def lookup_face():
global CUSTOMER_STATUS
global SESSION_NAME
submitted_name = request.values.get("name")
submitted_account = request.values.get("account")
submitted_pin = request.values.get("pin").replace('-', '')
frame = g_camera.get_frame(return_raw_image=True)
vector, return_status = pfs.get_image_with_bbs(frame,
is_mxnet=mxnet_face_model,
return_vector=True,
classify_face=True,
customer_status=CUSTOMER_STATUS,
session_name=SESSION_NAME)
collection = MongoClient(DB_HOST)[DB_NAME][COLLECTION_NAME]
if return_status == True:
records = list(collection.find({"name": submitted_name}))
if not records:
return Response("Number not found. Unable to authenticate customer.")
record_vec = np.array(records[0]['vector']).reshape((1, 128))
similarity = get_distances(record_vec, vector.reshape((1, 128)))
is_similar_img = similarity < 1.1
is_correct_pin = records[0]["pin"] == submitted_pin
if is_correct_pin and is_similar_img:
CUSTOMER_STATUS = "verified"
SESSION_NAME = submitted_name
return Response("{0} was verified.".format(submitted_name))
else:
CUSTOMER_STATUS = "unverified"
return Response("Customer not authenticated. Number Status: {0}, Facial Recognition Status: {1} -- Image similarity is {2}".format(is_correct_pin,
is_similar_img,
similarity))
no_face_detected_response = "Unable to verify identity. No face was detected."
return Response(no_face_detected_response)
def facenet_classify_image_data(cv2_image):
facenet_result = None
if pk_enable_ws:
cv2_image_string = cv2.imencode('.png', cv2_image)[1].tostring()
response = requests.post('http://localhost:5000/get-image-with-bbs',
files={'image_data': cv2_image_string})
file_bytes = np.asarray(bytearray(response.content), dtype=np.uint8)
facenet_result = cv2.imdecode(file_bytes, cv2.IMREAD_UNCHANGED)
else:
facenet_result = pfs.get_image_with_bbs(cv2_image)
return facenet_result
def facenet_classify_image_data(cv2_image, docs, is_mxnet=mxnet_face_model, return_vector=False, classify_face=False, customer_status="unknown", session_name=None):
facenet_result = None
if pk_enable_ws:
cv2_image_string = cv2.imencode('.png', cv2_image)[1].tostring()
response = requests.post('http://localhost:5000/get-image-with-bbs', files={'image_data': cv2_image_string})
file_bytes = np.asarray(bytearray(response.content), dtype=np.uint8)
facenet_result = cv2.imdecode(file_bytes, cv2.IMREAD_UNCHANGED), False
else:
# print "facenet_classify_image_data: Classify Face ", classify_face
facenet_result = pfs.get_image_with_bbs(cv2_image, docs,
is_mxnet=mxnet_face_model,
return_vector=False,
classify_face=classify_face,
customer_status=customer_status,
session_name=session_name)
return facenet_result
def register_face():
name = request.values.get("name")
pin = request.values.get("pin").replace('-', '')
frame = g_camera.get_frame(return_raw_image=True)
print "Frame: ", frame.shape
vector, return_status = pfs.get_image_with_bbs(frame, is_mxnet=mxnet_face_model, return_vector=True)
print vector.shape, return_status
collection = MongoClient(DB_HOST)[DB_NAME][COLLECTION_NAME]
if return_status == True:
new_doc = {"name": name,
"vector": vector.tolist(),
"pin": pin}
collection.update_many({"name":name},
{"$set": {"pin":pin,
"vector": vector.tolist()}}, upsert=True)
return Response("{0} added to records.".format(name))
return Response("Unable to locate a face in the camera. Try another perspective.".format(name))
def fl_get_image_with_bbs():
# This returns an image with bbs superimposed on it
if "image_url" in request.form.keys() and request.form['image_url']:
image_url = request.form['image_url']
image_data = urllib.urlopen(image_url, context=gcontext).read()
else:
image_data = request.files['image_data'].read()
file_bytes = np.asarray(bytearray(image_data), dtype=np.uint8)
cv2_image = cv2.imdecode(file_bytes, cv2.IMREAD_UNCHANGED)
bbs = pfs.get_image_bbs(cv2_image)
faces = pfs.fp.extract_faces(cv2_image, bbs)
face_vecs = pfs.pkfn.vectorize(faces)
names = [get_identity(vec, pfs.reference_table) for vec in face_vecs]
cv2_image_result = pfs.get_image_with_bbs(cv2_image)
cv2_image_string = cv2.imencode('.png', cv2_image_result)[1].tostring()
#DBG print('length:', len(cv2_image_string))
response = make_response(cv2_image_string)
response.headers['Content-Type'] = 'image/png'
# response.headers['Content-Disposition'] = 'attachment; filename=image_result.jpg'
return response