#OpenCV – Detect and blur faces 😁 using haar cascades in Python 🐍

Hi !

Quick post today: detect a face using haar cascades and blur the face area. Easy to implement, and also easy to read:

Code below

	# Copyright (c) 2022
	# Author : Bruno Capuano
	# Create Time : 2022 Feb
	# Change Log :
	# - Open a camera feed from a local webcam and analyze each frame to detect faces using haar cascades
	# - When a face is detected, the app will blur the face zone
	# - Press [Q] to quit the app
	#
	# The MIT License (MIT)
	#
	# Permission is hereby granted, free of charge, to any person obtaining a copy
	# of this software and associated documentation files (the "Software"), to deal
	# in the Software without restriction, including without limitation the rights
	# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	# copies of the Software, and to permit persons to whom the Software is
	# furnished to do so, subject to the following conditions:
	#
	# The above copyright notice and this permission notice shall be included in
	# all copies or substantial portions of the Software.
	#
	# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
	# THE SOFTWARE.
	import cv2
	import time
	video_capture = cv2.VideoCapture(0)
	time.sleep(2)
	# enable face detection
	face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
	# open
	while True:
	try:
	_, frameOrig = video_capture.read()
	frame = cv2.resize(frameOrig, (640, 480))
	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
	faces = face_cascade.detectMultiScale(gray, 1.3, 5)
	for (top, right, bottom, left) in faces:
	cv2.rectangle(frame,(top,right),(top+bottom,right+left),(0,0,255),2)
	face = frame[right:right+left, top:top+bottom]
	face = cv2.GaussianBlur(face,(23, 23), 30)
	# merge this blurry rectangle to our final image
	frame[right:right+face.shape[0], top:top+face.shape[1]] = face
	cv2.imshow('@elbruno - Face Blur', frame)
	except Exception as e:
	print(f'exc: {e}')
	pass
	if cv2.waitKey(1) & 0xFF == ord('q'):
	break
	video_capture.release()
	cv2.destroyAllWindows()

view raw opencvdetectandblurface.py hosted with ❤ by GitHub

Important: Haar cascades are easy to implement and learn, however, not recommented as a good solution to detect faces.

Face Recognition and Face Detection series in Python

1. Detecting Faces with 20 lines in Python
2. Face Recognition with 20 lines in Python
3. Detecting Facial Features with 20 lines in Python
4. Facial Features and Face Recognition with 20 lines in Python
5. Performance improvements with code
6. Resize the camera input with OpenCV
7. Working with Haar Cascades and OpenCV
8. Detect and blur faces using haar cascades

Original Post

Happy coding!

Greetings

El Bruno

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Comments

[Vinícius Gajo]

Hey, is it possible to write this with F#?

[El Bruno]

Hi Vinicius, yes sure !
I need to update my F# skills, however, I'll give it a try

[Vinícius Gajo]

Cool, thanks for the reply. I'm currently studying opencv using Python since it is easy to find books and docs, but my final goal is to implement it with F#