How do I convert a PIL Image into a NumPy array?

Open I as an array:

>>> I = numpy.asarray(PIL.Image.open('test.jpg'))

Do some stuff to I, then, convert it back to an image:

>>> im = PIL.Image.fromarray(numpy.uint8(I))

Source: Filter numpy images with FFT, Python

If you want to do it explicitly for some reason, there are pil2array() and array2pil() functions using getdata() on this page in correlation.zip.

You need to convert your image to a numpy array this way:

import numpy
import PIL

img = PIL.Image.open("foo.jpg").convert("L")
imgarr = numpy.array(img) 

The example, I have used today:

import PIL
import numpy
from PIL import Image

def resize_image(numpy_array_image, new_height):
    # convert nympy array image to PIL.Image
    image = Image.fromarray(numpy.uint8(numpy_array_image))
    old_width = float(image.size[0])
    old_height = float(image.size[1])
    ratio = float( new_height / old_height)
    new_width = int(old_width * ratio)
    image = image.resize((new_width, new_height), PIL.Image.ANTIALIAS)
    # convert PIL.Image into nympy array back again
    return array(image)

I am using Pillow 4.1.1 (the successor of PIL) in Python 3.5. The conversion between Pillow and numpy is straightforward.

from PIL import Image
import numpy as np
im = Image.open('1.jpg')
im2arr = np.array(im) # im2arr.shape: height x width x channel
arr2im = Image.fromarray(im2arr)

One thing that needs noticing is that Pillow-style im is column-major while numpy-style im2arr is row-major. However, the function Image.fromarray already takes this into consideration. That is, arr2im.size == im.size and arr2im.mode == im.mode in the above example.

We should take care of the HxWxC data format when processing the transformed numpy arrays, e.g. do the transform im2arr = np.rollaxis(im2arr, 2, 0) or im2arr = np.transpose(im2arr, (2, 0, 1)) into CxHxW format.

If your image is stored in a Blob format (i.e. in a database) you can use the same technique explained by Billal Begueradj to convert your image from Blobs to a byte array.

In my case, I needed my images where stored in a blob column in a db table:

def select_all_X_values(conn):
    cur = conn.cursor()
    cur.execute("SELECT ImageData from PiecesTable")    
    rows = cur.fetchall()    
    return rows

I then created a helper function to change my dataset into np.array:

X_dataset = select_all_X_values(conn)
imagesList = convertToByteIO(np.array(X_dataset))

def convertToByteIO(imagesArray):
    """
    # Converts an array of images into an array of Bytes
    """
    imagesList = []

    for i in range(len(imagesArray)):  
        img = Image.open(BytesIO(imagesArray[i])).convert("RGB")
        imagesList.insert(i, np.array(img))

    return imagesList

After this, I was able to use the byteArrays in my Neural Network.

plt.imshow(imagesList[0])

def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()

You can transform the image into numpy
by parsing the image into numpy() function after squishing out the features( unnormalization)

Convert Numpy to PIL image and PIL to Numpy

import numpy as np
from PIL import Image

def pilToNumpy(img):
    return np.array(img)

def NumpyToPil(img):
    return Image.fromarray(img)

I can vouch for svgtrace, I found it both super simple and relatively fast. Find it here: https://pypi.org/project/svgtrace/

This is how I used it:

from svgtrace import trace

asset_path = 'image.png'
save_path = 'traced_image.svg'

Path(save_path).write_text(trace(asset_path), encoding='utf-8')

It took an average of 3 seconds for a 1080x1080px image on my machine. (MacBook Pro 2017)