You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
80 lines
3.1 KiB
80 lines
3.1 KiB
import face_recognition
|
|
import cv2
|
|
import numpy as np
|
|
|
|
# This is a super simple (but slow) example of running face recognition on live video from your webcam.
|
|
# There's a second example that's a little more complicated but runs faster.
|
|
|
|
# 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(2)
|
|
|
|
# Load a sample picture and learn how to recognize it.
|
|
Ruben_image = face_recognition.load_image_file("Ruben.jpg")
|
|
Ruben_face_encoding = face_recognition.face_encodings(Ruben_image)[0]
|
|
|
|
# Load a second samp=le picture and learn how to recognize it.
|
|
Marjo_image = face_recognition.load_image_file("Marjo2.jpg")
|
|
Marjo_face_encoding = face_recognition.face_encodings(Marjo_image)[0]
|
|
|
|
# Create arrays of known face encodings and their names
|
|
known_face_encodings = [
|
|
Ruben_face_encoding,
|
|
Marjo_face_encoding
|
|
]
|
|
known_face_names = [
|
|
"Ruben",
|
|
"Marjo"
|
|
]
|
|
|
|
while True:
|
|
# Grab a single frame of video
|
|
ret, frame = video_capture.read()
|
|
|
|
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
|
|
rgb_frame = frame[:, :, ::-1]
|
|
|
|
# Find all the faces and face enqcodings in the frame of video
|
|
face_locations = face_recognition.face_locations(rgb_frame)
|
|
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
|
|
|
|
# Loop through each face in this frame of video
|
|
for (top, right, bottom, left), face_encoding in zip(face_locations, 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]
|
|
|
|
# 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)
|
|
font = cv2.FONT_HERSHEY_DUPLEX
|
|
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'):
|
|
break
|
|
|
|
# Release handle to the webcam
|
|
video_capture.release()
|
|
cv2.destroyAllWindows()
|