def __init__(self,
prev,
curr,
fusion_params={},
skin_params={},
motion_params={}):
super(self.__class__, self).__init__(self.segment, **fusion_params)
self.coseg = GaussianSkinSegmenter(model_params=skin_params)
self.moseg = MotionSegmenter(prev, curr, model_params=motion_params)
self.bbox = None
self.com = None
self.backprojection = None
self.skin = None
self.motion = None
class SkinMotionSegmenter(Processor):
'''
Blends skin and motion segmentation together to get more accurate
segmentation
Parameters
----------
alpha : float
Skin-to-motion blending parameter; i.e., fusion = w*skin + (1-w)*motion
'''
def __init__(self,
prev,
curr,
fusion_params={},
skin_params={},
motion_params={}):
super(self.__class__, self).__init__(self.segment, **fusion_params)
self.coseg = GaussianSkinSegmenter(model_params=skin_params)
self.moseg = MotionSegmenter(prev, curr, model_params=motion_params)
self.bbox = None
self.com = None
self.backprojection = None
self.skin = None
self.motion = None
def segment(self, img):
self.bbox = self.com = None
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
self.motion = self.moseg.segment(gray)
self.skin = self.coseg.segment(img)
if self.moseg.bbox is None:
self.backprojection = np.zeros_like(self.motion, dtype=float)
return self.motion
x, y, w, h = self.moseg.bbox
self.backprojection = np.zeros_like(self.coseg.backprojection)
self.backprojection[y:y+h,x:x+w] = self.coseg.backprojection[y:y+h,x:x+w] \
/ self.coseg.backprojection[y:y+h,x:x+w].max()
self.backprojection[y:y + h, x:x + w] *= self.alpha
self.backprojection[y:y + h,
x:x + w] += (1 - self.alpha) * self.motion[y:y + h,
x:x + w]
fused = np.zeros_like(gray, dtype=bool)
fused[y:y + h, x:x +
w] = self.skin[y:y + h, x:x + w] | self.motion[y:y + h, x:x + w]
if fused.any():
self.bbox, self.com = findBBoxCoM(fused)
return fused
def __init__(self,prev,curr,fusion_params={},skin_params={},motion_params={}):
super(self.__class__, self).__init__(self.segment,**fusion_params)
self.coseg = GaussianSkinSegmenter(model_params=skin_params)
self.moseg = MotionSegmenter(prev,curr,model_params=motion_params)
self.bbox = None
self.com = None
self.backprojection = None
self.skin = None
self.motion = None
class SkinMotionSegmenter(Processor):
'''
Blends skin and motion segmentation together to get more accurate
segmentation
Parameters
----------
alpha : float
Skin-to-motion blending parameter; i.e., fusion = w*skin + (1-w)*motion
'''
def __init__(self,prev,curr,fusion_params={},skin_params={},motion_params={}):
super(self.__class__, self).__init__(self.segment,**fusion_params)
self.coseg = GaussianSkinSegmenter(model_params=skin_params)
self.moseg = MotionSegmenter(prev,curr,model_params=motion_params)
self.bbox = None
self.com = None
self.backprojection = None
self.skin = None
self.motion = None
def segment(self,img):
self.bbox = self.com = None
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
self.motion = self.moseg.segment(gray)
self.skin = self.coseg.segment(img)
if self.moseg.bbox is None:
self.backprojection = np.zeros_like(self.motion,dtype=float)
return self.motion
x,y,w,h = self.moseg.bbox
self.backprojection = np.zeros_like(self.coseg.backprojection)
self.backprojection[y:y+h,x:x+w] = self.coseg.backprojection[y:y+h,x:x+w] \
/ self.coseg.backprojection[y:y+h,x:x+w].max()
self.backprojection[y:y+h,x:x+w] *= self.alpha
self.backprojection[y:y+h,x:x+w] += (1-self.alpha)*self.motion[y:y+h,x:x+w]
fused = np.zeros_like(gray,dtype=bool)
fused[y:y+h,x:x+w] = self.skin[y:y+h,x:x+w] | self.motion[y:y+h,x:x+w]
if fused.any():
self.bbox,self.com = findBBoxCoM(fused)
return fused
