def test_SomePermittedUsers_DoorOpenOnce(
self, MockRepository: mock.MagicMock,
MockRecognizer: mock.MagicMock,
MockAccessControlSystem: mock.MagicMock):
mock_acs = MockAccessControlSystem.return_value
mock_acs.has_access.return_value = True
mock_recognizer = MockRecognizer.return_value
mock_recognizer.extract.return_value = Vector('1')
mock_recognizer.compare_vectors.return_value = 1
mock_repository = MockRepository.return_value
mock_repository.get_users.return_value = [
User('1', Vector('1'), ''),
User('2', Vector('2'), ''),
User('3', Vector('3'), '')
]
mock_repository.get_control_points.return_value = [
ControlPoint('1', '1')
]
mock_repository.get_cameras.return_value = [
Camera('1', ControlPoint('1', '1'))
]
authorizer = Authorizer(mock_repository, mock_recognizer, mock_acs)
authorizer.authorize(None)
mock_acs.open_door.assert_called_once()
def test_OneScoreIsNone_ContinueAuthorization(
self, MockRepository: mock.MagicMock,
MockRecognizer: mock.MagicMock,
MockAccessControlSystem: mock.MagicMock):
mock_acs = MockAccessControlSystem.return_value
mock_acs.has_access.return_value = True
mock_recognizer = MockRecognizer.return_value
mock_recognizer.extract.return_value = Vector('1')
mock_recognizer.compare_vectors.side_effect = [None, 1]
mock_repository = MockRepository.return_value
mock_repository.get_users.return_value = [
User('1', Vector('1'), ''),
User('2', Vector('2'), '')
]
mock_repository.get_control_points.return_value = [
ControlPoint('1', '1')
]
mock_repository.get_cameras.return_value = [
Camera('1', ControlPoint('1', '1'))
]
authorizer = Authorizer(mock_repository, mock_recognizer, mock_acs)
authorizer.authorize(None)
mock_acs.open_door.assert_called()
def getBoundingBoxes(self, image, threshold=0.8):
bodies = self.model.detect([self.preprocessImage(image)], verbose=0)[0]
boxes = []
for id, score, box in zip(bodies["class_ids"], bodies["scores"],
bodies["rois"]):
if id == 1 and score > threshold:
boxes.append(
BoundingBox(Vector(int(box[1]), int(box[0])),
Vector(int(box[3]), int(box[2]))))
return image, boxes
def getBoundingBoxesAndMasks(self, image, threshold=0.8):
bodies = self.model.detect([self.preprocessImage(image)], verbose=0)[0]
boxes = []
masks = []
for index, (id, score, box) in enumerate(
zip(bodies["class_ids"], bodies["scores"], bodies["rois"])):
if id == 1 and score > threshold:
boxes.append(
BoundingBox(Vector(int(box[1]), int(box[0])),
Vector(int(box[3]), int(box[2]))))
masks.append(bodies["masks"][:, :, index])
return (boxes, masks)
def extract(self, picture) -> Vector:
try:
if picture is None:
return None
headers = {
'Content-Type': "image/jpeg",
'Content-Length': str(len(picture)),
}
req = requests.Request('POST',
self._url + "extract",
headers=headers)
prep = req.prepare()
prep.body = picture
s = requests.Session()
response = s.send(prep)
if response.status_code != 200:
if response.json()['code'] not in ['BPE-003002']:
logger = logging.getLogger(__name__)
logger.exception(response.text)
return None
return Vector(response.content)
except Exception as e:
logger = logging.getLogger(__name__)
logger.exception(e)
return None
def getFaces(self, image):
faces = self.model.detect_faces(image)
if faces is not None:
return [
Face(boundingBox=BoundingBox(
origin=Vector(int(face["box"][0]), int(face["box"][1])),
end=Vector(int(face["box"][0] + face["box"][2]),
int(face["box"][1] + face["box"][3])),
),
leftEye=Vector(int(face["keypoints"]["left_eye"][0]),
int(face["keypoints"]["left_eye"][1])),
rightEye=Vector(int(face["keypoints"]["right_eye"][0]),
int(face["keypoints"]["right_eye"][1])),
nose=Vector(int(face["keypoints"]["nose"][0]),
int(face["keypoints"]["nose"][1])))
for face in faces
]
def getBoundingBoxes(self, image, threshold=0.5):
blob = cv.dnn.blobFromImage(image,
1 / 255.,
self.inputDim,
swapRB=False,
crop=False)
self.model.setInput(blob)
outs = self.model.forward(self.getOutputLayers())
boxVectors = []
confidence = []
for out in outs:
for detection in out:
id = np.argmax(detection[5:])
score = detection[5:][id]
if id == 0 and score > threshold:
boxVector = detection[:4] * np.array([
image.shape[1], image.shape[0], image.shape[1],
image.shape[0]
])
boxVectors.append([
int(boxVector[0] - (boxVector[2] / 2)),
int(boxVector[1] - (boxVector[3] / 2)),
int(boxVector[2]),
int(boxVector[3])
])
confidence.append(float(score))
boxes = []
if boxVectors:
indexes = cv.dnn.NMSBoxes(boxVectors, confidence, threshold,
self.nmsThreshold)
boxes = [
BoundingBox(
Vector(boxVector[0], boxVector[1]),
Vector(boxVector[0] + boxVector[2],
boxVector[1] + boxVector[3]))
for boxVector in map(lambda i: boxVectors[i[0]], indexes)
]
return image, boxes
def getVectorByName(self, name):
query = {"name":name}
results = self.get(query)
vector = None
for result in results:
vector = Vector(
result["name"],
result["desc"],
result["nodes"]
)
break
return vector
def __init__(self):
FlexServ = win32com.client.Dispatch("FlexServer.FlexServerGlobal")
token = FlexServ.AuthenticateUser("admin", "admin", False)
self._FlexACS = FlexServ.GetObject(token, "FlexACSModule.FlexACS")
self._FlexACS.ConnectAll(None, 0)
self._FlexDB = FlexServ.GetObject(token, "FlexDB.FlexDBModule")
self._path = "D:\\Октаграм\\client_temp\\"
self._last_time_accessed = datetime.datetime.now() - datetime.timedelta(seconds=6)
self._last_user_accessed = User('','', Vector(''))
logger = logging.getLogger(__name__)
logger.debug("FlexACS: {}".format(str(self._FlexACS)))
logger.debug("FlexDB: {}".format(str(self._FlexDB)))
def get_unidentified_users(self) -> List[User]:
return [
User(
''.join(
random.choice(string.ascii_lowercase) for i in range(10)),
''.join(
random.choice(string.ascii_lowercase) for i in range(10)),
Vector('')),
User(
''.join(
random.choice(string.ascii_lowercase) for i in range(10)),
''.join(
random.choice(string.ascii_lowercase) for i in range(10)),
Vector('')),
User(
''.join(
random.choice(string.ascii_lowercase) for i in range(10)),
''.join(
random.choice(string.ascii_lowercase) for i in range(10)),
Vector(''))
]
def get_unidentified_users(self) -> List[User]:
try:
users = [User(user.strSID,
user.strFirstName+' '+user.strLastName,
Vector(''))
for user in self._FlexDB.GetUsers("", False, "")]
except Exception as e:
logger = logging.getLogger(__name__)
logger.exception(e)
return None
return users
def test_ScoreIsHigherThanThreshold_DoNoting(
self, MockRepository: mock.MagicMock,
MockRecognizer: mock.MagicMock,
MockAccessControlSystem: mock.MagicMock):
mock_acs = MockAccessControlSystem.return_value
mock_acs.has_access.return_value = True
mock_recognizer = MockRecognizer.return_value
mock_recognizer.extract.return_value = Vector('1')
mock_repository = MockRepository.return_value
mock_repository.get_users.return_value = [User('1', Vector('1'), '')]
mock_repository.get_control_points.return_value = [
ControlPoint('1', '1')
]
mock_repository.get_cameras.return_value = [
Camera('1', ControlPoint('1', '1'))
]
authorizer = Authorizer(mock_repository, mock_recognizer, mock_acs)
mock_recognizer.compare_vectors.return_value = authorizer._threshold + 0.0000000001
authorizer.authorize(None)
mock_acs.open_door.assert_called()
def getBoundingBoxes(self, image, threshold=0.7):
with tf.Session(graph=self.graph) as sess:
detectionClasses, numDetections, detectionBoxes, detectionScores = sess.run(
[
self.detectionClassesTensor, self.numDetectionsTensor,
self.detectionBoxesTensor, self.detectionScoresTensor
],
feed_dict={
self.inputImage:
np.expand_dims(self.preprocessImage(image), axis=0)
})
boxes = []
for i in range(int(numDetections[0])):
if detectionClasses[0][
i] == 1 and detectionScores[0][i] > threshold:
boxes.append(
BoundingBox(
Vector(int(detectionBoxes[0][i][1] * image.shape[1]),
int(detectionBoxes[0][i][0] * image.shape[0])),
Vector(int(detectionBoxes[0][i][3] * image.shape[1]),
int(detectionBoxes[0][i][2] * image.shape[0]))))
return image, boxes
def test_NoUsers_DoNothing(self, MockRepository: mock.MagicMock,
MockRecognizer: mock.MagicMock,
MockAccessControlSystem: mock.MagicMock):
mock_acs = MockAccessControlSystem.return_value
mock_acs.has_access.return_value = True
mock_recognizer = MockRecognizer.return_value
mock_recognizer.extract.return_value = Vector('1')
mock_recognizer.compare_vectors.return_value = 1
mock_repository = MockRepository.return_value
mock_repository.get_users.return_value = []
mock_repository.get_control_points.return_value = [
ControlPoint('1', '1')
]
mock_repository.get_cameras.return_value = [
Camera('1', ControlPoint('1', '1'))
]
authorizer = Authorizer(mock_repository, mock_recognizer, mock_acs)
authorizer.authorize(None)
mock_acs.open_door.assert_not_called()
def getVectors(self):
vectors = [
Vector(vector["name"], vector["desc"], vector["nodes"]) for vector in self.get(None)
]
return vectors
def add_vector(self, vid, vector_array):
self.vectors.append(Vector(vid, vector_array))
def extract(self, picture) -> Vector:
return Vector('asrtastsrt')
