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face_recognition.py
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face_recognition.py
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import os
import dlib
import numpy as np
from skimage import io
import cv2
data_dir = os.path.expanduser('~/data')
faces_folder_path = data_dir + '/kodemaker/'
# Globals
face_detector = dlib.get_frontal_face_detector()
shape_predictor = dlib.shape_predictor(data_dir + '/dlib/shape_predictor_68_face_landmarks.dat')
face_recognition_model = dlib.face_recognition_model_v1(data_dir + '/dlib/dlib_face_recognition_resnet_model_v1.dat')
def get_face_encodings(face):
bounds = face_detector(face, 1)
faces_landmarks = [shape_predictor(face, face_bounds) for face_bounds in bounds]
return [np.array(face_recognition_model.compute_face_descriptor(face, face_pose, 1)) for face_pose in faces_landmarks]
def get_face_matches(known_faces, face):
return np.linalg.norm(known_faces - face, axis=1)
def find_match(known_faces, person_names, face):
matches = get_face_matches(known_faces, face) # get a list of True/False
min_index = matches.argmin()
min_value = matches[min_index]
if min_value < 0.55:
return person_names[min_index]+"! ({0:.2f})".format(min_value)
if min_value < 0.58:
return person_names[min_index]+" ({0:.2f})".format(min_value)
if min_value < 0.65:
return person_names[min_index]+"?"+" ({0:.2f})".format(min_value)
return 'Not Found'
def load_face_encodings(faces_folder_path):
image_filenames = filter(lambda x: x.endswith('.jpg'), os.listdir(faces_folder_path))
image_filenames = sorted(image_filenames)
person_names = [x[:-4] for x in image_filenames]
full_paths_to_images = [faces_folder_path + x for x in image_filenames]
face_encodings = []
win = dlib.image_window()
for path_to_image in full_paths_to_images:
face = io.imread(path_to_image)
faces_bounds = face_detector(face, 1)
if len(faces_bounds) != 1:
print("Expected one and only one face per image: " + path_to_image + " - it has " + str(len(faces_bounds)))
exit()
face_bounds = faces_bounds[0]
face_landmarks = shape_predictor(face, face_bounds)
face_encoding = np.array(face_recognition_model.compute_face_descriptor(face, face_landmarks, 1))
win.clear_overlay()
win.set_image(face)
win.add_overlay(face_bounds)
win.add_overlay(face_landmarks)
face_encodings.append(face_encoding)
print(face_encoding)
#dlib.hit_enter_to_continue()
return face_encodings, person_names
def recognize_faces_in_video(face_encodings, person_names):
faceClassifier = cv2.CascadeClassifier(data_dir + '/opencv/haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 360)
while True:
ret, frame = cap.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
face_rects = faceClassifier.detectMultiScale(
gray,
scaleFactor = 1.1,
minNeighbors = 5,
minSize = (50, 50),
flags = cv2.CASCADE_SCALE_IMAGE)
for (x, y, w, h) in face_rects:
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
face = frame[y:y + h, x:x + w]
face_encodings_in_image = get_face_encodings(face)
if (face_encodings_in_image):
match = find_match(face_encodings, person_names, face_encodings_in_image[0])
cv2.putText(frame, match, (x+5, y-15), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
cv2.imshow("bilde", frame)
if cv2.waitKey(10) == 27:
break
cap.release()
cv2.destroyAllWindows()
face_encodings, person_names = load_face_encodings(faces_folder_path)
recognize_faces_in_video(face_encodings, person_names)