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)
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):
""" 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 add_person_from_file(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 = "Irshan"
folder = people_folder + person_name
os.mkdir(folder)
folderSample = people_folder + "IrshanSample"
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)
for filename in os.listdir(folderSample):
imgFrame = cv2.imread(os.path.join(folderSample,filename),0)
print("FileName",os.path.join(folderSample,filename))
face_coord = detector.detect(imgFrame)
print(len(face_coord))
if len(face_coord):
imgFrame, face_img = get_images_fromfile(imgFrame, face_coord, shape)
print(face_img)
# save a face every second, we start from an offset '5' because
# the first frame of the camera gets very high intensity
# readings.
cv2.imwrite(folder + '/' + str(counter) + '.jpg',
face_img[0])
counter += 1
print ('Images Saved:' + str(counter))
cv2.imshow('Saved Face', face_img[0])
cv2.imshow('Video Feed', imgFrame)
cv2.waitKey(1)
timer += 5
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 func_addpersonpics(folder, shape):
count = 0
mkdir('data/pics')
mkdir('data/pos')
try:
file_images = raw_input("Enter the images' direction: ")
except:
print("NO FOUND FOLDER !!!")
sys.exit()
cascade_faces = [
"haarcascade_frontalface_default.xml",
"haarcascade_frontalface_alt.xml", "haarcascade_frontalface_alt2.xml",
"haarcascade_frontalface_alt_tree.xml", "haarcascade_profileface.xml"
]
#copy file to pics folder
print("Copying images .....")
for file_type in [file_images]:
for img in os.listdir(file_type):
imagePath = file_type + '/' + img
newimagePath = "data/pics/" + img
shutil.copy2(imagePath, newimagePath)
print("Complete to copy images")
processpercent = 0
print("Faces detecing ..... " + str(processpercent) + "%")
for i in cascade_faces:
cascadePath = "haarcascade/" + i
#print (cascadePath)
detector = FaceDetector(cascadePath)
for file_type in ['data/pics']:
for img in os.listdir(file_type):
imagePath = file_type + '/' + img
frame = cv2.imread(imagePath)
face_coord = detector.detect(frame)
if len(face_coord):
frame, face_img = get_images(frame, face_coord, shape)
now = datetime.datetime.now()
#cv2.imwrite(folder + '/' + img, face_img[0])
#after create new function detect faces, will delete this line
cv2.imwrite('data/crop/' + img, face_img[0])
print("Images Saved: " + img)
cv2.imshow("frame after", cv2.resize(frame, (240, 320)))
cv2.waitKey(100)
count += 1
#copy to pos
newimagePath = "data/pos/" + img
shutil.move(imagePath, newimagePath)
processpercent += 20
print("Faces detecing ..... " + str(processpercent) + "%")
#os.rmdir()
#shutil.rmtree()
while True:
ans = raw_input("Do you want add more? (y/n)")
if (ans == "y"):
func_addpersonpics(folder, shape)
else:
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):
""" 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):
# 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):
""" 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 "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 = 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
recognizer = cv2. face. 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)
#print labels_people
try:
recognizer.train(images, np.array(labels))
except:
print "\nOpenCV Error: Do you have at least two people in the database?\n"
sys.exit()
file_ = "/var/www/html/facerecognitionapp/f.txt"
engine = pyttsx.init()
engine.setProperty('rate',200)
#video = VideoCamera()
fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = True)
stream=urllib.urlopen('http://192.168.206.175//mjpg/video.mjpg')
bytes=''
while(True):
bytes+=stream.read(1024)
bytes+=stream.read(1024)
a = bytes.find('\xff\xd8')
b = bytes.find('\xff\xd9')
if a!=-1 and b!=-1:
jpg = bytes[a:b+2]
bytes= bytes[b+2:]
frame = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8),cv2.IMREAD_COLOR)
fgmask = fgbg.apply(frame)
#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)
print labels_people[int(pred)]
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)#
with open (file_,"a+") as f:
for Line in f:
Line = Line.split(";")
for i in range(0,len(Line)/2):
if Line [i*2] == labels_people[int(pred)]: #Line[i*2+1]=int(time.time())
break
else:
f.write(";{};{}".format(labels_people[int(pred)],time.strftime("%a %b %d %H:%M:%S %Y", time.localtime())))
command = "Welcome " + labels_people[int(pred)]+" to Winlab";
print command
engine.say(command)
engine.runAndWait()
time.sleep(1)
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)
line1 = np.array([[666,0],[666,750]],np.int32).reshape((-1,1,2))
frame = cv2.polylines(frame,[line1],False,(0,255,0),thickness=3)
cv2.imshow('Video test....', frame)
line2 = np.array([[666,0],[666,300]],np.int32).reshape((-1,1,2))
fgmask = cv2.polylines(fgmask,[line2],False,(0,255,255),thickness=3)
cv2.imshow('Background',fgmask)
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()
def recognize_people_from_external_video(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 = 85
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 = ExternalVideo()
# Define the codec and create VideoWriter object
#fourcc = cv2.VideoWriter_fourcc(*'H264')
#myvideo = cv2.VideoWriter('video/video_rod_garage2.avi',-1, 10.0, (640,480))
# Get the width and height of frame
# Define the codec and create VideoWriter object
counter = 0;
while True:
frame = video.get_frame()
if counter == 0:
print("Exceuted if")
[h, w] = frame.shape[:2]
out = cv2.VideoWriter("video/garage_out.avi", 0, 25.0, (w, h))
counter = 1
faces_coord = detector.detect(frame, False)
if len(faces_coord):
frame, faces_img = get_images(frame, faces_coord, shape)
print("Version",__version__)
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)
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, 2.5, (206, 0, 209), 2,
cv2.LINE_AA)
else:
cv2.putText(frame, "Unknown",
(faces_coord[i][0] , faces_coord[i][1]),
cv2.FONT_HERSHEY_PLAIN, 2.5, (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)
out.write(frame)
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == 27:
out.release()
sys.exit()