CODEWITHSUNDEEP
pythonopencvroi
vallete7
vallete7

Reputation: 123

Compare cropped images python opencv

I want to compare two images to check if they are equal or not, but for that i need to compare a specific region (ROI) of both images. I've cropped the areas i want to compare, but now i would like to know how can i do that process, because i can't directly compare the cropped images. How can i for example get the average pixels values of both cropped images and compare them?

Update: I've solved the situation. Current code:

import cv2
import numpy as np
from skimage.measure import compare_ssim as ssim


def mse(imageA, imageB):
    # the 'Mean Squared Error' between the two images is the sum of the squared difference between the two images;
    err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2) 
    err /= float(imageA.shape[0] * imageA.shape[1])


polarity_ok = cv2.resize(cv2.imread("polarity_OK_edited.jpg"),None,fx=0.2, fy=0.2) #resize the image to be smaller
polarity_nok = cv2.resize(cv2.imread("Polarity_NOK1.JPG"), None,fx=0.2, fy=0.2) #resize the image to be smaller

polarity_ok_cropped = polarity_ok[350:408, 97:111]
polarity_nok_cropped = polarity_nok[350:408, 97:111]

polarity_ok_cropped1 = polarity_ok[359:409, 232:240]
polarity_nok_cropped1 = polarity_nok[359:409, 232:240]

polarity_ok_cropped2 = polarity_ok[118:153, 44:69]
polarity_nok_cropped2 = polarity_nok[118:153, 44:69]

polarity_ok_cropped3 = polarity_ok[94:142, 192:197]
polarity_nok_cropped3 = polarity_nok[94:142, 192:197]

m = mse(polarity_ok_cropped, polarity_nok_cropped)
s = ssim(polarity_ok_cropped, polarity_nok_cropped, multichannel=True)

diff = cv2.subtract(polarity_ok_cropped, polarity_nok_cropped)
result = not np.any(diff)

m1 = mse(polarity_ok_cropped1, polarity_nok_cropped1)
s1 = ssim(polarity_ok_cropped1, polarity_nok_cropped1, multichannel=True)

diff1 = cv2.subtract(polarity_ok_cropped1, polarity_nok_cropped1)
result1 = not np.any(diff1)

m2 = mse(polarity_ok_cropped2, polarity_nok_cropped2)
s2 = ssim(polarity_ok_cropped2, polarity_nok_cropped2, multichannel=True)

diff2 = cv2.subtract(polarity_ok_cropped2, polarity_nok_cropped2)
result2 = not np.any(diff2)

m3 = mse(polarity_ok_cropped2, polarity_nok_cropped2)
s3 = ssim(polarity_ok_cropped2, polarity_nok_cropped2, multichannel=True)

diff3 = cv2.subtract(polarity_ok_cropped3, polarity_nok_cropped3)
result3 = not np.any(diff3)


if (result and result1 and result2 and result3):
    print ("The polarity is correct. Awesome :)")
else:
    print ("Nice try, but the polarity is incorrect. Take another chance!")

Upvotes: 0

Views: 2876

Answers (3)

M. D. P
M. D. P

Reputation: 764

One more way to do the same :

from PIL import Image
import math, operator

i1 = Image.open('./image1.png')
i2 = Image.open('./image2.png')

#this will resize any format of image file
assert i1.mode == i2.mode, "Different kinds of images."
assert i1.size == i2.size, "Different sizes."
 
pairs = zip(i1.getdata(), i2.getdata())
if len(i1.getbands()) == 1:
    # for gray-scale jpegs
    dif = sum(abs(p1-p2) for p1,p2 in pairs)
else:
    dif = sum(abs(c1-c2) for p1,p2 in pairs for c1,c2 in zip(p1,p2))
 
ncomponents = i1.size[0] * i1.size[1] * 3
print ("Difference (percentage):", (dif / 255.0 * 100) / ncomponents)

You need to install pillow. hope this will help you.

Upvotes: 0

kocica
kocica

Reputation: 6465

If you know exactly where the objects you want to compare are, simple and fast method using OpenCV to compare two images is to extract histograms using calcHistogram() for each channel (RGB or HSV) and then compare them using compareHist().

Further infos and examples might be found here: Histogram comparsion.

Upvotes: 1

Amjad sibili
Amjad sibili

Reputation: 1149

You can use the Structural Similarity Index (SSIM) as giving the 2 images as input and returning a score value in the range [-1, 1]. A score of 1 indicating a perfect similarity between 2 input images (In case of both images are equal) 

from skimage.measure import compare_ssim
(score, diff) = compare_ssim(image1, image2, full=True)

Btw Converting the input images before comparison into grayscale is prefered.

Upvotes: 0

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