How to get only possible non overlapping blocks from images of any given size and show them in Python

I have images of varying resolutions, and I would like to extract non-overlapping blocks from these images.

However, because the images have not fixed size and my block size is big (64x64), I would like to get only non-overlapping blocks that could be found in an image. If the block exceeds the image borders, I don't want to get them.

I tried the view_as_blocks function from scikit-image as below:

from skimage.util import view_as_blocks
for elem in listOfFiles:
    # Reading image
    print("Reading image "+elem)

    img = cv2.imread(elem)
    print(img.shape) #for example, one image is (2059, 2059, 3)

    Blocks = view_as_blocks(img, block_shape=(64, 64, 3))

The code returns the following error:

ValueError: 'block_shape' is not compatible with 'arr_in'

I also tried the Patch Extractor from scikit-learn, as follows:

from sklearn.feature_extraction import image
import cv2
import numpy 

for elem in listOfFiles:
    # Reading image
    print("Reading image "+elem)

    img = cv2.imread(elem)
    print(img.shape)

    pe = image.PatchExtractor(patch_size=(64,64))
    pe_fit = pe.fit(img)
    pe_trans = pe.transform(img)
    print('Patches shape: {}'.format(pe_trans.shape))

The error that returns to me is the following:

ValueError: negative dimensions are not allowed

the function image.extract_patches_2d from sklearns runs perfectly, but unfortunatelly it works only for overlapping blocks as you can see here.

These functions also don't help me because I also want to show the image with these blocks selected, so I also need another matrix with coordinates of such blocks and show the selected blocks.

Is that possible to that in Python?

Solution

Since you don't care about the incomplete blocks at the edges, you can manually check the number of blocks along each dimension, and crop your image to that shape:

from skimage.util import view_as_blocks


for elem in listOfFiles:
    # Reading image
    print("Reading image "+elem)

    img = cv2.imread(elem)
    print(img.shape) #for example, one image is (2059, 2059, 3)
    block_shape = np.array((64, 64, 3))
    nblocks = np.array(img.shape) // block_shape  # integer division
    crop_r, crop_c, crop_ch = nblocks * block_shape
    cropped_img = img[:crop_r, :crop_c, :crop_ch]
    Blocks = view_as_blocks(cropped_img, block_shape=(64, 64, 3))