def print_R_script(scene, tree, classifier, step_size):
print "# " + scene.filename + "\n"
# Some R functions for plotting.
print "library(scales)" # for alpha blending
rtools.print_plot_focus_measures(scene.fvalues)
# The correct classifications.
classes = [ "left" if classifier(scene, lens_pos) else "right"
for lens_pos in range(2 * step_size, scene.step_count) ]
# What we actually get.
results = []
for lens_pos in range(2 * step_size, scene.step_count):
initial_positions = featuresfirststep.first_three_lens_pos(
lens_pos, step_size)
first, second, third = scene.get_focus_values(initial_positions)
norm_lens_pos = float(lens_pos) / (scene.step_count - 1)
evaluator = featuresfirststep.firststep_feature_evaluator(
first, second, third, norm_lens_pos)
evaluation = evaluatetree.evaluate_tree(tree, evaluator)
results.append(evaluation)
rtools.print_classification_points(classes, results, ["left", "right"])
print "# Plot me!\n"
def _get_first_direction(self):
"""Direction in which we should start sweeping initially."""
first, second, third = self.camera.get_fvalues(
self.camera.visited_positions[-3:])
norm_lens_pos = float(self.initial_pos) / (self.scene.step_count - 1)
evaluator = featuresfirststep.firststep_feature_evaluator(
first, second, third, norm_lens_pos)
return Direction(evaluatetree.evaluate_tree(
self.params.left_right_tree, evaluator))
def print_R_script(scene, tree, step_size):
print "# " + scene.filename + "\n"
# Some R functions for plotting.
print "library(scales)" # for alpha blending
rtools.print_plot_focus_measures(scene.fvalues)
classes = []
results = []
for lens_pos in range(2 * step_size, scene.step_count):
# The correct classification.
go_left = featuresfirststep.nearest_on_left(scene, lens_pos)
initial_positions = featuresfirststep.first_three_lens_pos(
lens_pos, step_size)
if go_left:
initial_positions.reverse()
last_pos = initial_positions[-1]
if scene.distance_to_closest_left_peak(last_pos) <= 15:
classes.append("fine")
else:
classes.append("coarse")
else:
last_pos = initial_positions[-1]
if scene.distance_to_closest_right_peak(last_pos) <= 15:
classes.append("fine")
else:
classes.append("coarse")
# The classification obtained by evaluating the decision tree.
first, second, third = scene.get_focus_values(initial_positions)
norm_lens_pos = float(lens_pos) / (scene.step_count - 1)
evaluator = featuresfirststep.firststep_feature_evaluator(
first, second, third, norm_lens_pos)
evaluation = evaluatetree.evaluate_tree(tree, evaluator)
results.append(evaluation)
rtools.print_classification_points(classes, results, ["coarse", "fine"])
print "# Plot me!\n"