def encode_all_people_in_db():
crud = Crud()
people = crud.get_person({})
for person in people:
image_location = person["photo_location"]
image_encoding = encode_image(image_location)
if len(image_encoding) > 1:
image_encoding = image_encoding[0]
if len(image_encoding)>0:
crud.update_person(person, {"encoded_face":list(image_encoding), "has_encodings":True})
class TestPersonSeen(TestCase):
def setUp(self):
self.sc = SocketClient()
self.crud = Crud()
def test_identify_and_insert_alex(self):
picture = self.sc.get_picture()
known_faces = get_feature_encoding_list()
people_similar = compare_face_to_known(picture, known_faces, True)
if people_similar is not None:
most_similar = people_similar[0]
self.assertEqual(most_similar["name"], "Alex Young")
self.crud.person_seen_db_insert(most_similar)
person_seen = self.crud.find_someone_seen_in_last_x_seconds(2)
self.assertEqual(person_seen[0], "Alex Young")
else:
self.fail("No faces found")
class TestEncodeTestImages(TestCase):
def setUp(self):
insert_test_faces_into_db()
self.crud = Crud()
def test_encode_images(self):
encode_all_people_in_db()
person = self.crud.get_person({"name":"AJ Cook"})
self.assertTrue("encoded_face" in person[0])
self.assertTrue(len(person[0]["encoded_face"]) > 2)
def insert_test_faces_into_db():
crud = Crud()
crud.delete_all_people()
for person in os.listdir(DATA_DIR):
person_dir = DATA_DIR + person + os.sep
for pic in os.listdir(person_dir):
if "0001.jpg" in pic:
name = " ".join(person.split("_"))
role = ROLES[random.randint(0, 6)]
person_data_pic = DATA_DIR + person + os.sep + pic
person_target_dir = PICTURE_DIR + person
person_target_pic = person_target_dir + os.sep + person + ".jpg"
if not os.path.exists(person_target_dir):
os.makedirs(person_target_dir)
copyfile(person_data_pic, person_target_pic)
person_data = {
"name": name,
"role": role,
"photo_location":
PICTURE_DIR + person + os.sep + person + ".jpg",
"has_encodings": False
}
crud.insert_person(person_data)
def compare_face_to_known(face_to_match, known_face_list, to_match_top=1):
# Load a test image and get encondings for it
encode_face_to_match = encode_image(face_to_match)
encoded_known_faces = known_face_list[1]
names_known_faces = known_face_list[0]
if len(encode_face_to_match) == 0:
return None
# See how far apart the test image is from the known faces
face_distances = face_recognition.face_distance(encoded_known_faces,
encode_face_to_match)
person_distance_from_match = list(zip(names_known_faces, face_distances))
person_distance_from_match.sort(key=lambda x: x[1])
similar_people = person_distance_from_match[0:to_match_top]
similar_people_records = []
crud = Crud()
for person in similar_people:
if person[1] < 0.5:
person = crud.get_person({"name": person[0]})[0]
similar_people_records.append(person)
return similar_people_records
def setUp(self):
self.sc = SocketClient()
self.crud = Crud()
def setUp(self):
insert_test_faces_into_db()
self.crud = Crud()