def main():
# Load training data into model
print 'Loading training data...'
model = cv2.createEigenFaceRecognizer()
model.load(config.TRAINING_FILE)
print 'Training data loaded!'
# Initialize camer and box.
camera = config.get_camera()
door = hardware.Door()
# Move box to locked position.
door.lock()
print 'Running Lock...'
print 'Press button to lock (if unlocked), or unlock if the correct face is detected.'
print 'Press Ctrl-C to quit.'
while True:
try:
# Check if capture should be made.
# TODO: Check if button is pressed.
if door.is_button_up() or is_letter_input('l'):
if not door.is_locked:
# Lock the door if it is unlocked
door.lock()
print 'Door is now locked.'
else:
print 'Button pressed, looking for face...'
# Check for the positive face and unlock if found.
image = camera.read()
# Convert image to grayscale.
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Get coordinates of single face in captured image.
result = face.detect_single(image)
if result is None:
print 'Could not detect single face! Check the image in capture.pgm' \
' to see what was captured and try again with only one face visible.'
soundChannelC.play(soundC)
sleep(.01)
continue
x, y, w, h = result
# Crop and resize image to face.
crop = face.resize(face.crop(image, x, y, w, h))
# Test face against model.
label, confidence = model.predict(crop)
print 'Predicted {0} face with confidence {1} (lower is more confident).'.format(
'POSITIVE' if label == config.POSITIVE_LABEL else 'NEGATIVE',
confidence)
if label == config.POSITIVE_LABEL and confidence < config.POSITIVE_THRESHOLD:
print 'Recognized face! Unlocking Door Now...'
door.unlock()
soundChannelA.play(soundA)
sleep(.01)
else:
print 'Did not recognize face!'
soundChannelB.play(soundB)
sleep(.01)
except KeyboardInterrupt:
door.clean()
sys.exit()
def comp():
datab = request.form['comp']
#print datab
#open image path to save after decode
inital = open("limage/attempt.jpg", "wb")
inital.write(datab.decode('base64'))
inital.close()
#open image and convert to pgm format
second = Image.open('limage/attempt.jpg')
second = second.convert('RGB')
second.save('limage/attempt.pgm')
print 'Loading training data...'
#initalize opencv facerecognizer class
model = cv2.createEigenFaceRecognizer()
#loads xml training file creaded by train.py
model.load(config.TRAINING_FILE)
print 'Training data loaded!'
print 'Capturing Profile...'
#start loop to process users image
while True:
#read in converted pgm image and change to grayscale
third= cv2.imread('limage/attempt.pgm')
#print type(third)
compare = cv2.cvtColor(third,cv2.COLOR_RGB2GRAY)
#run face detect cv process
result = face.detect_single(compare)
if result is None:
print 'Could not detect one face!'
#return "User Not Detected"
flash("User Not Detected! Please retake image", 'danger')
return render_template('facelogin.html')
break
x, y, w, h = result
# Crop and resize image to face.
crop = face.resize(face.crop(compare, x, y, w, h))
#write debug image after crop and resize peformed
cv2.imwrite('limage/debug.pgm',crop)
#read croped image for model to process--prevents wrong shape matrices error
final = cv2.imread('limage/debug.pgm',0)
# Test user face against model
label, confidence = model.predict(final)
print 'Predicted face with confidence {1} (lower is more confident).'.format(
'POSITIVE' if label == config.POSITIVE_LABEL else 'NEGATIVE',
confidence)
#if confidence level is less than set threshold in config.py user is accepted
if label == config.POSITIVE_LABEL and confidence < config.POSITIVE_THRESHOLD:
#return 'Accepted User'
flash("User Accepted", 'success')
return render_template('facelogin.html')
#user is denied if confidence level is greater than set threshold in config.py
else:
print 'Did not recognize user!'
#return 'User Not Accepted !'
flash("User Not Accepted!", 'danger')
return render_template('facelogin.html')
def matchFace(image, coordinate):
x, y, w, h = coordinate
# Crop and resize image to face.
crop = face.resize(face.crop(image, x, y, w, h))
cv2.imwrite('crop.jpeg', crop)
# Test face against model.
label, confidence = model.predict(crop)
if label == config.POSITIVE_LABEL and confidence < config.POSITIVE_THRESHOLD:
return config.HIT
else:
return config.NO_HIT
def main(argv):
pid = int(sys.argv[1])
print 'PID is: ', pid
# Load training data into model
print 'Loading training data...'
model = cv2.createEigenFaceRecognizer()
print 'Model created'
model.load(config.TRAINING_FILE)
print 'Training data loaded!'
# Initialize camera and box.
camera = config.get_camera()
print 'Press Ctrl-C to quit.'
goodpicture = False;
while goodpicture == False:
print 'Looking for face...'
print 'Check for the positive face and unlock if found.'
image = camera.read()
print 'Convert image to grayscale.'
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
print 'Get coordinates of single face in captured image.'
result = face.detect_single(image)
if result is None:
print 'Could not detect single face! Check the image in capture.pgm to see what was captured and try again with only one face visible.'
#continue
else: goodpicture = True;
x, y, w, h = result
print 'Crop and resize image to face.'
crop = face.resize(face.crop(image, x, y, w, h))
print 'Test face against model.'
label, confidence = model.predict(crop)
print 'Predicted {0} face with confidence {1} (lower is more confident).'.format(
'POSITIVE' if label == config.POSITIVE_LABEL else 'NEGATIVE',
confidence)
print 'Starting to print in file'
fo = open("foo.txt", "wr")
if label == config.POSITIVE_LABEL and confidence < config.POSITIVE_THRESHOLD:
print 'Recognized face!'
fo.write("recognized")
else:
print 'Did not recognize face!'
fo.write("echec")
fo.close()
os.kill(pid, signal.SIGUSR2)
def ImageCapture(cls,ID):
labels=[]
images=[]
# make sure this is the right file name
faceCascade = cv2.CascadeClassifier(cascadePath)
counter=0
#counter2=0
foldername=ID;
if not os.path.exists(foldername):
os.makedirs(foldername)
name=foldername+"/Images"
camera=PiCamera()
camera.resolution=(320,240)
camera.framerate=32
rawCapture=PiRGBArray(camera,size=(320,240))
time.sleep(3)
cv2.namedWindow("Preview")
camera.capture(rawCapture,format="bgr",use_video_port=True)
while rawCapture is not None and counter<30:
image=rawCapture.array
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
result=face.detect_single(gray)
cv2.imshow("Preview",image)
if result is None:
flag=0
print "could not detect single face. Please retry."
else:
x,y,w,h=result
flag=1
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
scaled_byRatio=face.crop(gray,x,y,w,h)
resized=face.resize(scaled_byRatio)
print "Saved captured image No."+str(counter)
counter=counter+1
filename = name + str(counter) + ".pgm"
cv2.imwrite(filename,resized)
rawCapture.truncate(0)
camera.capture(rawCapture,format="bgr",use_video_port=True)
key=cv2.waitKey(1)
camera.close()
cv2.destroyWindow("Preview")
def comp():
#import cv2
#import config
#import face
# Load training data into model
print 'Loading training data...'
model = cv2.createEigenFaceRecognizer()
model.load(config.TRAINING_FILE)
print 'Training data loaded!'
# Initialize camera.
camera = config.get_camera()
print 'Capturing Profile...'
while True:
image = camera.read()
# Convert image to grayscale.
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Get coordinates of single face in captured image.
result = face.detect_single(image)
if result is None:
print 'Could not detect one face! Check the image capture.pgm'
return "User Not Detected"
break
x, y, w, h = result
# Crop and resize image to face.
crop = face.resize(face.crop(image, x, y, w, h))
# Test face against model.
label, confidence = model.predict(crop)
print 'Predicted {0} face with confidence {1} (lower is more confident).'.format(
'POSITIVE' if label == config.POSITIVE_LABEL else 'NEGATIVE',
confidence)
#user_login for the redirect refers to the def user_login not /user_login
#return redirect(url_for('user_login'))
if label == config.POSITIVE_LABEL and confidence < config.POSITIVE_THRESHOLD:
break
else:
print 'Did not recognize face!'
return 'User Not Accepted !'
def scan():
print("Looking for face...")
image = camera.read()
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
result = face.detect_single(image)
if result is None:
print("Could not detect a single face. There may be other faces in view. Check the image in capture.pgm to see what was captured.")
AuthenticateJSON.writeToJSON(False)
return
x, y, w, h = result
crop = face.resize(face.crop(image, x, y, w, h))
label, confidence = model.predict(crop)
positiveId = ""
if label == config.POSITIVE_LABEL:
positiveId = "POSITIVE"
else:
positiveId = "NEGATIVE"
print('Predicted {0} face with confidence {1} (Lower number is higher confidence).'.format(positiveId, confidence))
if label == config.POSITIVE_LABEL and confidence < config.POSITIVE_THRESHOLD:
AuthenticateJSON.writeToJSON(True)
print('Face recognized. Access granted.')
print('Timeout for 30 seconds commencing -- CHANGE BACK TO 30 AFTER PRESENTATION')
print('This will allow the user to stay "authenticated" on the webserver"')
print('Goodbye')
time.sleep(10)
AuthenticateJSON.writeToJSON(False)
else:
AuthenticateJSON.writeToJSON(False)
print('Face was unrecognized. Access denied.')
# Check for the positive face and unlock if found.
image = camera.read()
# Convert image to grayscale.
image_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Get coordinates of single face in captured image.
result = face.detect_single(image_gray)
print (result)
if result is None:
cv2.imshow('image',image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
print ('Could not detect single face! Check the image in capture.pgm' \
' to see what was captured and try again with only one face visible.')
continue
x, y, w, h = result
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imshow('image',image)
# Crop and resize image to face.
crop = face.resize(face.crop(image_gray, x, y, w, h))
# Test face against recognizer.
predicted = recognizer.predict(crop)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
print 'Button pressed, looking for face...'
# Check for the positive face and unlock if found.
image = camera.read()
# Convert image to grayscale.
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Get coordinates of single face in captured image.
result = face.detect_single(image)
if result is None:
print 'Could not detect single face! Check the image in capture.pgm' \
' to see what was captured and try again with only one face visible.'
continue
x, y, w, h = result
# Crop and resize image to face.
crop = face.resize(face.crop(image, x, y, w, h))
# Test face against model.
label, confidence = model.predict(crop)
if confidence < config.Threshold:
print 'Recognized face!'
else:
print 'Did not recognize face!'
print 'name = {0} with confidence {1}'.format(label, confidence)
Database_1.data_entry(label)
print('Press Ctrl-C to quit.')
while True:
# Check for the positive face and unlock if found.
image = camera.read()
# Convert image to grayscale.
image_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Get coordinates of single face in captured image.
result = face.detect_single(image_gray)
print(result)
if result is None:
cv2.imshow('image', image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
print ('Could not detect single face! Check the image in capture.pgm' \
' to see what was captured and try again with only one face visible.')
continue
x, y, w, h = result
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow('image', image)
# Crop and resize image to face.
crop = face.resize(face.crop(image_gray, x, y, w, h))
# Test face against recognizer.
predicted = recognizer.predict(crop)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
def prepareImage(fileName):
return face.resize(cv2.imread(fileName, cv2.IMREAD_GRAYSCALE))
def prepareImage(fileName):
"""Read an image as grayscale and resize it to the appropriate size for
training the face recognition model.
"""
return face.resize(cv2.imread(fileName, cv2.IMREAD_GRAYSCALE))
def Authenticate(cls):
#load lookup table_ ky
tableName=LOOKUP_FILE
table=[]
samples=[]
#self.load_table(tableName,table,samples)
# Create window
cv2.namedWindow("Preview")
#cv2.namedWindow("Compared")
# Load training data into model
print 'Loading training data...'
model = cv2.createLBPHFaceRecognizer()
model.load(TRAINING_FILE)
print 'Training data loaded!'
confidences=[]
labels=[]
vc = cv2.VideoCapture(0)
print 'Looking for face...'
if vc.isOpened(): # try to get the first frame
rval, frame = vc.read()
print "opened"
while rval==False:
rval,frame=vc.read()
else:
print "error"
rval = False
count=30
recognition=0
while rval:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
result=face.detect_single(gray)
cv2.imshow("Preview",frame)
key=cv2.waitKey(1)
if result is None:
print "Please face to the camera "
else:
x, y, w, h = result
# Crop and resize image to face
crop = face.resize(face.crop(gray, x, y, w, h))
label, confidence = model.predict(crop)
confidences.append(confidence)
labels.append(label)
cv2.waitKey(1)
count -= 1
if count<=0:
break
rval, frame = vc.read()
print "finish capturing faces"
vc.release()
cv2.destroyWindow("Preview")
temp=[]
i=0
length=len(labels)
while length>0:
if i==0:
temp.append(labels[length-1])
i += 1
length -= 1
else:
tempi=0
while tempi<i:
if labels[length-1]!=temp[tempi]:
tempi += 1
else:
length -=1
break
if tempi == i:
temp.append(labels[length-1])
i += 1
length -= 1
#print labels
#print temp
#print i
tempi=0
numoflabel=0
if i > 5:
print "could not enter"
return 0,-1
else:
element=temp[tempi]
while tempi < i:
tempj=0
count=0
while tempj<len(labels):
if labels[tempj]==temp[tempi]:
count += 1
tempj += 1
if count > numoflabel :
numoflabel=count
element=temp[tempi]
tempi += 1
print "element is {}, numoflabel is {}".format(element, numoflabel)
tempi = 0
con=0
while tempi < len(labels):
if labels[tempi]==element:
con=con+confidences[tempi]
tempi += 1
ave=con/numoflabel
print "mean of confidences is {}".format(ave)
#print confidences
# print recognition
f=open(ENROLLMENT_FILE,'r')
s=f.readline()
flag=0
while s!="":
index=int(s)
#print index
if index==element:
flag=1
print "flag TRUE"
break
s=f.readline()
if ave < 52 and flag==1:
print "authenticated"
return 1,element
else:
print "could not enter"
return 0,element
os.system('xdotool windowminimize $(xdotool getactivewindow)')
count = count + 1
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('Video', frame)
if cv2.waitKey(1) & flag == True:
result = face.detect_single(gray)
if result is None:
print 'No face!'
lock.lock()
if result is not None:
x, y, w, h = result
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
crop = face.resize(face.crop(gray, x, y, w, h))
facecrop = gray[y:y + h, x:x + w]
print 'Image captured successfully!'
if result is None:
print 'Could not detect single face!'
else:
print 'Analysing image....'
model = cv2.createEigenFaceRecognizer()
model.load(config.TRAINING_FILE)
label, confidence = model.predict(crop)
print 'Predicted {0} face with confidence {1} (lower is more confident).'.format(
def Authenticate(cls):
#load lookup table_ ky
tableName=LOOKUP_FILE
table=[]
samples=[]
#self.load_table(tableName,table,samples)
# Create window
cv2.namedWindow("Preview")
#cv2.namedWindow("Compared")
# Load training data into model
print 'Loading training data...'
model = cv2.createLBPHFaceRecognizer()
model.load(TRAINING_FILE)
print 'Training data loaded!'
confidences=[]
labels=[]
camera=PiCamera()
camera.resolution=(320,240)
camera.framerate=32
rawCapture=PiRGBArray(camera,size=(320,240))
time.sleep(3)
count=30
reccognition=0
print 'Looking for face...'
camera.capture(rawCapture,format="bgr",use_video_port=True)
while rawCapture is not None:
image=rawCapture.array
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
result=face.detect_single(gray)
cv2.imshow("Preview",image)
key=cv2.waitKey(1)
if result is None:
print "Please face to the camera "
else:
x, y, w, h = result
# Crop and resize image to face
crop = face.resize(face.crop(gray, x, y, w, h))
label, confidence = model.predict(crop)
confidences.append(confidence)
labels.append(label)
cv2.waitKey(1)
count -= 1
if count<=0:
break
rawCapture.truncate(0)
camera.capture(rawCapture,format="bgr",use_video_port=True)
print "finish capturing faces"
camera.close()
cv2.destroyWindow("Preview")
temp=[]
i=0
length=len(labels)
while length>0:
if i==0:
temp.append(labels[length-1])
i += 1
length -= 1
else:
tempi=0
while tempi<i:
if labels[length-1]!=temp[tempi]:
tempi += 1
else:
length -=1
break
if tempi == i:
temp.append(labels[length-1])
i += 1
length -= 1
print "------LABELS:{}".format(labels)
print "------DIFFERENT LABELS:{}".format(temp)
print "------NUMBER OF DIFFERENT LABELS:{}".format(i)
tempi=0
numoflabel=0
if i > 5:
print "could not enter"
return 0,-1
else:
element=temp[tempi]
while tempi < i:
tempj=0
count=0
while tempj<len(labels):
if labels[tempj]==temp[tempi]:
count += 1
tempj += 1
if count > numoflabel :
numoflabel=count
element=temp[tempi]
tempi += 1
print "element is {}, numoflabel is {}".format(element, numoflabel)
tempi = 0
con=0
while tempi < len(labels):
if labels[tempi]==element:
con=con+confidences[tempi]
tempi += 1
ave=con/numoflabel
print "mean of confidences is {}".format(ave)
#print confidences
# print recognition
f=open(ENROLLMENT_FILE,'r')
s=f.readline()
flag=0
while s!="":
index=int(s)
#print index
if index==element:
flag=1
print "flag TRUE"
break
s=f.readline()
if ave < 52 and flag==1:
print "authenticated"
return 1,element
else:
print "could not enter"
return 0,-1
def LBPHupdate(ID):
labels = []
images = []
# make sure this is the right file name
faceCascade = cv2.CascadeClassifier(cascadePath)
cv2.namedWindow("preview")
vc = cv2.VideoCapture(1) # device ID may not be 0
counter = 0
#counter2=0
foldername = ID
if not os.path.exists(foldername):
os.makedirs(foldername)
name = foldername + "/Images"
if vc.isOpened(): # try to get the first frame
rval, frame = vc.read()
print "opened"
while rval == False:
rval, frame = vc.read()
else:
print "error"
rval = False
# if vc.isOpened(): # try to get the first frame
# rval, frame = vc.read()
# print "opened"
# else:
# print "error"
# rval = False
while rval and counter < 30:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#faces =faceCascade.detectMultiScale(gray)
result = face.detect_single(gray)
cv2.imshow("preview", frame)
#for (x, y, w, h) in face:
if result is None:
flag = 0
print "could not detect single face. Please retry."
else:
x, y, w, h = result
#print "hello"
flag = 1
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
scaled_byRatio = face.crop(gray, x, y, w, h)
resized = face.resize(scaled_byRatio)
#resized=cv2.resize(gray[y:y+h,x:x+w],(200,200),interpolation=cv2.INTER_CUBIC)
# keyword "interpolation" should not be left out
cv2.imshow("preview", frame)
#print "Ready for capture"
print "Saved captured image No." + str(counter)
counter = counter + 1
filename = name + str(counter) + ".pgm"
cv2.imwrite(filename, resized)
rval, frame = vc.read()
key = cv2.waitKey(1)
vc.release()
cv2.destroyWindow("preview")
#update database
print 'Loading training data...'
model = cv2.createLBPHFaceRecognizer()
model.load(TRAINING_FILE)
print 'Training data loaded!'
f = open(CSV_FILE, 'r+')
t = open(LOOKUP_FILE, 'r+')
en = open(ENROLLMENT_FILE, 'r+')
#Get label
f.seek(-10, 2)
s = f.readline()
#print s
list = s.split(';')
num = str(list[1]).split('\n')
#new label no.
label = int(num[0]) + 1
#print label
f.seek(0, 2)
t.seek(0, 2)
en.seek(0, 2)
faces = []
labels = []
DIRECTORY = foldername
#print DIRECTORY
SEPARATOR = ";"
for files in os.listdir(DIRECTORY):
abs_path = "%s\%s" % (DIRECTORY, files)
seq = ''.join([str(abs_path), str(SEPARATOR), str(label), '\n'])
f.write(seq)
t.write(''.join([str(DIRECTORY), ';', abs_path, ';\n']))
en.write(''.join([str(label), '\n']))
f.close()
t.close()
en.close()
for filename in walk_files(DIRECTORY, '*.pgm'):
#print filename
faces.append(prepare_image(filename))
labels.append(label)
model.update(np.asarray(faces), np.asarray(labels))
#print model
#Save model results
model.save(TRAINING_FILE)
print 'Training data saved to', TRAINING_FILE
print "successfully updated"
shutil.rmtree(foldername)
while rval and counter<30:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#faces =faceCascade.detectMultiScale(gray)
result=face.detect_single(gray)
cv2.imshow("preview",frame)
#for (x, y, w, h) in face:
if result is None:
flag=0
print "could not detect single face. Please retry."
else:
x,y,w,h=result
#print "hello"
flag=1
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
scaled_byRatio=face.crop(gray,x,y,w,h)
resized=face.resize(scaled_byRatio)
#resized=cv2.resize(gray[y:y+h,x:x+w],(200,200),interpolation=cv2.INTER_CUBIC)
# keyword "interpolation" should not be left out
cv2.imshow("preview", frame)
#print "Ready for capture"
print "Saved captured image No."+str(counter)
counter=counter+1
filename = name + str(counter) + ".pgm"
cv2.imwrite(filename,resized)
rval, frame = vc.read()
key=cv2.waitKey(1)
vc.release()
cv2.destroyWindow("preview")
def update(self, observable, arg):
print "UI sees change" , observable.currentState
if observable.currentState.authState == AuthStates.Assess:
self.ledsOff()
GPIO.output(lightPins, 1)
image = camera.read()
# Convert image to grayscale.
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
result = face.detect_single(image)
if result is None:
print 'Could not detect single face! Check the image in capture.pgm' \
' to see what was captured and try again with only one face visible.'
self.playAudio( sorryAudioFile )
self.controllable.failAuth()
else:
GPIO.output(lightPins, 0)
sleep(0.2)
GPIO.output(lightPins, 1)
sleep(0.2)
GPIO.output(lightPins, 0)
sleep(0.2)
GPIO.output(lightPins, 1)
x, y, w, h = result
# Crop and resize image to face.
crop = face.resize(face.crop(image, x, y, w, h))
# Test face against model.
label, confidence = model.predict(crop)
print 'Predicted {0} face with confidence {1} (lower is more confident).'.format(
'POSITIVE' if label == config.POSITIVE_LABEL else 'NEGATIVE',
confidence)
if label == config.POSITIVE_LABEL and confidence < config.POSITIVE_THRESHOLD:
print 'Recognized face!'
self.playAudio( welcomeAudioFile )
self.controllable.acceptAuth()
else:
print 'Did not recognize face!'
self.playAudio( intruderAudioFile )
self.controllable.rejectAuth()
elif observable.currentState.authState != AuthStates.Assess:
GPIO.output(lightPins, 0)
if observable.currentState.alarmState == AlarmStates.Disarmed:
if observable.currentState.authState == AuthStates.Accept:
self.flasher.flash(self.greenOnly, self.greenOnly, self.ledsOff, self.greenOnly, 3)
else:
self.flasher.flash(self.greenOnly, self.greenOnly, self.greenOnly, self.greenOnly, 3)
elif observable.currentState.alarmState == AlarmStates.Armed:
if observable.currentState.authState == AuthStates.Reject:
self.flasher.flash(self.redOnly, self.ledsOff, self.ledsOff, self.redOnly, 3)
elif observable.currentState.authState == AuthStates.Fail:
self.flasher.flash(self.redOnly, self.greenOn, self.greenOnly, self.redOnly, 3)
elif observable.currentState.authState == AuthStates.NoAuth:
self.flasher.flash(self.redOnly, self.greenOn, self.greenOnly, self.redOnly, 1)
if observable.currentState.alarmState == AlarmStates.Fire:
self.fireOn()
else:
self.fireOff()
def run(self):
debut = time.time()
self.ecrireStatus("INCONNU")
reconnu = False
# Load training data into model
print 'Loading training data...'
model = cv2.createEigenFaceRecognizer()
model.load(config.TRAINING_FILE)
print 'Training data loaded!'
# Initialize camer and box.
camera = cv2.VideoCapture(0)
print 'Press Ctrl-C to quit.'
while True:
if time.time() - debut > 3:
print reconnu
reconnu = False
ecrireStatus("INCONNU")
print "3 secondes passees sans reconnaissance"
ret, frame = camera.read()
# Convert image to grayscale.
image = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
# Get coordinates of single face in captured image.
result = face.detect_single(image)
if result is None:
os.system('cls' if os.name == 'nt' else 'clear')
print 'PAS DE VISAGE'
continue
#dessiner le carre
for (x, y, w, h) in result:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
#Display the resulting frame
#cv2.imshow('Video', frame)
nx = round(w * MagicMirror.coefW) + x
ny = round(h * MagicMirror.coefH) + y
nw = w - round(w * 2 * MagicMirror.coefW)
nh = h - round(h * 2 * MagicMirror.coefH)
cv2.imshow("Adding faces to traning set...", frame[ny:ny + nh,
nx:nx + nw])
cv2.waitKey(1)
x, y, w, h = result[0]
# Crop and resize image to face.
crop = face.resize(face.crop(image, nx, ny, nw, nh))
# Test face against model.
label, confidence = model.predict(crop)
if label == config.POSITIVE_LABEL and confidence < MagicMirror.confidenceTolerate:
os.system('cls' if os.name == 'nt' else 'clear')
print 'OUI'
print confidence
print x, " ", nx, " "
print y, " ", ny, " "
print w, " ", nw, " "
print h, " ", nh, " "
if reconnu == False:
print 'OUI'
print "changement de status"
ecrireStatus("Imade")
reconnu = True
debut = time.time()
else:
os.system('cls' if os.name == 'nt' else 'clear')
print 'NON'
print confidence
print x, " ", nx, " "
print y, " ", ny, " "
print w, " ", nw, " "
print h, " ", nh, " "
def LBPHupdate(ID):
labels=[]
images=[]
# make sure this is the right file name
faceCascade = cv2.CascadeClassifier(cascadePath)
counter=0
#counter2=0
foldername=ID;
if not os.path.exists(foldername):
os.makedirs(foldername)
name=foldername+"/Images"
camera=PiCamera()
camera.resolution=(320,240)
camera.framerate=32
rawCapture=PiRGBArray(camera,size=(320,240))
time.sleep(3)
cv2.namedWindow("Capturing new images")
camera.capture(rawCapture,format="bgr",use_video_port=True)
while rawCapture is not None and counter<30:
image=rawCapture.array
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
result=face.detect_single(gray)
cv2.imshow("Capturing new images",image)
if result is None:
flag=0
print "could not detect single face. Please retry."
else:
x,y,w,h=result
flag=1
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
scaled_byRatio=face.crop(gray,x,y,w,h)
resized=face.resize(scaled_byRatio)
print "Saved captured image No."+str(counter)
counter=counter+1
filename = name + str(counter) + ".pgm"
cv2.imwrite(filename,resized)
rawCapture.truncate(0)
camera.capture(rawCapture,format="bgr",use_video_port=True)
key=cv2.waitKey(1)
camera.close()
cv2.destroyWindow("Capturing new images")
#update database
print 'Loading training data...'
model=cv2.createLBPHFaceRecognizer()
model.load(TRAINING_FILE)
print 'Training data loaded!'
f=open(CSV_FILE,'r+')
t=open(LOOKUP_FILE,'r+')
en=open(ENROLLMENT_FILE,'r+')
#Get label
f.seek(-10,2)
s=f.readline()
#print s
list=s.split(';')
num=str(list[1]).split('\n')
#new label no.
label=int(num[0])+1
#print label
f.seek(0,2)
t.seek(0,2)
en.seek(0,2)
faces=[]
labels=[]
DIRECTORY=foldername
#print DIRECTORY
SEPARATOR=";"
for files in os.listdir(DIRECTORY):
abs_path="%s\%s"%(DIRECTORY,files)
seq=''.join([str(abs_path),str(SEPARATOR),str(label),'\n'])
f.write(seq)
t.write(''.join([str(DIRECTORY),';',abs_path,';\n']));
en.write(''.join([str(label),'\n']))
f.close()
t.close()
en.close()
for filename in walk_files(DIRECTORY,'*.pgm'):
#print filename
faces.append(prepare_image(filename))
labels.append(label)
model.update(np.asarray(faces), np.asarray(labels))
#print model
#Save model results
model.save(TRAINING_FILE)
print 'Training data saved to',TRAINING_FILE
print "successfully updated"
def prepare_image(filename): """Read an image as grayscale and resize it to the appropriate size for training the face recognition model. """ return face.resize(cv2.imread(filename, cv2.IMREAD_GRAYSCALE))
def Authenticate():
#load lookup table_ ky
tableName = LOOKUP_FILE
table = []
samples = []
load_table(tableName, table, samples)
# Create window
cv2.namedWindow("Preview")
#cv2.namedWindow("Compared")
# Load training data into model
print 'Loading training data...'
model = cv2.createLBPHFaceRecognizer()
model.load(TRAINING_FILE)
print 'Training data loaded!'
confidences = []
labels = []
vc = cv2.VideoCapture(1)
print 'Looking for face...'
if vc.isOpened(): # try to get the first frame
rval, frame = vc.read()
print "opened"
while rval == False:
rval, frame = vc.read()
else:
print "error"
rval = False
count = 30
recognition = 0
while rval:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
result = face.detect_single(gray)
cv2.imshow("Preview", frame)
key = cv2.waitKey(1)
if result is None:
print "Please face to the camera "
else:
x, y, w, h = result
# Crop and resize image to face
crop = face.resize(face.crop(gray, x, y, w, h))
label, confidence = model.predict(crop)
confidences.append(confidence)
labels.append(label)
cv2.waitKey(1)
count -= 1
if count <= 0:
break
rval, frame = vc.read()
print "finish capturing faces"
vc.release()
cv2.destroyWindow("Preview")
temp = []
i = 0
length = len(labels)
while length > 0:
if i == 0:
temp.append(labels[length - 1])
i += 1
length -= 1
else:
tempi = 0
while tempi < i:
if labels[length - 1] != temp[tempi]:
tempi += 1
else:
length -= 1
break
if tempi == i:
temp.append(labels[length - 1])
i += 1
length -= 1
#print labels
#print temp
#print i
tempi = 0
numoflabel = 0
if i > 5:
print "could not enter"
else:
element = temp[tempi]
while tempi < i:
tempj = 0
count = 0
while tempj < len(labels):
if labels[tempj] == temp[tempi]:
count += 1
tempj += 1
if count > numoflabel:
numoflabel = count
element = temp[tempi]
tempi += 1
print "element is {}, numoflabel is {}".format(element, numoflabel)
tempi = 0
con = 0
while tempi < len(labels):
if labels[tempi] == element:
con = con + confidences[tempi]
tempi += 1
ave = con / numoflabel
print "mean of confidences is {}".format(ave)
#print confidences
# print recognition
f = open(ENROLLMENT_FILE, 'r')
s = f.readline()
flag = 0
while s != "":
index = int(s)
#print index
if index == element:
flag = 1
print "flag TRUE"
break
s = f.readline()
if ave < 50 and flag == 1:
print "authenticated"
return 1, element
else:
print "could not enter"
return 0, -1
def LBPHupdate(ID):
labels = []
images = []
# make sure this is the right file name
faceCascade = cv2.CascadeClassifier(cascadePath)
counter = 0
#counter2=0
foldername = ID
if not os.path.exists(foldername):
os.makedirs(foldername)
name = foldername + "/Images"
camera = PiCamera()
camera.resolution = (320, 240)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(320, 240))
time.sleep(3)
cv2.namedWindow("Capturing new images")
camera.capture(rawCapture, format="bgr", use_video_port=True)
while rawCapture is not None and counter < 30:
image = rawCapture.array
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
result = face.detect_single(gray)
cv2.imshow("Capturing new images", image)
if result is None:
flag = 0
print "could not detect single face. Please retry."
else:
x, y, w, h = result
flag = 1
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
scaled_byRatio = face.crop(gray, x, y, w, h)
resized = face.resize(scaled_byRatio)
print "Saved captured image No." + str(counter)
counter = counter + 1
filename = name + str(counter) + ".pgm"
cv2.imwrite(filename, resized)
rawCapture.truncate(0)
camera.capture(rawCapture, format="bgr", use_video_port=True)
key = cv2.waitKey(1)
camera.close()
cv2.destroyWindow("Capturing new images")
#update database
print 'Loading training data...'
model = cv2.createLBPHFaceRecognizer()
model.load(TRAINING_FILE)
print 'Training data loaded!'
f = open(CSV_FILE, 'r+')
t = open(LOOKUP_FILE, 'r+')
en = open(ENROLLMENT_FILE, 'r+')
#Get label
f.seek(-10, 2)
s = f.readline()
#print s
list = s.split(';')
num = str(list[1]).split('\n')
#new label no.
label = int(num[0]) + 1
#print label
f.seek(0, 2)
t.seek(0, 2)
en.seek(0, 2)
faces = []
labels = []
DIRECTORY = foldername
#print DIRECTORY
SEPARATOR = ";"
for files in os.listdir(DIRECTORY):
abs_path = "%s\%s" % (DIRECTORY, files)
seq = ''.join([str(abs_path), str(SEPARATOR), str(label), '\n'])
f.write(seq)
t.write(''.join([str(DIRECTORY), ';', abs_path, ';\n']))
en.write(''.join([str(label), '\n']))
f.close()
t.close()
en.close()
for filename in walk_files(DIRECTORY, '*.pgm'):
#print filename
faces.append(prepare_image(filename))
labels.append(label)
model.update(np.asarray(faces), np.asarray(labels))
#print model
#Save model results
model.save(TRAINING_FILE)
print 'Training data saved to', TRAINING_FILE
print "successfully updated"
import fnmatch
import os
import cv2
import config
import face
def walk_files(directory, match="*"):
for root, dirs, files in os.walk(directory):
for filename in fnmatch.filter(files, match):
yield os.path.join(root, filename)
for filename in walk_files(config.CAPTURE_DIR, "*.jpg"):
img = cv2.imread(filename)
bw_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
result = face.detect_single(bw_img)
if result is None:
print "No face detected ... " + filename
else:
x, y, w, h = result
crop = face.resize(face.crop(img, x, y, w, h))
cv2.imwrite(filename, crop)
print "Cropped file ... ", filename
def run(self):
debut = time.time()
self.ecrireStatus("INCONNU")
reconnu=False
# Load training data into model
print 'Loading training data...'
model = cv2.createEigenFaceRecognizer()
model.load(config.TRAINING_FILE)
print 'Training data loaded!'
# Initialize camer and box.
camera = cv2.VideoCapture(0)
print 'Press Ctrl-C to quit.'
while True:
if time.time() - debut > 3:
print reconnu
reconnu=False
ecrireStatus("INCONNU")
print "3 secondes passees sans reconnaissance"
ret, frame = camera.read()
# Convert image to grayscale.
image = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
# Get coordinates of single face in captured image.
result = face.detect_single(image)
if result is None:
os.system('cls' if os.name=='nt' else 'clear')
print 'PAS DE VISAGE'
continue
#dessiner le carre
for (x, y, w, h) in result:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
#Display the resulting frame
#cv2.imshow('Video', frame)
nx = round(w*MagicMirror.coefW) + x
ny = round(h*MagicMirror.coefH) + y
nw = w - round(w*2*MagicMirror.coefW)
nh = h - round(h*2*MagicMirror.coefH)
cv2.imshow("Adding faces to traning set...", frame[ny: ny + nh, nx: nx + nw])
cv2.waitKey(1)
x, y, w, h = result[0]
# Crop and resize image to face.
crop = face.resize(face.crop(image, nx, ny, nw, nh))
# Test face against model.
label, confidence = model.predict(crop)
if label == config.POSITIVE_LABEL and confidence < MagicMirror.confidenceTolerate:
os.system('cls' if os.name=='nt' else 'clear')
print 'OUI'
print confidence
print x," ",nx," "
print y," ",ny," "
print w," ",nw," "
print h," ",nh," "
if reconnu == False:
print 'OUI'
print "changement de status"
ecrireStatus("Imade")
reconnu = True
debut = time.time()
else:
os.system('cls' if os.name=='nt' else 'clear')
print 'NON'
print confidence
print x," ",nx," "
print y," ",ny," "
print w," ",nw," "
print h," ",nh," "
def Authenticate(cls):
#load lookup table_ ky
tableName = LOOKUP_FILE
table = []
samples = []
#self.load_table(tableName,table,samples)
# Create window
cv2.namedWindow("Preview")
#cv2.namedWindow("Compared")
# Load training data into model
print 'Loading training data...'
model = cv2.createLBPHFaceRecognizer()
model.load(TRAINING_FILE)
print 'Training data loaded!'
confidences = []
labels = []
camera = PiCamera()
camera.resolution = (320, 240)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(320, 240))
time.sleep(3)
count = 30
reccognition = 0
print 'Looking for face...'
camera.capture(rawCapture, format="bgr", use_video_port=True)
while rawCapture is not None:
image = rawCapture.array
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
result = face.detect_single(gray)
cv2.imshow("Preview", image)
key = cv2.waitKey(1)
if result is None:
print "Please face to the camera "
else:
x, y, w, h = result
# Crop and resize image to face
crop = face.resize(face.crop(gray, x, y, w, h))
label, confidence = model.predict(crop)
confidences.append(confidence)
labels.append(label)
cv2.waitKey(1)
count -= 1
if count <= 0:
break
rawCapture.truncate(0)
camera.capture(rawCapture, format="bgr", use_video_port=True)
print "finish capturing faces"
camera.close()
cv2.destroyWindow("Preview")
temp = []
i = 0
length = len(labels)
while length > 0:
if i == 0:
temp.append(labels[length - 1])
i += 1
length -= 1
else:
tempi = 0
while tempi < i:
if labels[length - 1] != temp[tempi]:
tempi += 1
else:
length -= 1
break
if tempi == i:
temp.append(labels[length - 1])
i += 1
length -= 1
print "------LABELS:{}".format(labels)
print "------DIFFERENT LABELS:{}".format(temp)
print "------NUMBER OF DIFFERENT LABELS:{}".format(i)
tempi = 0
numoflabel = 0
if i > 5:
print "could not enter"
return 0, -1
else:
element = temp[tempi]
while tempi < i:
tempj = 0
count = 0
while tempj < len(labels):
if labels[tempj] == temp[tempi]:
count += 1
tempj += 1
if count > numoflabel:
numoflabel = count
element = temp[tempi]
tempi += 1
print "element is {}, numoflabel is {}".format(element, numoflabel)
tempi = 0
con = 0
while tempi < len(labels):
if labels[tempi] == element:
con = con + confidences[tempi]
tempi += 1
ave = con / numoflabel
print "mean of confidences is {}".format(ave)
#print confidences
# print recognition
f = open(ENROLLMENT_FILE, 'r')
s = f.readline()
flag = 0
while s != "":
index = int(s)
#print index
if index == element:
flag = 1
print "flag TRUE"
break
s = f.readline()
if ave < 52 and flag == 1:
print "authenticated"
return 1, element
else:
print "could not enter"
return 0, -1
# TODO: Check if button is pressed.
# if is_letter_input('c'):
# Check for the positive face and unlock if found.
image = camera.read()
# Convert image to grayscale.
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Get coordinates of single face in captured image.
result = face.detect_single(image)
if result is None:
print "No Face"
time.sleep(10)
personPresent = 0
continue
x, y, w, h = result
# Crop and resize image to face.
crop = face.resize(face.crop(image, x, y, w, h))
# Test face against model.
label, confidence = model.predict(crop)
print "Predicted {0} face with confidence {1} (lower is more confident).".format(
"POSITIVE" if label == config.POSITIVE_LABEL else "NEGATIVE", confidence
)
if label == config.POSITIVE_LABEL and confidence < config.POSITIVE_THRESHOLD and personPresent == 0:
print "Recognized face!"
personPresent = 1
with open("/home/ubuntu/chatbotio", "w") as file:
file.write("Greetings Human")
time.sleep(10)
else:
print "Did not recognize face"
time.sleep(10)
def LBPHupdate(cls,ID):
labels=[]
images=[]
# make sure this is the right file name
faceCascade = cv2.CascadeClassifier(cascadePath)
cv2.namedWindow("preview")
vc = cv2.VideoCapture(0) # device ID may not be 0
counter=0
#counter2=0
foldername=ID;
if not os.path.exists(foldername):
os.makedirs(foldername)
name=foldername+"/Images"
if vc.isOpened(): # try to get the first frame
rval, frame = vc.read()
print "opened"
while rval==False:
rval,frame=vc.read()
else:
print "error"
rval = False
# if vc.isOpened(): # try to get the first frame
# rval, frame = vc.read()
# print "opened"
# else:
# print "error"
# rval = False
while rval and counter<30:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#faces =faceCascade.detectMultiScale(gray)
result=face.detect_single(gray)
cv2.imshow("preview",frame)
#for (x, y, w, h) in face:
if result is None:
flag=0
print "could not detect single face. Please retry."
else:
x,y,w,h=result
#print "hello"
flag=1
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
scaled_byRatio=face.crop(gray,x,y,w,h)
resized=face.resize(scaled_byRatio)
#resized=cv2.resize(gray[y:y+h,x:x+w],(200,200),interpolation=cv2.INTER_CUBIC)
# keyword "interpolation" should not be left out
cv2.imshow("preview", frame)
#print "Ready for capture"
print "Saved captured image No."+str(counter)
counter=counter+1
filename = name + str(counter) + ".pgm"
cv2.imwrite(filename,resized)
rval, frame = vc.read()
key=cv2.waitKey(1)
vc.release()
cv2.destroyWindow("preview")
#update database
print 'Loading training data...'
model=cv2.createLBPHFaceRecognizer()
model.load(TRAINING_FILE)
print 'Training data loaded!'
f=open(CSV_FILE,'r+')
t=open(LOOKUP_FILE,'r+')
en=open(ENROLLMENT_FILE,'r+')
#Get label
f.seek(-10,2)
s=f.readline()
#print s
list=s.split(';')
num=str(list[1]).split('\n')
#new label no.
label=int(num[0])+1
#print label
f.seek(0,2)
t.seek(0,2)
en.seek(0,2)
faces=[]
labels=[]
DIRECTORY=foldername
#print DIRECTORY
SEPARATOR=";"
for files in os.listdir(DIRECTORY):
abs_path="%s\%s"%(DIRECTORY,files)
seq=''.join([str(abs_path),str(SEPARATOR),str(label),'\n'])
f.write(seq)
t.write(''.join([str(DIRECTORY),';',abs_path,';\n']));
en.write(''.join([str(label),'\n']))
f.close()
t.close()
en.close()
for filename in cls.walk_files(DIRECTORY,'*.pgm'):
#print filename
faces.append(cls.prepare_image(filename))
labels.append(label)
model.update(np.asarray(faces), np.asarray(labels))
#print model
#Save model results
model.save(TRAINING_FILE)
print 'Training data saved to',TRAINING_FILE
print "successfully updated"
shutil.rmtree(foldername)
return label
