def deletar(name):
all_face_encodings = {}
pickle_file = 'dataset_faces.pickle'
try:
all_face_encodings = pickle.load(pickle_file)
except:
print("Arquivo vazio")
try:
all_face_encodings.pop(name)
pickle.safe_dump(all_face_encodings, pickle_file)
print("Depois : " + str(all_face_encodings))
except:
return "Ocorreu um erro ao salvar os dados"
return "Usuário removido com sucesso."
def atualizar(file, name):
all_face_encodings = {}
pickle_file = 'dataset_faces.pickle'
load_image = face_recognition.load_image_file(file)
image_encoding = face_recognition.face_encodings(load_image)[0]
try:
all_face_encodings = pickle.load(pickle_file)
except:
print("Arquivo vazio")
try:
all_face_encodings[name] = image_encoding
pickle.safe_dump(all_face_encodings, pickle_file)
except:
raise
return "Ocorreu um erro ao salvar os dados"
return "Foto atualizada com sucesso"
def cadastrar(file, name):
all_face_encodings = {}
pickle_file = 'dataset_faces.pickle'
load_image = face_recognition.load_image_file(file)
image_encoding = face_recognition.face_encodings(load_image)[0]
try:
all_face_encodings = pickle.load(pickle_file)
except:
print("Arquivo vazio")
print("Depois : " + str(all_face_encodings))
try:
all_face_encodings[name] = image_encoding
pickle.safe_dump(all_face_encodings, pickle_file)
except:
raise
return "Ocorreu um erro ao salvar os dados"
return image_encoding
def procurar(file):
print("TIPO :::::: ")
print(type(file))
# Load face encodings
all_face_encodings = {}
pickle_file = 'dataset_faces.pickle'
erro = ""
try:
all_face_encodings = pickle.load(pickle_file)
print(all_face_encodings)
if all_face_encodings:
print(all_face_encodings.keys())
else:
erro = "Ninguem Cadastrado"
except:
erro = "Não foi possivel acessar o banco de dados"
return erro
# Grab the list of names and the list of encodings
face_names = list(all_face_encodings.keys())
print("FACE NAMES")
print(face_names)
face_encodings = np.array(list(all_face_encodings.values()))
# Try comparing an unknown image
unknown_image = face_recognition.load_image_file(file)
#unknown_image = file
unknown_face = face_recognition.face_encodings(unknown_image)
result = face_recognition.compare_faces(face_encodings, unknown_face)
# Print the result as a list of names with True/False
names_with_result = dict(zip(result, face_names))
if True in names_with_result.keys():
return names_with_result[True]
return "Não encontrou ninguém"
def test_compress():
"""Test whether data compression is working.
"""
pk.safe_dump({"value": 1}, path_gz, enable_verbose=False)
assert pk.load(path_gz, enable_verbose=False) == {"value": 1}
os.remove(path_gz)
def acesso():
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
# Load face encodings
known_face_encodings_file = {}
known_face_encodings = []
known_face_names = []
exit_msg = "404"
pickle_file = 'dataset_faces.pickle'
known_face_encodings_file = pickle.load(pickle_file)
if known_face_encodings_file:
print(known_face_encodings_file.keys())
known_face_encodings = list(known_face_encodings_file.values())
known_face_names = list(known_face_encodings_file.keys())
else:
return "Ninguem cadastrado"
achou = 0
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
name = ""
#temporizador
tstart = time.process_time()
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(
rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(
known_face_encodings, face_encoding)
name = "Unknown"
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(
known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
exit_msg = name
achou = 1
#chamar o arduino
face_names.append(name)
process_this_frame = not process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations,
face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom),
(0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0,
(255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
tstop = time.process_time()
if (achou == 1 or (tstop - tstart) > 5):
video_capture.release()
cv2.destroyAllWindows()
return exit_msg
