Python Read Pil Image Into 2-d Numpy Array

Images are an easier way to represent the working model. In Machine Learning, Python uses the epitome data in the format of Height, Width, Aqueduct format. i.east. Images are converted into Numpy Array in Height, Width, Channel format.

Modules Needed:

• NumPy: By default in higher versions of Python similar 3.ten onwards, NumPy is available and if non  available(in lower versions), one can install by using

pip install numpy

• Pillow: This has to be explicitly installed in later versions besides. It is a preferred image manipulation tool. In Python 3, Pillow python library which is nothing but the upgradation of PIL only. It can be installed using

pip install Pillow

One can hands cheque the version of installed Pillow past using the below lawmaking

Python3

import PIL

print ( 'Installed Pillow Version:' , PIL.__version__)

Output:

Installed Pillow Version: vii.2.0        

Loading the images via Pillow Library

Let us check for an image that is in the PNG or JPEG format. The image can exist referred via its path. Image class is the heart of PIL. It has open up() function which opens upwards an paradigm and digital file format tin can be retrieved besides equally pixel format.

Paradigm Used:

Python3

from PIL import Image

prototype = Image. open up ( 'Sample.png' )

print (image. format )

print (paradigm.size)

impress (image.mode)

Output :

PNG (400, 200) RGB

Converting an image into NumPy Array

Python provides many modules and API's for converting an image into a NumPy array. Let's talk over a few of them in particular.

Using NumPy module

Numpy module in itself provides various methods to exercise the aforementioned. These methods are –

Method 1: Using asarray() function

asarray() function is used to convert PIL images into NumPy arrays. This part converts the input to an array

Python3

from PIL import Image

from numpy import asarray

img = Image. open ( 'Sample.png' )

numpydata = asarray(img)

impress ( type (numpydata))

print (numpydata.shape)

Output :

<grade 'numpy.ndarray'> (200, 400, 3)

Method ii: Using numpy.assortment() part

Past using numpy.array() function which takes an prototype as the argument and converts to NumPy array

Python3

from PIL import Image

import numpy

img = Image. open up ( "Sample.png" )

np_img = numpy.array(img)

print (np_img.shape)

Output :

(200, 400, iii)

In club to get the value of each pixel of the NumPy array image, nosotros need to print the retrieved data that got either from asarray() function or array() part.

Python3

from PIL import Image

from numpy import asarray

img = Image. open ( 'Sample.png' )

numpydata = asarray(img)

print (numpydata)

Output :

[[[111  60   0]  [116  65   0]  [122  69   0]  ...  [ 97  47   0]  [ 99  47   0]  [100  49   0]] [[111  61   0]  [118  65   0]  [122  69   0]  ...  [ 97  47   0]  [ 99  48   0]  [100  49   0]] [[118  65   0]  [122  69   0]  [126  73   3]  ...  [ 98  48   0]  [100  49   0]  [100  49   0]] ... [[ 96  44   7]  [ 95  43   half dozen]  [ 93  41   4]  ...  [225  lxxx   3]  [228  80   0]  [229  78   0]] [[ 93  40   6]  [ 90  37   5]  [ 85  32   0]  ...  [226  81   4]  [231  80   1]  [232  79   1]] [[ 89  36   4]  [ 84  31   0]  [ 79  26   0]  ...  [228  81   4]  [232  81   four]  [233  80   ii]]]

Getting back the prototype from converted Numpy Array

Image.fromarray() function helps to get back the image from converted numpy array. Nosotros get back the pixels also same afterwards converting back and forth. Hence, this is very much efficient

Python3

from PIL import Image

from numpy import asarray

img = Prototype. open ( 'Sample.png' )

numpydata = asarray(img)

print ( type (numpydata))

print (numpydata.shape)

pilImage = Image.fromarray(numpydata)

print ( type (pilImage))

print (pilImage.fashion)

impress (pilImage.size)

Output :

<grade 'numpy.ndarray'> (200, 400, 3) <class 'PIL.Image.Image'> RGB (400, 200)

Converting Images using Keras API

Keras API provides the functions for loading, converting, and saving epitome data. Keras is possible to run on the top of the TensorFlow framework and hence that is mandatory to have. Deep learning computer vision images require Keras API. To install it type the below control in the final

pip install keras

Every bit Keras requires TensorFlow 2.ii or higher. If not there, demand to install it. To install it type the below command in the terminal.

pip install tensorflow

Python3

from keras.preprocessing.image import load_img

import warnings

img = load_img( 'sample.png' )

impress ( type (img))

impress (img. format )

print (img.mode)

print (img.size)

Output :

<course 'PIL.PngImagePlugin.PngImageFile'> PNG RGB (400, 200)

Using Keras API, catechumen images to Numpy Array and reverting the prototype from Numpy Array

Python3

from keras.preprocessing.image import load_img

import warnings

from keras.preprocessing.image import img_to_array

from keras.preprocessing.image import array_to_img

img = load_img( 'sample.png' )

print ( type (img))

impress (img. format )

impress (img.way)

impress (img.size)

img_numpy_array = img_to_array(img)

print ( "Image is converted and NumPy array information :" )

print ( type (img_numpy_array))

print ( "type:" , img_numpy_array.dtype)

print ( "shape:" , img_numpy_array.shape)

img_pil_from_numpy_array = array_to_img(img_numpy_array)

print ( "converting NumPy array into prototype:" ,

blazon (img_pil_from_numpy_array))

Output :

<course 'PIL.PngImagePlugin.PngImageFile'> PNG RGB (400, 200) Image is converted and NumPy array information : <grade 'numpy.ndarray'> type: float32 shape: (200, 400, 3) converting NumPy array into image: <class 'PIL.Image.Image'>

From the to a higher place output, we can check that the source image PIL.Paradigm.Epitome and destination image types are the aforementioned.

Using OpenCV Library

OpenCV version from 3.x has DNN and Caffe frameworks, and they are very helpful to solve deep learning problems. It can be installed by using

pip install opencv-contrib-python

cv2 package has the post-obit methods

• imread() function is used to load the paradigm and It also reads the given image (PIL paradigm) in the NumPy assortment format.
• Then nosotros need to catechumen the image colour from BGR to RGB.
• imwrite() is used to relieve the image in the file.

Python3

import cv2

epitome = cv2.imread( 'Sample.png' )

img = cv2.cvtColor(prototype, cv2.COLOR_BGR2RGB)

cv2.imwrite( 'opncv_sample.png' , img)

print ( blazon (img))

Output :

<form 'numpy.ndarray'>

Conclusion

Python is a very flexible tool and we have seen ways of converting images into Numpy Array and similarly back to images using different APIs. Manipulating the converted array and forming different image data and one can feed into deep learning neural networks.

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Source: https://www.geeksforgeeks.org/how-to-convert-images-to-numpy-array/

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