def _applyFPS(self, event):
item = self.app_state.current_item
if (item is None or item.data is None):
return
if (item.type != item.TypePointCloud):
self.app_state.showError(
'This operation can only be applyed to a Point Clouds.')
return
if (len(self.tf_centroid_count.text()) <= 0):
return
count = int(self.tf_centroid_count.text())
rateo = count / item.data.pos.size(0)
if (rateo >= 1):
self.app_state.showError(
'The number of point to select with FPS({}) have to be less than the total number of points({}).'
.format(count, item.data.pos.size(0)))
return
item.data.pos = item.data.pos.double()
idx = FPS(item.data.pos,
torch.zeros(item.data.pos.size(0)).long(), rateo)
new_item = Item(name='{}-FPS'.format(item.name),
data=Data(pos=item.data.pos[idx]),
type=Item.TypePointCloud)
self.app_state.setNewItem(new_item)
def _loadTorch(path):
tensor = torch.load(path)
data = Data(pos=tensor.pos.float)
return Item(data=data,
type=Item.TypePointCloud,
name=os.path.basename(path))
def _applySaveEdit(self, event):
sourceItem = self.app_state.current_item
item = Item(data=sourceItem.data,
name=sourceItem.name,
type=sourceItem.type)
self.app_state.addItem(item)
def _applyFaceImportanceSampling(self, event):
item = self.app_state.current_item
if (item is None or item.data is None):
return
if (item.type != item.TypeMesh):
self.app_state.showError(
'This operation can only be applyed to be a Mesh object.')
return
if (len(self.tf_importance_point_count.text()) <= 0):
return
count = int(self.tf_importance_point_count.text())
item.data.pos = item.data.pos.float()
new_data = SamplePoints(count)(item.data)
new_item = Item(name='{}-ImportanceSamped'.format(item.name),
data=new_data,
type=Item.TypePointCloud)
self.app_state.setNewItem(new_item)
def _applyPoissonDiskSampling(self, event):
item = self.app_state.current_item
if (item is None or item.data is None):
return
if (item.type != item.TypeMesh):
self.app_state.showError(
'This operation can only be applyed to be a Mesh object.')
return
if (len(self.tf_poisson_point_count.text()) <= 0
or len(self.tf_poisson_radius.text()) <= 0):
return
count = int(self.tf_poisson_point_count.text())
radio = float(self.tf_poisson_radius.text())
item.data.pos = item.data.pos.float()
new_data = PoissonDiskSampling(count, radio)(item.data)
new_item = Item(name='{}-Poisson'.format(item.name),
data=new_data,
type=Item.TypePointCloud)
self.app_state.setNewItem(new_item)
def _loadOff(path):
off_obj = Off.OffParser(path)
data = Data(pos=torch.Tensor(off_obj.points).double(),
face=torch.Tensor(off_obj.faces).transpose(0, 1).long())
return Item(data=data, type=Item.TypeMesh, name=os.path.basename(path))