def is_smiling(img):
"""Given an image (bytes), return True if a mouth is discovered and it is smiling."""
mouth = m.findmouth(img)
# show(mouth)
if mouth != 2: # did not return error
mouthimg = crop(mouth)
cv.SaveImage("webcam-m.jpg", mouthimg)
# predict the captured emotion
result = lr.predict(vectorize("webcam-m.jpg"))
return result == 1
else:
# "failed to detect mouth. Try hold your head straight and make sure there is only one face."
return False
def is_smiling(lr, image):
mouth_image_name = 'temp-mouth.jpg'
mouth = m.findmouth(image)
# show(mouth)
if mouth != 2: # did not return error
mouthimg = crop(mouth)
cv.SaveImage(mouth_image_name, mouthimg)
# predict the captured emotion
result = lr.predict(vectorize(mouth_image_name))
if result == 1:
return 1
else:
return 0
else:
return -1;
def is_smiling(img=None,filename=None):
if img is None:
if filename is None:
raise Exception("You need to specify an img or a filename")
img = open_as_cv(filename)
"""Given an image (bytes), return True if a mouth is discovered and it is smiling."""
mouth = m.findmouth(img)
print "is_smiling: mouth {}".format(mouth)
if mouth != 2: # did not return error
mouthimg = crop(img, mouth)
cv.SaveImage("webcam-m.jpg", mouthimg)
# predict the captured emotion
result = SMILE_LR.predict(vectorize('webcam-m.jpg'))
return (result == 1)
else:
#print "failed to detect mouth. Try hold your head straight and make sure there is only one face."
return False
def do_POST(self):
logging.warning("======= POST STARTED =======")
logging.warning(self.headers)
form = cgi.FieldStorage(
fp=self.rfile,
headers=self.headers,
environ={'REQUEST_METHOD':'POST',
'CONTENT_TYPE':self.headers['Content-Type'],
})
logging.warning("======= POST VALUES =======")
image_base64 = form.getvalue("image")
g = open("out.jpg", "w")
g.write(image_base64.decode('base64'))
g.close()
img = cv.LoadImage("out.jpg") # input image
mouth = m.findmouth(img)
# show(mouth)
if mouth != 2: # did not return error
mouthimg = crop(mouth, img)
cv.SaveImage("webcam-m.jpg", mouthimg)
# predict the captured emotion
result = lr.predict(vectorize('webcam-m.jpg'))
if result == 1:
print "you are smiling! :-) "
ans = "S"
else:
print "you are not smiling :-| "
ans = "F"
else:
ans = "D"
print "failed to detect mouth. Try hold your head straight and make sure there is only one face."
logging.warning("\n")
self.send_response(200, "OK")
self.send_header("Content-type", "application/json")
self.end_headers()
print ans
self.wfile.write("{result:" + ans + "}")
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face.detectMultiScale(gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.cv.CV_HAAR_SCALE_IMAGE)
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
if True:
cv.SaveImage("webcam.jpg", cv.fromarray(frame))
img = cv.LoadImage("webcam.jpg") # input image
mouth = m.findmouth(img)
smile = 0
nosmile = 0
#show(mouth)
if mouth != 2: # did not return error
mouthimg = crop(mouth)
cv.SaveImage("webcam-m.jpg", mouthimg)
# predict the captured emotion
result = lr.predict(vectorize('webcam-m.jpg'))
if result == 1:
print "You are smiling! :-) "
smile = smile + 1
else:
print "You are not smiling :-/ "
nosmile = nosmile + 1
else:
cv.SaveImage("webcam.jpg", cv.fromarray(frame))
print x,y,w,h
img = cv.LoadImage("webcam.jpg")
cropping=img[y-30: y + h+50, x-30: x + w+50]
if(i==1):
cv.SaveImage("crop1.jpg", cropping)
else:
cv.SaveImage("crop2.jpg", cropping)
if(i==1):
img = cv.LoadImage("crop1.jpg")
else:
img= cv.LoadImage("crop2.jpg")
mouth = m.findmouth(img)
if mouth != 2:
mouthimg = crop(mouth)
if(i==1):
cv.SaveImage("webcam-m1.jpg", mouthimg)
else:
cv.SaveImage("webcam-m2.jpg", mouthimg)
if(i==1):
result = lr.predict(vectorize('webcam-m1.jpg'))
else:
result = lr.predict(vectorize('webcam-m2.jpg'))
if result == 1:
print "face",i, ": You are smiling! :-) "
smile=smile+1
phi[idx] = vectorize(PATH + filename)
labels.append(2)'''
"""
training the data with logistic regression
"""
lr = logistic.Logistic(dim)
lr.train(phi, labels)
"""
open webcam and capture images
"""
img = cv2.imread("/home/qbuser/Desktop/4.jpeg") # input image
mouth = mouthdetection.findmouth(img)
# show(mouth)
#if mouth != 2: # did not return error
#mouthimg = crop(mouth)
cv2.imwrite("webcam-m.jpg", mouth)
# predict the captured emotion
result = lr.predict(vectorize('webcam-m.jpg'))
if result == 1:
print "you are smilinggggggggg! :-) "
elif result == 0:
print "you are neutral "
#elif result==2:
print "you are sad"
#else:
# print "failed to detect mouth. Try hold your head straight and make sure there is only one face."
# frame : np array
else:
rval = False
print "\n\n\n\n\npress q to exit"
while rval:
cv2.imshow("preview", frame)
rval, frame = vc.read()
if cv2.waitKey(10) & 0xFF == ord('q'): # exit on ESC
break
else :
t0 = time.time()
face, mouth = m.findmouth(frame, haarFace, haarMouth)
t1 = time.time()
print t1 - t0
try:
(x, y, w, h) = face
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
(x, y, w, h) = mouth
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 1)
except ValueError:
continue
# shows(mouth)
if len(mouth) != 0: # did not return error
# ksize Size of the filter returned.
# sigma Standard deviation of the gaussian envelope.
# theta Orientation of the normal to the parallel stripes of a Gabor function.
# lambd Wavelength of the sinusoidal factor.
# gamma Spatial aspect ratio.
# psi Phase offset.
# ktype Type of filter coefficients. It can be CV_32F or CV_64F .
thets = np.arange(0, np.pi, np.pi/8)
for thet in thets :
kern = cv2.getGaborKernel(ksize = (9, 9), sigma = 3, theta = thet, lambd = 3.13,
gamma = 3, psi = 0, ktype=cv2.CV_32F)
cap = cv2.VideoCapture(video_jordan)
while cap.isOpened():
ret, frame = cap.read()
if frame is not None:
detected_object = mouthdetection.findmouth(frame, haar_face, haar_mouth)[0]
frame = smile_detection.crop(detected_object, frame)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.filter2D(gray, cv2.CV_8UC3, kern)
edges = cv2.Canny(gray,100,100)
cv2.imshow('frame', edges)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
else:
break
