class Mood_Detect():
def __init__(self, nose_cascade = None, face_cascade = None, eye_cascade = None, sample = None):
self.nose = Image_X(nose_cascade)
self.face = Image_X(face_cascade)
self.eye = Image_X(eye_cascade)
self.nose_casc = nose_cascade
self.face_casc = face_cascade
self.eye_casc = eye_cascade
val = self.GetDimensions(cv2.imread(sample))
self.dimensions = val['dimensions']
self.dist = val['distance']
def GetImage(self, port):
'''
When two eyes and face are detected, capture the image
'''
cam_port = port
cam = cv2.VideoCapture(port)
while True:
ret, image = cam.read()
face = self.identify(self.face_casc, image)
if len(face) is 1:
for x in face:
if len(self.identify(self.eye_casc, image[x[1]:x[1]+x[3], x[0]:x[0]+x[2]])) is 2:
del(cam)
return self.crop(image)
def GetDimensions(self, img):
val = self.GetCentre(img)
centre = val['centre']
#return (up, down, left, right)
try:
return {'dimensions' : (img.shape[1] - centre[1], centre[1], centre[0], img.shape[0] - centre[0]),
'distance' : val['dist']}
except:
return None
def GetCentre(self, img):
#print img
eye = self.GetEyes_haar(img)
return {'centre' : ((eye[0][0]+eye[1][0])/2., eye[0][1]), 'dist' : abs(eye[0][0]-eye[1][0])}
def IdError(self, img):
face = self.identify(self.face_casc, img)
if len(face) is not 1: raise Exception('Error in detection')
for (x, y, w, h) in face:
return img[y:y+h , x:x+w]
def identify(self, cascade, image):
Cascade = cv2.CascadeClassifier(cascade)
try:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
except:
gray = image
result = []
objects = Cascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags = cv2.cv.CV_HAAR_SCALE_IMAGE
)
print "Found {0} objects!".format(len(objects))
for parameters in objects:
result.append(parameters)
return result
def GetAngle(self, corners):
tangent = (corners[0][1] - corners[1][1])/float(corners[0][0] - corners[1][0])
angle = math.degrees(math.atan(tangent))
return angle
def Resize(self, img):
#print img
dist = self.GetCentre(img)['dist']
print dist
scaleFactor = self.dist/float(dist)
print scaleFactor
return cv2.resize(img, (0,0), fx = scaleFactor, fy = scaleFactor)
def GetEyes(self, img):
'''Use Shi-Tomasi corner detection'''
try:
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
except:
gray = img
result = []
eyes = self.identify(self.eye_casc, img)
if len(eyes) is 2:
for (x, y, w, h) in eyes:
corners = cv2.goodFeaturesToTrack(gray[y:y+h , x:x+w],1,0.01,10)
corners = np.int0(corners)
for i in corners:
u,v = i.ravel()
result.append((u+x, v+y))
return result
def GetEyes_haar(self, img):
#print img
try:
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
except:
gray = img
result = []
for (x, y, w, h) in self.identify(self.eye_casc, img):
result.append((x+w/2.,y+h/2.))
return result
def AlignEyes(self, img):
return self.eye.rotateImage(img, self.GetAngle(self.GetEyes_haar(img)))
def alignFace(self, img):
'''
x -> left
y -> down
w -> right + left
h -> up + down
'''
image = self.Resize(self.AlignEyes(img))
#print image
centre = self.GetCentre(image)['centre']
dim = self.dimensions
try:
return image, (centre[0] - dim[2],
centre[1] - dim[1],
dim[2] + dim[3],
dim[0] + dim[1])
except:
return image, None
def crop(self, image):
image, x = self.alignFace(image)
return image[x[1]:x[1]+x[3] , x[0]:x[0]+x[2]]
'''def detect(self,img,face_no):