def evaluate(self):
#print(self.players[self.myId]['position'], self.myId, self.whoPlay)
return self.players[self.myId]['hp'] - self.players[
self.opponentId]['hp']
if self.players[self.myId]['hp'] // self.players[
self.myId]['maxHp'] < 0.5:
return (self.players[self.myId]['hp'] - self.players[
self.opponentId]['hp']) * 1000 + lib.getDistance(
self.players[self.myId]['position'],
self.players[self.opponentId]['position'])
else:
return (self.players[self.myId]['hp'] - self.players[
self.opponentId]['hp']) * 1000 - lib.getDistance(
self.players[self.myId]['position'], [8, 8])
def moveMap(center, mp):
moveMap = [center]
for y in range(lib.getMapHeight()):
for x in range(lib.getMapWidth()):
if lib.getDistance([x, y], center) < mp + 1 and lib.getCellContent(
[x, y]) == lib.CELL_EMPTY:
path = lib.getPath([x, y], center)
if path != -1 and len(path) <= mp:
moveMap.append([x, y])
return moveMap
def compute_distances(self):
""" compute distances from the first point to the last point """
current_lat, current_lon = 0.0, 0.0
dist = 0
x, t = 0, 0
tp = Trace_point.objects.filter(trace=self).order_by('time')
for p in tp:
x = lib.getDistance(current_lat, current_lon, p.latitude, p.longitude)
dist = dist + x
p.distance = dist
p.save()
current_lat = p.latitude
current_lon = p.longitude
transaction.commit()
def compute_distances(self):
""" compute distances from the first point to the last point """
current_lat, current_lon = 0.0, 0.0
dist = 0
x, t = 0, 0
tp = Trace_point.objects.filter(trace=self).order_by('time')
for p in tp:
x = lib.getDistance(current_lat, current_lon, p.latitude,
p.longitude)
dist = dist + x
p.distance = dist
p.save()
current_lat = p.latitude
current_lon = p.longitude
transaction.commit()
def get_closest_tracks(tr_id, lat, lon):
"""Renvoie les traces les plus proches du point passé en paramètre
à l'exception de tr_id (trace courante)
Calcul fait sur la base du point "moyen" des traces
"""
#boundbox progressivement agrandie, on s'arrete quand on a plus de 10 traces, on les ordonne par distance puis on renvoie les 10 premiers
boxsize, trs, trsdis = gps.settings.SEARCH_BOX_SIZE, [], []
#TODO optimiser ?
while len(trs) < 10 and boxsize < 2000:
boxsize = boxsize * 2 + gps.settings.SEARCH_BOX_SIZE #on augmente de plus en plus vite
trs = Trace.get_tracks_in_bounds(lat - boxsize, lon - boxsize, lat + boxsize, lon + boxsize)
#recherche des 10 plus proches
#trsdis = [(t,lib.getDistanceAB(lat,lon,t.getFirstPoint().latitude,t.getFirstPoint().longitude)) for t in trs]
for t in trs:
if t.id != tr_id:
avgPt = t.get_avg_lat_lon()
trsdis.append((t, lib.getDistance(lat, lon, avgPt['lat'], avgPt['lon'])))
trs = sorted(trsdis, key=lambda trk: trk[1])[0:10]
return trs
def get_closest_tracks(tr_id, lat, lon):
"""Renvoie les traces les plus proches du point passé en paramètre
à l'exception de tr_id (trace courante)
Calcul fait sur la base du point "moyen" des traces
"""
#boundbox progressivement agrandie, on s'arrete quand on a plus de 10 traces, on les ordonne par distance puis on renvoie les 10 premiers
boxsize, trs, trsdis = gps.settings.SEARCH_BOX_SIZE, [], []
#TODO optimiser ?
while len(trs) < 10 and boxsize < 2000:
boxsize = boxsize * 2 + gps.settings.SEARCH_BOX_SIZE #on augmente de plus en plus vite
trs = Trace.get_tracks_in_bounds(lat - boxsize, lon - boxsize,
lat + boxsize, lon + boxsize)
#recherche des 10 plus proches
#trsdis = [(t,lib.getDistanceAB(lat,lon,t.getFirstPoint().latitude,t.getFirstPoint().longitude)) for t in trs]
for t in trs:
if t.id != tr_id:
avgPt = t.get_avg_lat_lon()
trsdis.append(
(t, lib.getDistance(lat, lon, avgPt['lat'], avgPt['lon'])))
trs = sorted(trsdis, key=lambda trk: trk[1])[0:10]
return trs
def main():
print('---USER 1---')
lib.setWeapon(lib.WEAPON_SIMPLE_GUN)
selfId = lib.getMyId()
selfPos = lib.getCell(selfId)
selfMp = lib.getMp(selfId)
enemyId = lib.getEnemyId()
enemyPos = lib.getCell(enemyId)
enemyMp = lib.getMp(enemyId)
tab = []
OBSTACLES = lib.getObstacles()
# Movemap
selfMoveMap = moveMap(selfPos, selfMp)
#for cell in selfMoveMap:
# lib.mark(cell, 'green')
enemyMoveMap = moveMap(enemyPos, enemyMp)
print(enemyPos, enemyMp, enemyMoveMap, file=sys.stderr)
#for cell in enemyMoveMap:
# lib.mark(cell, 'red')
# Find safe cell / attack cell
canHit = False
bestMove = [[], -1]
for simulated in selfMoveMap:
# Find far cell where we can attack
if lib.getDistance(simulated, enemyPos) <= 5 and lib.getLineOfSight(
simulated, enemyPos):
if lib.getDistance(simulated, enemyPos) >= bestMove[1]:
bestMove = [
simulated.copy(),
lib.getDistance(simulated, enemyPos)
]
canHit = True
if canHit:
lib.mark(bestMove[0], 'blue')
path = lib.getPath(selfPos, bestMove[0])
if path != -1:
for move in path:
lib.moveOn(move)
selfMp -= 1
lib.attackOn(enemyPos)
lib.mark(bestMove[0], 'yellow')
selfPos = bestMove[0]
# Flee on safe cell
canFlee = False
bestFlee = [[], 99999]
if (selfMp > 0):
selfMoveMap = moveMap(selfPos, selfMp)
for selfSimu in selfMoveMap:
safeCell = True
for enemySimu in enemyMoveMap:
print('Simualted (E/S):',
enemySimu,
'/',
selfSimu,
file=sys.stderr)
if lib.getDistance(selfSimu,
enemySimu) <= 5 and lib.getLineOfSight(
selfSimu, enemySimu):
# If user can hit us, then cell isn't safe
safeCell = False
break
if safeCell:
lib.mark(selfSimu, 'green')
canFlee = True
if (bestFlee[1] >= lib.getDistance(selfSimu, enemyPos)):
bestFlee = [[selfSimu.copy()],
lib.getDistance(selfSimu, enemyPos)]
elif (bestFlee[1] == lib.getDistance(selfSimu, enemyPos)):
bestFlee[0].append(selfSimu.copy())
else:
lib.mark(selfSimu, 'red')
if canFlee:
bestFlee[0] = random.choice(bestFlee[0])
lib.mark(bestFlee[0], 'blue')
path = lib.getPath(selfPos, bestFlee[0])
if path != -1:
for move in path:
lib.moveOn(move)
selfMp -= 1
selfPos = bestFlee[0]
def availableMoves(self):
mp = self.players[self.whoPlay]['mp']
x, y = self.players[self.whoPlay]['position']
#print('X/Y', x, y)
path = lib.getPath(list(self.players[self.whoPlay]['position']),
list(self.players[1 - self.whoPlay]['position']))
if path != -1:
moves = [path[0]]
moves[0].append('[FLEE]')
"""if len(path) <= 4:
print('path', path[2])
moves.append[path[max(0, len(path) - 2)]]
moves[1].append('[ATTACK]', self.weapons[lib.WEAPON_SWORD])"""
else:
moves = [list(self.players[self.whoPlay]['position'])]
moves[0].append('[FLEE]')
if self.whoPlay == self.myId:
for weapon in self.weapons:
accessibleCells = [[x, y]]
enemyPos = lib.getCell(1 - self.whoPlay)
weaponRange = weapon['maxRange']
last = [[x, y]]
while mp >= 0:
tmp = []
for cell in last:
if lib.getLineOfSight(
cell, enemyPos) and lib.getDistance(
cell, enemyPos) <= weaponRange:
moves.append(
tuple(cell) + tuple(['[ATTACK]', weapon]))
mp = 0
else:
for dx, dy in [(0, 1), (0, -1), (-1, 0), (1, 0)]:
if lib.getCellContent(
(cell[0] + dx,
cell[1] + dy)) == lib.CELL_EMPTY and not [
cell[0] + dx, cell[1] + dy
] in accessibleCells:
tmp.append([cell[0] + dx, cell[1] + dy])
accessibleCells.append(
[cell[0] + dx, cell[1] + dy])
mp -= 1
last = tmp.copy()
else:
accessibleCells = [[x, y]]
enemyPos = lib.getCell(1 - self.whoPlay)
weaponRange = self.weapons[self.players[self.whoPlay]
['currentWeapon']]['maxRange']
last = [[x, y]]
while mp >= 0:
tmp = []
for cell in last:
if lib.getLineOfSight(cell, enemyPos) and lib.getDistance(
cell, enemyPos) <= weaponRange:
moves.append(
tuple(cell) + tuple([
'[ATTACK]', self.weapons[self.players[
self.whoPlay]['currentWeapon']]
]))
mp = 0
else:
for dx, dy in [(0, 1), (0, -1), (-1, 0), (1, 0)]:
if lib.getCellContent(
(cell[0] + dx,
cell[1] + dy)) == lib.CELL_EMPTY and not [
cell[0] + dx, cell[1] + dy
] in accessibleCells:
tmp.append([cell[0] + dx, cell[1] + dy])
accessibleCells.append(
[cell[0] + dx, cell[1] + dy])
mp -= 1
last = tmp.copy()
# Defensive moves
mp = self.players[self.whoPlay]['mp']
last = [[x, y]]
accessibleCells = [[x, y]]
defMoves = []
while mp > 0:
tmp = []
h = False
if defMoves:
h = True
for cell in last:
for dx, dy in [(0, 1), (0, -1), (-1, 0), (1, 0)]:
if (lib.getCellContent((cell[0] + dx, cell[1] + dy))
== lib.CELL_EMPTY) and not [
cell[0] + dx, cell[1] + dy
] in accessibleCells:
tmp.append([cell[0] + dx, cell[1] + dy])
accessibleCells.append([cell[0] + dx, cell[1] + dy])
if not lib.getLineOfSight([cell[0] + dx, cell[1] + dy],
enemyPos):
if h:
defMoves = []
h = False
defMoves.append(
(cell[0] + dx, cell[1] + dy, '[FLEE]'))
mp -= 1
last = tmp.copy()
for move in defMoves:
moves.append(move)
return moves
def get_matching_segments(self, tr2_id):
""" repérage et TODO: stockage des segments communs entre self et tr2
on passe une tolerance en longueur pour le match (plus c'est élevé plus on tolère de mismatchs
TODO: stockage en base des repérages de segments matchés
TODO: search_small_step est le levier d'amélioration dans le paramétrage
"""
search_big_step = 30 # nombre de points entre 2 tests en recherche grosse maille
search_small_step = 2 # nombre de points entre 2 tests après avoir trouvé le premier match
dist_tolerance = 0.030
min_seg_dist = 0.300 # 300m
mismatch_tolerance = 2 #tolerance de perte de chemin commun en nombre de points
lonlat_delta = 10
seg2_search_length = 100 #range of points to be search further after first match found
def get_matching_points(tp1, tr2_id, excluded_ranges=''):
""" renvoie pour tp1 les points de t2 susceptible de matcher les points de t1
avec un order_num >= à order_min
"""
match = {}
tps = Trace_point.objects.filter(
trace=tr2_id).order_by('order_num')
# if num_min != 0:
# tps = tps.filter(order_num__lt = num_min + seg2_search_length)
#tps = tps.filter(order_num__gt = num_min)
if excluded_ranges != '':
excluded_ranges = 'not (' + excluded_ranges[0:-4] + ')'
tps = tps.extra(where=[excluded_ranges])
tps = tps.extra(where=[
'10000*(abs(' + str(tp1.latitude) + '-latitude)+abs(' +
str(tp1.longitude) + '-longitude)) <' + str(lonlat_delta)
])
tps = tps.order_by('order_num')
# print tps.query
if tps.count() > 0:
min_dist = 1 #on commence avec 1 km
tp2 = tps[0]
for t in tps:
#dist = lib.getDistance(tp1.latitude, tp1.longitude,t.latitude, t.longitude)
dist = lib.getQuickDistance(tp1.latitude, tp1.longitude,
t.latitude, t.longitude)
if dist < min_dist:
min_dist = dist
tp2 = t
if min_dist < dist_tolerance:
match[tp1] = tp2
else:
return {}
return match
tps = Trace_point.objects.filter(trace=self).order_by(
'order_num'
)[::search_big_step] #query with a step equals to tolerance
matches = [] #liste de points matchants
t1_order_num = 0
range_extra = ''
matching_segments = [
] #liste de matches dont la longueur est suffisante
for tp1 in tps:
if tp1.order_num > t1_order_num:
t1_order_num = tp1.order_num
match = get_matching_points(tp1, tr2_id, range_extra)
if match != {}:
# matches.append((match.keys()[0].order_num, match.values()[0][0].order_num))
t1_order_num = t1_order_num - search_big_step
segtps = Trace_point.objects.filter(trace=self).filter(
order_num__gt=t1_order_num)
n_unmatch, first_match_found = 0, 0
#boucle en small step
for tp in segtps[::search_small_step]:
match = get_matching_points(tp, tr2_id, range_extra)
if match == {}:
n_unmatch += 1
if n_unmatch > mismatch_tolerance and first_match_found == 1:
break
else:
matches.append((match.keys()[0].order_num,
match.values()[0].order_num))
n_unmatch, first_match_found = 0, 1
if len(matches) > 0:
start = Trace_point.objects.filter(trace=self).filter(
order_num=matches[0][0])[0]
end = Trace_point.objects.filter(trace=self).filter(
order_num=matches[-1][0])[0]
dist_seg = lib.getDistance(start.latitude,
start.longitude,
end.latitude, end.longitude)
if dist_seg > min_seg_dist:
matching_segments.append(matches)
t1_order_num = matches[-1][
0] + 1 # on reprendra au dernier point matché sur t1
if len(matches) > 0:
range_extra += '(order_num >' + str(
matches[0][1]) + ' and order_num <' + str(
matches[-1][1]) + ') or '
matches = []
return matching_segments
def get_matching_segments_old(self, tr2_id):
""" repérage et TODO: stockage des segments communs entre self et tr2
on passe une tolerance en longueur pour le match (plus c'est élevé plus on tolère de mismatchs
TODO: stockage en base des repérages de segments matchés
TODO: search_small_step est le levier d'amélioration dans le paramétrage
"""
search_big_step = 30 # nombre de points entre 2 tests en recherche grosse maille
search_small_step = 1 # nombre de points entre 2 tests après avoir trouvé le premier match
dist_tolerance = 0.040
min_seg_dist = 0.300 # 300m
mismatch_tolerance = 5 #tolerance de perte de chemin commun en nombre de points
lonlat_delta = 10
seg2_search_length = 100 #range of points to be search further after first match found
#DONE: use excluded ranges instead of excluded lists
def get_matching_points(tp1, tr2_id, num_min=0, exclude_list=[]):
""" renvoie pour tp1 les points de t2 susceptible de matcher les points de t1
avec un order_num >= à order_min
"""
match = {}
tps = Trace_point.objects.filter(trace=tr2_id)
if num_min != 0:
tps = tps.filter(order_num__lt=num_min + seg2_search_length)
if len(exclude_list) > 950:
#handling sqlite limitations (but exclude list should not be so long) i should use excluded_ranges cf previous to do.
# this is artificially introducing a maxlength to the matching segment
return {}
tps = tps.filter(order_num__gt=num_min).exclude(
order_num__in=exclude_list)
tps = tps.extra(where=[
'10000*(abs(' + str(tp1.latitude) + '-latitude)+abs(' +
str(tp1.longitude) + '-longitude)) <' + str(lonlat_delta)
])
tps = tps.order_by('order_num')
# print tps.query
if tps.count() > 0:
min_dist = 1 #on commence avec 1 km
tp2 = tps[0]
for t in tps:
#dist = lib.getDistance(tp1.latitude, tp1.longitude,t.latitude, t.longitude)
dist = lib.getQuickDistance(tp1.latitude, tp1.longitude,
t.latitude, t.longitude)
if dist < min_dist:
min_dist = dist
tp2 = t
if min_dist < dist_tolerance:
match[tp1] = tp2
else:
return {}
return match
tps = Trace_point.objects.filter(
trace=self
)[::search_big_step] #query with a step equals to tolerance
matches = [] #liste de points matchants
t1_order_num = 0
exclude_list = [] #liste des points déjà matchés dans t2=> à exclure
matching_segments = [
] #liste de matches dont la longueur est suffisante
for tp1 in tps:
if tp1.order_num > t1_order_num:
t1_order_num = tp1.order_num
match = get_matching_points(tp1, tr2_id, 0, exclude_list)
if match != {}:
# matches.append((match.keys()[0].order_num, match.values()[0][0].order_num))
t2_min_num = match.values()[0].order_num - search_big_step
t1_order_num = t1_order_num - search_big_step
# try to build a segment ( with first match, i go back search_step points) and find the first matching point
segtps = Trace_point.objects.filter(trace=self).filter(
order_num__gt=t1_order_num)
n_unmatch, first_match_found = 0, 0
#boucle en small step
for tp in segtps[::search_small_step]:
match = get_matching_points(
tp, tr2_id, t2_min_num - search_small_step,
exclude_list)
if match == {}:
n_unmatch += 1
if n_unmatch > mismatch_tolerance and first_match_found == 1:
break
else:
matches.append((match.keys()[0].order_num,
match.values()[0].order_num))
t2_min_num = match.values()[0].order_num
n_unmatch, first_match_found = 0, 1
if len(matches) > 0:
start = Trace_point.objects.filter(trace=self).filter(
order_num=matches[0][0])[0]
end = Trace_point.objects.filter(trace=self).filter(
order_num=matches[-1][0])[0]
dist_seg = lib.getDistance(start.latitude,
start.longitude,
end.latitude, end.longitude)
if dist_seg > min_seg_dist:
matching_segments.append(matches)
if len(matches) > 0:
exclude_list += range(matches[0][1],
matches[-1][1])
matches = []
return matching_segments
def get_matching_segments(self, tr2_id):
""" repérage et TODO: stockage des segments communs entre self et tr2
on passe une tolerance en longueur pour le match (plus c'est élevé plus on tolère de mismatchs
TODO: stockage en base des repérages de segments matchés
TODO: search_small_step est le levier d'amélioration dans le paramétrage
"""
search_big_step = 30# nombre de points entre 2 tests en recherche grosse maille
search_small_step = 2 # nombre de points entre 2 tests après avoir trouvé le premier match
dist_tolerance = 0.030
min_seg_dist = 0.300 # 300m
mismatch_tolerance = 2 #tolerance de perte de chemin commun en nombre de points
lonlat_delta = 10
seg2_search_length = 100 #range of points to be search further after first match found
def get_matching_points(tp1 ,tr2_id, excluded_ranges=''):
""" renvoie pour tp1 les points de t2 susceptible de matcher les points de t1
avec un order_num >= à order_min
"""
match = {}
tps = Trace_point.objects.filter(trace=tr2_id).order_by('order_num')
# if num_min != 0:
# tps = tps.filter(order_num__lt = num_min + seg2_search_length)
#tps = tps.filter(order_num__gt = num_min)
if excluded_ranges != '':
excluded_ranges = 'not (' +excluded_ranges[0:-4] +')'
tps = tps.extra(where=[excluded_ranges])
tps = tps.extra(where=['10000*(abs('+str(tp1.latitude)+'-latitude)+abs('+str(tp1.longitude)+'-longitude)) <'+str(lonlat_delta)])
tps = tps.order_by('order_num')
# print tps.query
if tps.count()>0:
min_dist = 1 #on commence avec 1 km
tp2 = tps[0]
for t in tps:
#dist = lib.getDistance(tp1.latitude, tp1.longitude,t.latitude, t.longitude)
dist = lib.getQuickDistance(tp1.latitude, tp1.longitude,t.latitude, t.longitude)
if dist < min_dist:
min_dist = dist
tp2 = t
if min_dist < dist_tolerance:
match[tp1] = tp2
else:
return {}
return match
tps = Trace_point.objects.filter(trace=self).order_by('order_num')[::search_big_step] #query with a step equals to tolerance
matches = [] #liste de points matchants
t1_order_num = 0
range_extra=''
matching_segments = [] #liste de matches dont la longueur est suffisante
for tp1 in tps:
if tp1.order_num > t1_order_num:
t1_order_num = tp1.order_num
match = get_matching_points(tp1, tr2_id, range_extra)
if match != {}:
# matches.append((match.keys()[0].order_num, match.values()[0][0].order_num))
t1_order_num = t1_order_num-search_big_step
segtps = Trace_point.objects.filter(trace=self).filter(order_num__gt=t1_order_num)
n_unmatch , first_match_found = 0,0
#boucle en small step
for tp in segtps[::search_small_step]:
match = get_matching_points(tp,tr2_id,range_extra)
if match == {}:
n_unmatch += 1
if n_unmatch > mismatch_tolerance and first_match_found==1:
break
else:
matches.append((match.keys()[0].order_num, match.values()[0].order_num))
n_unmatch, first_match_found=0,1
if len(matches) > 0:
start = Trace_point.objects.filter(trace= self).filter(order_num = matches[0][0])[0]
end = Trace_point.objects.filter(trace= self).filter(order_num = matches[-1][0])[0]
dist_seg = lib.getDistance(start.latitude,start.longitude, end.latitude, end.longitude)
if dist_seg > min_seg_dist:
matching_segments.append(matches)
t1_order_num = matches[-1][0] +1 # on reprendra au dernier point matché sur t1
if len(matches) >0:
range_extra += '(order_num >' + str(matches[0][1]) +' and order_num <'+str(matches[-1][1])+') or '
matches = []
return matching_segments
def get_matching_segments_old(self, tr2_id):
""" repérage et TODO: stockage des segments communs entre self et tr2
on passe une tolerance en longueur pour le match (plus c'est élevé plus on tolère de mismatchs
TODO: stockage en base des repérages de segments matchés
TODO: search_small_step est le levier d'amélioration dans le paramétrage
"""
search_big_step = 30 # nombre de points entre 2 tests en recherche grosse maille
search_small_step = 1 # nombre de points entre 2 tests après avoir trouvé le premier match
dist_tolerance = 0.040
min_seg_dist = 0.300 # 300m
mismatch_tolerance = 5 #tolerance de perte de chemin commun en nombre de points
lonlat_delta = 10
seg2_search_length = 100 #range of points to be search further after first match found
#DONE: use excluded ranges instead of excluded lists
def get_matching_points(tp1 ,tr2_id, num_min = 0, exclude_list = []):
""" renvoie pour tp1 les points de t2 susceptible de matcher les points de t1
avec un order_num >= à order_min
"""
match = {}
tps = Trace_point.objects.filter(trace=tr2_id)
if num_min != 0:
tps = tps.filter(order_num__lt = num_min + seg2_search_length)
if len(exclude_list)>950:
#handling sqlite limitations (but exclude list should not be so long) i should use excluded_ranges cf previous to do.
# this is artificially introducing a maxlength to the matching segment
return {}
tps = tps.filter(order_num__gt = num_min).exclude(order_num__in=exclude_list)
tps = tps.extra(where=['10000*(abs('+str(tp1.latitude)+'-latitude)+abs('+str(tp1.longitude)+'-longitude)) <'+str(lonlat_delta)])
tps = tps.order_by('order_num')
# print tps.query
if tps.count()>0:
min_dist = 1 #on commence avec 1 km
tp2 = tps[0]
for t in tps:
#dist = lib.getDistance(tp1.latitude, tp1.longitude,t.latitude, t.longitude)
dist = lib.getQuickDistance(tp1.latitude, tp1.longitude,t.latitude, t.longitude)
if dist < min_dist:
min_dist = dist
tp2 = t
if min_dist < dist_tolerance:
match[tp1] = tp2
else:
return {}
return match
tps = Trace_point.objects.filter(trace=self)[::search_big_step] #query with a step equals to tolerance
matches = [] #liste de points matchants
t1_order_num = 0
exclude_list = [] #liste des points déjà matchés dans t2=> à exclure
matching_segments = [] #liste de matches dont la longueur est suffisante
for tp1 in tps:
if tp1.order_num > t1_order_num:
t1_order_num = tp1.order_num
match = get_matching_points(tp1, tr2_id, 0, exclude_list)
if match != {}:
# matches.append((match.keys()[0].order_num, match.values()[0][0].order_num))
t2_min_num = match.values()[0].order_num-search_big_step
t1_order_num = t1_order_num-search_big_step
# try to build a segment ( with first match, i go back search_step points) and find the first matching point
segtps = Trace_point.objects.filter(trace=self).filter(order_num__gt=t1_order_num)
n_unmatch , first_match_found = 0,0
#boucle en small step
for tp in segtps[::search_small_step]:
match = get_matching_points(tp,tr2_id,t2_min_num-search_small_step,exclude_list)
if match == {}:
n_unmatch += 1
if n_unmatch > mismatch_tolerance and first_match_found==1:
break
else:
matches.append((match.keys()[0].order_num, match.values()[0].order_num))
t2_min_num = match.values()[0].order_num
n_unmatch, first_match_found=0,1
if len(matches) > 0:
start = Trace_point.objects.filter(trace= self).filter(order_num = matches[0][0])[0]
end = Trace_point.objects.filter(trace= self).filter(order_num = matches[-1][0])[0]
dist_seg = lib.getDistance(start.latitude,start.longitude, end.latitude, end.longitude)
if dist_seg > min_seg_dist:
matching_segments.append(matches)
if len(matches) >0: exclude_list += range(matches[0][1],matches[-1][1])
matches = []
return matching_segments
def start():
ret, frame = vc.read()
# Timer for saving strum timings and saving music
start = time.time()
gap = 0
prevStrum = ""
song = []
prevTime = 0.00
elapsed = 0.00
# Motion detection flags
prevPos = 0
direction = 0
up = 0
down = 0
firstframe = 1
while ret:
# Change color space for better detection
hsvframe = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
fingerImages = lib.filterFingers(hsvframe)
cv2.imshow('red', fingerImages[0])
cv2.imshow('green', fingerImages[1])
cv2.imshow('blue', fingerImages[2])
fingerPositions = lib.getPositions(fingerImages)
# Detect the mode of playback
mode = lib.getMode(fingerPositions)
distance = lib.getDistance(fingerPositions)
# Show the mode on screen
cv2.putText(frame, mode , (100, 100), cv2.FONT_HERSHEY_SIMPLEX, 4, 255)
cv2.imshow('preview', frame)
# Find the postion of lower strumming hand
lowerPos = lib.getLowerBlob(hsvframe)
# Motion detection for lower hand
if prevPos != 0:
disp = lowerPos[0][1] - prevPos
direction = direction + disp
if direction < -200:
up = 1
direction = 0
if direction > 200:
down = 1
direction = 0
if firstframe == 1:
firstframe = 0
prevPos = lowerPos[0][1]
# Perform the playback and append the strum in song
if down == 1:
if gap == 1:
elapsed = time.time() - start
song.append([prevStrum, elapsed])
gap == 0
if prevTime != 0.00:
song.append([prevStrum, (elapsed-prevTime)])
else:
song.append([prevStrum, 0.00])
prevTime = elapsed
play.play(lib.getPattern(mode,distance,'down'))
if gap == 0:
start = time.time()
prevStrum = lib.getPattern(mode,distance, 'down')
gap = 1
down = 0
else:
if up == 1:
if gap == 1:
elapsed = time.time() - start
song.append([prevStrum, elapsed])
gap == 0
if prevTime != 0.00:
song.append([prevStrum, (elapsed-prevTime)])
else:
song.append([prevStrum, 0.00])
prevTime = elapsed
play.play(lib.getPattern(mode,distance,'up'))
if gap == 0:
start = time.time()
prevStrum = lib.getPattern(mode,distance,'up')
gap = 1
up = 0
ret, frame = vc.read()
key = cv2.waitKey(20)
if key == 27: #Ends the session and save the song. Press ESCAPE key
play.save('muse', song, 0.05, [0, prevTime, 0.1])
break
