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#Read image
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im = Image.open( 'image.jpg' )
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#Display image
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im.show()
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#Applying a filter to the image
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im_sharp = im.filter( ImageFilter.SHARPEN )
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#Saving the filtered image to a new file
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im_sharp.save( 'image_sharpened.jpg', 'JPEG' )
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#Splitting the image into its respective bands, i.e. Red, Green,
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#and Blue for RGB
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r,g,b = im_sharp.split()
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#Viewing EXIF data embedded in image
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exif_data = im._getexif()
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exif_data
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There are more examples of the Pillow library in the
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`Pillow tutorial <http://pillow.readthedocs.org/en/3.0.x/handbook/tutorial.html>`_.
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OpenSource Computer Vision
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---------------------------
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OpenSource Computer Vision, or OpenCV in short, is a slightly more advanced and useful
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image manipulation and processing software than PIL. It has been implemented in several
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languages and is very widely used.
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Installation
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~~~~~~~~~~~~~
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In Python, image processing using OpenCV is implemented using the **cv2** and **NumPy** modules.
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Check the installation instructions for OpenCV `here <https://help.ubuntu.com/community/OpenCV>`_.
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NumPy can be easily downloaded from the Python Package Index(PyPI):
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.. code-block:: console
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$ pip install numpy
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Example
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~~~~~~~~
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.. code-block:: python
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from cv2 import *
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import numpy as np
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#Read Image
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img = cv2.imread('testimg.jpg')
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#Display Image
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cv2.imshow('image',img)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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#Applying Grayscale filter to image
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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#Saving filtered image to new file
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cv2.imwrite('graytest.jpg',gray)
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There are more examples of OpenCV in the documentation
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`here <http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_tutorials.html>`_.
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