如何使用opencv copyTo()函数? [英] How to use opencv copyTo() function?
问题描述
我已阅读 copyTo()的文档,但对于如何将此函数应用于以下代码仍然感到困惑. 此答案指出我们可以使用copyTo函数而不是255-x.在这种情况下如何应用此功能?我希望您能看到一个代码段.
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
推荐答案
对不起,我在那儿误导了你.尽管它在C ++中可以很好地工作,但是我在Python中找不到绑定.但是,您可以使用 numpy.copyto 函数.
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.
这是一个小示例,演示了两种方法(bitwise_not
和copyto
)产生相同的结果.
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)
请注意,在遮罩数组中填充了额外的尺寸,以便可以广播.
Note that an extra dimension was padded to the mask array so that it can be broadcasted.
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