Pythoninformer
pythoninformer.com › python-libraries › numpy › numpy-and-images
PythonInformer - Image processing with pillow and NumPy
October 2, 2022 - The PIL function Image.fromarray function creates a PIL image from a NumPy array. If the NumPy array has the shape (height, width, 3) it will automatically create an RGB image. We can then use the PIL function save to save the image.
Readthedocs
pc-pillow.readthedocs.io › en › latest › Image_class › Image_fromarray.html
3. Image fromarray — PC-Pillow
from PIL import Image import numpy ... Image.fromarray(data, 'RGB') # im.show() im.save("Image/Image_fromarray.png") The image is put in an numpy array, inverted and then given to the fromarray method to make an image....
python - Image.fromarray changes size - Stack Overflow
I have data that I want to store into an image. I created an image with width 100 and height 28, my matrix has the same shape. When I use Image.fromarray(matrix) the shape changes: from PIL import... More on stackoverflow.com
Image.fromarray() cannot handle 1-D arrays
Running Pillow 7.0.0 (EDIT: now 7.1.2) on Python 3.7.6 on Windows 10 x64 I am converting a float32 grayscale array to Pillow so that I can use some of the image operations. This is not strictly an ... More on github.com
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May 3, 2020python - How to read image from numpy array into PIL Image? - Stack Overflow
I am trying to read an image from a numpy array using PIL, by doing the following: from PIL import Image import numpy as np #img is a np array with shape (3,256,256) Image.fromarray(img) and am ge... More on stackoverflow.com
[np and PIL] how do you load images as array and then reshape back to view the image
Image.fromarray takes an array of type np.uint8 so you can either initialize your array as np.uint8 or convert the final image to np.uint8 arr = np.zeros((20, 5000), np.uint8) Image.fromarray(np.uint8(arr[0].reshape((50, 100)), "L") More on reddit.com
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September 11, 2022Nrao
casa.nrao.edu › docs › CasaRef › image.fromarray.html
1.1.1.0 image.fromarray - Function
""" # make an image with a default RA/Dec/Stokes/Frequency coordinate system # having all pixels set to 2.5. ary = ia.makearray(v=2.5, shape=[64, 64, 4, 128]) # the ia tool does not need to reference an image in this case (ie open() # need not have been called), if it does reference another image, that reference # will be lost and replaced with a reference to the newly created image. res = ia.fromarray(outfile=’test.data’, pixels=ary, overwrite=true) if res: # perform operations on the newly created image if desired and make sure # to close it when done to free up system resources (eg memory) ia.shape() ia.done() """
Note.nkmk.me
note.nkmk.me › home › python › numpy
Image processing with Python, NumPy | note.nkmk.me
October 20, 2020 - from PIL import Image import numpy as np im = np.array(Image.open('data/src/lena_square.png').resize((256, 256))) print(im.shape) # (256, 256, 3) im_0 = im[:, :100] im_1 = im[:, 100:] print(im_0.shape) # (256, 100, 3) print(im_1.shape) # (256, 156, 3) Image.fromarray(im_0).save('data/dst/lena_numpy_split_0.jpg') Image.fromarray(im_1).save('data/dst/lena_numpy_split_1.jpg')
Pillow Documentation
pillow.readthedocs.io › en › stable › reference › Image.html
Image module - Pillow (PIL Fork) 12.3.0 documentation
from PIL import Image import numpy as np a = np.zeros((5, 5)) im = Image.fromarray(a) If obj is not contiguous, then the tobytes method is called and frombuffer() is used. In the case of NumPy, be aware that Pillow modes do not always correspond to NumPy dtypes.
ProgramCreek
programcreek.com › python › example › 82086 › Image.fromarray
Python Examples of Image.fromarray
def transform_rgb_to_bgr(image): sub = image.convert("RGB") data = np.array(sub) red, green, blue = data.T data = np.array([blue, green, red]) data = data.transpose() return Image.fromarray(data) ... def _save(self): tt = datetime.now() time_string = tt.strftime('%mm-�-%Hh-%Mm-%Ss') W = self.model.conv1.W.get_value() W = W.transpose(1, 2, 0, 3) row_list = [] img_list = [] k = 0 rows = W.shape[3] / 16 bar = 0.5 * numpy.ones((W.shape[0], 1, 3)) for i in range(rows): row_list.append(bar) for j in range(16): W0 = W[:, :, :, k] W0 -= W0.min() W0 /= W0.max() # W0[:, :,0] -= W0[:, :,0].min() # W0[:
scikit-image
scikit-image.org › skimage-tutorials › lectures › 00_images_are_arrays.html
Images are numpy arrays — Image analysis in Python
Type: <class 'numpy.ndarray'> dtype: uint8 shape: (303, 384)
Pluralsight
pluralsight.com › blog › tech guides & tutorials
Importing Image Data Into NumPy Arrays | Pluralsight
April 15, 2025 - from PIL import Image from numpy import asarray # load the image image = Image.open('kolala.jpeg') # convert image to numpy array data = asarray(image) print(type(data)) # summarize shape print(data.shape) # create Pillow image image2 = Image.fromarray(data) print(type(image2)) # summarize image details print(image2.mode) print(image2.size)
Programtalk
programtalk.com › python-examples › PIL.Image.fromarray
PIL.Image.fromarray Example - Program Talk
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Top answer 1 of 2
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PIL and numpy have different indexing systems. matrix[a, b] gives you the point at x position b, and y position a, but img.getpixel((a, b)) gives you the point at x position a, and y position b. As a result of this, when you are converting between numpy and PIL matrices, they switch their dimensions. To fix this, you could take the transpose (matrix.transpose()) of the matrix.
Here's what's happening:
import numpy as np
from PIL import Image
img = Image.new('L', (100, 28))
img.putpixel((5, 3), 17)
matrix = np.array(img)
print matrix[5, 3] #This returns 0
print matrix[3, 5] #This returns 17
matrix = matrix.transpose()
print matrix[5, 3] #This returns 17
print matrix[3, 5] #This returns 0
2 of 2
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NumPy and PIL have different indexing systems. So a (100, 28) numpy array will be interpreted as an image with width 28 and height 100.
If you want a 28x100 image, then you should swap the dimensions for your image instantiation.
img = Image.new('L', (28, 100))
If you want a 100x28 image, then you should transpose the numpy array.
tmp = Image.fromarray(matrix.transpose())
More generally, if you're working with RGB, you can use transpose() to only swap the first two axes.
>>> arr = np.zeros((100, 28, 3))
>>> arr.shape
(100, 28, 3)
>>> arr.transpose(1, 0, 2).shape
(28, 100, 3)
Medium
medium.com › @whyamit101 › step-by-step-guide-to-convert-numpy-array-to-pil-image-f7f8492cd785
Step-by-Step Guide to Convert NumPy Array to PIL Image | by why amit | Medium
February 9, 2025 - The shape of the array is (100, 100), meaning it’s a 100x100 pixel image. No RGB channels—just simple brightness values. ... Now comes the exciting part. We’ll use Image.fromarray() again, but with a small twist—this time, we’ll specify ...
GitHub
github.com › python-pillow › Pillow › issues › 4606
Image.fromarray() cannot handle 1-D arrays · Issue #4606 · python-pillow/Pillow
May 3, 2020 - When I try to load the array into the Pillow Image class, it throws an error because __array_interface__['shape'][1] is missing on a 1-D array. This is the Numpy intended behavior (see https://numpy.org/doc/stable/reference/generated/numpy.ndarray.shape.html). There is no check for this in Image.fromarray().
Author: python-pillow
Coderz Column
coderzcolumn.com › tutorials › python › pillow
Image Processing using Pillow by Sunny Solanki
November 4, 2019 - We can create Image instance from numpy array again with Image.fromarray() function. nparr = np.array(img) nparr.shape, nparr.dtype, nparr ·
GeeksforGeeks
geeksforgeeks.org › python › how-to-convert-images-to-numpy-array
How to Convert images to NumPy array? - GeeksforGeeks
July 15, 2025 - Explanation: It converts a PIL image to a NumPy array using img_to_array() for processing, then back to a PIL image using array_to_img(), confirming smooth transformation with array type float32 and shape (200, 400, 3).
Readthedocs
image-utils.readthedocs.io
Image Utils — Image Utils documentation
Simply wrap your NumPy array, PyTorch Tensor, or PIL image with Im() and let it handle conversions between shapes, formats, etc. We can replace this: numpy_img = (torch.rand(10, 3, 256, 256).permute(0, 2, 3, 1).detach().float().cpu().numpy() * 255).astype(np.uint8) Image.fromarray(numpy_img[0]).save('output.png') With this: Im(torch.rand(10, 3, 256, 256)).save() The powerful part is not that this works for a specific input shape/dtype/range, but [almost] any combination.
Guiwitz
guiwitz.github.io › PyImageCourse_beginner › 02-Images_as_arrays.html
2. Images as arrays — Image processing with Python for beginners
Above we have used a function that takes an array as input and returns again an array (of the same shape) as output. However there are functions that summarize the array and that return for example a single number.
MachineLearningMastery
machinelearningmastery.com › home › blog › how to load and manipulate images for deep learning in python with pil/pillow
How to Load and Manipulate Images for Deep Learning in Python With PIL/Pillow - MachineLearningMastery.com
September 12, 2019 - The process can be reversed converting a given array of pixel data into a Pillow Image object using the Image.fromarray() function. This can be useful if image data is manipulated as a NumPy array and you then want to save it later as a PNG or JPEG file. The example below loads the photo as a Pillow Image object and converts it to a NumPy array, then converts it back to an Image object again. Running the example first loads the photo as a Pillow image then converts it to a NumPy array and reports the shape of the array.
Reddit
reddit.com › r/learnpython › [np and pil] how do you load images as array and then reshape back to view the image
r/learnpython on Reddit: [np and PIL] how do you load images as array and then reshape back to view the image
September 11, 2022 -
arr = np.zeros((20,5000))
for idx,file in enumerate(images):
file_to_open = folder / file
print(file_to_open)
img = Image.open(file_to_open)
img = img.reduce(4)
print(img.size)
arr[idx]=np.asarray(img).reshape((1,5000))I have 19 images of 80 W x 61 H ( grayscale)I just want to stack 20x 5000 for machine learning.
examplebut problem occurs when I wanted to display only 1 image after the combine and I wasn't able to re-construct the image array to display the it as gif using the code below
Image.fromarray(arr[0].reshape((50,100),'L')
I think it didn't reshape properly..
Apologies if it's not entirely Python.
anyone familiar with PIL and reshape? Thanks
w3resource
w3resource.com › python-exercises › numpy › python-numpy-exercise-109.php
Python NumPy: Convert a numpy array to an image - w3resource
August 29, 2025 - # Importing the Image module from the PIL library from PIL import Image # Importing the NumPy library and aliasing it as 'np' import numpy as np # Defining image width and height img_w, img_h = 200, 200 # Creating a NumPy array 'data' filled with zeros of shape (img_h, img_w, 3) representing RGB channels data = np.zeros((img_h, img_w, 3), dtype=np.uint8) # Setting a specific pixel (100, 100) in the 'data' array to the color red [255, 0, 0] data[100, 100] = [255, 0, 0] # Creating an image object 'img' from the NumPy array 'data', specifying the color mode as 'RGB' img = Image.fromarray(data, 'RGB') # Saving the image as 'test.png' img.save('test.png') # Displaying the image img.show() Sample Output: Explanation: In the above code – ·