def predict_corners(self, new_corners):
'''
If in new_corners are Nones then this function tries to predict where
not found corners are
@param new_corners:
'''
db.db_break()
new_corners = self._improve_corners(new_corners)
self.validate_corners(new_corners)
corners = filter(lambda x: x is not None, new_corners)
def cmp(x, y):
if x.similarity > y.similarity:
return 1
else: return - 1
corners = sorted(corners, cmp)
self.avg_sim = reduce(lambda x, y: x + y.similarity, corners, 0)
while len(corners) > 0:
ncorners = list(new_corners)
self.avg_sim /= len(corners)
self._predict_corners(ncorners)
if all(ncorners):
if cv.CheckContourConvexity([x.p for x in ncorners]) == 1:
le = len(ncorners)
for i,c in enumerate(ncorners):
n= (i+1)%le
ncorners[n].prev = c.p
c.next = ncorners[n].p
for c in ncorners:c.measure()
return ncorners
last = corners[-1]
new_corners[last.id] = None
self.avg_sim = self.avg_sim * len(corners) - last.similarity
corners.remove(last)
return None
def find_corners(self):
'''
Tries to find corners or to predict them. Return empty list,
if every corner is found, or list of points in the area which tell where
to do futher checking
'''
time = self.m_d.time
if time-self.last_seen >= MAX_SEEN:
return False
#cv.ResetImageROI(draw_img)
ncorners = []
bounds = []
for c in self.corners:
db.db_break("find_corners")
cands = c.get_candidates(self.m_d)
min = NOT_SIMILLAR;
bestcand = None;
for cand in cands:
if cand.similarity < min:
min = cand.similarity;
bestcand = cand
ncorners.append(bestcand)
bounds.extend(cvrect(cv.GetImageROI(self.m_d.bw_img)))
if bestcand is None:
pass
db.pr([ncorners], "find_corners")
for cor in ncorners:
if cor is not None:
cor.draw(self.m_d.tmp_img)
if None in ncorners:
pass
CP = CornerPredictor(self.corners, time, self.m_d)
ncorners = CP.predict_corners(ncorners)
db.pr([ncorners], "new corners")
if ncorners is not None:
points = [x.p for x in ncorners]
correctness = self.code_correctness(points)
if correctness>REQUIRED_CORRECTNESS:
self.set_new_position(ncorners)
return True
else:
db.PolyLine(self.m_d.tmp_img,[points], True, (255,255,255),
"%.2f"%correctness)
self.m_d.borders.extend(CP.bounds)
return False
def find_corners(self):
'''
Tries to find corners or to predict them. Return empty list,
if every corner is found, or list of points in the area which tell where
to do futher checking
'''
time = self.m_d.time
if time - self.last_seen >= MAX_SEEN:
return False
#cv.ResetImageROI(draw_img)
ncorners = []
bounds = []
for c in self.corners:
db.db_break("find_corners")
cands = c.get_candidates(self.m_d)
min = NOT_SIMILLAR
bestcand = None
for cand in cands:
if cand.similarity < min:
min = cand.similarity
bestcand = cand
ncorners.append(bestcand)
bounds.extend(cvrect(cv.GetImageROI(self.m_d.bw_img)))
if bestcand is None:
pass
db.pr([ncorners], "find_corners")
for cor in ncorners:
if cor is not None:
cor.draw(self.m_d.tmp_img)
if None in ncorners:
pass
CP = CornerPredictor(self.corners, time, self.m_d)
ncorners = CP.predict_corners(ncorners)
db.pr([ncorners], "new corners")
if ncorners is not None:
points = [x.p for x in ncorners]
correctness = self.code_correctness(points)
if correctness > REQUIRED_CORRECTNESS:
self.set_new_position(ncorners)
return True
else:
db.PolyLine(self.m_d.tmp_img, [points], True, (255, 255, 255),
"%.2f" % correctness)
self.m_d.borders.extend(CP.bounds)
return False
