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本文将学习如何使用 OpenCV 和 Python 在直播中模糊人脸。这将是一个非常有趣的博客，让我们开始吧！
我们最终结果的快照：
第 1 步：导入所需的库
import cv2
import numpy as np
第 2 步：定义模糊函数
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它需要 2 个参数，图像 img 和模糊因子 k 。
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然后我们通过将高度和宽度除以模糊因子来简单地计算内核高度和内核宽度。kw 和 kh 越小，模糊度越高。
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然后我们检查 kw 和 kh 是否为奇数，如果它们是偶数，则减 1 以使它们为奇数。
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然后简单地我们将高斯模糊应用于我们的图像并返回它。我们还可以应用任何其他模糊操作，请在此处阅读有关它们的更多信息：
https://machinelearningprojects.net/blurrings-in-cv2/
def blur(img,k):
h,w = img.shape[:2]
kh,kw = h//k,w//k
if kh%2==0:
kh-=1
if kw%2==0:
kw-=1
img = cv2.GaussianBlur(img,ksize=(kh,kw),sigmaX=0)
return img
第 3 步：定义 pixelate_face 函数
def pixelate_face(image, blocks=10):
# divide the input image into NxN blocks
(h, w) = image.shape[:2]
xSteps = np.linspace(0, w, blocks + 1, dtype="int")
ySteps = np.linspace(0, h, blocks + 1, dtype="int")
# loop over the blocks in both the x and y direction
for i in range(1, len(ySteps)):
for j in range(1, len(xSteps)):
# compute the starting and ending (x, y)-coordinates
# for the current block
startX = xSteps[j - 1]
startY = ySteps[i - 1]
endX = xSteps[j]
endY = ySteps[i]
# extract the ROI using NumPy array slicing, compute the
# mean of the ROI, and then draw a rectangle with the
# mean RGB values over the ROI in the original image
roi = image[startY:endY, startX:endX]
(B, G, R) = [int(x) for x in cv2.mean(roi)[:3]]
cv2.rectangle(image, (startX, startY), (endX, endY),
(B, G, R), -1)
# return the pixelated blurred image
return image
第 4 步：让我们在实时提要中模糊面孔
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定义一个级联分类器对象 face_cascade 来检测人脸。
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下载 haarcascade_frontalface_default.xml 文件
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https://raw.githubusercontent.com/sharmaji27/face-blur-using-opencv/main/haarcascade_frontalface_default.xml
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然后简单地运行一个无限循环，从网络摄像头读取图像，检测其中的人脸，然后用像素化的人脸替换该人脸部分。
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阅读更多关于使用 HAARCASCADES 进行面部和眼睛检测的信息
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https://machinelearningprojects.net/face-and-eye-detection-in-cv2/)
factor = 3
cap = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
while 1:
ret,frame = cap.read()
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.5, 5)
for (x,y,w,h) in faces:
frame[y:y+h,x:x+w] = pixelate_face(blur(frame[y:y+h,x:x+w],factor))
cv2.imshow('Live',frame)
if cv2.waitKey(1)==27:
break
cap.release()
cv2.destroyAllWindows()
让我们看看完整代码
import cv2
import numpy as np
def blur(img,k):
h,w = img.shape[:2]
kh,kw = h//k,w//k
if kh%2==0:
kh-=1
if kw%2==0:
kw-=1
img = cv2.GaussianBlur(img,ksize=(kh,kw),sigmaX=0)
return img
def pixelate_face(image, blocks=10):
# divide the input image into NxN blocks
(h, w) = image.shape[:2]
xSteps = np.linspace(0, w, blocks + 1, dtype="int")
ySteps = np.linspace(0, h, blocks + 1, dtype="int")
# loop over the blocks in both the x and y direction
for i in range(1, len(ySteps)):
for j in range(1, len(xSteps)):
# compute the starting and ending (x, y)-coordinates
# for the current block
startX = xSteps[j - 1]
startY = ySteps[i - 1]
endX = xSteps[j]
endY = ySteps[i]
# extract the ROI using NumPy array slicing, compute the
# mean of the ROI, and then draw a rectangle with the
# mean RGB values over the ROI in the original image
roi = image[startY:endY, startX:endX]
(B, G, R) = [int(x) for x in cv2.mean(roi)[:3]]
cv2.rectangle(image, (startX, startY), (endX, endY),
(B, G, R), -1)
# return the pixelated blurred image
return image
factor = 3
cap = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
while 1:
ret,frame = cap.read()
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.5, 5)
for (x,y,w,h) in faces:
frame[y:y+h,x:x+w] = pixelate_face(blur(frame[y:y+h,x:x+w],factor))
cv2.imshow('Live',frame)
if cv2.waitKey(1)==27:
break
cap.release()
cv2.destroyAllWindows()
下载1：OpenCV-Contrib扩展模块中文版教程
