Recognize and manipulate faces from Python or from the command line with the world’s simplest face recognition library. In this post we’ll be blurring the face from a live video.
Built using dlib’s state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38% on the Labeled Faces in the Wild benchmark.
- Python 3.3+ or Python 2.7
- macOS or Linux (Windows not officially supported, but might work)
First, make sure you have dlib already installed with Python bindings:
- How to install dlib from source on macOS or Ubuntu go to link :https://gist.github.com/ageitgey/629d75c1baac34dfa5ca2a1928a7aeaf
Then, install this module from pypi using
pip2 for Python 2):
pip3 install face_recognition
Below is the code to blur the face in a live camera.
import face_recognition import cv2 # PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam. # OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this # specific demo. If you have trouble installing it, try any of the other demos that don't require it instead. # Get a reference to webcam #0 (the default one) video_capture = cv2.VideoCapture(0) # Initialize some variables face_locations =  while True: # Grab a single frame of video ret, frame = video_capture.read() # Resize frame of video to 1/4 size for faster face detection processing small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) # Find all the faces and face encodings in the current frame of video face_locations = face_recognition.face_locations(small_frame, model="cnn") # Display the results for top, right, bottom, left in face_locations: # Scale back up face locations since the frame we detected in was scaled to 1/4 size top *= 4 right *= 4 bottom *= 4 left *= 4 # Extract the region of the image that contains the face face_image = frame[top:bottom, left:right] # Blur the face image face_image = cv2.GaussianBlur(face_image, (99, 99), 30) # Put the blurred face region back into the frame image frame[top:bottom, left:right] = face_image # Display the resulting image cv2.imshow('Video', frame) # Hit 'q' on the keyboard to quit! if cv2.waitKey(1) & 0xFF == ord('q'): break # Release handle to the webcam video_capture.release() cv2.destroyAllWindows()
Live demo image:
Please feel free to add-on or point out correction on my post in the comments section.