def convert(): # Create the directory for positive training images if it doesn't exist. if not os.path.exists(config.TRAINING_DIR + CAPTURE_DIR): os.makedirs(config.TRAINING_DIR + CAPTURE_DIR) # Find the largest ID of existing positive images. # Start new images after this ID value. files = sorted(glob.glob(os.path.join(config.TRAINING_DIR + CAPTURE_DIR, '[0-9][0-9][0-9].pgm'))) count = 0 if len(files) > 0: # Grab the count from the last filename. count = int(files[-1][-7:-4])+1 for filename in walk_files(RAW_DIR, '*.jpg'): image = filename image = cv2.imread(image) 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!' continue x, y, w, h = result # Crop image as close as possible to desired face aspect ratio. # Might be smaller if face is near edge of image. crop = face.crop(image, x, y, w, h) # Save image to file. filename = os.path.join(config.TRAINING_DIR + CAPTURE_DIR, '%03d.pgm' % count) cv2.imwrite(filename, crop) print 'Found face and wrote training image', filename count += 1
def training(root):
fn_name = askname()
camera = config.get_camera()
if not os.path.exists(config.POSITIVE_DIR):
os.makedirs(config.POSITIVE_DIR)
path = os.path.join(config.POSITIVE_DIR,fn_name)
if not os.path.isdir(path):
os.mkdir(path)
files = sorted(glob.glob(os.path.join(path,'[0-9][0-9][0-9].pgm')))
count = 0
if len(files) > 0:
count = int(files[-1][-7:-4])+1
c =0
while c < 2 :
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 image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = face.crop(image, x, y, w, h)
# Save image to file.
filename = os.path.join(path,'%03d.pgm' % count)
cv2.imwrite(filename, crop)
print 'Found face and wrote training image', filename
c += 1
count += 1
popuptrained()
def check():
"""Checks if it can recognize a face. Returns name is successful and None otherwise"""
print("Looking for face...")
image = camera.read()
image = cv2.cvtColor(image)
# Get coordinates of single face in captured image.
result = face.detect_single(image)
if result is None:
print("Could not detect face!")
return
x, y, w, h = result
# Crop and resize image to face.
crop = face.resize(face.crop(image, x, y, w, h))
# Test face against models.
prob = {}
for file, model in models.items():
label, confidence = model.predict(crop)
print('Predicted {0} face with confidence {1} for {2}'.format(
'POSITIVE' if label == config.POSITIVE_LABEL else 'NEGATIVE',
confidence, file))
if label == config.POSITIVE_LABEL:
prob[file] = confidence
name = None
conf = 10000000
for file, confidence in prob.items():
print("Checking {0} with {1}".format(file, confidence))
if confidence < conf:
conf = confidence
name = file
return name
def face_check():
#Only run face_check() for a certain amount of time according to the config file
time_out_start = time.time()
while time.time() < time_out_start + config.TIME_OUT:
# Initialize camera'
camera = cv2.VideoCapture(config.WEBCAM_ID)
ret1, frame1 = camera.read()
image = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
result = face.detect_single(image)
#Check if any face presents
if result is None:
lcd.clear()
lcd.message('NO FACE DETECTED' + '\n')
GPIO.output(Relay_Pin, GPIO.LOW)
else:
lcd.clear()
x, y, w, h = result
crop = face.resize(face.crop(image, x, y, w, h))
label, confidence = model.predict(crop)
if label == config.POSITIVE_LABEL and confidence < config.POSITIVE_THRESHOLD:
lcd.clear()
lcd.message(str(label) + ' OK. OPEN LOCK!' + '\n' + str(confidence) + '\n')
GPIO.output(Relay_Pin, GPIO.HIGH)
else:
lcd.clear()
lcd.message(str(label) + ' NOT RECOGNIZED' + '\n' + str(confidence)+ '\n')
GPIO.output(Relay_Pin, GPIO.LOW)
camera.release()
def convert():
# Create the directory for positive training images if it doesn't exist.
if not os.path.exists(config.TRAINING_DIR + CAPTURE_DIR):
os.makedirs(config.TRAINING_DIR + CAPTURE_DIR)
# Find the largest ID of existing positive images.
# Start new images after this ID value.
files = sorted(glob.glob(os.path.join(config.TRAINING_DIR + CAPTURE_DIR, '[0-9][0-9][0-9].pgm')))
count = 0
if len(files) > 0:
# Grab the count from the last filename.
count = int(files[-1][-7:-4]) + 1
for filename in walk_files(RAW_DIR, '*.jpg'):
image = filename
image = cv2.imread(image)
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!'
continue
x, y, w, h = result
# Crop image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = face.crop(image, x, y, w, h)
# Save image to file.
filename = os.path.join(config.TRAINING_DIR + CAPTURE_DIR, '%03d.pgm' % count)
cv2.imwrite(filename, crop)
print 'Found face and wrote training image', filename
count += 1
def classify(model,model1):
print "predicting"
image = camera.read()
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.'
return False
print "Single face detected"
x, y, w, h = result
crop = face.resize(face.crop(image, x, y, w, h))
# Test face against model.
label, confidence = model.predict(crop)
label1, confidence1 = model1.predict(crop)
print 'Predicted {0} face with confidence {1} (lower is more confident).'.format('POSITIVE' if label == config.POSITIVE_LABEL else 'NEGATIVE', confidence)
print 'Predicted {0} face with confidence {1} (lower is more confident).'.format('POSITIVE' if label1 == config.POSITIVE_LABEL else 'NEGATIVE', confidence1)
if (label == config.POSITIVE_LABEL and confidence < config.POSITIVE_THRESHOLD) or (label1 == config.POSITIVE_LABEL and confidence1 < config.POSITIVE_THRESHOLD) :
print 'Recognized face!'
'''TO DO'''
return True
else:
print 'Did not recognize face!'
'''TO DO'''
return False
def admincapture():
if request.method == 'GET':
# Prefix for positive training image filenames.
POSITIVE_FILE_PREFIX = 'positive_'
camera = config.get_camera()
# Create the directory for positive training images if it doesn't exist.
if not os.path.exists(config.POSITIVE_DIR):
os.makedirs(config.POSITIVE_DIR)
# Find the largest ID of existing positive images.
# Start new images after this ID value.
files = sorted(glob.glob(os.path.join(config.POSITIVE_DIR,
POSITIVE_FILE_PREFIX + '[0-9][0-9][0-9].pgm')))
count = 0
if len(files) > 0:
# Grab the count from the last filename.
count = int(files[-1][-7:-4])+1
while True:
print 'Capturing image...'
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.'
return 'Could not detect single face! Check the image in capture.pgm' \
' to see what was captured and try again with only one face visible.'
break
#continue
x, y, w, h = result
# Crop image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = face.crop(image, x, y, w, h)
# Save image to file.
filename = os.path.join(config.POSITIVE_DIR, POSITIVE_FILE_PREFIX + '%03d.pgm' % count)
cv2.imwrite(filename, crop)
print 'Found face and wrote training image', filename
return 'Found face and wrote training image'
if True:
print"succesful capture"
delay()
if True:
return redirect(url_for('/new'))
#<----*****THis count will need to be edited -->
count += 1
if count >4:
break
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 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 detect_face(self):
# Check for the positive face and unlock if found.
image = self.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)
#x, y, w, h = result
#print(result)
return result
def admincapture():
data = request.form['mydata']
#print data
#need to decode data here
#open image path to save after decode
img = open("userimg/capture.jpg", "wb")
img.write(data.decode('base64'))
img.close()
#open image and convert to pgm format
im = Image.open('userimg/capture.jpg')
im = im.convert('RGB')
im.save('userimg/capture.pgm')
# Prefix for positive training image files
POSITIVE_FILE_PREFIX = 'positive_'
# Create the directory for positive training images if it doesn't exist.
if not os.path.exists(config.POSITIVE_DIR):
os.makedirs(config.POSITIVE_DIR)
# Find the largest ID of existing positive images.
# Start new images after this ID value.
files = sorted(glob.glob(os.path.join(config.POSITIVE_DIR,
POSITIVE_FILE_PREFIX + '[0-9][0-9][0-9].pgm')))
count = 0
if len(files) > 0:
# Grab the count from the last filename.
count = int(files[-1][-7:-4])+1
while True:
xx= cv2.imread('userimg/capture.pgm')
print type(xx)
yy = cv2.cvtColor(xx,cv2.COLOR_RGB2GRAY)
cv2.imwrite('userimg/capture2.pgm',yy)
image = cv2.imread('userimg/capture2.pgm')
result = face.detect_single(image)
if result is None:
print 'not detected'
flash("User Profile Not Detected",'danger')
#return 'not detected'
return render_template("capture.html")
break
x, y, w, h = result
crop = face.crop(image, x, y, w, h)
filename = os.path.join(config.POSITIVE_DIR, POSITIVE_FILE_PREFIX + '%03d.pgm' % count)
cv2.imwrite(filename,crop)
if True:
print 'Captured sucess!'
flash("Profile Captured Sucessfuly", 'success')
time.sleep(3)
#return 'capture sucess'
return render_template("capture.html")
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 convertImage(file):
print('Converting image...')
tmpimage = cv2.imread(file, cv2.COLOR_RGB2GRAY)
# Get coordinates of single face in captured image.
tmpresult = face.detect_single(tmpimage)
if tmpresult is None:
print('Could not detect single face! File: ' + file)
return None
x, y, w, h = tmpresult
# Crop image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = face.crop(tmpimage, x, y, w, h)
return crop
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 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 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.')
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 captureImage():
print('Capturing image...')
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.')
exit
x, y, w, h = result
# Crop image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = face.crop(image, x, y, w, h)
# Save image to file.
filename = os.path.join(serverConfig.IMAGE_DIR, serverConfig.POSITIVE_DIR, POSITIVE_FILE_PREFIX + '%03d.pgm' % count)
cv2.imwrite(filename, crop)
print('Found face and wrote training image', filename)
def FaceRec(self):
while True:
# Check if capture should be made.
# TODO: Check if button is pressed.
if door.is_button_up() or is_letter_input('l'):
led.LedOn()
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.'
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()
led.LedOff()
self.IRstatus()
return self.main()
else:
print 'Did not recognize face!'
sleep(.01)
def capture():
camera = config.get_camera()
# Create the directory for positive training images if it doesn't exist.
if not os.path.exists(config.TRAINING_DIR + CAPTURE_DIR):
os.makedirs(config.TRAINING_DIR + CAPTURE_DIR)
# Find the largest ID of existing positive images.
# Start new images after this ID value.
files = sorted(glob.glob(os.path.join(config.TRAINING_DIR + CAPTURE_DIR, '[0-9][0-9][0-9].pgm')))
count = 0
if len(files) > 0:
# Grab the count from the last filename.
count = int(files[-1][-7:-4]) + 1
print 'Capturing positive training images.'
print 'Press enter to capture an image.'
print 'Press Ctrl-C to quit.'
while True:
try:
raw_input()
print 'Capturing image...'
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.'
filename = os.path.join(config.TRAINING_DIR , 'capture.pgm')
cv2.imwrite(filename, image)
continue
x, y, w, h = result
# Crop image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = face.crop(image, x, y, w, h)
# Save image to file.
filename = os.path.join(config.TRAINING_DIR + CAPTURE_DIR, '%03d.pgm' % count)
cv2.imwrite(filename, crop)
print 'Found face and wrote training image', filename
count += 1
except KeyboardInterrupt:
camera.stop()
break
def recognizeFace(model):
# Initialize camer and box.
camera = serverConfig.get_camera()
# Check for the positive face and unlock if found.
print("Trying to read an image from the camera.")
image = camera.read()
# Convert image to grayscale.
print("Converting image to greyscale.")
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
cv2.equalizeHist(image, image)
# Get coordinates of single face in captured image.
print("Trying to detect a single face.")
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.')
return 'NoFace'
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(label)
print(confidence)
if label == serverConfig.NEGATIVE_LABEL:
return 'Neg'
else:
for i in range(len(serverConfig.USERS)):
if label == serverConfig.POSITIVE_LABELS[
i] and confidence < serverConfig.POSITIVE_THRESHOLD:
print('Found a match')
return serverConfig.USERS[i]
# Must not be a match
print('No Match')
return 'Neg'
def capture_positive(num, name):
"""Capture one positive image. Return 1 if successful and 0 otherwise"""
path = config.POSITIVE_DIR + "/" + name
print('Capturing image...')
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!')
return 0
x, y, w, h = result
# Crop image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = face.crop(image, x, y, w, h)
# Save image to file.
filename = os.path.join(path,
const.POSITIVE_FILE_PREFIX + '%03d.pgm' % num)
cv2.imwrite(filename, crop)
print('Found face and wrote training image', filename)
return 1
def capture1():
camera = config.get_camera()
# Create the directory for positive training images if it doesn't exist.
if not os.path.exists(config.POSITIVE_DIR1):
os.makedirs(config.POSITIVE_DIR1)
# Find the largest ID of existing positive images.
# Start new images after this ID value.
files = sorted(glob.glob(os.path.join(config.POSITIVE_DIR1,
POSITIVE_FILE_PREFIX + '[0-9][0-9][0-9].pgm')))
count = 0
if len(files) > 0:
# Grab the count from the last filename.
count = int(files[-1][-7:-4])+1
print 'Capturing image...'
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.'
return False
#if buttons.decideAdd_button():
x, y, w, h = result
# Crop image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = face.crop(image, x, y, w, h)
# Save image to file.
filename = os.path.join(config.POSITIVE_DIR1, POSITIVE_FILE_PREFIX + '%03d.pgm' % count)
cv2.imwrite(filename, crop)
print 'Found face and wrote training image', filename
return True
def CapturePic(self):
files = sorted(
glob.glob(
os.path.join(config.POSITIVE_DIR + ID,
POSITIVE_FILE_PREFIX + '[0-9][0-9][0-9].pgm')))
count = 0
if len(files) > 0:
# Grab the count from the last filename.
count = int(files[-1][-7:-4]) + 1
print 'Capturing positive training images.'
print 'Press button or type c (and press enter) to capture an image.'
print 'Press Ctrl-C to quit.'
while True:
if door.is_button_up() or is_letter_input('c'):
led.LedOn()
print 'Capturing image...'
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 image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = face.crop(image, x, y, w, h)
# Save image to file.
filename = os.path.join(
config.POSITIVE_DIR + ID,
POSITIVE_FILE_PREFIX + '%03d.pgm' % count)
cv2.imwrite(filename, crop)
print 'Found face and wrote training image', filename
led.LedOff()
count += 1
def capture():
camera = config.get_camera()
# Create the directory for positive training images if it doesn't exist.
if not os.path.exists(config.TRAINING_DIR + CAPTURE_DIR):
os.makedirs(config.TRAINING_DIR + CAPTURE_DIR)
# Find the largest ID of existing positive images.
# Start new images after this ID value.
files = sorted(glob.glob(os.path.join(config.TRAINING_DIR + CAPTURE_DIR,
'[0-9][0-9][0-9].pgm')))
count = 0
if len(files) > 0:
# Grab the count from the last filename.
count = int(files[-1][-7:-4])+1
print 'Capturing positive training images.'
print 'Type c (and press enter) to capture an image.'
print 'Press Ctrl-C to quit.'
while True:
# Check if button was pressed or 'c' was received, then capture image.
if is_letter_input('c'):
print 'Capturing image...'
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 image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = face.crop(image, x, y, w, h)
# Save image to file.
filename = os.path.join(config.TRAINING_DIR + CAPTURE_DIR, '%03d.pgm' % count)
cv2.imwrite(filename, crop)
print 'Found face and wrote training image', filename
count += 1
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.')
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)
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"
# 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
if __name__ == '__main__':
result = config.NO_HIT
imgPath = sys.argv[1]
if not imgPath:
sys.exit(0)
image = cv2.imread(imgPath)
if image is None:
sys.exit(0)
# Load training data into model
#model = cv2.createEigenFaceRecognizer()
#model = cv2.createFisherFaceRecognizer()
model = cv2.createLBPHFaceRecognizer()
model.load(config.TRAINING_FILE)
# Convert image to grayscale.
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
cv2.imwrite('gray.jpeg', image)
faces = face.detect_single(image)
if faces is not config.NO_HIT :
result = matchFace(image, faces)
print result
# Initialize camera and box.
camera = config.get_camera()
while True:
# Getting image
image = camera.read()
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
cv2.equalizeHist(gray_image,gray_image)
gray_image = cv2.resize(gray_image,(160,120))
# Time calculation
#t = cv2.getTickCount()
# Human face detection
result = face.detect_single(gray_image,config.HAAR_FACES)
if result is None:
pass
else:
# print 'Human Face found'
x, y, w, h = result
cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,0),2)
cv2.imwrite(config.DEBUG_IMAGE, image)
emailer.sendMail(["*****@*****.**"],
"Message from Your Scarecrow",
"There is someone in the garden",
["capture.jpg"])
#t = cv2.getTickCount() - t
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
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
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(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
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
# 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
if __name__ == '__main__':
result = config.NO_HIT
imgPath = sys.argv[1]
if not imgPath:
sys.exit(0)
image = cv2.imread(imgPath)
if image is None:
sys.exit(0)
# Load training data into model
#model = cv2.createEigenFaceRecognizer()
#model = cv2.createFisherFaceRecognizer()
model = cv2.createLBPHFaceRecognizer()
model.load(config.TRAINING_FILE)
# Convert image to grayscale.
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
cv2.imwrite('gray.jpeg', image)
faces = face.detect_single(image)
if faces is not config.NO_HIT:
result = matchFace(image, faces)
print result
def main():
debut = time.time()
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 * coefW) + x
ny = round(h * coefH) + y
nw = w - round(w * 2 * coefW)
nh = h - round(h * 2 * 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 < 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, " "
image = frame.array
######################################################################################################################
if command == config.STATE_DETECT or command == config.STATE_SEARCH:
# create grayscale version
grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
crop = image.copy()
#cv2.equalizeHist(grayscale, grayscale)
#faces = cascade.detectMultiScale(grayscale, scaleFactor=1.3,minNeighbors=5, minSize=(30, 30), flags = cv2.cv.CV_HAAR_SCALE_IMAGE)face.detect_single(image)
#if len(faces)>0:
# for i in faces:
# x, y, w, h = i
# crop = face.crop(image, x, y, w, h)
result = face.detect_single(grayscale)
if result != None:
x, y, w, h = result
# Crop and resize image to face.
crop = face.resize(face.crop(grayscale, x, y, w, h))
#################################################################################################################
if ALIVE:
if command == config.STATE_SEARCH:
rec_result, data_name, confidence = face_recognize(
crop)
if rec_result:
if confidence <= 45:
lightLed(config.GREEN_LED_PIN)
print 'green'
recordTime(data_name)
def ReC():
Pinlbl = Label(top, text="Insert The Pin Code", font=myfont, width=16)
Pinlbl.grid(row=0, column=3)
Pinlbl.configure(bg='#ff7700')
pinCode = code.get()
print("Pin code: ", pinCode) #Debug
c.execute("SELECT * FROM RFID WHERE ADMIN = '%s'" %
(pinCode)) #PinCode = 711003
if c.rowcount != 1:
print("Access Denied, YOU SHALL NOT PASS!")
else:
if __name__ == '__main__':
camera = config.get_camera()
# Create the directory for positive training images if it doesn't exist.
if not os.path.exists(config.POSITIVE_DIR):
os.makedirs(config.POSITIVE_DIR)
# Find the largest ID of existing positive images.
# Start new images after this ID value.
files = sorted(
glob.glob(
os.path.join(
config.POSITIVE_DIR,
POSITIVE_FILE_PREFIX + '[0-9][0-9][0-9].pgm')))
count = 0
if len(files) > 0:
# Grab the count from the last filename.
count = int(files[-1][-7:-4]) + 1
print 'Capturing positive training images.'
while True:
print 'Capturing image...'
Pinlbl = Label(top,
text="Capturing Image",
font=myfont,
width=16)
Pinlbl.grid(row=0, column=3)
Pinlbl.configure(bg='#ff7700')
GPIO.output(LED, GPIO.HIGH) #Turn off LED
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.'
Pinlbl = Label(top,
text="Waiting Detection Face",
font=myfont,
width=16)
Pinlbl.grid(row=0, column=3)
Pinlbl.configure(bg='#ff7700')
continue
x, y, w, h = result
# Crop image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = face.crop(image, x, y, w, h)
# Save image to file.
filename = os.path.join(
config.POSITIVE_DIR,
POSITIVE_FILE_PREFIX + '%03d.pgm' % count)
cv2.imwrite(filename, crop)
print 'Found face and wrote training image', filename
count += 1
Pinlbl = Label(topwin,
text="Face Detected",
font=myfont,
width=16)
Pinlbl.grid(row=0, column=3)
Pinlbl.configure(bg='#ff7700')
GPIO.output(LED, GPIO.LOW) #Turn off LED
break
def camera():
if __name__ == '__main__':
# Load training data into model
print 'Loading training data...'
Pinlbl = Label(top2,
text="Loading Training Data",
font=myfont,
width=16)
Pinlbl.grid(row=0, column=3)
Pinlbl.configure(bg='#ff7700')
model = cv2.face.EigenFaceRecognizer_create()
model.read(config.TRAINING_FILE)
print 'Training data loaded!'
# Initialize camer and box.
camera = config.get_camera()
# Move box to locked position.
print 'Camera Running'
Pinlbl = Label(top2, text="Camera Running", font=myfont, width=16)
Pinlbl.grid(row=0, column=3)
Pinlbl.configure(bg='#ff7700')
while True:
GPIO.output(LED, GPIO.HIGH) #Turn off LED
# 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.'
Pinlbl = Label(top2,
text="Waiting Detection Face",
font=myfont,
width=12)
Pinlbl.grid(row=0, column=3)
Pinlbl.configure(bg='#ff7700')
else:
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)
config.POSITIVE_THRESHOLD = 5000
if label == config.POSITIVE_LABEL:
print 'Recognized face!'
Pinlbl = Label(top2,
text="Recognize Face",
font=myfont,
width=12)
Pinlbl.grid(row=0, column=3)
Pinlbl.configure(bg='#ff7700')
GPIO.output(Relay, GPIO.HIGH) #Turn on Relay
time.sleep(5) #Wait 5 Seconds
GPIO.output(LED, GPIO.LOW) #Turn off LED
GPIO.output(Relay, GPIO.LOW) #Turn off Relay
break
else:
print 'Did not recognize face!'
Pinlbl = Label(top2,
text="Did Not Recognize Face",
font=myfont,
width=16)
Pinlbl.grid(row=0, column=3)
Pinlbl.configure(bg='#ff7700')
GPIO.output(Buzzer, GPIO.HIGH) #Turn on Relay
time.sleep(5)
GPIO.output(LED, GPIO.LOW) #Turn off LED
break
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 cv2
import config
import face
print 'Loading training data...'
model = cv2.createEigenFaceRecognizer()
model.load(config.TRAINING_FILE)
print 'Training data loaded!'
# Initialize camera and box.
camera = config.get_camera()
while True:
n = raw_input()
print 'press c for taking photo'
if n == 'c':
image = camera.read()
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
result = face.detect_single(image)
if result is None:
print 'Could not detect face'
continue
x, y, w, h = result
crop = face.resize(face.crop(image, x, y, w, h))
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!'
else:
print 'Did not recognize face!'
import cv2
import time
import config
import face
import camera
model = cv2.createEigenFaceRecognizer()
model.load(config.TRAINING_FILE)
print "Model loaded ..."
while True:
filename = config.TEST_FILE
camera.take_pic(filename)
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 ... :("
else:
x, y, w, h = result
crop = face.resize(face.crop(bw_img, x, y, w, h))
label, confidence = model.predict(crop)
print "label ... '%s'" % label, "confidence ... '%s'" % confidence
time.sleep(5)
# Initialize camer and box.
camera = config.get_camera()
personPresent = 0
print "Running Hal 9000 recognition"
print "Press Ctrl-C to quit."
while True:
# Check if capture should be made.
# 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!"
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)
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 mark_attendance(root):
flag = 1
#print "Reading training images...!"
while flag == True :
faces = []
labels = []
names = {}
pos_count = 0
neg_count = 0
id = 1
# Read all positive images
for (subdirs, dirs, files) in os.walk(config.POSITIVE_DIR):
for subdir in dirs:
names[id] = subdir
subjectpath = os.path.join(config.POSITIVE_DIR, subdir)
for filename in os.listdir(subjectpath):
path = subjectpath + '/' + filename
faces.append(prepare_image(path))
label = id
labels.append(int(label))
pos_count += 1
id += 1
# Read all negative images
for filename in walk_files(config.NEGATIVE_DIR, '*.pgm'):
faces.append(prepare_image(filename))
labels.append(config.NEGATIVE_LABEL)
neg_count += 1
# Train model
flag = 0
model = cv2.createEigenFaceRecognizer()
model.train(np.asarray(faces), np.asarray(labels))
# Save model results
model.save(config.TRAINING_FILE)
# Save mean and eignface images which summarize the face recognition model.
mean = model.getMat("mean").reshape(faces[0].shape)
cv2.imwrite(MEAN_FILE, normalize(mean, 0, 255, dtype=np.uint8))
eigenvectors = model.getMat("eigenvectors")
pos_eigenvector = eigenvectors[:,0].reshape(faces[0].shape)
cv2.imwrite(POSITIVE_EIGENFACE_FILE, normalize(pos_eigenvector, 0, 255, dtype=np.uint8))
neg_eigenvector = eigenvectors[:,1].reshape(faces[0].shape)
cv2.imwrite(NEGATIVE_EIGENFACE_FILE, normalize(neg_eigenvector, 0, 255, dtype=np.uint8))
# 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()
c = 0
while c < 1:
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 not label == config.NEGATIVE_LABEL and confidence < config.POSITIVE_THRESHOLD:
tattend=time.asctime(time.localtime(time.time()))
c+=1
name = names[label]
mark(name,tattend)
popup(name)
else:
popuperror()
c+=1