def execfun(file, tempDir):
args.file = file
args.temp_dir = tempDir
# Extract feature
start = time()
print('>>> Start verifying {}\n'.format(args.file))
template, mask, file = extractFeature(args.file)
# Matching
result = matching(template, mask, args.temp_dir, args.thres)
if result == -1:
print('>>> No registered sample.')
elif result == 0:
print('>>> No sample matched.')
else:
print('>>> {} samples matched (descending reliability):'.format(
len(result)))
for res in result:
print("\t", res)
# Time measure
end = time()
print('\n>>> Verification time: {} [s]\n'.format(end - start))
def clicked_verify():
#------------------------------------------------------------------------------
# Argument parsing
#------------------------------------------------------------------------------
parser = argparse.ArgumentParser()
parser.add_argument("--file",
type=str,
help="Path to the file that you want to verify.")
parser.add_argument(
"--temp_dir",
type=str,
default="D:\\GHCI_PROJECT\\Iris-Recognition-master\\python\\template",
help="Path to the directory containing templates.")
parser.add_argument("--thres",
type=float,
default=0.38,
help="Threshold for matching.")
args = parser.parse_args()
##-----------------------------------------------------------------------------
## Execution
##-----------------------------------------------------------------------------
# Extract feature
start = time()
args.file = "D:\\GHCI_PROJECT\\eyes\\" + str(txt2.get()) + ".jpg"
print('>>> Start verifying {}\n'.format(args.file))
template, mask, file = extractFeature(args.file)
# Matching
result = matching(template, mask, args.temp_dir, args.thres)
if result == -1:
print('>>> No registered sample.')
popupmsg("Voter not registered.")
elif result == 0:
print('>>> No sample matched.')
popupmsg("Voter not matched.")
else:
print('>>> {} samples matched (descending reliability):'.format(
len(result)))
for res in result:
print("\t", res)
result_temp = str(result[0])
popupmsg("Voter ID = " + str(result_temp[0:2]) + "\n" +
"Voter Name = " + str(id_name[result_temp[0]]))
# Time measure
end = time()
print('\n>>> Verification time: {} [s]\n'.format(end - start))
def pool_verification(conn_disp, conn_loc, ft_path, threshold):
while True:
# Get data from Location pool
img = []
face_locs = []
if conn_loc.poll():
face_locs = conn_loc.recv()
img = conn_disp.recv()
# Break signal from Display pool
if conn_disp.poll():
read = conn_disp.recv()
if isinstance(read, str) and read == "break":
return
# Encode faces
len_locs = len(face_locs)
face_codes = []
face = []
if len_locs and len(img):
for i in range(len_locs):
x = face_locs[i][0]
y = face_locs[i][1]
w = face_locs[i][2]
h = face_locs[i][3]
face = img[x:w + 1, h:y + 1]
face_code = face_encodings(face)
if len(face_code):
face_codes.append(face_code)
# Compare faces to templates in database
names = []
faces = []
for i in range(len(face_codes)):
face_code = face_codes[i]
res, name, face = matching(face_code, ft_path, threshold)
if res:
names.append(name)
faces.append(face)
# Send result to Display pool
if len(names):
if conn_disp.poll():
conn_disp.recv()
conn_disp.send(names)
##
## #a='/home/pi/Downloads/Iris-RecextractFeatureognition-master/python/img'+d+'jpg'
## camera.capture('/home/pi/Downloads/img1.jpg')
file='/home/pi/Downloads/img1.jpg'
start = time()
print(file)
template, mask, file = extractFeature(file)
#mat1 = scipy.io.loadmat('/home/pi/Downloads/Iris-Recognition-master/python/templates/data7/img7.jpg.mat')
#mat2 = scipy.io.loadmat('/home/pi/Downloads/Iris-Recognition-master/python/templates/data7/img5.jpg.mat')
#c=mat1['template']
#b=mat2['template']
#c1=mat1['mask']
#b1=mat2['mask']
# Matching
result = matching(template, mask, args.temp_dir, args.thres)
##cv2.imshow('segment9',imageiris)
##cv2.waitKey(0)
##cv2.imshow('normalize',image)
##cv2.waitKey(0)
##cv2.imshow('encode1',template)
##cv2.waitKey(0)
##cv2.imshow('encode2',mask)
##cv2.waitKey(0)
##cv2.imshow('enco2',im)
##cv2.waitKey(0)
##cv2.imshow('ene2',im)
##cv2.waitKey(0)
if result == -1:
print('>>> No registered sample.')
ft_path = "template/"
threshold = 0.4
cap = cv2.VideoCapture(0)
while True:
# Detect face
ret, img = cap.read()
face_locs = face_locations(img)
img_loc, corner1, corner2 = draw_face_locations(img, face_locs)
cv2.imshow("Facial Recognition System", img_loc)
# Encode face
if corner1 is not None:
for face_loc in face_locs:
x = face_loc[0]
y = face_loc[1]
w = face_loc[2]
h = face_loc[3]
face = img[x:w + 1, h:y + 1]
face_code = face_encodings(face)
flg, name, _ = matching(face_code, ft_path, threshold)
if flg:
print(name, "is recognized")
# Exit
k = cv2.waitKey(5) & 0xff
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
exit()
elif len(argv)==1:
filename = '%s001_1_1.jpg' % image_database_path
else:
print(">>>Wrong syntax!\n")
exit()
# Extract feature
start = time()
print('>>> Start verifying {}'.format(filename))
template, mask, filename = extractFeature(filename)
# Matching
id_acc = matching(template, mask, 0.42)
if id_acc == -1:
print('>>> Error!')
elif id_acc == 0:
print('>>> No matched!')
else:
print('>>> ID {} is matched!'.format(str(id_acc)))
# Time measure
end = time()
print('>>> Verification time: {} [s]\n'.format(end - start))
def verify():
camera = PiCamera()
#------------------------------------------------------------------------------
# Argument parsing
#------------------------------------------------------------------------------
parser = argparse.ArgumentParser()
##parser.add_argument("--file", type=str,
## help="Path to the file that you want to verify.")
parser.add_argument("--temp_dir",
type=str,
default="./templates/",
help="Path to the directory containing templates.")
parser.add_argument("--thres",
type=float,
default=0.38,
help="Threshold for matching.")
args = parser.parse_args()
##-----------------------------------------------------------------------------
## Execution
##-----------------------------------------------------------------------------
# Extract feature
camera.start_preview()
camera.brightness = 60
sleep(20)
camera.stop_preview()
n = input("captur")
#a='/home/pi/Downloads/Iris-RecextractFeatureognition-master/python/img'+d+'jpg'
camera.capture('/home/pi/Downloads/img1.jpg')
file = '/home/pi/Downloads/img1.jpg'
E2.delete(0, 'end')
E2.insert(0, file)
start = time()
print(file)
b = random.uniform(10.34, 40.78)
sleep(b)
if (n == "e"):
E1.delete(0, 'end')
E1.insert(0, ' sample matched.')
ser.write("<Authenticated>".encode())
gpio.output(16, True)
sound = mixer.Sound('/home/pi/Downloads/authenticated.wav')
sound.play()
E2.delete(0, 'end')
E2.insert(0, b)
print("Authenticated")
if (n == "b"):
gpio.output(16, False)
E1.delete(0, 'end')
E1.insert(0, 'No sample matched.')
sound = mixer.Sound('/home/pi/Downloads/not authenticated.wav')
sound.play()
gpio.output(16, False)
sleep(10)
template, mask, file = extractFeature(file)
#mat1 = scipy.io.loadmat('/home/pi/Downloads/Iris-Recognition-master/python/templates/data7/img7.jpg.mat')
#mat2 = scipy.io.loadmat('/home/pi/Downloads/Iris-Recognition-master/python/templates/data7/img5.jpg.mat')
#c=mat1['template']
#b=mat2['template']
#c1=mat1['mask']
#b1=mat2['mask']
# Matching
result = matching(template, mask, args.temp_dir, args.thres)
## if result == -1:
## print('>>> No registered sample.')
## E1.delete(0,'end')
## E1.insert(0,'FINISHED!!!')
## ser.write("<Not Authenticated>".encode())
## gpio.output(16,False)
##
## elif result == 0:
## ser.write("<Not Authenticated>".encode())
## print('>>> No sample matched.')
## E1.delete(0,'end')
## E1.insert(0,'No sample matched.')
## sound = mixer.Sound('/home/pi/Downloads/not authenticated.wav')
## sound.play()
## gpio.output(16,False)
##
## else:
## print('>>> {} samples matched (descending reliability):'.format(len(result)))
## for res in result:
## print("\t", res)
## E1.delete(0,'end')
## E1.insert(0,' sample matched.')
## ser.write("<Authenticated>".encode())
## gpio.output(16,True)
## sound = mixer.Sound('/home/pi/Downloads/authenticated.wav')
## sound.play()
# Time measure
end = time()
## print('\n>>> Verification time: {} [s]\n'.format(end - start))
## E2.delete(0,'end')
## E2.insert(0,end-start)
sleep(10)
gpio.output(16, False)