def __init__(
self,
ip_address,
port,
api_key,
timeout,
detect_only,
save_file_folder,
save_timestamped_file,
camera_entity,
name=None,
):
"""Init with the API key and model id."""
super().__init__()
self._dsface = ds.DeepstackFace(ip_address, port, api_key, timeout)
self._detect_only = detect_only
self._last_detection = None
self._save_file_folder = save_file_folder
self._save_timestamped_file = save_timestamped_file
self._camera = camera_entity
if name:
self._name = name
else:
camera_name = split_entity_id(camera_entity)[1]
self._name = "{} {}".format(CLASSIFIER, camera_name)
self._faces = []
self._matched = {}
self.total_faces = None
def test_DeepstackFace():
"""Test a good response from server."""
with requests_mock.Mocker() as mock_req:
mock_req.post(
FACE_DETECTION_URL,
status_code=ds.HTTP_OK,
json=MOCK_FACE_DETECTION_RESPONSE,
)
dsface = ds.DeepstackFace(MOCK_IP_ADDRESS, MOCK_PORT)
predictions = dsface.detect(MOCK_BYTES)
assert predictions == MOCK_FACE_DETECTION_RESPONSE["predictions"]
def __init__(self,
ip_address,
port,
api_key,
timeout,
detect_only,
camera_entity,
name=None):
"""Init with the API key and model id."""
super().__init__()
self._dsface = ds.DeepstackFace(ip_address, port, api_key, timeout)
self._detect_only = detect_only
self._camera = camera_entity
if name:
self._name = name
else:
camera_name = split_entity_id(camera_entity)[1]
self._name = "{} {}".format(CLASSIFIER, camera_name)
self._matched = {}
if deepstack_mode == FACE:
st.title("Deepstack Face recognition")
img_file_buffer = st.file_uploader("Upload an image",
type=["png", "jpg", "jpeg"])
## Process image
if img_file_buffer is not None:
pil_image = Image.open(img_file_buffer)
else:
pil_image = Image.open(FACE_TEST_IMAGE)
dsface = ds.DeepstackFace(
ip=DEEPSTACK_IP,
port=DEEPSTACK_PORT,
api_key=DEEPSTACK_API_KEY,
timeout=DEEPSTACK_TIMEOUT,
min_confidence=MIN_CONFIDENCE_THRESHOLD,
)
predictions = process_image_face(pil_image, dsface)
faces = utils.get_faces(predictions, pil_image.width, pil_image.height)
recognised_faces = [
face for face in faces if face["confidence"] > CONFIDENCE_THRESHOLD
]
# Draw object boxes
draw = ImageDraw.Draw(pil_image)
for face in faces:
confidence = face["confidence"]
name = face["name"]
box_label = f"{name}"