I have an array of image pixel values that I would like to upscale for input into my neural network. It is an array of shape (28000, 48, 48, 1). These are normalized image pixel values and would like to upscale these to a higher resolution for input into my CNN. The arrays look like this...
array([[[[-0.6098866 ],
[-0.4592209 ],
[-0.40325198],
...,
[-0.7694696 ],
[-0.90518403],
[-0.95160526]],
[[-0.66049284],
[-0.68162924],
[-0.694159 ],
Both my X_train and y_train image arrays have shape of (28000,48,48,1). I would like to upscale or resize these 28000 image arrays to size 75x75. Please help. Should I convert arrays back to non-normalized arrays or images and then maybe use cv2 to upscale? How would I do this?
One easy way to resize images is using the Python module PIL (Python Image Library), which you can install with pip install pillow. Example below to demonstrate resizing a single image:
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
# Open image
panda_pil = Image.open("panda.jpg")
print(np.array(panda_pil).shape)
# (613, 696, 3)
panda_pil_resized = panda_pil.resize((75, 75))
print(np.array(panda_pil_resized).shape)
# (75, 75, 3)
plt.imshow(np.array(panda_pil_resized))
plt.show()
You can download the panda image as follows:
import urllib.request
panda_fname = "panda.jpg"
panda_url = "https://upload.wikimedia.org/wikipedia/commons/f/fe/Giant_Panda_in_Beijing_Zoo_1.JPG"
urllib.request.urlretrieve(panda_url, panda_fname)
To resize all 28000 images, one approach would be to do this as a preprocessing step in a for-loop, and save the images to a numpy array.
Edit: You can loop through your original 28000x2304 image array and upscale each image individually in a for-loop. To get the PIL.Image object from a np.ndarray object, you can use Pil.Image.from_array, as shown below (I have just generated a random array of Gaussian noise but it should work the same with your images):
import numpy as np
from PIL import Image
from time import perf_counter
old_width, old_height = 48, 48
new_width, new_height = 75, 75
num_images = 28000
old_image_array = np.random.normal(size=[num_images, old_width*old_height])
new_image_array = np.empty(shape=[num_images, new_width*new_height])
print("Starting conversion...")
t0 = perf_counter()
# Loop over each image individually
for i in range(num_images):
# Get the ith image and reshape
old_image = old_image_array[i].reshape(old_width, old_height)
# Convert to PIL.Image
old_image_pil = Image.fromarray(old_image)
# Upscale resolution
new_image_pil = old_image_pil.resize((new_width, new_height))
# Convert to numpy array
new_image = np.array(new_image_pil)
# Reshape and store in new image array
new_image_array[i] = new_image.reshape(new_width*new_height)
t1 = perf_counter()
print("Time taken = {:.3f} s".format(t1 - t0))
print(old_image_array.shape, new_image_array.shape)
Console output:
Starting conversion...
Time taken = 2.771 s
(28000, 2304) (28000, 5625)
There may well be a more efficient way of doing this, but this method is simple, and uses tools which are useful to know about if you don't know about them already (PIL is a good module for manipulating images, see this blog post if you want to learn more about PIL).
I saved an numpy array to an image as follows:
plt.imshow(xNext[0,:,:,0]) #xNext has shape (1,64,25,1)
print(xNext[0,:,:,0].shape) #outputs (64,25)
plt.savefig(os.path.join(root,filename)+'.png')
np.save(os.path.join(root,filename)+'.npy',xNext[0,:,:,0])
How can I obtain the same numpy array back from the .png saved image? Can you also please show me if I had saved as .jpg image?
I've tried the following and works with 3D array (v1) where resulting image close to the original numpy array produced image (original).
image = Image.open(imageFilename) #brings in as 3D array
box = (315,60,500,540)
image = image.crop(box)
image = image.resize((25,64)) #to correct to desired shape
arr = np.asarray(image)
plt.imshow(arr)
plt.savefig('v1.png')
plt.close()
However, when I convert the 3D array to 2D array, the resulting image is different (v1b and v1c).
arr2 = arr[:,:,0]
plt.imshow(arr2)
plt.savefig('v1b.png')
plt.close()
arr3 = np.dot(arr[...,:3],[0.299,0.587,0.11])
plt.imshow(arr3)
plt.savefig('v1c.png')
plt.close()
How can I convert the 3D to 2D correctly? Thanks for your help.
original, v1 (saved from 3D array)
v1b, v1c (saved from 2D arrays)
original (with original size)
If your objective is to save a numpy array as an image, your approach have a problem. The function plt.savefig saves an image of the plot, not the array. Also transforming an array into an image may carry some precision loss (when converting from float64 or float32 to uint16). That been said, I suggest you use skimage and imageio:
import imageio
import numpy as np
from skimage import img_as_uint
data = np.load('0058_00086_brown_2_recording1.wav.npy')
print("original", data.shape)
img = img_as_uint(data)
imageio.imwrite('image.png', img)
load = imageio.imread('image.png')
print("image", load.shape)
This script loads the data you provided and prints the shape for verification
data = np.load('0058_00086_brown_2_recording1.wav.npy')
print("original", data.shape)
then it transform the data to uint, saves the image as png and loads it:
img = img_as_uint(data)
imageio.imwrite('image.png', img)
load = imageio.imread('image.png')
the output of the script is:
original (64, 25)
image (64, 25)
i.e. the image is loaded with the same shape that data. Some notes:
image.png is saved as a grayscale image
To save to .jpg just change to imageio.imwrite('image.jpg', img)
In the case of .png the absolute average distance from the original image was 3.890e-06 (this can be verified using np.abs(img_as_float(load) - data).sum() / data.size)
Information about skimage and imageio can be found in the respectives websites. More on saving numpy arrays as images can be found in the following answers: [1], [2], [3] and [4].
link
from scipy.misc import imread
image_data = imread('test.jpg').astype(np.float32)
This should give you the numpy array (I would suggest using imread from scipy)
I have a matrix in the type of a Numpy array. How would I write it to disk it as an image? Any format works (png, jpeg, bmp...). One important constraint is that PIL is not present.
An answer using PIL (just in case it's useful).
given a numpy array "A":
from PIL import Image
im = Image.fromarray(A)
im.save("your_file.jpeg")
you can replace "jpeg" with almost any format you want. More details about the formats here
This uses PIL, but maybe some might find it useful:
import scipy.misc
scipy.misc.imsave('outfile.jpg', image_array)
EDIT: The current scipy version started to normalize all images so that min(data) become black and max(data) become white. This is unwanted if the data should be exact grey levels or exact RGB channels. The solution:
import scipy.misc
scipy.misc.toimage(image_array, cmin=0.0, cmax=...).save('outfile.jpg')
With matplotlib:
import matplotlib.image
matplotlib.image.imsave('name.png', array)
Works with matplotlib 1.3.1, I don't know about lower version. From the docstring:
Arguments:
*fname*:
A string containing a path to a filename, or a Python file-like object.
If *format* is *None* and *fname* is a string, the output
format is deduced from the extension of the filename.
*arr*:
An MxN (luminance), MxNx3 (RGB) or MxNx4 (RGBA) array.
There's opencv for python (documentation here).
import cv2
import numpy as np
img = ... # Your image as a numpy array
cv2.imwrite("filename.png", img)
useful if you need to do more processing other than saving.
Pure Python (2 & 3), a snippet without 3rd party dependencies.
This function writes compressed, true-color (4 bytes per pixel) RGBA PNG's.
def write_png(buf, width, height):
""" buf: must be bytes or a bytearray in Python3.x,
a regular string in Python2.x.
"""
import zlib, struct
# reverse the vertical line order and add null bytes at the start
width_byte_4 = width * 4
raw_data = b''.join(
b'\x00' + buf[span:span + width_byte_4]
for span in range((height - 1) * width_byte_4, -1, - width_byte_4)
)
def png_pack(png_tag, data):
chunk_head = png_tag + data
return (struct.pack("!I", len(data)) +
chunk_head +
struct.pack("!I", 0xFFFFFFFF & zlib.crc32(chunk_head)))
return b''.join([
b'\x89PNG\r\n\x1a\n',
png_pack(b'IHDR', struct.pack("!2I5B", width, height, 8, 6, 0, 0, 0)),
png_pack(b'IDAT', zlib.compress(raw_data, 9)),
png_pack(b'IEND', b'')])
... The data should be written directly to a file opened as binary, as in:
data = write_png(buf, 64, 64)
with open("my_image.png", 'wb') as fh:
fh.write(data)
Original source
See also: Rust Port from this question.
Example usage thanks to #Evgeni Sergeev: https://stackoverflow.com/a/21034111/432509
You can use PyPNG. It's a pure Python (no dependencies) open source PNG encoder/decoder and it supports writing NumPy arrays as images.
If you have matplotlib, you can do:
import matplotlib.pyplot as plt
plt.imshow(matrix) #Needs to be in row,col order
plt.savefig(filename)
This will save the plot (not the images itself).
for saving a numpy array as image, U have several choices:
1) best of other: OpenCV
import cv2
cv2.imwrite('file name with extension(like .jpg)', numpy_array)
2) Matplotlib
from matplotlib import pyplot as plt
plt.imsave('file name with extension(like .jpg)', numpy_array)
3) PIL
from PIL import Image
image = Image.fromarray(numpy_array)
image.save('file name with extension(like .jpg)')
4) ...
scipy.misc gives deprecation warning about imsave function and suggests usage of imageio instead.
import imageio
imageio.imwrite('image_name.png', img)
You can use 'skimage' library in Python
Example:
from skimage.io import imsave
imsave('Path_to_your_folder/File_name.jpg',your_array)
Addendum to #ideasman42's answer:
def saveAsPNG(array, filename):
import struct
if any([len(row) != len(array[0]) for row in array]):
raise ValueError, "Array should have elements of equal size"
#First row becomes top row of image.
flat = []; map(flat.extend, reversed(array))
#Big-endian, unsigned 32-byte integer.
buf = b''.join([struct.pack('>I', ((0xffFFff & i32)<<8)|(i32>>24) )
for i32 in flat]) #Rotate from ARGB to RGBA.
data = write_png(buf, len(array[0]), len(array))
f = open(filename, 'wb')
f.write(data)
f.close()
So you can do:
saveAsPNG([[0xffFF0000, 0xffFFFF00],
[0xff00aa77, 0xff333333]], 'test_grid.png')
Producing test_grid.png:
(Transparency also works, by reducing the high byte from 0xff.)
For those looking for a direct fully working example:
from PIL import Image
import numpy
w,h = 200,100
img = numpy.zeros((h,w,3),dtype=numpy.uint8) # has to be unsigned bytes
img[:] = (0,0,255) # fill blue
x,y = 40,20
img[y:y+30, x:x+50] = (255,0,0) # 50x30 red box
Image.fromarray(img).convert("RGB").save("art.png") # don't need to convert
also, if you want high quality jpeg's
.save(file, subsampling=0, quality=100)
matplotlib svn has a new function to save images as just an image -- no axes etc. it's a very simple function to backport too, if you don't want to install svn (copied straight from image.py in matplotlib svn, removed the docstring for brevity):
def imsave(fname, arr, vmin=None, vmax=None, cmap=None, format=None, origin=None):
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
fig = Figure(figsize=arr.shape[::-1], dpi=1, frameon=False)
canvas = FigureCanvas(fig)
fig.figimage(arr, cmap=cmap, vmin=vmin, vmax=vmax, origin=origin)
fig.savefig(fname, dpi=1, format=format)
Imageio is a Python library that provides an easy interface to read and write a wide range of image data, including animated images, video, volumetric data, and scientific formats. It is cross-platform, runs on Python 2.7 and 3.4+, and is easy to install.
This is example for grayscale image:
import numpy as np
import imageio
# data is numpy array with grayscale value for each pixel.
data = np.array([70,80,82,72,58,58,60,63,54,58,60,48,89,115,121,119])
# 16 pixels can be converted into square of 4x4 or 2x8 or 8x2
data = data.reshape((4, 4)).astype('uint8')
# save image
imageio.imwrite('pic.jpg', data)
The world probably doesn't need yet another package for writing a numpy array to a PNG file, but for those who can't get enough, I recently put up numpngw on github:
https://github.com/WarrenWeckesser/numpngw
and on pypi: https://pypi.python.org/pypi/numpngw/
The only external dependency is numpy.
Here's the first example from the examples directory of the repository. The essential line is simply
write_png('example1.png', img)
where img is a numpy array. All the code before that line is import statements and code to create img.
import numpy as np
from numpngw import write_png
# Example 1
#
# Create an 8-bit RGB image.
img = np.zeros((80, 128, 3), dtype=np.uint8)
grad = np.linspace(0, 255, img.shape[1])
img[:16, :, :] = 127
img[16:32, :, 0] = grad
img[32:48, :, 1] = grad[::-1]
img[48:64, :, 2] = grad
img[64:, :, :] = 127
write_png('example1.png', img)
Here's the PNG file that it creates:
Also, I used numpngw.write_apng to create the animations in Voronoi diagram in Manhattan metric.
Assuming you want a grayscale image:
im = Image.new('L', (width, height))
im.putdata(an_array.flatten().tolist())
im.save("image.tiff")
If you happen to use [Py]Qt already, you may be interested in qimage2ndarray. Starting with version 1.4 (just released), PySide is supported as well, and there will be a tiny imsave(filename, array) function similar to scipy's, but using Qt instead of PIL. With 1.3, just use something like the following:
qImage = array2qimage(image, normalize = False) # create QImage from ndarray
success = qImage.save(filename) # use Qt's image IO functions for saving PNG/JPG/..
(Another advantage of 1.4 is that it is a pure python solution, which makes this even more lightweight.)
If you are working in python environment Spyder, then it cannot get more easier than to just right click the array in variable explorer, and then choose Show Image option.
This will ask you to save image to dsik, mostly in PNG format.
PIL library will not be needed in this case.
Use cv2.imwrite.
import cv2
assert mat.shape[2] == 1 or mat.shape[2] == 3, 'the third dim should be channel'
cv2.imwrite(path, mat) # note the form of data should be height - width - channel
In the folowing answer has the methods as proposed by #Nima Farhadi in time measurement.
The fastest is CV2 , but it's important to change colors order from RGB to BGR. The simples is matplotlib.
It's important to assure, that the array have unsigned integer format uint8/16/32.
Code:
#Matplotlib
from matplotlib import pyplot as plt
plt.imsave('c_plt.png', c.astype(np.uint8))
#PIL
from PIL import Image
image = Image.fromarray(c.astype(np.uint8))
image.save('c_pil.png')
#CV2, OpenCV
import cv2
cv2.imwrite('c_cv2.png', cv2.cvtColor(c, cv2.COLOR_RGB2BGR))
With pygame
so this should work as I tested (you have to have pygame installed if you do not have pygame install it by using pip -> pip install pygame (that sometimes does not work so in that case you will have to download the wheel or sth but that you can look up on google)):
import pygame
pygame.init()
win = pygame.display.set_mode((128, 128))
pygame.surfarray.blit_array(win, yourarray)
pygame.display.update()
pygame.image.save(win, 'yourfilename.png')
just remember to change display width and height according to your array
here is an example, run this code:
import pygame
from numpy import zeros
pygame.init()
win = pygame.display.set_mode((128, 128))
striped = zeros((128, 128, 3))
striped[:] = (255, 0, 0)
striped[:, ::3] = (0, 255, 255)
pygame.surfarray.blit_array(win, striped)
pygame.display.update()
pygame.image.save(win, 'yourfilename.png')
I attach an simple routine to convert a npy to an image.
from PIL import Image
import matplotlib
img = np.load('flair1_slice75.npy')
matplotlib.image.imsave("G1_flair_75.jpeg", img)
You can use this code for converting your Npy data into an image:
from PIL import Image
import numpy as np
data = np.load('/kaggle/input/objects-dataset/nmbu.npy')
im = Image.fromarray(data, 'RGB')
im.save("your_file.jpeg")