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A simple face recognition system using opencv2 and dlib to perform face encodings, and calculate Euclidean distances from a database of previously encoded faces.
import face_recognition
import cv2
import numpy as np
import glob
import os
import logging
import pickle
# adapted from the Deep Learning: Face Recognition course from LinkedIn Learning
IMAGES_PATH = './intranet_images' # put your reference images in here
#IMAGES_PATH = './images' # put your reference images in here
CAMERA_DEVICE_ID = 0
MAX_DISTANCE = 0.6 # increase to make recognition less strict, decrease to make more strict
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output/brim.avi', fourcc, 7.0, (640, 480))
def get_face_embeddings_from_image(image, convert_to_rgb=False):
"""
Take a raw image and run both the face detection and face embedding model on it
"""
# Convert from BGR to RGB if needed
if convert_to_rgb:
image = image[:, :, ::-1]
# run the face detection model to find face locations
face_locations = face_recognition.face_locations(image)
# run the embedding model to get face embeddings for the supplied locations
face_encodings = face_recognition.face_encodings(image, face_locations)
return face_locations, face_encodings
def load_database_from_file():
with open('database', 'rb') as fp:
database = pickle.load(fp)
return database
def setup_database():
"""
Load reference images and create a database of their face encodings
"""
database = {}
count = 0
for filename in glob.glob(os.path.join(IMAGES_PATH, '*.jpg')):
# load image
image_rgb = face_recognition.load_image_file(filename)
# use the name in the filename as the identity key
identity = os.path.splitext(os.path.basename(filename))[0]
# get the face encoding and link it to the identity
locations, encodings = get_face_embeddings_from_image(image_rgb)
if len(encodings) == 0:
print(f'Face encodings not found for user {identity}.')
else:
print(f'Encoding face for user #{count}: {identity}')
database[identity] = encodings[0]
count = count + 1
with open('database', 'wb') as fp:
pickle.dump(database, fp)
return database
def paint_detected_face_on_image(frame, location, name=None):
"""
Paint a rectangle around the face and write the name
"""
# unpack the coordinates from the location tuple
top, right, bottom, left = location
if name is None:
name = 'Unknown'
color = (0, 0, 255) # red for unrecognized face
else:
color = (0, 128, 0) # dark green for recognized face
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), color, 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), color, cv2.FILLED)
cv2.putText(frame, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_DUPLEX, 1.0, (255, 255, 255), 1)
def run_face_recognition(database):
"""
Start the face recognition via the webcam
"""
# Open a handler for the camera
video_capture = cv2.VideoCapture(CAMERA_DEVICE_ID)
#video_capture.set(3, 900)
#video_capture.set(4, 1600)
# the face_recognitino library uses keys and values of your database separately
known_face_encodings = list(database.values())
known_face_names = list(database.keys())
while video_capture.isOpened():
# Grab a single frame of video (and check if it went ok)
ok, frame = video_capture.read()
if not ok:
logging.error("Could not read frame from camera. Stopping video capture.")
break
# flip image
frame = cv2.flip(frame, 1)
# run detection and embedding models
face_locations, face_encodings = get_face_embeddings_from_image(frame, convert_to_rgb=True)
# Loop through each face in this frame of video and see if there's a match
for location, face_encoding in zip(face_locations, face_encodings):
# get the distances from this encoding to those of all reference images
distances = face_recognition.face_distance(known_face_encodings, face_encoding)
# select the closest match (smallest distance) if it's below the threshold value
if np.any(distances <= MAX_DISTANCE):
best_match_idx = np.argmin(distances)
name = known_face_names[best_match_idx]
else:
name = None
# put recognition info on the image
paint_detected_face_on_image(frame, location, name)
# Display the resulting image
cv2.imshow('Video', frame)
# write the flipped frame
out.write(frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
out.release()
cv2.destroyAllWindows()
database = setup_database()
# if you want to load the face encoding from a file instead of processing it at each run, use the function load_database_from_file()
#database = load_database_from_file()
run_face_recognition(database)
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