def align_faces(faces, image, predictor, required_size=(160, 160)):
# Récupération de la taille de l'image
(s_height, s_width) = image.shape[:2]
aligned_faces = []
# Boucler sur les visages détecté
for i, det in enumerate(faces):
# Récupérer les traits du visage
shape = predictor(image, det)
# Récupérer les coordonnées des yeux grâce au traits du visages
left_eye = extract_left_eye_center(shape)
right_eye = extract_right_eye_center(shape)
# Récupérer la matice de rotation du visage grace à la position des yeux
M = get_rotation_matrix(left_eye, right_eye)
# Appliquer une rotation à l'image afin d'avoir le ième visage aligné
rotated = cv2.warpAffine(
image, M, (s_width, s_height), flags=cv2.INTER_CUBIC)
# Rogner l'image afin de garder uniquement le ième visage
cropped = crop_image(rotated, det)
cropped = cv2.resize(cropped, required_size)
# Ajouter le visage rogné à la liste
aligned_faces.append(cropped)
return asarray(aligned_faces)
def crop_from_detected(det):
shape = self.predictor(img, det)
left_eye = extract_left_eye_center(shape)
right_eye = extract_right_eye_center(shape)
M = get_rotation_matrix(left_eye, right_eye)
rotated = cv2.warpAffine(img,
M, (s_width, s_height),
flags=cv2.INTER_CUBIC)
cropped = crop_image(rotated, det)
return cropped
def detectar(img):
detecs = detector(img, 1) # Vetor de detecção de rostos
rostos = []
s_height, s_width = img.shape[:2]
for i, det in enumerate(detecs):
shape = predictor(img, det)
left_eye = extract_left_eye_center(shape)
right_eye = extract_right_eye_center(shape)
M = get_rotation_matrix(left_eye, right_eye)
rotated = cv2.warpAffine(img, M, (s_height, s_width), flags=cv2.INTER_CUBIC)
cropped = crop_image(rotated, det)
squared = resizeAndPad(cropped, (img_h,img_w), 127)
rostos.append(squared)
return detecs, rostos
def prepare(raw_coll, dest_dir):
for filename in raw_coll:
img = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
s_height, s_width = img.shape[:2]
dets = detector(img, 1)
print('Processing ' + filename)
for i, det in enumerate(dets):
if i > 0:
print("Mais de um rosto detectado!!!!!")
break
shape = predictor(img, det)
left_eye = extract_left_eye_center(shape)
right_eye = extract_right_eye_center(shape)
M = get_rotation_matrix(left_eye, right_eye)
rotated = cv2.warpAffine(img, M, (s_height, s_width), flags=cv2.INTER_CUBIC)
cropped = crop_image(rotated, det)
squared = resizeAndPad(cropped, (img_h,img_w), 127)
output_image_path = dest_dir + os.path.basename(filename)
cv2.imwrite(output_image_path, squared)
def process_image(self, input_image, output_image, scale=2):
img = cv2.imread(input_image)
# cv2.imshow('image', img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
height, width = img.shape[:2]
s_height, s_width = height // scale, width // scale
img = cv2.resize(img, (s_width, s_height))
dets = self.detector(img, 1)
for i, det in enumerate(dets):
shape = self.predictor(img, det)
left_eye = extract_left_eye_center(shape)
right_eye = extract_right_eye_center(shape)
M = get_rotation_matrix(left_eye, right_eye)
rotated = cv2.warpAffine(img,
M, (s_width, s_height),
flags=cv2.INTER_CUBIC)
cropped = crop_image(rotated, det)
# cv2.imshow('image', cropped)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# if output_image.endswith('.jpg'):
# output_image_path = output_image.replace('.jpg', '_%i.jpg' % i)
# elif output_image.endswith('.png'):
# output_image_path = output_image.replace('.png', '_%i.jpg' % i)
# else:
# output_image_path = output_image + ('_%i.jpg' % i)
#
output_image_path = os.path.join(
output_image,
input_image.split("/")[-1] + ('_%i.jpg' % i))
cv2.imwrite(output_image_path, cropped)
def alignFace(image):
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
height, width = image.shape[:2]
s_height, s_width = height, width
image = cv2.resize(image, (s_width, s_height))
dets = detector(image, 1)
if len(dets) == 1:
shape = predictor(image, dets[0])
left_eye = extract_left_eye_center(shape)
right_eye = extract_right_eye_center(shape)
M = get_rotation_matrix(left_eye, right_eye)
rotated = cv2.warpAffine(image,
M, (s_width, s_height),
flags=cv2.INTER_CUBIC)
return rotated
else:
print("Not a single face")
return None
def run(self):
old = ""
self.count, self.count_u, confidence = 0, 0, 0
print("thread is : ", int(self.thread().currentThreadId()))
local_DB = sqlite3.connect('Data/users.db')
cur = local_DB.cursor()
start = time.time()
while True:
dur = time.time() - start
dirty = False
if dur > 10.0:
queryb = {'id': 1}
if float(self.temp()) > 60 :
self.activate_fan()
else :
self.deactivate_fan()
try:
res1 = requests.post(urlb, data=queryb)
#print(res1.text)
start = time.time()
print("pulsed")
# mergeDB()
except requests.ConnectionError as e:
print("no connection")
dirty = True
while self.working:
dure = time.clock()
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
# grab the raw NumPy array representing the image, then initialize the timestamp
# and occupied/unoccupied text
if (not self.working):
rawCapture.seek(0)
rawCapture.truncate(0)
break
frame = frame.array
height, width, channels = frame.shape
# Convert to grayscalel
gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
# Resize to speed up detection (optinal, change size above)
mini = cv.resize(
frame, (int(gray.shape[1] / size), int(gray.shape[0] / size)))
# Detect faces and loop through each one
# faces = self.lbp_cascade.detectMultiScale(mini, 1.1, 3)
dets = detector(gray, 1)
um_faces = len(dets)
if um_faces == 0:
self.status.emit("Denied")
self.user.emit("aucun personne")
for i, det in enumerate(dets):
shape = predictor(frame, det)
left_eye = extract_left_eye_center(shape)
right_eye = extract_right_eye_center(shape)
M = get_rotation_matrix(left_eye, right_eye)
rotated = cv.warpAffine(
frame, M, (int(width / size), int(height / size)), flags=cv.INTER_CUBIC)
cropped = crop_image(rotated, det)
cropped = cv.cvtColor(cropped, cv.COLOR_BGR2GRAY)
# Try to recognize the face
if cropped is None:
continue
prediction, confidence = self.model.predict(cropped)
print(confidence)
cv.rectangle(frame, (det.left(), det.top()),
(det.right(), det.bottom()), (255, 255, 0), 2)
if confidence < 160:
# Grant accesss
cur.execute("SELECT prenom, depatement FROM users WHERE nom = ?",(self.names[prediction],))
row = cur.fetchone()
print(row)
print(prediction)
if old == self.names[prediction]:
self.count += 1
if self.count > 3:
count = 0
self.status.emit("Autoriser")
self.user.emit(
"Bienvenue, Mr,{}".format(old))
cur.execute(
"INSERT INTO log (nom,prenom,departement,date) VALUES (?,?,?,datetime('now'))", (old, row[0], row[1]))
local_DB.commit()
query = {'name': old,
'prenom': row[0],
'departement':row[1]}
if not dirty:
res = requests.post(url, data=query)
self.stop()
else :
old = self.names[prediction]
else:
self.status.emit("Denied")
self.user.emit("non reconue")
self.count_u += 1
if self.count_u > 5:
self.count_u = 0
self.stop()
# Show the image and check for ESC being pressed
qtimg = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
# qtimg = cv.resize(qtimg, dim, interpolation = cv.INTER_AREA)
image = QtGui.QImage(qtimg, width,
height, width * 3, QtGui.QImage.Format_RGB888)
pixmap = QtGui.QPixmap.fromImage(image)
pixmap_resized = pixmap.scaled(
350, 260, QtCore.Qt.KeepAspectRatio)
pixmapItem = QtWidgets.QGraphicsPixmapItem(pixmap)
self.changePixmap.emit(pixmapItem)
rawCapture.seek(0)
rawCapture.truncate(0)
if (time.clock() - dure) > 20:
self.stop()
QtWidgets.QApplication.processEvents()
img_gray = cv2.cvtColor(img_color, cv2.COLOR_BGR2GRAY)
# Face detection
dets = detector(img_gray)
det = correct_detection(dets[0], img_gray)
ldmrks = predictor(img_gray, det)
roll, pitch, yaw = rpy.estimate(det, img_color)
axis_image = img_color.copy()
draw_axis(axis_image, yaw, pitch, roll)
cv2.imwrite("img_axes.png", axis_image)
# Face rotation
left_eye = extract_left_eye_center(ldmrks)
right_eye = extract_right_eye_center(ldmrks)
dlib_angle = angle_between_2_points(left_eye, right_eye)
if abs(roll.item()) > abs(dlib_angle):
angle = roll.item()
else:
angle = dlib_angle
M = cv2.getRotationMatrix2D(
(ldmrks.part(NOSE_CENTER).x, ldmrks.part(NOSE_CENTER).y), angle, 1)
rotated = cv2.warpAffine(img_color,
M, (img_color.shape[1], img_color.shape[0]),
flags=cv2.INTER_CUBIC)
cv2.imwrite("img_rotated.png", rotated)
# Rotated landmarls
ldmrks = predictor(cv2.cvtColor(rotated, cv2.COLOR_BGR2GRAY), det)
def capture(self, id, name):
try:
user_name = name
id = id
except:
print("Vous devez entrer un nom")
path = os.path.join(users_dir, user_name)
if not os.path.isdir(path):
os.mkdir(path)
# Generate name for image file
pin = sorted(
[int(n[:n.find('.')])
for n in os.listdir(path) if n[0] != '.'] + [0])[-1] + 1
print("Le programmes va capturer 20 images. \
\nDeplacez votre tete pour augmenter la precision pendant le fonctionnement.\n"
)
# The program loops until it has 20 images of the face.
count = 0
pause = 0
t = 0
count_max = 20
# Loop until the camera is working
# Loop until the camera is working
for frame in camera.capture_continuous(rawCapture,
format="bgr",
use_video_port=True):
if count >= count_max:
rawCapture.seek(0)
rawCapture.truncate(0)
break
# grab the raw NumPy array representing the image, then initialize the timestamp
# and occupied/unoccupied text
frame = frame.array
# Get image size
height, width, channels = frame.shape
r = 100.0 / width
dim = (100, int(height * r))
# Flip frame
frame = cv.flip(frame, 1, 0)
# Convert to grayscale
gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
# Detect faces
dets = detector(frame, 1)
for i, det in enumerate(dets):
shape = predictor(frame, det)
left_eye = extract_left_eye_center(shape)
right_eye = extract_right_eye_center(shape)
M = get_rotation_matrix(left_eye, right_eye)
rotated = cv.warpAffine(frame,
M, (width, height),
flags=cv.INTER_CUBIC)
cropped = crop_image(rotated, det)
cv.rectangle(frame, (det.left(), det.top()),
(det.right(), det.bottom()), (255, 255, 0), 2)
# Remove false positives
if (False):
print("Non claire")
else:
# To create diversity, only save every fith detected image
if (pause == 0):
print("enregistrement de la capture " +
str(count + 1) + "/" + str(count_max))
# Save image file
cv.imwrite('%s/%s.png' % (path, pin), cropped)
pin += 1
count += 1
pause = 1
if (pause > 0):
pause = (pause + 1) % 3
qtimg = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
#qtimg = cv.resize(qtimg, dim, interpolation = cv.INTER_AREA)
image = QtGui.QImage(qtimg, width, height, width * 3,
QtGui.QImage.Format_RGB888)
pixmap = QtGui.QPixmap.fromImage(image)
pixmap_resized = pixmap.scaled(350, 260, QtCore.Qt.KeepAspectRatio)
pixmapItem = QtWidgets.QGraphicsPixmapItem(pixmap_resized)
if count == count_max:
image = QtGui.QImage('camera.png')
pixmap = QtGui.QPixmap.fromImage(image)
pixmap_resized = pixmap.scaled(350, 260,
QtCore.Qt.KeepAspectRatio)
pixmapItem = QtWidgets.QGraphicsPixmapItem(pixmap_resized)
self.scene.addItem(pixmapItem)
rawCapture.seek(0)
rawCapture.truncate(0)
QtWidgets.QApplication.processEvents()
key = cv.waitKey(10)
if key == 27:
break
image = QtGui.QImage('/camera.png')
idle = QtGui.QPixmap.fromImage(image)
idle_resized = pixmap.scaled(350, 260, QtCore.Qt.KeepAspectRatio)
pixmapItem = QtWidgets.QGraphicsPixmapItem(pixmap_resized)
self.scene.addItem(pixmapItem)
cv.destroyAllWindows()
QtWidgets.QApplication.processEvents()
filename = os.fsdecode(file)
if filename.endswith(".jpg") or filename.endswith(
".png") or filename.endswith(".jpeg") or filename.endswith(
".bmp"):
print('transforming ' + str(counter) + '/' + str(maxFiles) + ': ' +
filename)
img = cv2.imread(input_image_folder + '/' + filename,
cv2.IMREAD_GRAYSCALE)
height, width = img.shape[:2]
s_height, s_width = height // scale, width // scale
img = cv2.resize(img, (s_width, s_height))
dets = detector(img, 1)
for i, det in enumerate(dets):
shape = predictor(img, det)
left_eye = extract_left_eye_center(shape)
right_eye = extract_right_eye_center(shape)
M = get_rotation_matrix(left_eye, right_eye)
rotated = cv2.warpAffine(img,
M, (s_width, s_height),
flags=cv2.INTER_CUBIC)
cropped = crop_image(rotated, det)
if filename.endswith('.jpg'):
output_image = filename.replace('.jpg', '_%i.jpg' % i)
elif filename.endswith('.png'):
output_image = filename.replace('.png', '_%i.jpg' % i)
elif filename.endswith('.jpeg'):
output_image = filename.replace('.jpeg', '_%i.jpg' % i)
elif filename.endswith('.bmp'):
def align_face(name):
input_image, output_image = source + name, destination + name
#reading image. If 0 bytes we delete it
output = input_image + ''
img = cv2.imread(input_image)
if img is None:
if (os.path.isfile(input_image)):
os.remove(input_image)
return 'fail'
#scaling images
height, width = img.shape[:2]
if (width == 0 or height == 0):
no_removes += 1
os.remove(input_image)
return 'fail'
s_height, s_width = height // scale, width // scale
img = cv2.resize(img, (s_width, s_height))
output += ' | ' + str(s_width) + ' | ' + str(s_height)
#detects faces. If no faces -> remove
dets = detector(img, 1)
output += ' | ' + str(len(dets)) + '\n'
if (len(dets) == 0):
os.remove(input_image)
return 'fail'
#looping through each face, rotating it, cropping, saving, then reading in again and outputting the landmark points
for i, det in enumerate(dets):
shape = predictor(img, det)
#rotate eyes to horizontal
left_eye = extract_left_eye_center(shape)
right_eye = extract_right_eye_center(shape)
output += 'eyes: ' + str(left_eye) + ' ' + str(right_eye) + ' | '
M = get_rotation_matrix(left_eye, right_eye)
rotated = cv2.warpAffine(img,
M, (s_width, s_height),
flags=cv2.INTER_CUBIC)
cropped = crop_image(rotated, det)
if (cropped.shape[1] == 0):
continue
#saving
if output_image.endswith('.jpg'):
output_image_path = output_image.replace('.jpg', '_%i.jpg' % i)
elif output_image.endswith('.png'):
output_image_path = output_image.replace('.png', '_%i.jpg' % i)
else:
output_image_path = output_image + ('_%i.jpg' % i)
output += ' | ' + output_image_path + ' | ' + str(cropped.shape)
cv2.imwrite(output_image_path, cropped)
#landmark detection
try:
LM_img = io.imread(output_image_path)
except:
os.remove(output_image_path)
return
dets = detector(LM_img, 1)
output += ("Number of faces detected: {}".format(len(dets)))
if (len(dets) == 0):
os.remove(output_image_path)
for k, d in enumerate(dets):
output += (
" | Detection {}: Left: {} Top: {} Ri: {} Bot: {}".format(
k, d.left(), d.top(), d.right(), d.bottom()))
shape = predictor(LM_img, d)
with open(output_image_path + '.txt', 'w') as lm:
for i in range(shape.num_parts):
lm.write(
str(shape.part(i).x) + ' ' + str(shape.part(i).y) +
'\n')
#print(output + '\n===============')
return 'success'