How to check similarity of two images that have different pixelization
Question:
I am running a python code to check similarity of Quora and Twitter users profiles photos, but i am not getting a positive result when images are the same.
This is the code for comparing the two images :
path_photo_quora= "/home/yousuf/Desktop/quora_photo.jpg"
path_photo_twitter="/home/yousuf/Desktop/twitter_photo.jpeg"
if open(path_photo_quora,"rb").read() == open(path_photo_twitter,"rb").read():
print('photos profile are identical')
despite images are the same, the console is not printing “photos profile are identical”, what can i do?
Answers:
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 8×8 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
I am running a python code to check similarity of Quora and Twitter users profiles photos, but i am not getting a positive result when images are the same.
This is the code for comparing the two images :
path_photo_quora= "/home/yousuf/Desktop/quora_photo.jpg"
path_photo_twitter="/home/yousuf/Desktop/twitter_photo.jpeg"
if open(path_photo_quora,"rb").read() == open(path_photo_twitter,"rb").read():
print('photos profile are identical')
despite images are the same, the console is not printing “photos profile are identical”, what can i do?
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 8×8 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