CVLIB -如何将模糊的子面添加到原始图像？

Question

朋友们，我需要实现一个代码，从给定的图像中模糊人脸(我不是开发人员，所以这对我来说真的很难)。我发现我可以使用OpenCV和cvlib来完成这项工作，并找到了一个示例代码(来自cvlib的存储库)，它完成了部分工作。

我知道我需要得到子面孔，并将面部模糊应用于它们，我可以做到这一点，但现在我不知道如何将模糊的脸添加到原始图像中。有人能帮我一下吗？

import cvlib as cv
import sys
from cv2 import cv2
import os 

# read input image
image = cv2.imread('path')

# apply face detection
faces, confidences = cv.detect_face(image)

print(faces)
print(confidences)

# loop through detected faces
for face,conf in zip(faces,confidences):

    (startX,startY) = face[0],face[1]
    (endX,endY) = face[2],face[3]

    subFace = image[startY:endY,startX:endX]
    subFace = cv2.GaussianBlur(subFace,(23, 23), 30)
# display output
# press any key to close window           
cv2.imshow("face_detection", image)
cv2.waitKey()

cv2.imshow("face_detection", subFace)


# release resources
cv2.destroyAllWindows()

Answer 1

我终于想出了怎么做：

import cvlib as cv
import sys
from cv2 import cv2
import os 

# read input image
image = cv2.imread('path')

# apply face detection
faces, confidences = cv.detect_face(image)

# print the array with the coordinates and the confidence
print(faces)
print(confidences)

# loop through detected faces
for face,conf in zip(faces,confidences):

    (startX,startY) = face[0],face[1]
    (endX,endY) = face[2],face[3]
    
    # get the subface
    subFace = image[startY:endY,startX:endX]
    # apply gaussian blur over subfaces
    subFace = cv2.GaussianBlur(subFace,(23, 23), 30)
    # add the subfaces to de original image
    image[startY:startY+subFace.shape[0], startX:startX+subFace.shape[1]] = subFace
         
cv2.imshow("face_detection", image)
cv2.waitKey()

# save output
cv2.imwrite("face_detection.jpg", image)

# release resources
cv2.destroyAllWindows()