def load_encoding_image():
list_csv = []
contrato_user = current_user.contrato
cant_user = User.get_by_count(contrato_user)
users = User.get_by_contrato(contrato_user)
encoding_dir = current_app.config['CSV_DIR']
for user in users:
file_path = os.path.join(encoding_dir, user.csv_name)
data = loadtxt(file_path)
list_csv.append(data)
return list_csv
def list_users():
contrato_user = current_user.contrato
#users = User.get_all()
users = User.get_by_contrato(contrato_user)
return render_template("admin/users.html", users=users)
def face_user():
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
# video_capture = cv2.VideoCapture("https://192.168.1.81:8080/video")
# Load a sample picture and learn how to recognize it.
known_face_encodings = load_encoding_image()
# Create arrays of known face encodings and their names
contrato_user = current_user.contrato
users = User.get_by_contrato(contrato_user)
known_face_names = []
for user in users:
known_face_names.append(user.name)
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
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"
# If a match was found in known_face_encodings, just use the first one.
if True in matches:
first_match_index = matches.index(True)
name = known_face_names[first_match_index]
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!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
