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 Track(self, img):
mask = self.segmenter.segment(img)
if self.segmenter.moseg.bbox is not None:
x, y, w, h = self.segmenter.moseg.bbox
mask.fill(0)
mask[y:y + h, x:x + w] = True
x, y, w, h = self.tracker.track(
self.segmenter.coseg.converted_image, mask)
# it's possible that there is still motion but that tracking failed
# so make sure backprojection is not all zeros
if self.tracker.backprojection.any():
bbox, (xc, yc) = findBBoxCoM(self.tracker.backprojection)
self.waypts.append((int(xc), int(yc)))
return # success! keep tracking...
# if we got to this point then tracking has failed
if len(self.waypts) > MINWAYPTS and self.callback is not None:
# Find best gesture match
x, y = zip(*[(self.imshape[1] - x, self.imshape[0] - y)
for x, y in self.waypts])
matches = dollar.query(x, y, scale, samplesize, self.template_ds)
score, theta, clsid = matches[0]
if score > self.match_threshold:
ds = self.template_ds[clsid][0]
self.callback((x, y), (ds['x'], ds['y']), score, theta, clsid)
self.waypts = []
return self.Wait
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 Track(self,img):
mask = self.segmenter.segment(img)
if self.segmenter.moseg.bbox is not None:
x,y,w,h = self.segmenter.moseg.bbox
mask.fill(0)
mask[y:y+h,x:x+w] = True
x,y,w,h = self.tracker.track(self.segmenter.coseg.converted_image,mask)
# it's possible that there is still motion but that tracking failed
# so make sure backprojection is not all zeros
if self.tracker.backprojection.any():
bbox,(xc,yc) = findBBoxCoM(self.tracker.backprojection)
self.waypts.append((int(xc),int(yc)))
return # success! keep tracking...
# if we got to this point then tracking has failed
if len(self.waypts) > MINWAYPTS and self.callback is not None:
# Find best gesture match
x,y = zip(*[(self.imshape[1]-x,self.imshape[0]-y) for x,y in self.waypts])
matches = dollar.query(x,y,scale,samplesize,self.template_ds)
score,theta,clsid = matches[0]
if score > self.match_threshold:
ds = self.template_ds[clsid][0]
self.callback((x,y),(ds['x'],ds['y']),score,theta,clsid)
self.waypts = []
return self.Wait
def segment(self, img):
'''
Parameters
----------
img : array_like
Grayscale image
'''
self.bbox = self.com = None
prv, cur, nxt = self._buff + (img, )
T = self.T
bkgnd = self.background
moving = (cv2.absdiff(prv, nxt) > T) & (cv2.absdiff(cur, nxt) > T)
# if any motion was found, attempt to fill in the objects detected
# TODO: needs to work with multiple independent objects
if moving.any():
self.bbox, self.com = findBBoxCoM(moving)
x, y, w, h = self.bbox
motionfill = cv2.absdiff(nxt[y:y + h, x:x + w],
bkgnd[y:y + h, x:x + w]) > self.T0
moving[y:y + h, x:x + w] |= motionfill
# TODO replace boolean indexing with boolean multiply where possible
# Updating threshold depends on current background model
# so always update this before updating background
notmoving = ~moving
T[notmoving] = self.alpha*T[notmoving] \
+ (1-self.alpha)*5*cv2.absdiff(nxt,bkgnd)[notmoving]
# T[moving] = T[moving]
T[T < self.T0] = self.T0
bkgnd[notmoving] = self.alpha * bkgnd[notmoving] + (
1 - self.alpha) * nxt[notmoving]
bkgnd[moving] = nxt[moving]
self._buff = (cur, nxt)
return moving
def segment(self,img):
'''
Parameters
----------
img : array_like
Grayscale image
'''
self.bbox = self.com = None
prv,cur,nxt = self._buff + (img,)
T = self.T
bkgnd = self.background
moving = (cv2.absdiff(prv,nxt) > T) & (cv2.absdiff(cur,nxt) > T)
# if any motion was found, attempt to fill in the objects detected
# TODO: needs to work with multiple independent objects
if moving.any():
self.bbox, self.com = findBBoxCoM(moving)
x,y,w,h = self.bbox
motionfill = cv2.absdiff(nxt[y:y+h,x:x+w],bkgnd[y:y+h,x:x+w]) > self.T0
moving[y:y+h,x:x+w] |= motionfill
# TODO replace boolean indexing with boolean multiply where possible
# Updating threshold depends on current background model
# so always update this before updating background
notmoving = ~moving
T[notmoving] = self.alpha*T[notmoving] \
+ (1-self.alpha)*5*cv2.absdiff(nxt,bkgnd)[notmoving]
# T[moving] = T[moving]
T[T<self.T0] = self.T0
bkgnd[notmoving] = self.alpha*bkgnd[notmoving] + (1-self.alpha)*nxt[notmoving]
bkgnd[moving] = nxt[moving]
self._buff = (cur,nxt)
return moving
