def test_identification_banana_vs_bowl_vs_food_can(self):
""" Try to identify with 3 categories. """
# Getting the dataset
bowl_ids = ['fa61e604661d4aa66658ecd96794a1cd',
'f74bba9a22e044dea3769fcd5f96f4',
'd2e1dc9ee02834c71621c7edb823fc53']
banana_ids = ['f6e6117261dca163713c042b393cc65b',
'ba0d56295321002718ddbf38fa69c501',
'7d78e217e0ba160fe2b248b8bb97d290']
bowls = []
for bowl_id in bowl_ids:
bowls.append(SketchupModel.find_google_id(bowl_id))
bananas = []
for banana_id in banana_ids:
bananas.append(SketchupModel.find_google_id(banana_id))
# Training
iden = Identifier()
iden.add_models(bananas, 'banana')
iden.add_models(bowls, 'bowl')
iden.train()
# Identification
for i in range(20):
example = Example.get_random(['banana', 'bowl'])
pcd_file = example.pcd_file()
print "Identification of file {}".format(example)
cloud = PointCloud.load_pcd(pcd_file.name)
iden.identify(cloud)
def setUp(self):
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")
def test_google_id_is_unique(self):
""" Test that the Field google is unique. """
count = SketchupModel.objects.filter(google_id='test_unique').count()
self.assertEqual(count, 0)
SketchupModel(google_id='test_unique').save()
count = SketchupModel.objects.filter(google_id='test_unique').count()
self.assertEqual(count, 1)
SketchupModel(google_id='test_unique').save()
count = SketchupModel.objects.filter(google_id='test_unique').count()
self.assertEqual(count, 1)
def setUp(self):
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")
def retreive_model(google_id):
""" Return the SketchupModel corresponding to the google_id. """
json_data = api_get("GetEntity", id=google_id)
try:
model = SketchupModel.objects.get(google_id=google_id)
except SketchupModel.DoesNotExist:
model = SketchupModel()
try:
_parse_model_entry(model, json_data)
model.save()
return model
except KeyError:
return None
def retreive_model(google_id):
""" Return the SketchupModel corresponding to the google_id. """
json_data = api_get("GetEntity", id=google_id)
try:
model = SketchupModel.objects.get(google_id=google_id)
except SketchupModel.DoesNotExist:
model = SketchupModel()
try:
_parse_model_entry(model, json_data)
model.save()
return model
except KeyError:
return None
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 perform_learning(self, options):
""" Perfrom the learning from the dataset. """
# Retreiving the dataset models
print 'Training using a {} classifier.'.format(
options['classifier_type'])
models = {}
for category in self.dataset.keys():
models[category] = []
for google_id in self.dataset[category]:
models[category].append(
SketchupModel.find_google_id(google_id))
# Training
classifier = self.classifiers_available[options['classifier_type']]
self.identifier = Identifier(classifier=classifier)
for category in models.keys():
self.identifier.add_models(models[category], category)
# (x, y, w) = self.identifier._get_example_matrix()
# import matplotlib.pyplot as plt
# import numpy as np
# # w = np.array(w)
# # print y
# # print w
# plt.plot(y, w, 'ro')
# print self.dataset.keys()
# plt.show()
self.identifier.train(options['entropy'])
def perform_learning(self, options):
""" Perfrom the learning from the dataset. """
# Retreiving the dataset models
print 'Training using a {} classifier.'.format(
options['classifier_type'])
models = {}
for category in self.dataset.keys():
models[category] = []
for google_id in self.dataset[category]:
models[category].append(
SketchupModel.find_google_id(google_id))
# Training
classifier = self.classifiers_available[options['classifier_type']]
self.identifier = Identifier(classifier=classifier)
for category in models.keys():
self.identifier.add_models(models[category], category)
# (x, y, w) = self.identifier._get_example_matrix()
# import matplotlib.pyplot as plt
# import numpy as np
# # w = np.array(w)
# # print y
# # print w
# plt.plot(y, w, 'ro')
# print self.dataset.keys()
# plt.show()
self.identifier.train(options['entropy'])
def detailed_view(request, google_id):
model = SketchupModel.find_google_id(google_id)
print "model {} has {} views.".format(model, model.partialview_set.count())
if model is None:
return HttpResponseNotFound()
return render_to_response('model_detailed_view.html', {'model': model},
context_instance=RequestContext(request))
def test_exception_when_no_existing_category(self):
""" Test that if the identifier is empty it throws. """
pointcloud = PointCloud.load_pcd("pointcloud/fixtures/cloud.pcd")
identifier = Identifier()
self.assertRaises(IndexError, identifier.identify, pointcloud)
# training will add a category :
model = SketchupModel()
model.google_id = "test1"
model.mesh = file("sketchup_models/fixtures/mesh_can.tri").read()
identifier.add_models([model], "test_category")
try:
identifier.identify(pointcloud)
except IndexError:
self.fail("identifier.identify() raised IndexError unexpectedly!")
except:
# can raise if Indentification failed
pass
def search_for_models(keywords):
models = []
if keywords:
model_ids = search_by_keywords(keywords)
for model_id in model_ids:
models.append(SketchupModel.find_google_id(model_id))
# sleep(0.100)
return models
def search_for_models(keywords):
models = []
if keywords:
model_ids = search_by_keywords(keywords)
for model_id in model_ids:
models.append(SketchupModel.find_google_id(model_id))
# sleep(0.100)
return models
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 model_mesh(request, google_id):
try:
model = SketchupModel.find_google_id(google_id)
if model.mesh is None:
return HttpResponseNotFound()
response = HttpResponse(model.mesh, mimetype="text/plain")
response['Content-Disposition'] = 'attachment; filename=%s.tri' % model
return response
except SketchupModel.DoesNotExist, SketchupModel.AttributeError:
return HttpResponseNotFound()
def test_empty_pointcloud(self):
"""
The pointcloud is sometimes returned empty.
It seems to be random and about 1/10 of the time
We test here the stability.
"""
test_model = SketchupModel.find_google_id("fb01ac920e6b68fd7236c5b88164c0b8")
number_of_attempt = 10
for i in range(number_of_attempt):
print "Compute cloud {} on {}".format(i, number_of_attempt)
# Generate the pointcloud
PartialView.compute_all_views( test_model )
def test_empty_pointcloud(self):
"""
The pointcloud is sometimes returned empty.
It seems to be random and about 1/10 of the time
We test here the stability.
"""
test_model = SketchupModel.find_google_id(
"fb01ac920e6b68fd7236c5b88164c0b8")
number_of_attempt = 10
for i in range(number_of_attempt):
print "Compute cloud {} on {}".format(i, number_of_attempt)
# Generate the pointcloud
PartialView.compute_all_views(test_model)
def search_by_keywords(keywords, create_models=False):
""" Search the API for the keywords, return a list of model_ids. """
params = {
"startRow": 1,
"endRow": NUMBER_OF_RESULTS,
"q": keywords,
"type": "SKETCHUP_MODEL",
"class": "entity",
"Lk": True,
}
json_data = api_get("Search", **params)
if create_models:
models = []
for entry in json_data["entries"]:
try:
model = SketchupModel()
_parse_model_entry(model, entry)
model.save()
models.append(model)
except KeyError as exception:
print exception
return models
else:
return [entry["id"] for entry in json_data["entries"]]
def search_by_keywords(keywords, create_models=False):
""" Search the API for the keywords, return a list of model_ids. """
params = {
'startRow': 1,
'endRow': NUMBER_OF_RESULTS,
'q': keywords,
'type': 'SKETCHUP_MODEL',
'class': 'entity',
'Lk': True
}
json_data = api_get('Search', **params)
if create_models:
models = []
for entry in json_data['entries']:
try:
model = SketchupModel()
_parse_model_entry(model, entry)
model.save()
models.append(model)
except KeyError as exception:
print exception
return models
else:
return [entry['id'] for entry in json_data['entries']]
def train_identifier(request, evaluation_session_id):
""" Train the identifier then redirect to a new attempt. """
session = get_object_or_404(EvaluationSession, pk=evaluation_session_id)
attempt_index = int(request.GET['identification_attempt_index'])
attempt = session.attempts[attempt_index]
category = request.GET['category']
if request.GET['google_ids']:
model_ids = request.GET['google_ids'].split(",")
else:
model_ids = []
models = [SketchupModel.find_google_id(id) for id in model_ids]
attempt.selected_model_ids = model_ids
session.identifier.add_models(models, category)
session.identifier.train()
session.save()
return redirect('/system/session/{}/attempt/new'.format(session.pk))
def train_identifier(request, evaluation_session_id):
""" Train the identifier then redirect to a new attempt. """
session = get_object_or_404(EvaluationSession, pk=evaluation_session_id)
attempt_index = int(request.GET['identification_attempt_index'])
attempt = session.attempts[attempt_index]
category = request.GET['category']
if request.GET['google_ids']:
model_ids = request.GET['google_ids'].split(",")
else:
model_ids = []
models = [SketchupModel.find_google_id(id) for id in model_ids]
attempt.selected_model_ids = model_ids
session.identifier.add_models(models, category)
session.identifier.train()
session.save()
return redirect('/system/session/{}/attempt/new'.format(session.pk))
def _get_example_matrix(self, use_entropy):
""" Return the input matrix of example (X). """
X = numpy.zeros([0, SHAPE_DISTRIBUTION_SIZE])
Y = numpy.array([])
W = numpy.array([]) # Weights of the samples
for idx, (category, model_ids) in (enumerate(
self.dict_categories.items())):
for model_id in model_ids:
model = SketchupModel.find_google_id(model_id)
if model.partialview_set.count() == 0:
PartialView.compute_all_views(model)
(x, w) = self._get_model_example_matrix(model, use_entropy)
y = numpy.array([idx for _ in range(x.shape[0])])
X = numpy.vstack([X, x])
Y = numpy.concatenate([Y, y])
W = numpy.concatenate([W, w])
return (X, Y, W)
def _get_example_matrix(self, use_entropy):
""" Return the input matrix of example (X). """
X = numpy.zeros([0, SHAPE_DISTRIBUTION_SIZE])
Y = numpy.array([])
W = numpy.array([]) # Weights of the samples
for idx, (category,
model_ids) in (enumerate(self.dict_categories.items())):
for model_id in model_ids:
model = SketchupModel.find_google_id(model_id)
if model.partialview_set.count() == 0:
PartialView.compute_all_views(model)
(x, w) = self._get_model_example_matrix(model, use_entropy)
y = numpy.array([idx for _ in range(x.shape[0])])
X = numpy.vstack([X, x])
Y = numpy.concatenate([Y, y])
W = numpy.concatenate([W, w])
return (X, Y, W)
def test_mesh_attribute_can_be_used_as_a_string(self):
"""
As mesh is store in a gridfsfield, the attribute mesh would
normally be returned as a GridFSOut object.
The model has been modified so that it is a string instead.
"""
model = SketchupModel()
model.google_id = "test1"
model.mesh = file("sketchup_models/fixtures/mesh_can.tri").read()
def error_msg(model):
return ("The mesh should be of type str but is {} instead.".
format(model.mesh.__class__))
msg = error_msg(model)
self.assertTrue(isinstance(model.mesh, basestring))
model.save()
msg = error_msg(model)
self.assertTrue(isinstance(model.mesh, basestring))
model = SketchupModel.find_google_id("test1")
msg = error_msg(model)
self.assertTrue(isinstance(model.mesh, basestring), msg=msg)
def test_mesh_attribute_can_be_used_as_a_string(self):
"""
As mesh is store in a gridfsfield, the attribute mesh would
normally be returned as a GridFSOut object.
The model has been modified so that it is a string instead.
"""
model = SketchupModel()
model.google_id = "test1"
model.mesh = file("sketchup_models/fixtures/mesh_can.tri").read()
def error_msg(model):
return "The mesh should be of type str but is {} instead.".format(model.mesh.__class__)
msg = error_msg(model)
self.assertTrue(isinstance(model.mesh, basestring))
model.save()
msg = error_msg(model)
self.assertTrue(isinstance(model.mesh, basestring))
model = SketchupModel.find_google_id("test1")
msg = error_msg(model)
self.assertTrue(isinstance(model.mesh, basestring), msg=msg)
