'OpenCV - Fastest method to check if two images are 100% same or not

There are many questions over here which checks if two images are "nearly" similar or not.

My task is simple. With OpenCV, I want to find out if two images are 100% identical or not.

They will be of same size but can be saved with different filenames.

pythonc++opencv


Solution 1:[1]

the sum of the differences should be 0 (for all channels):

bool equal(const Mat & a, const Mat & b)
{
    if ( (a.rows != b.rows) || (a.cols != b.cols) )
        return false;
    Scalar s = sum( a - b );
    return (s[0]==0) && (s[1]==0) && (s[2]==0);
}

Solution 2:[2]

import cv2
import numpy as np
a = cv2.imread("picture1.png")
b = cv2.imread("picture2.png")
difference = cv2.subtract(a, b)    
result = not np.any(difference)
if result is True:
    print("Pictures are the same")
else:
    print("Pictures are different")

Solution 3:[3]

If they are same files except being saved in different file-names, you can check whether their Checksums are identical or not.

Solution 4:[4]

Importing the packages we’ll need — matplotlib for plotting, NumPy for numerical processing, and cv2 for our OpenCV bindings. Structural Similarity Index method is already implemented for us by scikit-image, so we’ll just use their implementation

# import the necessary packages
from skimage.measure import structural_similarity as ssim
import matplotlib.pyplot as plt
import numpy as np
import cv2

Then define the compare_images function which we’ll use to compare two images using both MSE and SSIM. The mse function takes three arguments: imageA and imageB, which are the two images we are going to compare, and then the title of our figure.

We then compute the MSE and SSIM between the two images. We also simply display the MSE and SSIM associated with the two images we are comparing.

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

    # return the MSE, the lower the error, the more "similar"
    # the two images are
    return err
def compare_images(imageA, imageB, title):
    # compute the mean squared error and structural similarity
    # index for the images
    m = mse(imageA, imageB)
    s = ssim(imageA, imageB)
    # setup the figure
    fig = plt.figure(title)
    plt.suptitle("MSE: %.2f, SSIM: %.2f" % (m, s))
    # show first image
    ax = fig.add_subplot(1, 2, 1)
    plt.imshow(imageA, cmap = plt.cm.gray)
    plt.axis("off")
    # show the second image
    ax = fig.add_subplot(1, 2, 2)
    plt.imshow(imageB, cmap = plt.cm.gray)
    plt.axis("off")
    # show the images
    plt.show()

Load images off disk using OpenCV. We’ll be using original image, contrast adjusted image, and our Photoshopped image

We then convert our images to grayscale

# load the images -- the original, the original + contrast,
# and the original + photoshop
original = cv2.imread("images/jp_gates_original.png")
contrast = cv2.imread("images/jp_gates_contrast.png")
shopped = cv2.imread("images/jp_gates_photoshopped.png")
# convert the images to grayscale
original = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)
contrast = cv2.cvtColor(contrast, cv2.COLOR_BGR2GRAY)
shopped = cv2.cvtColor(shopped, cv2.COLOR_BGR2GRAY)

We will generate a matplotlib figure, loop over our images one-by-one, and add them to our plot. Our plot is then displayed to us.

Finally, we can compare our images together using the compare_images function.

# initialize the figure
fig = plt.figure("Images")
images = ("Original", original), ("Contrast", contrast), ("Photoshopped", shopped)
# loop over the images
for (i, (name, image)) in enumerate(images):
    # show the image
    ax = fig.add_subplot(1, 3, i + 1)
    ax.set_title(name)
    plt.imshow(image, cmap = plt.cm.gray)
    plt.axis("off")
# show the figure
plt.show()
# compare the images
compare_images(original, original, "Original vs. Original")
compare_images(original, contrast, "Original vs. Contrast")
compare_images(original, shopped, "Original vs. Photoshopped")

Reference- https://www.pyimagesearch.com/2014/09/15/python-compare-two-images/

Solution 5:[5]

I have done this task.

Compare file sizes. Compare exif data. Compare first 'n' byte, where 'n' is 128 to 1024 or so. Compare last 'n' bytes. Compare middle 'n' bytes. Compare checksum

Sources

This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.

Source: Stack Overflow

Solution Source
Solution 1 berak
Solution 2 Ishaan Sharma
Solution 3 herohuyongtao
Solution 4 Shadab Hussain
Solution 5 Doug Rogers

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