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_scaling_to_unit_and_back():
tmpObj = DataCollector('../data/Landmarks/original/landmarks1-1.txt')
print tmpObj.points
print "*" * 50
tmpObj.scale_to_unit()
print "centroid distance: ", tmpObj.check_distance()
tmpObj.rescale()
print tmpObj.points
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_scaling_to_unit_and_back():
tmpObj = DataCollector('../data/Landmarks/original/landmarks1-1.txt')
print tmpObj.points
print "*" * 50
tmpObj.scale_to_unit()
print "centroid distance: ", tmpObj.check_distance()
tmpObj.rescale()
print tmpObj.points
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_rotating_landmarks():
tmpObj = DataCollector('../data/Landmarks/original/landmarks1-1.txt')
Plotter.render_landmarks(tmpObj)
tmpObj.rotate(1)
Plotter.render_landmarks(tmpObj)
def example_translate_to_origin():
tmpObj = DataCollector('../data/Landmarks/original/landmarks1-1.txt')
print numpy.mean(tmpObj.points, axis=0)
tmpObj.translate_to_origin()
print tmpObj.centroid
def example_rotating_landmarks():
tmpObj = DataCollector('../data/Landmarks/original/landmarks1-1.txt')
Plotter.render_landmarks(tmpObj)
tmpObj.rotate(1)
Plotter.render_landmarks(tmpObj)
def example_translate_to_origin():
tmpObj = DataCollector('../data/Landmarks/original/landmarks1-1.txt')
print numpy.mean(tmpObj.points, axis=0)
tmpObj.translate_to_origin()
print tmpObj.centroid
def example_read_landmarks_and_plot_over_original_image():
TmpObj = DataCollector('../data/Landmarks/original/landmarks1-4.txt')
img = cv2.imread('../data/Radiographs/01.tif')
Plotter.render_over_image(TmpObj, img)
def example_reading_landmarks_and_display_shape():
TmpObj = DataCollector('../data/Landmarks/original/landmarks1-1.txt')
Plotter.render_landmarks(TmpObj)