def priors_of_wall():
rj = request.json
res = {'object': [], 'mapping': {}, 'coarseSemantic': {}}
if 'auxiliaryWallObj' in rj:
res_prev = rj['auxiliaryWallObj']
res['emptyChoice'] = res_prev['emptyChoice']
else:
res_prev = res.copy()
res['emptyChoice'] = '781'
res['object'].append(res['emptyChoice'])
res['categoryCodec'] = categoryCodec
for obj in rj['objList']:
if 'key' not in obj:
continue
if obj['key'] in res_prev['mapping']:
res['mapping'][obj['key']] = res_prev['mapping'][obj['key']]
continue
_mageAddW = wallRelation[getobjCat(obj['modelId'])]['_mageAddWall']
if len(_mageAddW) == 0:
res['mapping'][obj['key']] = 'null'
continue
else:
res['mapping'][obj['key']] = random.choice(objListCat[random.choice(_mageAddW)])
# filling objects to be loaded to the front-end;
for thekey in res['mapping']:
if res['mapping'][thekey] not in res['object']:
if res['mapping'][thekey] == 'null':
continue
res['object'].append(res['mapping'][thekey])
for newobjname in res['object']:
res['coarseSemantic'][newobjname] = getobjCat(newobjname)
return json.dumps(res)
def keyObjectKeyFunction(obj):
if obj is None:
return -1
if 'modelId' not in obj:
return -1
cat = getobjCat(obj['modelId'])
if cat == "Unknown Category" or cat not in res_ratio_dom:
return -1
return res_ratio_dom[cat][obj['roomType']]
def mageAddSwapInstance():
insname = request.json['insname']
existList = request.json['existList']
inscat = getobjCat(insname)
# a special mechanism for wall objects and categories with only one object;
if(len(objListCat[inscat]) <= 1):
wallalternative = ["313", "781", "124", "633"]
newinsname = insname
if insname in wallalternative:
while newinsname == insname:
newinsname = random.choice(wallalternative)
return newinsname
# the following algorithm alleviate both IO and user satisfaction;
insindex = existList.index(insname)
while insindex < len(existList):
existi = existList[insindex]
insindex += 1
if inscat == getobjCat(existi) and insname != existi:
return existi
# if being executed till then, means we do not find another newinsname;
global SWAP_RESTART
if SWAP_RESTART:
for existi in existList:
if inscat == getobjCat(existi) and insname != existi:
SWAP_RESTART = not SWAP_RESTART
return existi
else:
newinsname = random.choice(objListCat[inscat])
while newinsname == insname:
newinsname = random.choice(objListCat[inscat])
SWAP_RESTART = not SWAP_RESTART
return newinsname
# if being executed till then, means we still dont find another newinsname;
newinsname = random.choice(objListCat[inscat])
while newinsname == insname:
newinsname = random.choice(objListCat[inscat])
SWAP_RESTART = not SWAP_RESTART
return newinsname
def patternChain(pri, secs, tier={'238': 0, '153': 0, "235": 0}):
print(f'start to generate pattern chain: {pri} & {secs}')
for s in secs:
if s not in tier:
tier[s] = 1
secs.sort()
ps = []
bbs = []
lsfornextlevel = []
for sec in secs:
with open('./latentspace/pos-orient-4/{}.json'.format(pri)) as f:
p = np.array(json.load(f)[getobjCat(sec)])
ps.append(p)
with open(f'./dataset/object/{sec}/{sec}-4p.json') as f:
bbs.append(np.array(json.load(f)))
lsfornextlevel.append(range(len(p)))
res = []
# vis_patches = []
for i in range(100):
# Create figure and axes
# vis_patches = []
r = checkbb(bbs.copy(), ps.copy(), secs.copy(), lsfornextlevel.copy(),
tier.copy())
checkr = deepcopy(r)
try:
for item in secs:
checkr[item].pop()
except Exception:
continue
if i % 50 == 0:
print(r)
res.append(r)
print(f'A new hyper-relation is generated - {pri} & {secs}')
if len(res) == 0:
return
with open(
'./latentspace/pos-orient-3/hyper/{}-{}.json'.format(
pri, '_'.join(secs)), 'w') as f:
json.dump(res, f)
def priors_of_objlist():
if request.method == 'GET':
return "Please refer to POST method for acquiring priors. "
# indexIndicator = 0
# 'existPair': ['i_dom-c_sec': 'i_sec']
res = {'prior': [], 'index': [], 'object': [], 'existPair': {}, 'belonging': [], 'coarseSemantic': {}, 'catMask': []}
room_json = request.json
if 'auxiliarySecObj' in room_json:
aso = room_json['auxiliarySecObj']
else:
aso = res.copy()
# note that we currently do not consider a dominant object with two copies;
instancePairCount = {}
for obj in room_json['objList']:
if 'mageAddDerive' not in obj:
continue
if obj['mageAddDerive'] == "":
continue
if obj['mageAddDerive'] not in instancePairCount:
instancePairCount[obj['mageAddDerive']] = 1
else:
instancePairCount[obj['mageAddDerive']] += 1
for obj in room_json['objList']: # for each existing object:
if obj is None:
continue
if 'modelId' not in obj:
continue
ppri = f'./latentspace/pos-orient-4/{obj["modelId"]}.json'
if os.path.exists(ppri):
with open(ppri) as f:
pri = json.load(f)
else:
continue
for c_sec in pri:
# e.g., Loveseat Sofa;
if c_sec not in categoryRelation[getobjCat(obj['modelId'])]:
continue
pairid = f'{obj["modelId"]}-{c_sec}'
if pairid in aso['existPair']:
objname = aso['existPair'][pairid]
else:
if 'share' in categoryRelation[getobjCat(obj['modelId'])][c_sec]:
objname = random.choice(objListCat[
random.choice(categoryRelation[getobjCat(obj['modelId'])][c_sec]['share'])
])
# print(f'selected: {objname} of {getobjCat(objname)}')
else:
objname = random.choice(objListCat[c_sec])
pairInsid = f'{obj["modelId"]}-{objname}'
res['existPair'][pairid] = objname
if objname not in res['object']:
res['object'].append(objname)
if pairInsid in instancePairCount and 'max' in categoryRelation[getobjCat(obj['modelId'])][c_sec]:
if instancePairCount[pairInsid] >= categoryRelation[getobjCat(obj['modelId'])][c_sec]['max']:
continue
# the following priors are involved in real-time calculation;
res['prior'] += priorTransform(pri[c_sec], obj['translate'], obj['orient'], obj['scale'])
res['index'] += np.full(len(pri[c_sec]), objname).tolist()
res['catMask'] += np.full(len(pri[c_sec]), categoryCodec[getobjCat(objname)]).tolist()
res['belonging'] += np.full(len(pri[c_sec]), obj["modelId"]).tolist()
# for objname in pri:
# if objname not in res['object']:
# res['object'].append(objname)
# res['prior'] += priorTransform(pri[objname], obj['translate'], obj['orient'], obj['scale'])
# res['index'] += np.full(len(pri[objname]), objname).tolist()
# np.arange(indexIndicator, indexIndicator + len(pri[objname])).tolist()
# indexIndicator = indexIndicator + len(pri[objname])
# print(instancePairCount)
for newobjname in res['object']:
res['coarseSemantic'][newobjname] = getobjCat(newobjname)
return json.dumps(res)
def priors_of_roomShape():
rj = request.json
existingCatList = []
for obj in rj['objList']:
if obj is None:
continue
if 'modelId' not in obj:
continue
if obj['modelId'] not in objCatList:
continue
if len(objCatList[obj['modelId']]) == 0:
continue
if objCatList[obj['modelId']][0] not in existingCatList:
existingCatList.append(objCatList[obj['modelId']][0])
existingPendingCatList = existingCatList.copy()
res = {'object': [], 'prior': [], 'index': [], 'coarseSemantic': {}, 'catMask': []}
if 'auxiliaryDomObj' in rj:
if 'heyuindex' in rj['auxiliaryDomObj']:
res['heyuindex'] = rj['auxiliaryDomObj']['heyuindex']
for objname in rj['auxiliaryDomObj']['object']:
if objCatList[objname][0] not in existingPendingCatList:
existingPendingCatList.append(objCatList[objname][0])
# print(existingCatList)
# print(existingPendingCatList)
# load and process room shapes;
room_meta = p2d('.', f'/dataset/room/{rj["origin"]}/{rj["modelId"]}f.obj')
room_meta = room_meta[:, 0:2]
wallSecIndices = np.arange(1, len(room_meta)).tolist() + [0]
res['room_meta'] = room_meta.tolist()
rv = room_meta[:] - room_meta[wallSecIndices]
normals = rv[:, [1,0]]
normals[:, 1] = -normals[:, 1]
res['room_orient'] = np.arctan2(normals[:, 0], normals[:, 1]).tolist()
res['room_oriNormal'] = normals.tolist()
# room_polygon = Polygon(room_meta[:, 0:2]) # requires python library 'shapely'
# currently, we hack few available coherent groups...
roomTypeSuggestedList = []
categoryList = []
for rt in rj['roomTypes']:
if 'heyuindex' not in res:
res['heyuindex'] = np.random.randint(len(roomTypeDemo[rt]))
categoryList += roomTypeDemo[rt][res['heyuindex']]
break
for cat in categoryList:
if cat in existingCatList:
continue
roomTypeSuggestedList.append(random.choice(objListCat[cat]))
if 'auxiliaryDomObj' not in rj: # if this is the first time calling this function for the pending room...
res['object'] = roomTypeSuggestedList
else:
# re-fuel the pending object list;
for newobjname in roomTypeSuggestedList:
if objCatList[newobjname][0] in existingPendingCatList:
continue
rj['auxiliaryDomObj']['object'].append(newobjname)
res['object'] = rj['auxiliaryDomObj']['object'].copy()
for objname in rj['auxiliaryDomObj']['object']:
# if the specific instance is already in the room;
if objCatList[objname][0] in existingCatList:
res['object'].remove(objname)
if len(res['object']) == 0: # if a recommendation list is full:
pass
# load wall priors;
for obj in res['object']:
with open(f'./latentspace/wdot-4/{obj}.json') as f:
wallpri = json.load(f)
res['prior'] += wallpri
res['index'] += np.full(len(wallpri), obj).tolist()
res['catMask'] += np.full(len(wallpri), categoryCodec[getobjCat(obj)]).tolist()
for newobjname in res['object']:
res['coarseSemantic'][newobjname] = getobjCat(newobjname)
return json.dumps(res)