在这种情况下， cv2.addWeighted 和 numpy mean 之间有什么区别？

Question

Suppose I have two OpenCV (python package cv2 ) loaded grayscale images img1 and img2 , both of same dimensions. Now, I wish to take the mean of both img1 and img2 . Here are two ways to do it:

# Method 1
mean = (img1 * 0.5) + (img2 * 0.5)

# Method 2
mean = cv2.addWeighted(img1,0.5,img2,0.5,0)

However, mean is visually different in both methods, when I display them using cv2.imshow . Why is this so?

Answer 1

I am glad that you have found a working solution to your problem, but this seems to be a workaround. The real reason for this behaviour lies somewhere else. The problem here is that mean = (img1 * 0.5) + (img2 * 0.5) is returning a matrix with float32 data type which contains values in range 0.0 - 255.0 . You can verify this by using print mean.dtype . Since the new matrix values have been converted to float unintentionally, we can revert this operation by using (img_1 * 0.5 + img_2 * 0.5).astype("uint8") . In case of cv2.addWeighted() it automatically returns you a matrix of data type uint8 and all things would work fine.

My concern is with the conclusion that you have drawn:

The issue is that the cv2.imshow() method used to display images, expects your image arrays to be normalized, ie in the range [0,1].

cv2.imshow() works just fine with range of [0-255] and [0.0-1.0] , but the issue arises when you pass a matrix whose values are in range [0-255] , but the dtype is float32 instead of uint8 .

Answer 2

Answering my own question, to help others who get confused by this:

Both methods 1 and 2 yield the same result. You can verify this by writing the mean image to disk using cv2.imwrite . The issue is not with the methods.

The issue is that the cv2.imshow method used to display images, expects your image arrays to be normalized, ie in the range [0,1]. In my case, both the image arrays are 8-bit unsigned integers and so, its pixel values are in the range [0,255]. Since mean is an average of the two arrays, its pixel values are also in the range [0,255]. So when I passed mean to cv2.imshow , pixels having values greater than 1 were interpreted as having a value of 255, resulting in vastly different visuals.

The solution is to normalize mean before passing it to cv2.imshow :

# Method 1
mean = (img1 * 0.5) + (img2 * 0.5)

# Method 2
mean = cv2.addWeighted(img1,0.5,img2,0.5,0)

# Note that the division by 255 results in the image array values being squeezed to [0,1].

cv2.imshow("Averaged", mean/255.)