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I have read through the documentation for copyTo() but am still confused on how this function would be applied to the following code. This anwer states that we can use the copyTo function instead of 255-x. How would this function be applied in this case? I would appreciate a code snippet.

#   Compute the gradient map of the image
def doLap(image):

    # YOU SHOULD TUNE THESE VALUES TO SUIT YOUR NEEDS
    kernel_size = 5         # Size of the laplacian window
    blur_size = 5           # How big of a kernal to use for the gaussian blur
                            # Generally, keeping these two values the same or very close works well
                            # Also, odd numbers, please...

    blurred = cv2.GaussianBlur(image, (blur_size,blur_size), 0)
    return cv2.Laplacian(blurred, cv2.CV_64F, ksize=kernel_size)

#
#   This routine finds the points of best focus in all images and produces a merged result...
#
def focus_stack(unimages):
    images = align_images(unimages)

    print "Computing the laplacian of the blurred images"
    laps = []
    for i in range(len(images)):
        print "Lap {}".format(i)
        laps.append(doLap(cv2.cvtColor(images[i],cv2.COLOR_BGR2GRAY)))

    laps = np.asarray(laps)
    print "Shape of array of laplacians = {}".format(laps.shape)

    output = np.zeros(shape=images[0].shape, dtype=images[0].dtype)

    abs_laps = np.absolute(laps)
    maxima = abs_laps.max(axis=0)
    bool_mask = abs_laps == maxima
    mask = bool_mask.astype(np.uint8)
    for i in range(0,len(images)):
        output = cv2.bitwise_not(images[i],output, mask=mask[i])

    return 255-output
john
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    the last part of the answer is about masking an image. copyTo provides a mask parameter to mask the image during copying. – Micka Jul 20 '18 at 19:39
  • @Micka could you provide a code snippet example? – john Jul 20 '18 at 20:19

1 Answers1

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Sorry that I kind of misled you there. Although it works nicely in C++, I cannot find the binding in Python. You can, however, use numpy.copyto function.

Here is a small demo that shows that both method (bitwise_not and copyto) produce identical result.

import cv2
import numpy as np

# Create two images
im1 = np.zeros((100, 100, 3), np.uint8)
im1[:] = (255, 0, 0)
im2 = np.zeros((100, 100, 3), np.uint8)
im2[:] = (0, 255, 0)

# Generate a random mask
ran = np.random.randint(0, 2, (100, 100), np.uint8)

# List of images and masks
images = [im1, im2]
mask = [ran, 1-ran]

not_output = np.zeros((100, 100, 3), np.uint8)
copy_output = np.zeros((100, 100, 3), np.uint8)

for i in range(0, len(images)):
    # Using the 'NOT' way
    not_output = cv2.bitwise_not(images[i], not_output, mask=mask[i])
    # Using the copyto way
    np.copyto(copy_output, images[i], where=mask[i][:, :, None].astype(bool))

cv2.imwrite('not.png', 255 - not_output)
cv2.imwrite('copy.png', copy_output)

enter image description here enter image description here

Note that an extra dimension was padded to the mask array so that it can be broadcasted.

hkchengrex
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