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python - CVLIB - 如何在原始图像中添加模糊的子面？

问题描述

朋友们，我需要实现一个代码，从给定的图像中模糊面孔（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 来做到这一点，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。

我知道我需要获取子面并将面部模糊应用于它们，我可以做到，但现在我不知道如何将模糊的面部添加到原始图像中。有人可以帮我吗？

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()

标签： pythonopencvcomputer-visioncvlib

解决方案

我终于想出了如何做到这一点：

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()

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