In Python, how can an image stored as a NumPy array be scaled in size?

You can use numpy.kron as suggested in the comment or you can use the following below options

1] Using PILLOW to maintain the Aspect Ratio

• If you want to maintain the aspect ratio of the image then you can use thumbnail() method

  from PIL import Imagedef scale_image(input_image_path,            output_image_path,            width=None,            height=None):    original_image = Image.open(input_image_path)    w, h = original_image.size    print('The original image size is {wide} wide x {height} '          'high'.format(wide=w, height=h))    if width and height:        max_size = (width, height)    elif width:        max_size = (width, h)    elif height:        max_size = (w, height)    else:        # No width or height specified        raise RuntimeError('Width or height required!')    original_image.thumbnail(max_size, Image.ANTIALIAS)    original_image.save(output_image_path)    scaled_image = Image.open(output_image_path)    width, height = scaled_image.size    print('The scaled image size is {wide} wide x {height} '          'high'.format(wide=width, height=height))if __name__ == '__main__':     scale_image(input_image_path='caterpillar.jpg',                 output_image_path='caterpillar_scaled.jpg',                 width=800)

• I used Image.ANTIALIAS flag which will apply a high quality down sampling filter which results in a better image

2] Using OpenCV

• OpenCV has cv2.resize() function

  import cv2image = cv2.imread("image.jpg")   # when reading the image the image original size is 150x150print(image.shape)scaled_image = cv2.resize(image, (24, 24))  # when scaling we scale original image to 24x24 print(scaled_image.shape)

• Output

  (150, 150)(24, 24)

• cv2.resize() function also has interpolation as argument by which you can specify how you want to resize the image

• INTERPOLATION METHODS:

  • INTER_NEAREST - a nearest-neighbor interpolation
  • INTER_LINEAR - a bilinear interpolation (used by default)
  • INTER_AREA - resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire’-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method.
  • INTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhood
  • INTER_LANCZOS4 - a Lanczos interpolation over 8x8 pixel neighborhood

3] Using PILLOW library

• Use Image.resize()

  from PIL import Imagesourceimage= Image.open("image.jpg")   # original image of size 150x150resized_image = sourceimage.resize((24, 24), resample=NEAREST)  # resized image of size 24x24resized_image.show()

4] Using SK-IMAGE library

• Use skimage.transform.resize()

  from skimage import ioimage = io.imread("image.jpg")print(image.shape)resized_image = skimage.transform.resize(image, (24, 24))print(resized_image.shape)

• Output

  (150, 150)(24, 24)

5] Use SciPy

• Use scipy.misc.imresize() function

  import numpy as npimport scipy.miscimage = scipy.misc.imread("image.jpg")print(image.shape)resized_image = scipy.misc.imresize(x, (24, 24))resized_imageprint(resized_image.shape)

• Output

  (150, 150)(24, 24)

In scikit-image, we have transform

from skimage import transform as tfimport matplotlib.pyplot as pltimport numpy as npdata = np.random.random((1, 15, 3))*255data = data.astype(np.uint8)new_data = tf.resize(data, (600, 300, 3), order=0) # order=0, Nearest-neighbor interpolationf, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(10, 10))ax1.imshow(data)ax2.imshow(new_data)ax3.imshow(tf.resize(data, (600, 300, 3), order=1))

Here's a snippet of code that resizes an image stored in a numpy array using PIL. In this example, img is a two-dimensional numpy array.

from PIL import Imageimport numpy as npnr,nc = img.shapeshrinkFactor = .5img_pil = Image.fromarray(img)img_pil = img_pil.resize((round(nc*shrinkFactor),round(nr*shrinkFactor)))img_resized = np.array(img_pil)