def get_distribution(self, save=True, force_computation=False):
""" Distribution of the frame, computed if needed, can be slow. """
if force_computation or not self._distribution:
distribution = ShapeDistribution.compute(self.pointcloud)
if save:
self._distribution = distribution
self.save()
return distribution
else:
return self._distribution
def get_distribution(self, save=True, force_computation=False):
""" Distribution of the frame, computed if needed, can be slow. """
if force_computation or not self._distribution:
distribution = ShapeDistribution.compute(self.pointcloud)
if save:
self._distribution = distribution
self.save()
return distribution
else:
return self._distribution
def setUp(self):
""" Run before starting testing. """
# TODO : use fixtures
test_model = SketchupModel()
test_model.google_id = "test1"
test_model.tags = ["tag1", "tag2"]
test_model.title = "title1"
test_model.text = "Description of 'title1' SketchupModel."
test_model.mesh = file("sketchup_models/fixtures/mesh_can.tri").read()
test_model.save()
self.test_model = SketchupModel.find_google_id("test1")
self.view = PartialView(model=self.test_model, theta=0.0, phi=0.0)
self.distribution = ShapeDistribution.compute(self.view.pointcloud)
def setUp(self):
""" Run before starting testing. """
# TODO : use fixtures
test_model = SketchupModel()
test_model.google_id = "test1"
test_model.tags = ["tag1", "tag2"]
test_model.title = "title1"
test_model.text = "Description of 'title1' SketchupModel."
test_model.mesh = file("sketchup_models/fixtures/mesh_can.tri").read()
test_model.save()
self.test_model = SketchupModel.find_google_id("test1")
self.view = PartialView(model=self.test_model, theta=0.0, phi=0.0)
self.distribution = ShapeDistribution.compute(self.view.pointcloud)
def identify_with_proba(self, data):
""" Return the category of the Pointcloud or Distribution object. """
if not self.dict_categories:
raise IndexError("Identifier is empty.")
if len(self.dict_categories) == 1:
# Only 1 category return it directly
return (self.dict_categories.keys()[0], 1.0)
if type(data) is PointCloud:
data = ShapeDistribution.compute(data)
if type(data) is ShapeDistribution:
data = data.as_numpy_array
result_proba = None
try:
result_proba = self.classifier.decision_function(data)
except AttributeError:
pass
try:
result_idx = int(self.classifier.predict(data)[0])
result_name = self.dict_categories.keys()[result_idx]
except ValueError:
result_name = 'Unknown'
return (result_name, result_proba)
def identify_with_proba(self, data):
""" Return the category of the Pointcloud or Distribution object. """
if not self.dict_categories:
raise IndexError("Identifier is empty.")
if len(self.dict_categories) == 1:
# Only 1 category return it directly
return (self.dict_categories.keys()[0], 1.0)
if type(data) is PointCloud:
data = ShapeDistribution.compute(data)
if type(data) is ShapeDistribution:
data = data.as_numpy_array
result_proba = None
try:
result_proba = self.classifier.decision_function(data)
except AttributeError:
pass
try:
result_idx = int(self.classifier.predict(data)[0])
result_name = self.dict_categories.keys()[result_idx]
except ValueError:
result_name = 'Unknown'
return (result_name, result_proba)
def distribution(self):
if not self._distribution:
self._distribution = ShapeDistribution.compute(self.pointcloud)
return self._distribution
def distribution(self):
if not self._distribution:
self._distribution = ShapeDistribution.compute(self.pointcloud)
return self._distribution
from sketchup_models.models import SketchupModel from partial_view.models import PartialView from shape_distribution.models import ShapeDistribution from common.libs.libpydescriptors import Distribution model = SketchupModel.objects.all()[0] view = PartialView.compute_view( model, 0.0, 0.0 ) dis = ShapeDistribution.compute( view.pointcloud )
