def ccs(self,remove_text=True,remove_inside_staffs=True,remove_classified=False):
""" Return the connected components of the image
Choose to get all, or with some parts removed
Keyword arguments:
remove_text --- Remove any thing that looks like text from the list
off ccs
remove_inside_staffs --- Remove all cc's that overlap with the
stafflines.
"""
self.l.debug("remove_text=%s, remove_inside_staffs=%s, remove_classified=%s",\
remove_text,remove_inside_staffs,remove_classified)
c = self.ccs_overall()
ccs = c["all"]
if (remove_text):
inccs = c["text"]
self.l.debug("Removing %d ccs as text",len(inccs))
ccs = ccs_remove(ccs,inccs)
if (remove_inside_staffs):
pre = ccs
ccs = ccs_intersect(ccs,c["outside"])
self.l.debug("Removing %d ccs as inside staffs",(len(pre)-len(ccs)))
if (remove_classified):
clasccs = ret["classified"]
self.l.debug("Removing %d ccs as classified",(len(classcs)))
ccs = ccs_remove(ccs,clasccs)
return ccs
def ccs(self,
remove_text=True,
remove_inside_staffs=True,
remove_classified=False):
""" Return the connected components of the image
Choose to get all, or with some parts removed
Keyword arguments:
remove_text --- Remove any thing that looks like text from the list
off ccs
remove_inside_staffs --- Remove all cc's that overlap with the
stafflines.
"""
self.l.debug("remove_text=%s, remove_inside_staffs=%s, remove_classified=%s",\
remove_text,remove_inside_staffs,remove_classified)
c = self.ccs_overall()
ccs = c["all"]
if (remove_text):
inccs = c["text"]
self.l.debug("Removing %d ccs as text", len(inccs))
ccs = ccs_remove(ccs, inccs)
if (remove_inside_staffs):
pre = ccs
ccs = ccs_intersect(ccs, c["outside"])
self.l.debug("Removing %d ccs as inside staffs",
(len(pre) - len(ccs)))
if (remove_classified):
clasccs = ret["classified"]
self.l.debug("Removing %d ccs as classified", (len(classcs)))
ccs = ccs_remove(ccs, clasccs)
return ccs
def ccs_overall(self):
""" Segment image in inside/outside staves,text,dynamics and return the
ccs for each of these segments as a dict:
return {
'all'
'outside'
'inside'
'text'
'classified'
}
"""
ret = {}
if self._ccs is None:
baseimg = self.without_staves()
ccs = set(baseimg.cc_analysis())
stavey = self.ms().get_staffpos()
if stavey is None:
raise NoStavesException,"No stafflines, no need for anything here. Abort"
cond = inout_staff_condition(self.ms().get_staffpos())
ret["all"] = ccs
ret["outside"] = [ c for c in ccs if not cond(c)]
ret["inside"] = [ c for c in ccs if cond(c)]
assert (len(ret['outside'])+len(ret["inside"]) == len(ccs))
ret["text"] = self._text().possible_text_ccs(image=self.without_insidestaves_info(),ccs=ret["outside"])
self._ccs = ret
else:
self.l.debug("Cached")
ret = self._ccs
if not "classified" in ret and not self.classifier is None:
ret["classified"] = self._classified_ccs(ccs_remove(ret['outside'],ret['text']))
self._ccs = ret
return ret
def ccs_overall(self):
""" Segment image in inside/outside staves,text,dynamics and return the
ccs for each of these segments as a dict:
return {
'all'
'outside'
'inside'
'text'
'classified'
}
"""
ret = {}
if self._ccs is None:
baseimg = self.without_staves()
ccs = set(baseimg.cc_analysis())
stavey = self.ms().get_staffpos()
if stavey is None:
raise NoStavesException, "No stafflines, no need for anything here. Abort"
cond = inout_staff_condition(self.ms().get_staffpos())
ret["all"] = ccs
ret["outside"] = [c for c in ccs if not cond(c)]
ret["inside"] = [c for c in ccs if cond(c)]
assert (len(ret['outside']) + len(ret["inside"]) == len(ccs))
ret["text"] = self._text().possible_text_ccs(
image=self.without_insidestaves_info(), ccs=ret["outside"])
self._ccs = ret
else:
self.l.debug("Cached")
ret = self._ccs
if not "classified" in ret and not self.classifier is None:
ret["classified"] = self._classified_ccs(
ccs_remove(ret['outside'], ret['text']))
self._ccs = ret
return ret
