def add_person(people_folder, shape):
person_name = raw_input('What is the name of the new person: ').lower()
folder = people_folder + person_name
if not os.path.exists(folder):
raw_input("I will now take 20 pictures. Press ENTER when ready.")
os.mkdir(folder)
video = VideoCamera()
detector = FaceDetector('face_recognition_system/frontal_face.xml')
counter = 1
timer = 0
cv2.namedWindow('Video Feed', cv2.WINDOW_AUTOSIZE)
cv2.namedWindow('Saved Face', cv2.WINDOW_NORMAL)
while counter < 21:
frame = video.get_frame()
face_coord = detector.detect(frame)
if len(face_coord):
frame, face_img = get_images(frame, face_coord, shape)
if timer % 100 == 5:
cv2.imwrite(folder + '/' + str(counter) + '.jpg',
face_img[0])
print('Images Saved:' + str(counter))
counter += 1
cv2.imshow('Saved Face', face_img[0])
cv2.imshow('Video Feed', cv2.flip(frame, 1))
cv2.waitKey(2)
timer += 5
else:
print "This name already exists."
sys.exit()
def func_addperson(people_folder, shape, folder):
count = 0
while (count < 5):
raw_input("I will now take 20 pictures. Press ENTER when ready.")
video = VideoCamera()
detector = FaceDetector('face_recognition_system/frontal_face.xml')
counter = 1
timer = 0
cv2.namedWindow('Video Feed', cv2.WINDOW_AUTOSIZE)
cv2.namedWindow('Saved Face', cv2.WINDOW_NORMAL)
while counter < 21:
frame = video.get_frame()
face_coord = detector.detect(frame)
if len(face_coord):
frame, face_img = get_images(frame, face_coord, shape)
# save a face every second, we start from an offset '5' because
# the first frame of the camera gets very high intensity
# readings.
if timer % 100 == 5:
now = datetime.datetime.now()
stringnow = str("%d" % now.year + "%d" % now.month +
"%d" % now.day + "%d" % now.hour +
"%d" % now.minute + "%d" % now.second)
cv2.imwrite(folder + '/' + str(stringnow) + '.jpg',
face_img[0])
print("Images Saved: " + str(stringnow))
cv2.imshow('Saved Face', face_img[0])
counter += 1
cv2.putText(frame, str(timer % 100), (5, frame.shape[0] - 5),
cv2.FONT_HERSHEY_PLAIN, 1.2, (206, 0, 209), 2,
cv2.LINE_AA)
cv2.imshow('Video Feed', frame)
cv2.waitKey(50)
timer += 5
count += 1
def add_person(people_folder, shape):
person_name = input('Name of the new person: ').lower()
folder = people_folder + person_name
if os.path.exists(folder):
shutil.rmtree(folder)
input("Press ENTER to start taking pictures")
os.mkdir(folder)
video = VideoCamera()
detector = FaceDetector('face_recognition_system/haarcascade_frontalface_alt2.xml')
counter = 1
timer = 0
while counter < 51:
frame = video.get_frame()
face_coord = detector.detect(frame)
if len(face_coord):
frame, face_img = get_images(frame, face_coord, shape)
if timer % 10 == 5:
cv2.imwrite(folder + '/' + str(counter) + '.jpg',
face_img[0])
counter += 1
cv2.imshow('Video Feed', frame)
cv2.waitKey(50)
timer += 5
cv2.destroyAllWindows()
def add_person(people_folder, shape):
person_name = input('Please give the name of the new person: ').lower()
folder = people_folder + person_name
if not os.path.exists(folder):
input("Press ENTER when ready.")
os.mkdir(folder)
video = VideoCamera()
detector = FaceDetector('face_recognition_system/frontal_face.xml')
counter = 1
timer = 0
cv2.namedWindow('Video Feed', cv2.WINDOW_AUTOSIZE)
cv2.namedWindow('Saved Face', cv2.WINDOW_NORMAL)
while counter < 21:
frame = video.get_frame()
face_coord = detector.detect(frame)
if len(face_coord):
frame, face_img = get_images(frame, face_coord, shape)
# save a face every second, we start from an offset '5' because
# the first frame of the camera gets very high intensity
# readings.
if timer % 100 == 5:
cv2.imwrite(folder + '/' + str(counter) + '.jpg',
face_img[0])
print('Images Saved:' + str(counter))
counter += 1
cv2.imshow('Saved Face', face_img[0])
cv2.imshow('Video Feed', frame)
cv2.waitKey(50)
timer += 5
else:
print("This name already exists.")
sys.exit()
def add_person(people_folder, shape, person):
#Directly taking argument instead of command line
folder = people_folder + person
if not os.path.exists(folder):
#raw_input("I will now take 20 pictures. Press ENTER when ready.")
os.mkdir(folder)
video = VideoCamera()
detector = FaceDetector('face_recognition_system/frontal_face.xml')
counter = 1
timer = 0
cv2.namedWindow('Video Feed', cv2.WINDOW_AUTOSIZE)
cv2.namedWindow('Saved Face', cv2.WINDOW_NORMAL)
while counter < 21:
frame = video.get_frame()
face_coord = detector.detect(frame)
if len(face_coord):
frame, face_img = get_images(frame, face_coord, shape)
# save a face every second, we start from an offset '5' because
# the first frame of the camera gets very high intensity
# readings.
if timer % 100 == 5:
cv2.imwrite(folder + '/' + str(counter) + '.jpg',
face_img[0])
print 'Images Saved:' + str(counter)
counter += 1
cv2.imshow('Saved Face', face_img[0])
cv2.imshow('Video Feed', frame)
cv2.waitKey(50)
timer += 5
else:
print "This name already exists."
sys.exit()
def recognize_people(people_folder, shape):
people = [person for person in os.listdir(people_folder)]
print (30 * '-')
detector = FaceDetector('face_recognition_system/haarcascade_frontalface_alt2.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
threshold = 105
images = []
labels = []
labels_people = {}
for i, person in enumerate(people):
labels_people[i] = person
for image in os.listdir(people_folder + person):
images.append(cv2.imread(people_folder + person + '/' + image, 0))
labels.append(i)
try:
recognizer.train(images, np.array(labels))
except:
print ("\nOpenCV Error: Do you have at least two people in the database?\n")
sys.exit()
video = VideoCamera()
while True:
frame = video.get_frame()
faces_coord = detector.detect(frame, False)
if len(faces_coord):
frame, faces_img = get_images(frame, faces_coord, shape)
for i, face_img in enumerate(faces_img):
if __version__ == "3.1.0":
collector = cv2.face.MinDistancePredictCollector()
recognizer.predict(face_img, collector)
conf = collector.getDist()
pred = collector.getLabel()
else:
pred, conf = recognizer.predict(face_img)
if conf < threshold:
cv2.putText(frame, labels_people[pred].capitalize(),
(faces_coord[i][0], faces_coord[i][1] - 2),
cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2,
cv2.LINE_AA)
else:
cv2.putText(frame, "Unknown",
(faces_coord[i][0], faces_coord[i][1]),
cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2,
cv2.LINE_AA)
cv2.putText(frame, "ESC to exit", (5, frame.shape[0] - 5),
cv2.FONT_HERSHEY_PLAIN, 1.2, (206, 0, 209), 2, cv2.LINE_AA)
cv2.imshow('Video', frame)
if cv2.waitKey(100) & 0xFF == 27:
cv2.destroyAllWindows()
sys.exit()
def add_person(people_folder, shape):
""" Funtion to add pictures of a person
:param people_folder: relative path to save the person's pictures in
:param shape: Shape to cut the faces on the captured images:
"rectangle" or "ellipse"
:type people_folder: String
:type shape: String
"""
person_name = raw_input('What is the name of the new person: ').lower()
folder = people_folder + person_name
if not os.path.exists(folder):
raw_input("I will now take 20 pictures. Press ENTER when ready.")
os.mkdir(folder)
video = VideoCamera()
detector = FaceDetector('face_recognition_system/frontal_face.xml')
counter = 1
timer = 0
cv2.namedWindow('Video Feed', cv2.WINDOW_AUTOSIZE)
#cv2.namedWindow('Saved Face', cv2.WINDOW_NORMAL)
mood_adj = "How do you look like?"
while counter < 21:
frame = video.get_frame()
face_coord = detector.detect(frame)
if len(face_coord):
cv2.putText(frame, mood_adj.capitalize(),
(face_coord[0][0], face_coord[0][1]),
cv2.FONT_HERSHEY_PLAIN, 1.7, (0, 3, 209), 2,
cv2.LINE_AA)
frame, face_img = get_images(frame, face_coord, shape)
# save a face every second (100), we start from an offset '5' because
# the first frame of the camera gets very high intensity
# readings.
if timer % 100 == 5:
(conf_adj, mood_adj) = analyze_face(frame)
cv2.imwrite(folder + '/' + str(counter) + '.jpg',
face_img[0])
print 'Images Saved:' + str(counter)
counter += 1
#cv2.imshow('Saved Face', face_img[0])
cv2.imshow('Video Feed', frame)
cv2.waitKey(50)
timer += 5
else:
print "This name already exists."
sys.exit()
def add_person(people_folder, shape, netcam):
""" Funtion to add pictures of a person
:param people_folder: relative path to save the person's pictures in
:param shape: Shape to cut the faces on the captured images:
"rectangle" or "ellipse"
:type people_folder: String
:type shape: String
"""
person_name = input('What is the name of the new person: ').lower()
folder = people_folder + person_name
if not os.path.exists(folder):
input("Ready to take 20 pictures. Press ENTER when ready.")
os.mkdir(folder)
video = VideoCamera(netcam)
detector = FaceDetector('face_recognition_system/frontal_face.xml')
counter = 1
timer = 0
cv2.namedWindow('Video Feed', cv2.WINDOW_NORMAL)
cv2.namedWindow('Saved Face', cv2.WINDOW_NORMAL)
while counter < 21:
frame = video.get_frame()
face_coord = detector.detect(frame)
if len(face_coord):
[frame, face_img] = get_images(frame, face_coord, shape)
# save a face every second, we start from an offset '5' because
# the first frame of the camera gets very high intensity
# readings.
if timer % 70 == 5: # 1 Second is = 100, less than a sec is < 100
cv2.imwrite(folder + '/' + str(counter) + '.jpg', face_img[0])
print('Images Saved:' + str(counter))
counter += 1
cv2.imshow('Saved Face', face_img[0])
cv2.imshow('Video Feed', frame)
timer += 5
k = cv2.waitKey(50) & 0xff
if k == 27:
break
del video
cv2.destroyAllWindows()
def add_person(people_folder, shape, user_name):
""" Funtion to add pictures of a person
"""
folder = people_folder + str(user_name)
if not os.path.exists(folder):
os.mkdir(folder)
video = VideoCamera()
detector = FaceDetector('face_recognition_system/frontal_face.xml')
counter = 1
timer = 0
cv2.namedWindow('Video Feed', cv2.WINDOW_AUTOSIZE)
cv2.namedWindow('Saved Face', cv2.WINDOW_NORMAL)
while counter < 5:
# I changed it to 11 to get 10 pics, it was 5
frame = video.get_frame()
frame = cv2.flip(frame, -1)
face_coord = detector.detect(frame)
if len(face_coord):
frame, face_img = get_images(frame, face_coord, shape)
# save a face every second, we start from an offset '5' because
# the first frame of the camera gets very high intensity
# readings.
if timer % 100 == 5:
cv2.imwrite(folder + '/' + str(counter) + '.jpg',
face_img[0])
print('Images Saved:' + str(counter))
counter += 1
cv2.imshow('Saved Face', face_img[0])
if counter == 5:
# I changed it to 11 to get 10 pics, it was 5
cv2.waitKey(1)
cv2.destroyAllWindows()
for i in range(0, 5):
cv2.waitKey(1)
break
cv2.imshow('Video Feed', frame)
cv2.waitKey(50)
timer += 5
else:
print("This name already exists.")
sys.exit()
def add_person(people_folder):
""" Funtion to add pictures of a person
:param people_folder: relative path to save the person's pictures in
:param shape: Shape to cut the faces on the captured images:
"rectangle" or "ellipse"
:type people_folder: String
:type shape: String
"""
person_name = input('What is the full name of the new person: ').lower()
folder = people_folder + person_name
if not os.path.exists(folder):
input("I will now take 20 pictures. Press ENTER when ready.")
os.mkdir(folder)
video = VideoCamera()
detector = FaceDetector('face_recognition_system/frontal_face.xml')
counter = 1
timer = 0
cv2.namedWindow('Video Feed', cv2.WINDOW_AUTOSIZE)
cv2.namedWindow('Saved Face', cv2.WINDOW_NORMAL)
while counter < 21:
frame = video.get_frame()
face_coord = detector.detect(frame)
if len(face_coord):
frame, face_img = get_images(frame, face_coord)
# save a face every second, we start from an offset '5' because
# the first frame of the camera gets very high intensity
# readings.
if timer % 100 == 5:
cv2.imwrite(folder + '/' + str(counter) + '.jpg',
face_img[0])
print ('Images Saved:' + str(counter))
counter += 1
cv2.imshow('Saved Face', face_img[0])
cv2.imshow('Video Feed', frame)
cv2.waitKey(50)
timer += 5
else:
print ("This name already exists.")
sys.exit()
def add_person(people_folder, shape):
""" Funtion to add pictures of a person
:param people_folder: relative path to save the person's pictures in
:param shape: Shape to cut the faces on the captured images:
"rectangle" or "ellipse"
:type people_folder: String
:type shape: String
"""
person_name = input('What is the name of the new person: ').lower()
folder = people_folder + person_name
if not os.path.exists(folder):
input("I will now take 20 pictures. Press ENTER when ready.")
os.mkdir(folder)
video = VideoCamera()
detector = FaceDetector('face_recognition_system/frontal_face.xml')
counter = 1
timer = 0
cv2.namedWindow('Video Feed', cv2.WINDOW_AUTOSIZE)
cv2.namedWindow('Saved Face', cv2.WINDOW_NORMAL)
while counter < 21:
frame = video.get_frame()
face_coord = detector.detect(frame)
if len(face_coord):
frame, face_img = get_images(frame, face_coord, shape)
# save a face every second, we start from an offset '5' because
# the first frame of the camera gets very high intensity
# readings.
if timer % 100 == 5:
cv2.imwrite(folder + '/' + str(counter) + '.jpg',
face_img[0])
print( 'Images Saved:' + str(counter))
counter += 1
cv2.imshow('Saved Face', face_img[0])
cv2.imshow('Video Feed', frame)
cv2.waitKey(50)
timer += 5
else:
print( "This name already exists.")
sys.exit()
def recognize_people(people_folder, shape):
""" Start recognizing people in a live stream with your webcam
:param people_folder: relative path to save the person's pictures in
:param shape: Shape to cut the faces on the captured images:
"rectangle" or "ellipse"
:type people_folder: String
:type shape: String
"""
try:
people = [person for person in os.listdir(people_folder)]
except:
print "Have you added at least one person to the system?"
sys.exit()
print "This are the people in the Recognition System:"
for person in people:
print "-" + person
print 30 * "-"
print " POSSIBLE RECOGNIZERS TO USE"
print 30 * "-"
print "1. EigenFaces"
print "2. FisherFaces"
print "3. LBPHFaces"
print 30 * "-"
choice = check_choice()
detector = FaceDetector("face_recognition_system/frontal_face.xml")
if choice == 1:
recognizer = cv2.face.createEigenFaceRecognizer()
threshold = 4000
elif choice == 2:
recognizer = cv2.face.createFisherFaceRecognizer()
threshold = 300
elif choice == 3:
recognizer = cv2.face.createLBPHFaceRecognizer()
threshold = 105
images = []
labels = []
labels_people = {}
for i, person in enumerate(people):
labels_people[i] = person
for image in os.listdir(people_folder + person):
images.append(cv2.imread(people_folder + person + "/" + image, 0))
labels.append(i)
try:
recognizer.train(images, np.array(labels))
except:
print "\nOpenCV Error: Do you have at least two people in the database?\n"
sys.exit()
video = VideoCamera()
while True:
frame = video.get_frame()
faces_coord = detector.detect(frame, False)
if len(faces_coord):
frame, faces_img = get_images(frame, faces_coord, shape)
for i, face_img in enumerate(faces_img):
if __version__ == "3.1.0":
collector = cv2.face.MinDistancePredictCollector()
recognizer.predict(face_img, collector)
conf = collector.getDist()
pred = collector.getLabel()
else:
pred, conf = recognizer.predict(face_img)
print "Prediction: " + str(pred)
print "Confidence: " + str(round(conf))
print "Threshold: " + str(threshold)
if conf < threshold:
cv2.putText(
frame,
labels_people[pred].capitalize(),
(faces_coord[i][0], faces_coord[i][1] - 2),
cv2.FONT_HERSHEY_PLAIN,
1.7,
(206, 0, 209),
2,
cv2.LINE_AA,
)
else:
cv2.putText(
frame,
"Unknown",
(faces_coord[i][0], faces_coord[i][1]),
cv2.FONT_HERSHEY_PLAIN,
1.7,
(206, 0, 209),
2,
cv2.LINE_AA,
)
cv2.putText(
frame, "ESC to exit", (5, frame.shape[0] - 5), cv2.FONT_HERSHEY_PLAIN, 1.2, (206, 0, 209), 2, cv2.LINE_AA
)
cv2.imshow("Video", frame)
if cv2.waitKey(100) & 0xFF == 27:
sys.exit()
def recognize_people(people_folder, shape):
""" Start recognizing people in a live stream with your webcam
:param people_folder: relative path to save the person's pictures in
:param shape: Shape to cut the faces on the captured images:
"rectangle" or "ellipse"
:type people_folder: String
:type shape: String
"""
try:
people = [person for person in os.listdir(people_folder)]
except:
print "Have you added at least one person to the system?"
sys.exit()
print "This are the people in the Recognition System:"
for person in people:
print "-" + person
print 30 * '-'
print " POSSIBLE RECOGNIZERS TO USE"
print 30 * '-'
print "1. EigenFaces"
print "2. FisherFaces"
print "3. LBPHFaces"
print 30 * '-'
choice = check_choice()
detector = FaceDetector('face_recognition_system/frontal_face.xml')
if choice == 1:
recognizer = cv2.face.EigenFaceRecognizer_create()
threshold = 4000
elif choice == 2:
recognizer = cv2.face.FisherFaceRecognizer_create()
threshold = 300
elif choice == 3:
recognizer = cv2.face.LBPHFaceRecognizer_create()
threshold = 105
images = []
labels = []
labels_people = {}
for i, person in enumerate(people):
labels_people[i] = person
for image in os.listdir(people_folder + person):
images.append(cv2.imread(people_folder + person + '/' + image, 0))
labels.append(i)
try:
recognizer.train(images, np.array(labels))
except:
print "\nOpenCV Error: Do you have at least two people in the database?\n"
sys.exit()
video = VideoCamera()
while True:
frame = video.get_frame()
faces_coord = detector.detect(frame, False)
if len(faces_coord):
frame, faces_img = get_images(frame, faces_coord, shape)
for i, face_img in enumerate(faces_img):
if __version__ == "3.1.0":
collector = cv2.face.MinDistancePredictCollector()
recognizer.predict(face_img, collector)
conf = collector.getDist()
pred = collector.getLabel()
else:
pred, conf = recognizer.predict(face_img)
print "Prediction: " + str(pred)
print 'Confidence: ' + str(round(conf))
print 'Threshold: ' + str(threshold)
if conf < threshold:
cv2.putText(frame, labels_people[pred].capitalize(),
(faces_coord[i][0], faces_coord[i][1] - 2),
cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2,
cv2.LINE_AA)
else:
cv2.putText(frame, "Unknown",
(faces_coord[i][0], faces_coord[i][1]),
cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2,
cv2.LINE_AA)
cv2.putText(frame, "ESC to exit", (5, frame.shape[0] - 5),
cv2.FONT_HERSHEY_PLAIN, 1.2, (206, 0, 209), 2, cv2.LINE_AA)
cv2.imshow('Video', frame)
if cv2.waitKey(100) & 0xFF == 27:
sys.exit()
def recognize_people(people_folder, shape, netcam):
""" Start recognizing people in a live stream with your webcam
:param people_folder: relative path to save the person's pictures in
:param shape: Shape to cut the faces on the captured images:
"rectangle" or "ellipse"
:type people_folder: String
:type shape: String
"""
try:
people = [person for person in os.listdir(people_folder)]
except:
print("Have you added at least one person to the system?")
sys.exit()
print("This are the people in the Recognition System:")
for i, person in enumerate(people):
iplus = i + 1
if (iplus < len(people)):
print(str(i + 1) + "- " + person + ",")
iplus += 1
elif (iplus == len(people)):
print(str(i + 1) + "- " + person)
detector = FaceDetector('face_recognition_system/frontal_face.xml')
recognizer = cv2.face.createLBPHFaceRecognizer()
images = []
labels = []
labels_people = {}
for i, person in enumerate(people):
labels_people[i] = person
for image in os.listdir(people_folder + person):
images.append(cv2.imread(people_folder + person + '/' + image, 0))
labels.append(i)
try:
recognizer.train(images, np.array(labels))
except:
print(
"\nOpenCV Error: Image Dimension Problems or you dont have at least two people in the database\n"
)
#sys.exit()
cv2.waitKey(100)
video = VideoCamera(netcam)
k = 1
threshsum = 0
data = []
f = open("predictdata.txt", "w+")
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID')
localtime = time.asctime(time.localtime(time.time()))
l = ''.join(e for e in localtime if e.isalnum())
out = cv2.VideoWriter(l + '.avi', fourcc, 20.0, (640, 480))
while True:
frame = video.get_frame()
faces_coord = detector.detect(frame, True)
if len(faces_coord):
[frame, faces_img] = get_images(frame, faces_coord, shape)
for i, face_img in enumerate(faces_img):
if (__version__ == "3.1.0"):
collector = cv2.face.MinDistancePredictCollector()
recognizer.predict(face_img, collector)
conf = collector.getDist()
pred = collector.getLabel()
else:
[pred, conf] = recognizer.predict(face_img)
print("Prediction: " + str(pred))
print('Confidence: ' + str(round(conf)))
avg = 20
if (k <= avg):
threshsum = conf + threshsum
threshold = 150
k = k + 1
else:
threshold = threshsum / (avg - 1)
print('Threshold Used: ' + str(threshold))
print("k val: " + str(k))
print("threshsum: " + str(threshsum))
if (conf <= threshold and k > avg):
cv2.putText(frame,
'Pred => ' + labels_people[pred].capitalize(),
(faces_coord[i][0], faces_coord[i][1] - 2),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255),
1, cv2.LINE_AA)
data.append(pred)
f.write("%d\n" % (pred))
elif (k <= avg):
cv2.putText(frame, "Finding Threshold",
(faces_coord[i][0], faces_coord[i][1]),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0),
1, cv2.LINE_AA)
elif (conf >= threshold and k > avg):
cv2.putText(frame, "Face Unknown",
(faces_coord[i][0], faces_coord[i][1]),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2,
cv2.LINE_AA)
dat = 'NaN'
data.append(dat)
f.write("%s\n" % (dat))
print('Appended Data: ' + str(data))
cv2.putText(frame, "Press ESC to Exit", (5, frame.shape[0] - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1, cv2.LINE_AA)
if len(frame):
# write the flipped frame
out.write(frame)
cv2.putText(frame, "Recording...", (490, frame.shape[0] - 420),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2,
cv2.LINE_AA)
cv2.imshow('Video Feed', frame)
if cv2.waitKey(100) & 0xFF == 27:
f.close()
out.release()
cv2.destroyAllWindows()
sys.exit()
def recognize_people(people_folder, shape, detection_mode):
""" Start recognizing people in a live stream with your webcam
"""
fingerprint_retries = []
counter = 1
try:
people = [person for person in os.listdir(people_folder)]
except:
print("Have you added at least one person to the system?")
sys.exit()
print("This are the people in the Recognition System:")
for person in people:
print("-" + person)
print(30 * '-')
detector = FaceDetector('face_recognition_system/frontal_face.xml')
recognizer = cv2.face.EigenFaceRecognizer_create()
threshold = 4000
images = []
labels = []
labels_people = {}
for i, person in enumerate(people):
labels_people[i] = person
for image in os.listdir(people_folder + person):
images.append(cv2.imread(people_folder + person + '/' + image, 0))
labels.append(i)
try:
recognizer.train(images, np.array(labels))
except:
print(
"\nOpenCV Error: Do you have at least two people in the database?\n"
)
sys.exit()
video = VideoCamera()
while True:
frame = video.get_frame()
frame = cv2.flip(frame, -1)
cv2.imwrite("2.jpg", frame)
faces_coord = detector.detect(frame, False)
if len(faces_coord):
frame, faces_img = get_images(frame, faces_coord, shape)
for i, face_img in enumerate(faces_img):
if __version__ == "3.1.0":
collector = cv2.face.MinDistancePredictCollector()
recognizer.predict(face_img, collector)
conf = collector.getDist()
pred = collector.getLabel()
else:
pred, conf = recognizer.predict(face_img)
print("Prediction: " + str(pred))
#print ('Confidence: ' + str(round(conf)))
#print ('Threshold: ' + str(threshold))
if conf < threshold:
cv2.putText(frame, labels_people[pred].capitalize(),
(faces_coord[i][0], faces_coord[i][1] - 2),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255),
1, cv2.LINE_AA)
if detection_mode == 'door_lock':
print('Accessing as ' + labels_people[pred] + '......')
print('Please verify yourself with fingerprint...')
fingerprint_label = recognize_finger()
if (int(fingerprint_label) == int(pred)):
#print (int(pred))
print('Access granted...')
print('Unlocking door....')
GPIO.output(14, True)
GPIO.output(24, False)
buzzer_success()
time.sleep(5)
GPIO.output(24, True)
GPIO.output(14, False)
print('Door Unlocked!')
now = datetime.now()
now_string = now.strftime("%d/%m/%Y %H:%M:%S")
with open('door_unlock_data.csv',
mode='a') as door_data:
door_writer = csv.writer(
door_data,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
door_writer.writerow([
str(counter),
str(pred),
str(labels_people[pred]),
str(now_string)
])
counter += 1
else:
#print (int(pred))
fingerprint_retries.append(labels_people[pred])
print(fingerprint_retries)
if len(fingerprint_retries) == 3:
if len(set(fingerprint_retries)) == 1:
malacious_sendmail()
fingerprint_retries = []
print('Fingerprint isn\'t matched')
print('Access Denied!!!')
buzzer_alert()
if pred != -1:
cv2.waitKey(1)
cv2.destroyAllWindows()
for i in range(0, 5):
cv2.waitKey(1)
for i in range(0, 100):
frame = video.get_frame()
frame = cv2.flip(frame, -1)
continue
elif detection_mode == 'attendance':
name_attended = labels_people[pred]
is_attended = 0
print('Detected ' + name_attended)
now = datetime.now()
now_string = now.strftime("%d/%m/%Y %H:%M:%S")
with open('attendance_data.csv',
mode='r') as attendance_data:
attendances = csv.reader(attendance_data)
for row in attendances:
if row[2] == name_attended:
is_attended = 1
if is_attended == 1:
print(name_attended + ' is already attended')
elif is_attended == 0:
with open('attendance_data.csv',
mode='a') as attendance_data:
attendance_writer = csv.writer(
attendance_data,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
attendance_writer.writerow([
str(counter),
str(pred),
str(labels_people[pred]),
str(now_string)
])
counter += 1
else:
cv2.putText(frame, "Unknown",
(faces_coord[i][0], faces_coord[i][1]),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255),
1, cv2.LINE_AA)
cv2.putText(frame, "ESC to exit", (5, frame.shape[0] - 5),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.imshow('Live Video Transmission', frame)
if cv2.waitKey(100) & 0xFF == 27:
sys.exit()
def recognize_people(people_folder, shape):
try:
people = [person for person in os.listdir(people_folder)]
except:
print("Have you added at least one person to the system?")
sys.exit()
print("This are the people in the Recognition System:")
for person in people:
print("-" + person)
print(" POSSIBLE RECOGNIZERS TO USE")
print("1. EigenFaces")
print("2. FisherFaces")
print("3. LBPHFaces")
choice = check_choice()
detector = FaceDetector('face_recognition_system/frontal_face.xml')
if choice == 1:
recognizer = cv2.face.EigenFaceRecognizer_create()
threshold = 4000
elif choice == 2:
recognizer = cv2.face.FisherFaceRecognizer_create()
threshold = 300
elif choice == 3:
recognizer = cv2.face.LBPHFaceRecognizer_create()
threshold = 105
#recognizer=VotingClassifier([('eigen',recognizer1),('lbph',recognizer2),('fisher',recognizer3)],voting='soft')
images = []
labels = []
labels_people = {}
for i, person in enumerate(people):
labels_people[i] = person
for image in os.listdir(people_folder + person):
images.append(cv2.imread(people_folder + person + '/' + image, 0))
labels.append(i)
try:
#print("printing images"+" "+" "+str(images))
#print("printing labesl"+" "+" "+str(labels)+" "+str(np.array(labels)))
recognizer.train(images, np.array(labels))
except:
print(
"\nOpenCV Error: Do you have at least two people in the database?\n"
)
sys.exit()
video = VideoCamera()
while True:
frame = video.get_frame()
faces_coord = detector.detect(frame, False)
if len(faces_coord):
frame, faces_img = get_images(frame, faces_coord, shape)
for i, face_img in enumerate(faces_img):
#if __version__ == "3.1.0":
global students
collector = cv2.face.StandardCollector_create()
#recognizer.predict(face_img)
#conf = collector.getDist()
#pred = collector.getLabel()
#print ("Prediction: " +pred)
#else:
pred, conf = recognizer.predict(face_img)
print("Prediction: " + str(pred))
students.add(pred)
print(students)
print('Confidence: ' + str(round(conf)))
print('Threshold: ' + str(threshold))
if conf < threshold:
cv2.putText(frame, labels_people[pred].capitalize(),
(faces_coord[i][0], faces_coord[i][1] - 2),
cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2,
cv2.LINE_AA)
else:
cv2.putText(frame, "Unknown",
(faces_coord[i][0], faces_coord[i][1]),
cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2,
cv2.LINE_AA)
cv2.putText(frame, "ESC to exit", (5, frame.shape[0] - 5),
cv2.FONT_HERSHEY_PLAIN, 1.2, (206, 0, 209), 2, cv2.LINE_AA)
cv2.imshow('Video', frame)
if cv2.waitKey(100) & 0xFF == 27:
dao = DAO(students)
dao.dbOperations()
print(
"*******************************Attendence Updated ****************************"
)
#dao.__del__()
sys.exit()
def recognize_people(people_folder, shape):
# type: (object, object) -> object
try:
people = [person for person in os.listdir(people_folder)]
except:
print "Have you added at least one person to the system?"
#sys.exit()
'''print "These are the people in the Recognition System:"
for person in people:
print "-" + person
print 30 * '-'
print " POSSIBLE RECOGNIZERS TO USE"
print 30 * '-'
print "1. EigenFaces"
print "2. FisherFaces"
print "3. LBPHFaces"
print 30 * '-'
'''
choice = 3
detector = FaceDetector('haarcascade_frontalface_default.xml')
if choice == 1:
recognizer = cv2.createEigenFaceRecognizer()
threshold = 3500
elif choice == 2:
recognizer = cv2.createFisherFaceRecognizer()
threshold = 300
elif choice == 3:
recognizer = cv2.createLBPHFaceRecognizer()
threshold = 100
images = []
labels = []
labels_people = {}
for i, person in enumerate(people):
labels_people[i] = person
for image in os.listdir(people_folder + person):
images.append(cv2.imread(people_folder + person + '/' + image, 0))
labels.append(i)
try:
recognizer.train(images, np.array(labels))
except:
print "\nOpenCV Error: Do you have at least two people in the database?\n"
#sys.exit()
video = VideoCamera()
while True:
frame = video.get_frame()
faces_coord = detector.detect(frame, False)
if len(faces_coord):
frame, faces_img = get_images(frame, faces_coord, shape)
for i, face_img in enumerate(faces_img):
if __version__ == "3.1.0":
collector = cv2.MinDistancePredictCollector()
recognizer.predict(face_img, collector)
conf = collector.getDist()
pred = collector.getLabel()
else:
pred, conf = recognizer.predict(face_img)
print "Prediction: " + str(pred)
print 'Confidence: ' + str(round(conf))
print 'Threshold: ' + str(threshold)
if conf < threshold:
cv2.putText(frame, labels_people[pred].capitalize(),
(faces_coord[i][0], faces_coord[i][1] - 2),
cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2,
cv2.CV_AA)
c.writerow(people[pred].split())
else:
cv2.putText(frame, "Unknown",
(faces_coord[i][0], faces_coord[i][1] - 2),
cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2,
cv2.CV_AA)
cv2.putText(frame, "ESC to exit", (5, frame.shape[0] - 5),
cv2.FONT_HERSHEY_PLAIN, 1.2, (206, 0, 209), 2, cv2.CV_AA)
cv2.imshow('Video', frame)
if cv2.waitKey(100) & 0xFF == 27:
exit()
def recognize_people(people_folder, shape):
""" Start recognizing people in a live stream with your webcam
"""
try:
people = [person for person in os.listdir(people_folder)]
except:
print ("Have you added at least one person to the system?")
sys.exit()
print ("This are the people in the Recognition System:")
for person in people:
print ("-" + person)
print (30 * '-')
print (" POSSIBLE RECOGNIZERS TO USE")
print (30 * '-')
print ("1. EigenFaces")
print ("2. FisherFaces")
print ("3. LBPHFaces")
print (30 * '-')
choice = check_choice()
detector = FaceDetector('face_recognition_system/frontal_face.xml')
if choice == 1:
recognizer = cv2.face.createEigenFaceRecognizer()
threshold = 4000
elif choice == 2:
recognizer = cv2.face.createFisherFaceRecognizer()
threshold = 300
elif choice == 3:
recognizer = cv2.face.createLBPHFaceRecognizer()
threshold = 80
images = []
labels = []
labels_people = {}
for i, person in enumerate(people):
labels_people[i] = person
for image in os.listdir(people_folder + person):
images.append(cv2.imread(people_folder + person + '/' + image, 0))
labels.append(i)
try:
recognizer.train(images, np.array(labels))
except:
print ("\nOpenCV Error: Do you have at least two people in the database?\n")
sys.exit()
video = VideoCamera()
while True:
frame = video.get_frame()
faces_coord = detector.detect(frame, False)
if len(faces_coord):
frame, faces_img = get_images(frame, faces_coord, shape)
for i, face_img in enumerate(faces_img):
if __version__ == "3.1.0":
collector = cv2.face.MinDistancePredictCollector()
recognizer.predict(face_img, collector)
conf = collector.getDist()
pred = collector.getLabel()
else:
pred, conf = recognizer.predict(face_img)
print ("Prediction: " + str(pred))
print ('Confidence: ' + str(round(conf)))
print ('Threshold: ' + str(threshold))
if conf < threshold:
cv2.putText(frame, labels_people[pred].capitalize(),
(faces_coord[i][0], faces_coord[i][1] - 2),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1,
cv2.LINE_AA)
else:
cv2.putText(frame, "Unknown",
(faces_coord[i][0], faces_coord[i][1]),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.putText(frame, "ESC to exit", (5, frame.shape[0] - 5),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255, 255), 1, cv2.LINE_AA)
cv2.imshow('Video', frame)
if cv2.waitKey(100) & 0xFF == 27:
sys.exit()
def recognize_people(people_folder, shape):
try:
people = [person for person in os.listdir(people_folder)]
except:
print "Have you added at least one person to the system?"
sys.exit()
'''
print "This are the people in the Recognition System:"
for person in people:
print "-" + person
'''
detector = FaceDetector('face_recognition_system/frontal_face.xml')
recognizer = cv2.createLBPHFaceRecognizer()
threshold = 105
images = []
labels = []
labels_people = {}
for i, person in enumerate(people):
labels_people[i] = person
for image in os.listdir(people_folder + person):
images.append(cv2.imread(people_folder + person + '/' + image, 0))
labels.append(i)
try:
recognizer.train(images, np.array(labels))
except:
print "\nOpenCV Error: Do you have at least two people in the database?\n"
sys.exit()
video = VideoCamera()
person_id = raw_input('Enter Your ID:')
boolean = "true"
while boolean == "true":
frame = video.get_frame()
faces_coord = detector.detect(frame, False)
if len(faces_coord):
frame, faces_img = get_images(frame, faces_coord, shape)
for i, face_img in enumerate(faces_img):
if __version__ == "3.1.0":
collector = cv2.face.MinDistancePredictCollector()
recognizer.predict(face_img, collector)
conf = collector.getDist()
pred = collector.getLabel()
else:
pred, conf = recognizer.predict(face_img)
#print "Prediction: " + str(pred)
#print 'Confidence: ' + str(round(conf))
#print 'Threshold: ' + str(threshold)
if conf < threshold:
cv2.putText(frame, labels_people[pred].capitalize(),
(faces_coord[i][0], faces_coord[i][1] - 2),
cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2,
cv2.CV_AA)
else:
cv2.putText(frame, "Unknown",
(faces_coord[i][0], faces_coord[i][1]),
cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2,
cv2.CV_AA)
cv2.imshow('Video', frame)
if (labels_people[pred] == str(person_id)
and boolean == "true"):
conn = pyodbc.connect(
"DRIVER={SQL Server};Server=DESKTOP-M4SA4AV;Database=Ds;uid=DESKTOP-M4SA4AV\fadel;pwd=;Trusted_Connection=yes;"
)
cursor = conn.cursor()
cursor.execute("SELECT * FROM grades where username='******'")
results = cursor.fetchone()
print("Welcome " + str(labels_people[pred]))
while results:
print("course:" + str(results[1]) + " grade:" +
str(results[2]))
results = cursor.fetchone()
#cv2.putText(frame, "ESC to exit", (5, frame.shape[0] - 5),cv2.FONT_HERSHEY_PLAIN, 1.2, (206, 0, 209), 2, cv2.CV_AA)
if cv2.waitKey(100) & 0xFF == 27:
sys.exit()