real_time_face_recognition.py

# This is a demo of running face recognition on live video from your webcam.
import face_recognition
import cv2
import numpy as np

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)

# Load sample pictures and learn how to recognize it.
erwin_image = face_recognition.load_image_file("img/imgA.jpg")
erwin_face_encoding = face_recognition.face_encodings(erwin_image)[0]
lai_image = face_recognition.load_image_file("img/imgC.jpg")
lai_face_encoding = face_recognition.face_encodings(lai_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
  erwin_face_encoding,
  lai_face_encoding
]
known_face_names = [
  "Erwin Hofmann",
  "Lai Jing An"
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

while True:
  # Grab a single frame of video
  ret, frame = video_capture.read()
  # Resize frame of video to 1/4 size
  small_frame = cv2.resize(frame, (0, 0), fx = 0.25, fy = 0.25)
  # Convert the image from BGR color to RGB color (which face_recognition uses)
  rgb_small_frame = cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB)
  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.
      # 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]
      face_names.append(name)
  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)
    font = cv2.FONT_HERSHEY_DUPLEX
    cv2.putText(frame, name, (left + 6, bottom -6), font, 1.0, (255, 255, 255), 1)
  cv2.imshow('Video', frame)
  # Hit 'q' to quit!
  if cv2.waitKey(1) & 0xFF == ord('q'):
    break

video_capture.release()
cv2.destroyAllWindows()