def run_image_learn(self):
is_manual_train = VARIABLE['hand_border']
self.tree = P2DHMMFactory.make(P2DHMM.P2DHMM_TYPE_TREE)
self.background = P2DHMMFactory.make(P2DHMM.P2DHMM_TYPE_BACKGROUND)
for root, dirs, files in os.walk(VARIABLE['directory']):
for file in files:
if file.endswith('.bmp'):
print(root+"/"+file)
self.piximage = piximage2.PixImage(root+"/"+file)
self.display()
if is_manual_train:
self.tree.manual_train(self.piximage)
else:
self.tree.auto_train(self.piximage.image_ycrcb)
self.tree.summarize()
self.tree.from_summaries()
self.background.train(self.piximage.image_ycrcb)
for ss_state in self.background.model.states:
if ss_state.name == self.background.superstate_names[0]:
for s_state in ss_state.distribution.model.states:
if s_state.name == ss_state.distribution.state_names[0]:
s_state.distribution = self.tree.backgorund_state.distribution
def run_image_denoise(self):
self.tree = P2DHMMFactory.make(P2DHMM.P2DHMM_TYPE_TREE)
self.tree.manual_train(self.piximage)