def test_gmm_segment(self):
"""
Apply the trained GMM
to unsegmented image and
generate a segmented image.
returns:
segmented_matrix = numpy.ndarray[numpy.ndarray[float]]
"""
image_file = 'images/party_spock.png'
image_matrix = image_to_matrix(image_file)
num_components = 3
gmm = GaussianMixtureModel(image_matrix, num_components)
gmm.initialize_training()
gmm.train_model()
segment = gmm.segment()
segment_num_components = len(np.unique(segment))
self.assertTrue(segment_num_components == num_components,
msg="Incorrect number of image segments produced")
def test_gmm_improvement(self):
"""
Tests whether the new mixture
model is actually an improvement
over the previous one: if the
new model has a higher likelihood
than the previous model for the
provided initial means.
returns:
original_segment = numpy.ndarray[numpy.ndarray[float]]
improved_segment = numpy.ndarray[numpy.ndarray[float]]
"""
image_file = 'images/self_driving.png'
image_matrix = image_to_matrix(image_file)
num_components = 4
initial_means = np.array([0.4627451, 0.20392157, 0.36078432, 0.47254905])
# first train original model with fixed means
gmm = GaussianMixtureModel(image_matrix, num_components)
gmm.initialize_training()
gmm.means = np.copy(initial_means)
gmm.train_model()
original_segment = gmm.segment()
original_likelihood = gmm.likelihood()
# then train improved model
gmm_improved = GaussianMixtureModelImproved(image_matrix,
num_components)
gmm_improved.initialize_training()
gmm_improved.train_model()
improved_segment = gmm_improved.segment()
improved_likelihood = gmm_improved.likelihood()
# then calculate likelihood difference
diff_thresh = 2.2e3
likelihood_diff = improved_likelihood - original_likelihood
print(improved_likelihood, original_likelihood, likelihood_diff)
self.assertTrue(likelihood_diff >= diff_thresh,
msg=("Model likelihood less than "
"%d higher than original model" % diff_thresh))