CODEWITHSUNDEEP
pythonnumpy
maxisme
maxisme

Reputation: 4245

How to get the MSE of each pixel in two images of the same dimension

I have two images that will be identical in dimensions.

The images are numpy arrays and I am itterating the pixels and aquiring the r g b of each pixel like this:

for i in range(len(img1)):
    for j in range(len(img1[0])):
        pimg1 = img1[i][j]
        pimg2 = img2[i][j]

        r1 = pimg1[0]
        r2 = pimg2[0]
        g1 = pimg1[1]
        g2 = pimg2[1]
        b1 = pimg1[2]
        b2 = pimg2[2]

I then acquire the MSE between the two pixels like:

mse = math.sqrt(((r1 - r2) ** 2) + ((g1 - g2) ** 2) + ((b1 - b2) ** 2))

The problem is this is ridiculously slow. Is there a more efficient way to do this?

The final goal

I am looking to make all pixels that have a certain "thresholded" similarity, between the two images, black. And all pixels that have a larger difference the pixel of img2.

if mse > threshold:
    new_img[i][j] = pimg2
else:
    new_img[i][j] = [0, 0, 0] # black pixel

background image

enter image description here

entered image

enter image description here

I am capturing images like:

for frame in cam.camera.capture_continuous(raw, format="rgb", use_video_port=True):
    img = frame.array
    cv2.imwrite("image.png", img)

I am getting the images like:

dir = 'images/compare/'
bg = cv2.imread(dir+'bg.png')
img = cv2.imread(dir+'in.png')

Upvotes: 3

Views: 4546

Answers (1)

Divakar
Divakar

Reputation: 221534

Simply use np.linalg.norm on the differences -

mse = np.linalg.norm(img1-img2,axis=2)

Faster one with np.einsum -

d = (img1-img2).astype(float)
mse = np.sqrt(np.einsum('...i,...i->...',d,d))

Runtime test -

In [46]: np.random.seed(0)
    ...: m,n = 1024,1024
    ...: img1 = np.random.randint(0,255,(m,n,3)).astype(np.uint8)
    ...: img2 = np.random.randint(0,255,(m,n,3)).astype(np.uint8)

In [47]: %timeit np.linalg.norm(img1-img2,axis=2)
10 loops, best of 3: 26.6 ms per loop

In [49]: %%timeit
    ...: d = (img1-img2).astype(float)
    ...: mse = np.sqrt(np.einsum('...i,...i->...',d,d))
100 loops, best of 3: 13 ms per loop

To create an output array that is set to black for pixels that have MSE values lesser than a certain threshold mse_thresh and select from img2 otherwise, here are the additional codes -

mask = mse >= mse_thresh
out = np.where(mask[...,None], img2, 0)

Stitching everything together - Using einsum to compute squared MSE values and comparing against the squared MSE threshold for major improvement and assigning back the output into img2 -

d = (img1-img2).astype(float)
mse = np.einsum('...i,...i->...',d,d)
img2[mse < mse_thresh**2] = 0

Upvotes: 5

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