class Selfie(object):
def __init__(self):
self.picture = Picture()
def share(self):
self.picture.capture()
picturepath = self.picture.path()
twythonTimelineSet("#NuupXe IoTPy Selfie Project!", picturepath)
class Selfie(object):
def __init__(self):
self.picture = Picture()
def share(self):
self.picture.capture()
picturepath = self.picture.path()
twythonTimelineSet("#NuupXe IoTPy Selfie Project!", picturepath)
class Selfie(object):
def __init__(self):
self.picture = Picture()
def share(self):
self.picture.capture()
picturepath = self.picture.path()
id = str(randint(0,99))
twythonTimelineSet("0x" + id + " #IoT #IoTLearningInit #IoTLearningInitiative IoTPy Selfie Project!", picturepath)
class Faces(object):
def __init__(self):
self.picture = Picture()
self.imageinput = None
self.cascPath = "libraries/haarcascade_frontalface_alt.xml"
self.imageoutput = "output/out.jpeg"
def execute(self):
faceCascade = cv2.CascadeClassifier(self.cascPath)
image = cv2.imread(self.imageinput)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.cv.CV_HAAR_SCALE_IMAGE)
print "Found {0} faces!".format(len(faces))
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.waitKey(0)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(image, 'IoT Lab!', (10, 100), font, 4, (255, 255, 255), 2)
cv2.imwrite(self.imageoutput, image)
return len(faces)
def share(self):
command = [
'libraries/voicerss.sh', 'es-mx',
"Hola! Buscare identificar el numero de personas en la foto! Listos? Comenzamos!"
]
proc = subprocess.call(command)
self.picture.capture()
self.imageinput = self.picture.path()
faces = self.execute()
messagefaces = "Segun mi algoritmo, hay {0} personas en la foto".format(
faces)
command = ['libraries/voicerss.sh', 'es-mx', messagefaces]
proc = subprocess.call(command)
class Faces(object):
def __init__(self):
self.picture = Picture()
self.imageinput = None
self.cascPath = "libraries/haarcascade_frontalface_alt.xml"
self.imageoutput = "output/out.jpeg"
def execute(self):
faceCascade = cv2.CascadeClassifier(self.cascPath)
image = cv2.imread(self.imageinput)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.cv.CV_HAAR_SCALE_IMAGE
)
print "Found {0} faces!".format(len(faces))
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.waitKey(0)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(image, "IoT Lab!", (10, 100), font, 4, (255, 255, 255), 2)
cv2.imwrite(self.imageoutput, image)
return len(faces)
def share(self):
command = [
"libraries/voicerss.sh",
"es-mx",
"Hola! Buscare identificar el numero de personas en la foto! Listos? Comenzamos!",
]
proc = subprocess.call(command)
self.picture.capture()
self.imageinput = self.picture.path()
faces = self.execute()
messagefaces = "Segun mi algoritmo, hay {0} personas en la foto".format(faces)
command = ["libraries/voicerss.sh", "es-mx", messagefaces]
proc = subprocess.call(command)
def __init__(self):
self.picture = Picture()
def __init__(self):
self.picture = Picture()
def __init__(self):
self.picture = Picture()
self.imageinput = None
self.cascPath = "libraries/haarcascade_frontalface_alt.xml"
self.imageoutput = "output/out.jpeg"
def __init__(self):
self.picture = Picture()
self.imageinput = None
self.cascPath = "libraries/haarcascade_frontalface_alt.xml"
self.imageoutput = "output/out.jpeg"
