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Last active August 13, 2020 07:17
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face recognition extension
#based on
# pip3 install cmake
# pip3 install face_recognition
# or pip3 install boost & pip --no-cache-dir install face_recognition
# pip3 install numpy
# pip3 install dlib
# pip3 install opencv-python
##for ChromeOS Linux
# as per current security limitation web camera is not available on ChromeOS yet:
# but it can be solved with Android Phone with "RTSP Camera Server" (
# which can be remotely accessed via RTSP protocol and "mounted" locally via ffmpeg
# eg. ffmpeg -i rtsp://@ -acodec copy -vcodec copy -f v4l2 ~/video1
# or pyton video source can be switched to rtsp stream it self
#code forked and tweaked from
#to extend, just add more people into the known_people folder
import face_recognition
import cv2
import numpy as np
import os
import glob
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
#make array of sample pictures with encodings
known_face_encodings = []
known_face_names = []
dirname = os.path.dirname(__file__)
path = os.path.join(dirname, 'known_people/')
#make an array of all the saved jpg files' paths
list_of_files = [f for f in glob.glob(path+'*.jpg')]
#find number of known faces
number_files = len(list_of_files)
names = list_of_files.copy()
for i in range(number_files):
globals()['image_{}'.format(i)] = face_recognition.load_image_file(list_of_files[i])
globals()['image_encoding_{}'.format(i)] = face_recognition.face_encodings(globals()['image_{}'.format(i)])[0]
# Create array of known names
names[i] = names[i].replace("known_people/", "")
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
while True:
# Grab a single frame of video
ret, frame =
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# # If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_names[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
process_this_frame = not process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
# 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
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
# Release handle to the webcam
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