While comparing various images with SSIM + Python, I found that some very similar images had a very low SSIM ratio. This is consequence of the interaction of the images background with the resize that has to be done to execute the calculations. This issue is important because I am comparing thousands of images and many dissimilar ones have higher similitude ratios that ones that are almost a morphological equal.
SSIM ratio = 0.2724580796
SSIM ratio = 0.3111611817
My code:
import cv2
import statistics
import numpy as np
from PIL import Image
import urllib.request
import matplotlib.pyplot as plt
from skimage.metrics import structural_similarity
class CompareImages_n():
def __init__(self, url_1, url_2):
self.img_url_1 = url_1
self.img_url_2 = url_2
def load_images(self, img_url, flag_color=False):
req = urllib.request.urlopen(img_url)
arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
image = cv2.imdecode(arr, -1) # Load image as it is
if not flag_color:
return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Change color to greyscale
else:
return image
def main_process_ssmi(self):
ima1 = self.load_images(self.img_url_1)
ima2 = self.load_images(self.img_url_2)
(H, W) = ima1.shape
ima2 = cv2.resize(ima2, (W, H))
(score, diff) = structural_similarity(ima1, ima2, full=True)
return score
How can I compare the images eliminating the background before the resize process? What other solution could be implemented? (Automated cropping?) Any library available?
