import face_recognition
import cv2
import numpy as np
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(1)
# Load a sample picture and learn how to recognize it.
image = face_recognition.load_image_file("me.jpg")
known_face_encodings = face_recognition.face_encodings(image)[0]
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 matches[0] == True:
print("It's a picture of me!")
else:
print("It's not a picture of me!")
# 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 = 'netkiller'
# 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()