If you're looking to quickly downsample an image, one simple method is to use array slicing like this: img_smaller = img[::10, ::10, :]. Keep in mind that this will decrease the size of the image by a factor of 10 without applying any interpolation, which can result in a coarse appearance and possible clipping of edges if the dimensions are not multiples of 10:
Here's how the image looks before downsampling:
https://i.stack.imgur.com/jYKuv.png
After downsampling using array slicing:
https://i.stack.imgur.com/g1Ec0.png
Alternatively, another approach is to downsample the image after loading it with the help of the PIL library. By setting downsample=12, you achieve a downsampling of 12x:
https://i.stack.imgur.com/5ua1E.png
Using PIL for downsampling:
from PIL import Image
#Load image or create a test image
np.random.seed(0)
img = Image.fromarray(np.random.randint(0, 255, size=(100, 100, 3), dtype=np.ubyte))
img = Image.open('../image.png')
#Downsample 12x and apply interpolation
downsample = 12
img_small = img.resize((img.height // downsample, img.width // downsample),
resample=Image.BICUBIC)
h, w = img_small.height, img_small.width
img_small_arr = np.asarray(img_small)
#Plotting
ax = plt.figure(figsize=(3, 3)).add_subplot(projection='3d')
xx, yy = np.meshgrid(np.linspace(0, 1, w), np.linspace(0, 1, h))
zz = np.ones((h, w))
ax.plot_surface(xx, yy, zz, facecolors=img_small_arr / 255, shade=False)
#facecolor=[0,0,0,0], linewidth=1) #optionally add a 'grid' effect
A one-liner method using slicing:
#Downsample using slicing
img_small_arr = np.asarray(img)[::10, ::10, :]
#New dimensions
h, w, _ = img_small_arr.shape
#Continue plotting as previously done
ax = plt.figure(figsize=(3, 3)).add_subplot(projection='3d')
xx, yy = np.meshgrid(np.linspace(0, 1, w), np.linspace(0, 1, h))
zz = np.ones((h, w))
ax.plot_surface(xx, yy, zz, facecolors=img_small_arr / 255, shade=False)