def main():
os.environ['REQUESTS_CA_BUNDLE'] = \
PyInstallerUtils.resourcePath('cacert.pem')
app = wx.App()
luxocator = Luxocator(
PyInstallerUtils.resourcePath('classifier.mat'),
verboseSearchSession=False, verboseClassifier=False)
luxocator.Show()
app.MainLoop()
def main():
app = wx.App()
recognizerPath = PyInstallerUtils.resourcePath(
'recognizers/lbph_human_faces.xml')
cascadePath = PyInstallerUtils.resourcePath(
# Uncomment the next argument for LBP.
#'cascades/lbpcascade_frontalface.xml')
# Uncomment the next argument for Haar.
'cascades/haarcascade_frontalface_alt.xml')
interactiveRecognizer = InteractiveRecognizer(
recognizerPath, cascadePath, title='Interactive Human Face Recognizer')
interactiveRecognizer.Show()
app.MainLoop()
def main():
app = wx.App()
recognizerPath = PyInstallerUtils.resourcePath(
'recognizers/lbph_human_faces.xml')
cascadePath = PyInstallerUtils.resourcePath(
# Uncomment the next argument for LBP.
#'cascades/lbpcascade_frontalface.xml')
# Uncomment the next argument for Haar.
'cascades/haarcascade_frontalface_alt.xml')
interactiveRecognizer = InteractiveRecognizer(
recognizerPath, cascadePath,
title='Interactive Human Face Recognizer')
interactiveRecognizer.Show()
app.MainLoop()
def main():
app = wx.App()
recognizerPath = PyInstallerUtils.resourcePath(
'recognizers/lbph_cat_faces.xml')
cascadePath = PyInstallerUtils.resourcePath(
# Uncomment the next argument for LBP.
#'cascades/lbpcascade_frontalcatface.xml')
# Uncomment the next argument for Haar with basic
# features.
'cascades/haarcascade_frontalcatface.xml')
# Uncomment the next argument for Haar with extended
# features.
#'cascades/haarcascade_frontalcatface_extended.xml')
interactiveRecognizer = InteractiveRecognizer(
recognizerPath, cascadePath,
minNeighbors=8,
title='Interactive Cat Face Recognizer')
interactiveRecognizer.Show()
app.MainLoop()
def main():
app = wx.App()
recognizerPath = PyInstallerUtils.resourcePath(
'recognizers/lbph_cat_faces.xml')
cascadePath = PyInstallerUtils.resourcePath(
# Uncomment the next argument for LBP.
#'cascades/lbpcascade_frontalcatface.xml')
# Uncomment the next argument for Haar with basic
# features.
#'cascades/haarcascade_frontalcatface.xml')
# Uncomment the next argument for Haar with extended
# features.
'cascades/haarcascade_frontalcatface_extended.xml')
interactiveRecognizer = InteractiveRecognizer(
recognizerPath,
cascadePath,
scaleFactor=1.2,
minNeighbors=1,
minSizeProportional=(0.125, 0.125),
title='Interactive Cat Face Recognizer')
interactiveRecognizer.Show()
app.MainLoop()
def main():
humanCascadePath = PyInstallerUtils.resourcePath(
# Uncomment the next argument for LBP.
#'cascades/lbpcascade_frontalface.xml')
# Uncomment the next argument for Haar.
'cascades/haarcascade_frontalface_alt.xml')
humanRecognizerPath = PyInstallerUtils.resourcePath(
'recognizers/lbph_human_faces.xml')
if not os.path.isfile(humanRecognizerPath):
sys.stderr.write('Human face recognizer not trained. Exiting.\n')
return
catCascadePath = PyInstallerUtils.resourcePath(
# Uncomment the next argument for LBP.
#'cascades/lbpcascade_frontalcatface.xml')
# Uncomment the next argument for Haar with basic
# features.
#'cascades/haarcascade_frontalcatface.xml')
# Uncomment the next argument for Haar with extended
# features.
'cascades/haarcascade_frontalcatface_extended.xml')
catRecognizerPath = PyInstallerUtils.resourcePath(
'recognizers/lbph_cat_faces.xml')
if not os.path.isfile(catRecognizerPath):
sys.stderr.write('Cat face recognizer not trained. Exiting.\n')
return
print('What email settings shall we use to send alerts?')
defaultSMTPServer = 'smtp.gmail.com:587'
print('Enter SMTP server (default: %s):' % defaultSMTPServer)
smtpServer = sys.stdin.readline().rstrip()
if not smtpServer:
smtpServer = defaultSMTPServer
print('Enter username:'******'Enter password:'******'')
defaultAddr = '*****@*****.**' % login
print('Enter "from" email address (default: %s):' % defaultAddr)
fromAddr = sys.stdin.readline().rstrip()
if not fromAddr:
fromAddr = defaultAddr
print('Enter comma-separated "to" email addresses (default: %s):' %
defaultAddr)
toAddrList = sys.stdin.readline().rstrip().split(',')
if toAddrList == ['']:
toAddrList = [defaultAddr]
print('Enter comma-separated "c.c." email addresses:')
ccAddrList = sys.stdin.readline().rstrip().split(',')
capture = cv2.VideoCapture(0)
imageWidth, imageHeight = \
ResizeUtils.cvResizeCapture(capture, (1280, 720))
minImageSize = min(imageWidth, imageHeight)
humanDetector = cv2.CascadeClassifier(humanCascadePath)
humanRecognizer = cv2.face.LBPHFaceRecognizer_create()
humanRecognizer.read(humanRecognizerPath)
humanMinSize = (int(minImageSize * 0.25), int(minImageSize * 0.25))
humanMaxDistance = 25
catDetector = cv2.CascadeClassifier(catCascadePath)
catRecognizer = cv2.face.LBPHFaceRecognizer_create()
catRecognizer.read(catRecognizerPath)
catMinSize = (int(minImageSize * 0.125), int(minImageSize * 0.125))
catMaxDistance = 25
while True:
success, image = capture.read()
if image is not None:
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
equalizedGrayImage = cv2.equalizeHist(grayImage)
humanRects = humanDetector.detectMultiScale(equalizedGrayImage,
scaleFactor=1.3,
minNeighbors=4,
minSize=humanMinSize)
if recognizeAndReport(humanRecognizer, grayImage, humanRects,
humanMaxDistance, 'human', smtpServer, login,
password, fromAddr, toAddrList, ccAddrList):
break
catRects = catDetector.detectMultiScale(equalizedGrayImage,
scaleFactor=1.2,
minNeighbors=1,
minSize=catMinSize)
# Reject any cat faces that overlap with human faces.
catRects = GeomUtils.difference(catRects, humanRects)
if recognizeAndReport(catRecognizer, grayImage, catRects,
catMaxDistance, 'cat', smtpServer, login,
password, fromAddr, toAddrList, ccAddrList):
break
def main():
app = wx.App()
configPath = PyInstallerUtils.resourcePath('config.dat')
livingHeadlights = LivingHeadlights(configPath)
livingHeadlights.Show()
app.MainLoop()
def main():
humanCascadePath = PyInstallerUtils.resourcePath(
# Uncomment the next argument for LBP.
#'cascades/lbpcascade_frontalface.xml')
# Uncomment the next argument for Haar.
'cascades/haarcascade_frontalface_alt.xml')
humanRecognizerPath = PyInstallerUtils.resourcePath(
'recognizers/lbph_human_faces.xml')
if not os.path.isfile(humanRecognizerPath):
print >> sys.stderr, \
'Human face recognizer not trained. Exiting.'
return
catCascadePath = PyInstallerUtils.resourcePath(
# Uncomment the next argument for LBP.
#'cascades/lbpcascade_frontalcatface.xml')
# Uncomment the next argument for Haar with basic
# features.
#'cascades/haarcascade_frontalcatface.xml')
# Uncomment the next argument for Haar with extended
# features.
'cascades/haarcascade_frontalcatface_extended.xml')
catRecognizerPath = PyInstallerUtils.resourcePath(
'recognizers/lbph_cat_faces.xml')
if not os.path.isfile(catRecognizerPath):
print >> sys.stderr, \
'Cat face recognizer not trained. Exiting.'
return
capture = cv2.VideoCapture(0)
imageWidth, imageHeight = \
ResizeUtils.cvResizeCapture(capture, (1280, 720))
minImageSize = min(imageWidth, imageHeight)
humanDetector = cv2.CascadeClassifier(humanCascadePath)
humanRecognizer = cv2.createLBPHFaceRecognizer()
humanRecognizer.load(humanRecognizerPath)
humanMinSize = (int(minImageSize * 0.25), int(minImageSize * 0.25))
humanMaxDistance = 25
catDetector = cv2.CascadeClassifier(catCascadePath)
catRecognizer = cv2.createLBPHFaceRecognizer()
catRecognizer.load(catRecognizerPath)
catMinSize = (int(minImageSize * 0.125), int(minImageSize * 0.125))
catMaxDistance = 25
while True:
success, image = capture.read()
if image is not None:
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
equalizedGrayImage = cv2.equalizeHist(grayImage)
humanRects = humanDetector.detectMultiScale(
equalizedGrayImage,
scaleFactor=1.3,
minNeighbors=4,
minSize=humanMinSize,
flags=cv2.cv.CV_HAAR_SCALE_IMAGE)
if recognizeAndReport(humanRecognizer, grayImage, humanRects,
humanMaxDistance, 'human'):
break
catRects = catDetector.detectMultiScale(
equalizedGrayImage,
scaleFactor=1.2,
minNeighbors=1,
minSize=catMinSize,
flags=cv2.cv.CV_HAAR_SCALE_IMAGE)
# Reject any cat faces that overlap with human faces.
catRects = GeomUtils.difference(catRects, humanRects)
if recognizeAndReport(catRecognizer, grayImage, catRects,
catMaxDistance, 'cat'):
break