This function is only for this Face Recoginition System.
if you want to import to another functions please modify
---------Created by ZOU Zijie :)
"""
#/------Initial Logger------/
log_timee = time.strftime("_%d-%b-%Y_%H:%M:%S", time.localtime())
#/---Dlib initial---/
model = './models/shape_predictor_68_face_landmarks.dat'
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(model)
fa = face_utils.FaceAligner(predictor, desiredFaceWidth=250)
def log_time():
#/--1 for info; 2 for warning; 3 for error--/
log_time = time.strftime("%d-%b-%Y_%H:%M:%S", time.localtime())
return log_time
def align_unknown():
user = "******"
path = './Users/people_ori/Unkonwn'
un_vec = './Users/people_vectors/Unkonwn'
count = 0
# 随机颜色
color = np.random.randint(0, 255, (100, 3))
cap = cv2.VideoCapture("stable.mp4")
cap.set(3, 1280)
cap.set(4, 720)
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("./68Marks.dat")
p0 = []
Face_Counter = 0
old_gray = None
aligner = face_utils.FaceAligner(predictor,
desiredFaceWidth=512,
desiredFaceHeight=512,
desiredLeftEye=(0.30, 0.30))
while True:
rect, frame = cap.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
old_gray = frame_gray
print(old_gray.shape)
dets = detector(frame_gray, 0)
if len(dets) != 0:
face = dets[0]
Face_Counter = Face_Counter + 1
left = face.left()
right = face.right()
bottom = face.bottom()
top = face.top()
cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
def __init__(self):
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
self.fa = face_utils.FaceAligner(self.predictor, desiredFaceWidth=256)
def main():
# return
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("Unable to connect to camera.")
return
counter = 0
frame_count = 0
record = False
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(face_landmark_path)
fa = face_utils.FaceAligner(predictor, desiredFaceWidth=256)
while cap.isOpened():
ret, img = cap.read()
#flip img
img = cv2.flip(img, 1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
res = img.copy()
if ret:
#detect faces from image
face_rects = detector(img, 0)
for i in range(len(face_rects)):
mask = np.zeros(img.shape[:2], dtype = np.uint8)
#get face landmarks
face_rect = face_rects[i]
shape = predictor(img, face_rect)
shape = face_utils.shape_to_np(shape)
reprojectdst, euler_angle = get_head_pose(shape)
#detect hull to crop face
hull = cv2.convexHull(shape)
cv2.fillPoly(mask, pts =[hull], color=1)
#visualize
cv2.polylines(res,[hull],True,(255, 0, 0))
for (x, y) in shape:
cv2.circle(res, (x, y), 1, (0, 255, 0), -1)
for start, end in line_pairs:
cv2.line(res, reprojectdst[start], reprojectdst[end], (0, 0, 255))
(x, y, w, h) = face_utils.rect_to_bb(face_rect)
x = np.clip(x, 0, img.shape[1])
y = np.clip(y, 0, img.shape[0])
w = np.clip(w, 1, img.shape[1]-x)
h = np.clip(h, 1, img.shape[0]-y)
img_masked = cv2.bitwise_and(img, img, mask = mask)
faceOrig = cv2.resize(img_masked[y:y + h, x:x + w], (256, 256))
faceAligned = cv2.resize(fa.align(img_masked, gray, face_rect), (img.shape[1], img.shape[0]))
if record:
if frame_count % 2 == 0:
path = './face_database/'+person_name+'/'+person_name+str(counter)+'.jpg'
counter+=1
cv2.imwrite(path, faceAligned)
res = np.hstack((res, faceAligned))
frame_count += 1
cv2.imshow("demo", res)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
elif key == ord('r'):
if not record:
record = True
print("recording...")
else:
record = False
print("stop recording")
def __init__(self):
self.detector = dlib.get_frontal_face_detector()
datapath = pathlib.Path(__file__).parent.absolute()
datapath = os.path.join(datapath, "shape_predictor_68_face_landmarks.dat")
self.predictor = dlib.shape_predictor(datapath)
self.fa = face_utils.FaceAligner(self.predictor, desiredFaceWidth=256)