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freco.py
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freco.py
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import face_recognition
import cv2
# This is a demo of running face recognition on live video from your webcam.
# It's a little more complicated than the
# other example, but it includes some basic performance tweaks
# to make things run a lot faster:
# 1. Process each video frame at 1/4 resolution
# (though still display it at full resolution)
# 2. Only detect faces in every other frame of video.
# 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)
# Load a sample picture and learn how to recognize it.
harsh_image = face_recognition.load_image_file("./img/harsh.jpg")
harsh_face_encoding = face_recognition.face_encodings(harsh_image)[0]
meet_image = face_recognition.load_image_file("./img/meet.jpg")
meet_face_encoding = face_recognition.face_encodings(meet_image)[0]
maharshi_image = face_recognition.load_image_file("./img/maharshi.jpg")
maharshi_face_encoding = face_recognition.face_encodings(maharshi_image)[0]
niraj_image = face_recognition.load_image_file("./img/niraj.jpg")
niraj_face_encoding = face_recognition.face_encodings(niraj_image)[0]
# 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 for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# 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(small_frame)
face_encodings = face_recognition.face_encodings(
small_frame, face_locations)
face_names = []
known_faces = [
harsh_face_encoding,
meet_face_encoding,
maharshi_face_encoding,
niraj_face_encoding
]
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
match = face_recognition.compare_faces(
known_faces, face_encoding)
name = "Unknown"
if match[0]:
name = "Harsh"
elif match[1]:
name = "Meet"
elif match[2]:
name = "Maharshi"
elif match[3]:
name = "Niraj"
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)
# 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()