def example_calculate_mean_image_and_display():
res = collect_vectors('../data/Landmarks/original', '5', 80)
referent = ActiveShapeModel.ReferentModel(res)
data_coll = DataCollector(None)
res = referent.mean_model()
data_coll.read_vector(referent.mean_model())
Plotter.render_landmarks(data_coll)
def example_calculate_mean_image_and_display():
res = collect_vectors('../data/Landmarks/original', '5', 80)
referent = ActiveShapeModel.ReferentModel(res)
data_coll = DataCollector(None)
res = referent.mean_model()
data_coll.read_vector(referent.mean_model())
Plotter.render_landmarks(data_coll)
def example_examine_principal_components():
res = collect_vectors('../data/Landmarks/original', '1', 80)
referent = ActiveShapeModel.ReferentModel(res)
referent.align()
referent.rescale_and_realign()
variance = ActiveShapeModel.VarianceModel(referent)
variance.obtain_components()
components = variance.get_components()
eigenvals = variance.get_eigenvalues()
shapes = utils.vary_component(referent.mean_shape, components.transpose(), eigenvals, 1, 10)
tmpObj = DataCollector(None)
Plotter.render_landmarks(referent.mean_shape)
for ind in range(len(shapes)):
tmpObj.read_vector(shapes[ind, :])
Plotter.render_landmarks(tmpObj)
def example_examine_principal_components():
res = collect_vectors('../data/Landmarks/original', '1', 80)
referent = ActiveShapeModel.ReferentModel(res)
referent.align()
referent.rescale_and_realign()
variance = ActiveShapeModel.VarianceModel(referent)
variance.obtain_components()
components = variance.get_components()
eigenvals = variance.get_eigenvalues()
shapes = utils.vary_component(referent.mean_shape, components.transpose(),
eigenvals, 1, 10)
tmpObj = DataCollector(None)
Plotter.render_landmarks(referent.mean_shape)
for ind in range(len(shapes)):
tmpObj.read_vector(shapes[ind, :])
Plotter.render_landmarks(tmpObj)