def runCapture(self):
imgdir = self._imagePath
cam = self._cam
cascade = self._cascade
faceSize= (90, 90)
model= cv2.createLBPHFaceRecognizer(threshold=70.0)
images,labels,namess = utils.retrain(imgdir,model,faceSize)
#print "Nouvel etat:",len(images),"images",len(namess),"personnes"
self.isSomebody = False
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#a voir si c'est necessaire
gray = cv2.equalizeHist(gray)
# dectection de visage
rects = cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5, minSize=(40, 40), flags=cv2.cv.CV_HAAR_SCALE_IMAGE) #flags = cv2.CASCADE_SCALE_IMAGE
# ne prend que la region de l'image qui nous interesse (le visage)
roi = None
if len(rects)>0:
(x, y, w, h) = rects[0]
# crop & resize it
roi = cv2.resize( gray[y:y+h, x:x+h], faceSize )
#calcule les coordonnees du rectangle et les affiche
xCentre1=(2*x+w)/2
yCentre1=(2*y+h)/2
xCentre=str(xCentre1)
yCentre=str(yCentre1)
centre="("+xCentre+":"+yCentre+")"
cv2.rectangle(img, (x,y),(x+w,y+h), (0,255,0),2)
if len(images)>0:
[p_label, p_confidence] = model.predict(np.asarray(roi))
name="unknown"
if p_label !=-1 :
name = namess[p_label]
self.isSomebody = True
cv2.putText( img, "%s %.2f %.2f" % (name,p_confidence,p_label),(x+10,y+20), cv2.FONT_HERSHEY_PLAIN,1.5, (0,255,0))
self.name = name
self.xPosition = xCentre1
self.yPosition = yCentre1
while True:
cv2.imshow('Face Recognition For Poppy', img)
k = cv2.waitKey(5) & 0xFF
# quitter avec Echap
if k == 27: break
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
serv_motion = self.add_preload_service('MotionSensor')
self.char_detected = serv_motion.configure_char('MotionDetected')
self.engine = ClassificationEngine("./models/classify.tflite")
self.is_trained = retrain()
self.labels = get_labels()
self.is_running = True
logging.info(self.setup_message())
def run(self):
while self.is_running:
if app_state.last_state == "shutdown":
self.is_running = False
os.system('kill $PPID')
if (app_state.last_state == "run") and self.is_trained:
detection = False
img = camera.returnPIL()
output = self.engine.ClassifyWithImage(img)
print("Detection: ", output[0][0])
if output[0][0] == int(self.labels["detection"]):
detection = True
logging.info("detection triggered")
self._detected(detection)
if app_state.last_state == "retrain":
logging.info("imprinting weights")
self.is_trained = retrain()
self.labels = get_labels()
if self.is_trained:
self.engine = ClassificationEngine(
"./models/classify.tflite")
app_state.last_state = "run"
logging.info("finished imprinting")
else:
app_state.last_state = "collect"
logging.warning(
"could not imprint weights. Please provide enough pictures"
)
if app_state.last_state == "collect_background":
camera.collect("background")
app_state.last_state = "collect"
if app_state.last_state == "collect_detection":
camera.collect("detection")
app_state.last_state = "collect"
def _runCaptureLoop2(self):
print " cliquer sur 'Echap' pour quitter"
print " cliquer sur 'a' pour ajouter une image a la base de donnees"
print " cliquer sur 't' pour retenir le modele"
# si le dossier n'existe pas, on le cree
imgdir = self._imagePath
try:
os.mkdir(imgdir)
except:
pass # le chemin existe deja
# taille des images dans la bdd
# open the webcam
cam = self._cam
if (not cam.isOpened()):
print "camera non detectee!"
sys.exit()
print "camera detectee!."
# load the cascadefile:
cascadePath = self._cascadePath
cascade = cv2.CascadeClassifier(cascadePath)
if (cascade.empty()):
print "aucune cascade precisee!"
sys.exit()
print "cascade:", cascadePath
faceSize = (90, 90)
model = cv2.createLBPHFaceRecognizer(threshold=70.0)
self.compteur = 0
images, labels, namess = utils.retrain(imgdir, model, faceSize)
print "Nouvel etat:", len(images), "images", len(namess), "personnes"
compteur = 0
while compteur < 10:
if self.compteur > 10:
self.isSomebody = False
compteur = 0
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#a voir si c'est necessaire
#gray = cv2.equalizeHist(gray)
# dectection de visage
rects = cascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.cv.CV_HAAR_SCALE_IMAGE
) #flags = cv2.CASCADE_SCALE_IMAGE
# ne prend que la region de l'image qui nous interesse (le visage)
roi = None
for x, y, w, h in rects:
self.isSomebody = True
# crop & resize it
roi = cv2.resize(gray[y:y + h, x:x + h], faceSize)
#calcule les coordonnees du rectangle et les affiche
xCentre1 = (2 * x + w) / 2
yCentre1 = (2 * y + h) / 2
xCentre = str(xCentre1)
yCentre = str(yCentre1)
centre = "(" + xCentre + ":" + yCentre + ")"
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
if len(images) > 0:
[p_label, p_confidence] = model.predict(np.asarray(roi))
name = "unknown"
if p_label != -1:
name = namess[p_label]
cv2.putText(img,
"%s %.2f %.2f" % (name, p_confidence, p_label),
(x + 10, y + 20), cv2.FONT_HERSHEY_PLAIN, 1.5,
(0, 255, 0))
self.name = name
self.xPosition = xCentre1
self.yPosition = yCentre1
break # use only 1st detected
self.compteur += 1
cv2.imshow('Face Recognition For Poppy', img)
k = cv2.waitKey(5) & 0xFF
# quitter avec Echap
if k == 27: break
# on clique sur 'a' et on ajoute la personne a la bdd
if (k == 97) and (roi != None):
print "Entrer le nom de la personne: "
name = sys.stdin.readline().strip('\r').strip('\n')
#creer un dossier pour cette personne
dirname = os.path.join(imgdir, name)
try:
os.mkdir(dirname)
except:
pass #on continue si le dossier existe deja
# on sauvegarde l'image
path = os.path.join(dirname,
"%d.png" % (rand.uniform(0, 10000)))
print "added:", path
cv2.imwrite(path, roi)
# on met a jour le modele
if (k == 116): # 't' pressed
images, labels, namess = utils.retrain(imgdir, model, faceSize)
print "Nouvel etat:", len(images), "images", len(
namess), "personnes"
compteur = compteur + 1
def runCapture(self):
imgdir = self._imagePath
cam = self._cam
cascade = self._cascade
faceSize = (90, 90)
model = cv2.createLBPHFaceRecognizer(threshold=70.0)
images, labels, namess = utils.retrain(imgdir, model, faceSize)
#print "Nouvel etat:",len(images),"images",len(namess),"personnes"
self.isSomebody = False
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#a voir si c'est necessaire
gray = cv2.equalizeHist(gray)
# dectection de visage
rects = cascade.detectMultiScale(
gray,
scaleFactor=1.2,
minNeighbors=5,
minSize=(40, 40),
flags=cv2.cv.CV_HAAR_SCALE_IMAGE) #flags = cv2.CASCADE_SCALE_IMAGE
# ne prend que la region de l'image qui nous interesse (le visage)
roi = None
if len(rects) > 0:
(x, y, w, h) = rects[0]
# crop & resize it
roi = cv2.resize(gray[y:y + h, x:x + h], faceSize)
#calcule les coordonnees du rectangle et les affiche
xCentre1 = (2 * x + w) / 2
yCentre1 = (2 * y + h) / 2
xCentre = str(xCentre1)
yCentre = str(yCentre1)
centre = "(" + xCentre + ":" + yCentre + ")"
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
if len(images) > 0:
[p_label, p_confidence] = model.predict(np.asarray(roi))
name = "unknown"
if p_label != -1:
name = namess[p_label]
self.isSomebody = True
cv2.putText(img,
"%s %.2f %.2f" % (name, p_confidence, p_label),
(x + 10, y + 20), cv2.FONT_HERSHEY_PLAIN, 1.5,
(0, 255, 0))
self.name = name
self.xPosition = xCentre1
self.yPosition = yCentre1
while True:
cv2.imshow('Face Recognition For Poppy', img)
k = cv2.waitKey(5) & 0xFF
# quitter avec Echap
if k == 27: break
def _runCaptureLoop2(self):
print " cliquer sur 'Echap' pour quitter"
print " cliquer sur 'a' pour ajouter une image a la base de donnees"
print " cliquer sur 't' pour retenir le modele"
# si le dossier n'existe pas, on le cree
imgdir=self._imagePath
try:
os.mkdir(imgdir)
except:
pass # le chemin existe deja
# taille des images dans la bdd
# open the webcam
cam = self._cam
if ( not cam.isOpened() ):
print "camera non detectee!"
sys.exit()
print "camera detectee!."
# load the cascadefile:
cascadePath= self._cascadePath
cascade = cv2.CascadeClassifier(cascadePath)
if ( cascade.empty() ):
print "aucune cascade precisee!"
sys.exit()
print "cascade:",cascadePath
faceSize= (90, 90)
model= cv2.createLBPHFaceRecognizer(threshold=70.0)
self.compteur = 0
images,labels,namess = utils.retrain(imgdir,model,faceSize)
print "Nouvel etat:",len(images),"images",len(namess),"personnes"
compteur = 0
while compteur < 10:
if self.compteur > 10:
self.isSomebody = False
compteur = 0
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#a voir si c'est necessaire
#gray = cv2.equalizeHist(gray)
# dectection de visage
rects = cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.cv.CV_HAAR_SCALE_IMAGE) #flags = cv2.CASCADE_SCALE_IMAGE
# ne prend que la region de l'image qui nous interesse (le visage)
roi = None
for x, y, w, h in rects:
self.isSomebody = True
# crop & resize it
roi = cv2.resize( gray[y:y+h, x:x+h], faceSize )
#calcule les coordonnees du rectangle et les affiche
xCentre1=(2*x+w)/2
yCentre1=(2*y+h)/2
xCentre=str(xCentre1)
yCentre=str(yCentre1)
centre="("+xCentre+":"+yCentre+")"
cv2.rectangle(img, (x,y),(x+w,y+h), (0,255,0),2)
if len(images)>0:
[p_label, p_confidence] = model.predict(np.asarray(roi))
name="unknown"
if p_label !=-1 :
name = namess[p_label]
cv2.putText( img, "%s %.2f %.2f" % (name,p_confidence,p_label),(x+10,y+20), cv2.FONT_HERSHEY_PLAIN,1.5, (0,255,0))
self.name = name
self.xPosition = xCentre1
self.yPosition = yCentre1
break # use only 1st detected
self.compteur += 1
cv2.imshow('Face Recognition For Poppy', img)
k = cv2.waitKey(5) & 0xFF
# quitter avec Echap
if k == 27: break
# on clique sur 'a' et on ajoute la personne a la bdd
if (k == 97) and (roi!=None):
print "Entrer le nom de la personne: "
name = sys.stdin.readline().strip('\r').strip('\n')
#creer un dossier pour cette personne
dirname = os.path.join(imgdir,name)
try:
os.mkdir(dirname)
except:
pass #on continue si le dossier existe deja
# on sauvegarde l'image
path=os.path.join(dirname,"%d.png" %(rand.uniform(0,10000)))
print "added:",path
cv2.imwrite(path,roi)
# on met a jour le modele
if (k == 116): # 't' pressed
images,labels,namess = utils.retrain(imgdir,model,faceSize)
print "Nouvel etat:",len(images),"images",len(namess),"personnes"
compteur = compteur + 1
