How to check similarity of two images that have different pixelization

The two images are NOT the same - only the thing imaged. The images obviously are different size, as you note yourself. Thus a comparison must fail.

You'll need to employ some kind of similarity check. The first step is to scale up the smaller image to the one of the larger one. Then you need to employ some mean of detecting and defining similarity. There are different ways and methods for that, and any combination of them might be valid.

For example see Checking images for similarity with OpenCV

You can use the imagehash library to compare similar images.

from PIL import Image
import imagehash
hash0 = imagehash.average_hash(Image.open('quora_photo.jpg')) 
hash1 = imagehash.average_hash(Image.open('twitter_photo.jpeg')) 
cutoff = 5  # maximum bits that could be different between the hashes. 

if hash0 - hash1 < cutoff:
  print('images are similar')
else:
  print('images are not similar')

Since the images are not exactly the same, there will be some differences, so therefore we use a cutoff value with an acceptable maximum difference. That difference between the hash objects is the number of bits that are flipped. But imagehash will work even if the images are resized, compressed, different file formats or with adjusted contrast or colors.

The hash (or fingerprint, really) is derived from a 8x8 monochrome thumbnail of the image. But even with such a reduced sample, the similarity comparisons give quite accurate results. Adjust the cutoff to find a balance between false positives and false negatives that is acceptable.

With 64 bit hashes, a difference of 0 means the hashes are identical. A difference of 32 means that there's no similarity at all. A difference of 64 means that one hash is the exact negative of the other.

import cv2

class CompareImage(object):

    def __init__(self, image_1_path, image_2_path):
        self.minimum_commutative_image_diff = 1
        self.image_1_path = image_1_path
        self.image_2_path = image_2_path

    def compare_image(self):
        image_1 = cv2.imread(self.image_1_path, 0)
        image_2 = cv2.imread(self.image_2_path, 0)
        commutative_image_diff = self.get_image_difference(image_1, image_2)

        if commutative_image_diff < self.minimum_commutative_image_diff:
            print "Matched"
            return commutative_image_diff
        return 10000 //random failure value

    @staticmethod
    def get_image_difference(image_1, image_2):
        first_image_hist = cv2.calcHist([image_1], [0], None, [256], [0, 256])
        second_image_hist = cv2.calcHist([image_2], [0], None, [256], [0, 256])

        img_hist_diff = cv2.compareHist(first_image_hist, second_image_hist, cv2.HISTCMP_BHATTACHARYYA)
        img_template_probability_match = cv2.matchTemplate(first_image_hist, second_image_hist, cv2.TM_CCOEFF_NORMED)[0][0]
        img_template_diff = 1 - img_template_probability_match

        # taking only 10% of histogram diff, since it's less accurate than template method
        commutative_image_diff = (img_hist_diff / 10) + img_template_diff
        return commutative_image_diff


    if __name__ == '__main__':
        compare_image = CompareImage('image1/path', 'image2/path')
        image_difference = compare_image.compare_image()
        print image_difference