Blurring and anonymizing faces using OpenCV and python

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Author URI ： Hai Yong
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In this paper , We will learn how to use OpenCV and Python Blur and anonymize faces .

So , We will use cascaded classifiers to detect faces . Make sure to download the same from this link xml file ：

drive.google.com/file/d/1PPO…

Method

• First , We use the built-in face detection algorithm , Face detection from real-time video or image . ad locum , We will use a cascaded classifier method to extract data from real-time video （ Use a webcam ） Face detection in .
• then , Read frames from real-time video . Store the latest frame and convert it to grayscale , To better understand the characteristics .
• Now? , In order to make the output beautiful , We will make a color border rectangle around the detected face . however , We want the detected face to be blurred , So we use the median fuzzy function to do the same thing , And mention the area where the face should be blurred .
• and , Now we want to show the blurred face , Use imshow The frame read by the function , We want it to be shown , Until we press a key .

Step by step implementation ：

step 1： Import face detection algorithm , It is called cascade classifier .

import cv2
#  Face detection 
cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
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step 2： Capture frames from video , To detect faces from frames

video_capture = cv2.VideoCapture(0)
while True:

	#  Capture the latest frame from the video 
	check, frame = video_capture.read()
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step 3： Change the captured frame to grayscale .

#  Convert frames to grayscale （ Black and white shadows ）
gray_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
face = cascade.detectMultiScale(gray_image,
								scaleFactor=2.0,
								minNeighbors=4)
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step 4： Draw a color rectangle around the detected face .

for x, y, w, h in face:

	#  Draw a border around the detected face 
	# （ The border color here is green , The thickness is 3）
	image = cv2.rectangle(frame, (x, y),
						(x+w, y+h),
						(0, 255, 0), 3)
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step 5： Blur the part inside the rectangle （ Contains the detected face ）.

#  Blur the face in the rectangle 
image[y:y+h, x:x+w] = cv2.medianBlur(image[y:y+h, x:x+w], 35)
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step 6： Show final output , That is, the detected face （ In rectangle ） It's fuzzy .

#  Show blurred faces in the video 
cv2.imshow('face blurred', frame)
key = cv2.waitKey(1)
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Here is the complete implementation ：

import cv2

#  Face detection 
cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")

# VideoCapture  It's a function , Used to capture video from cameras connected to the system 
#  You can pass on  0  or  1
# 0  For laptop webcam 
# 1  For external webcam 
video_capture = cv2.VideoCapture(0)

#  One while Loop runs infinite times , Capture an unlimited number of frames for video , Because video is a combination of frames 
while True:

	#  Capture the latest frame from the video 
	check, frame = video_capture.read()

	#  Convert frames to grayscale （ Black and white shadows ）
	gray_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

	#  Multiple faces are detected in the captured frame 
        # scaleFactor： Parameter specifies how much the image size is reduced at each image scale .
        # minNeighbors:  Parameter specifies how many neighbors each rectangle should have to keep it .
        #  The rectangle contains the detection object .
        #  The object here is the face .
	face = cascade.detectMultiScale(
		gray_image, scaleFactor=2.0, minNeighbors=4)

	for x, y, w, h in face:

		#  Draw a border around the detected face .
                #（ The border color here is green , The thickness is 3）
		image = cv2.rectangle(frame, (x, y), (x+w, y+h),
							(0, 255, 0), 3)

		#  Blur the face in the rectangle 
		image[y:y+h, x:x+w] = cv2.medianBlur(image[y:y+h, x:x+w],
											35)

	#  Show blurred faces in the video 
	cv2.imshow('face blurred', frame)
	key = cv2.waitKey(1)

	#  This statement runs only once per frame .
        #  Basically , If we get a key , And that key is a  q
	if key == ord('q'):
		break

#  We will pause and exit  while  loop ,
#  And then run ：
video_capture.release()
cv2.destroyAllWindows()
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Output ：

Write in the last

I've been writing a technology blog for a long time , And mainly through the Nuggets , This is one of my articles OpenCV and Python Blur and anonymize faces . I like to share technology and happiness through articles . You can visit my blog ： juejin.cn/user/204034… To learn more . I hope you will like ！

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