FACERECAGIZER PYTHON
import face_recognition
import os
import cv2
KNOWN_FACES_DIR = 'known_faces'
UNKNOWN_FACES_DIR = 'unknown_faces'
TOLERANCE = 0.6
FRAME_THICKNESS = 3
FONT_THICKNESS = 2
MODEL = 'cnn' # default: 'hog', other one can be 'cnn' - CUDA accelerated (if available) deep-learning pretrained model
# Returns (R, G, B) from name
def name_to_color(name):
# Take 3 first letters, tolower()
# lowercased character ord() value rage is 97 to 122, substract 97, multiply by 8
color = [(ord(c.lower())-97)*8 for c in name[:3]]
return color
print('Loading known faces...')
known_faces = []
known_names = []
# We oranize known faces as subfolders of KNOWN_FACES_DIR
# Each subfolder's name becomes our label (name)
for name in os.listdir(KNOWN_FACES_DIR):
# Next we load every file of faces of known person
for filename in os.listdir(f'{KNOWN_FACES_DIR}/{name}'):
# Load an image
image = face_recognition.load_image_file(f'{KNOWN_FACES_DIR}/{name}/{filename}')
# Get 128-dimension face encoding
# Always returns a list of found faces, for this purpose we take first face only (assuming one face per image as you can't be twice on one image)
encoding = face_recognition.face_encodings(image)[0]
# Append encodings and name
known_faces.append(encoding)
known_names.append(name)
print('Processing unknown faces...')
# Now let's loop over a folder of faces we want to label
for filename in os.listdir(UNKNOWN_FACES_DIR):
# Load image
print(f'Filename {filename}', end='')
image = face_recognition.load_image_file(f'{UNKNOWN_FACES_DIR}/{filename}')
# This time we first grab face locations - we'll need them to draw boxes
locations = face_recognition.face_locations(image, model=MODEL)
# Now since we know loctions, we can pass them to face_encodings as second argument
# Without that it will search for faces once again slowing down whole process
encodings = face_recognition.face_encodings(image, locations)
# We passed our image through face_locations and face_encodings, so we can modify it
# First we need to convert it from RGB to BGR as we are going to work with cv2
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# But this time we assume that there might be more faces in an image - we can find faces of dirrerent people
print(f', found {len(encodings)} face(s)')
for face_encoding, face_location in zip(encodings, locations):
# We use compare_faces (but might use face_distance as well)
# Returns array of True/False values in order of passed known_faces
results = face_recognition.compare_faces(known_faces, face_encoding, TOLERANCE)
# Since order is being preserved, we check if any face was found then grab index
# then label (name) of first matching known face withing a tolerance
match = None
if True in results: # If at least one is true, get a name of first of found labels
match = known_names[results.index(True)]
print(f' - {match} from {results}')
# Each location contains positions in order: top, right, bottom, left
top_left = (face_location[3], face_location[0])
bottom_right = (face_location[1], face_location[2])
# Get color by name using our fancy function
color = name_to_color(match)
# Paint frame
cv2.rectangle(image, top_left, bottom_right, color, FRAME_THICKNESS)
# Now we need smaller, filled grame below for a name
# This time we use bottom in both corners - to start from bottom and move 50 pixels down
top_left = (face_location[3], face_location[2])
bottom_right = (face_location[1], face_location[2] + 22)
# Paint frame
cv2.rectangle(image, top_left, bottom_right, color, cv2.FILLED)
# Wite a name
cv2.putText(image, match, (face_location[3] + 10, face_location[2] + 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (200, 200, 200), FONT_THICKNESS)
# Show image
cv2.imshow(filename, image)
cv2.waitKey(0)
cv2.destroyWindow(filename)
Mardax