#!/usr/bin/env python """ plots the metric values at the start and end of each slice registration Usage: before_and_after_registration values_dir """ from sys import argv from os import listdir from metric_values import MetricValues from numpy import * import matplotlib.pyplot as plt metric_values_dir = argv[1] basenames = listdir(metric_values_dir) # extract metric values vals = MetricValues(metric_values_dir) fig = plt.figure() ax = fig.gca() x = range(vals.number_of_slices()) ax.plot(x, vals.initial_values(), label="Initial values") ax.plot(x, vals.final_values(), label="Final values") plt.grid(axis="y") ax.legend() plt.show()
Rat28_root = registration_root + "results/Rat28/" thesis_root = "/Users/Matt/Documents/DPhil/written_work/thesis/" import sys from os import listdir from numpy import * import matplotlib.pyplot as plt # add registration graphing directory to python path sys.path.insert(0, registration_root + "graphing") from metric_values import MetricValues # metric values dirs metric_value_dir = registration_root + "results/Rat28/%s/MetricValues/" rigid_values = MetricValues(metric_value_dir % "rigid" + "CenteredRigid2DTransform" ) similarity_values = MetricValues(metric_value_dir % "size" + "CenteredSimilarity2DTransform") affine_values = MetricValues(metric_value_dir % "affine" + "CenteredAffineTransform") # check that they all have the same number of slices assert(rigid_values.number_of_slices() == similarity_values.number_of_slices() == affine_values.number_of_slices()) x = range(rigid_values.number_of_slices()) # plot figure fig = plt.figure(frameon=False) ax = fig.add_axes([0.05,0.05,0.9,0.9]) ax.plot(x, rigid_values.initial_values(), label="Initial values") ax.plot(x, rigid_values.final_values(), label="Final rigid values") ax.plot(x, similarity_values.final_values(), label="Final similarity values") ax.plot(x, affine_values.final_values(), 'y', label="Final affine values")
# plot all slices
else:
def plot_3d_lines(values, labels):
fig = plt.figure(frameon=False)
ax = fig.add_axes([0.0,0.0,1.0,1.0], projection='3d')
for i, slice in enumerate(values):
x = range(len(slice))
y = [i] * len(slice)
ax.plot(x, y, slice, label=labels[i])
ax.legend()
plt.xlabel('Iteration', fontsize='xx-large')
plt.ylabel('Slice Number', fontsize='xx-large')
ax.set_zlabel("Normalised Correlation", fontsize='xx-large')
plt.show()
basenames = listdir(metric_values_dir)
labels = [basename + " (" + str(i+1) + ")" for i, basename in enumerate(basenames)]
metric_values = MetricValues(metric_values_dir)
# plot slices in batches
if len(argv) == 4 and argv[2] == '--batch-size':
batch_size = int(argv[3])
for i in range(0,len(metric_values.values()), batch_size):
plot_3d_lines(metric_values.values()[i:i+batch_size], labels[i:i+batch_size])
# plot the whole set of slices together
else:
plot_3d_lines(metric_values.values(), labels)
plot_3d_lines(metric_values.delta_values(), labels)
Usage:
compare_final_metric_values dir1 [dir2 [dir3...]]
"""
from sys import argv
from metric_values import MetricValues
from numpy import *
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca()
for i, directory in enumerate(argv[1:]):
vals = MetricValues(directory)
x = range(vals.number_of_slices())
ax.plot(x, vals.final_values(), label=str(i+1) )
plt.grid(axis="y")
ax.legend()
plt.show()
# plot the differences if 2 directories are given
if len(argv) == 3:
fig = plt.figure()
ax = fig.gca()
vals1 = MetricValues(argv[1]).final_values()
vals2 = MetricValues(argv[2]).final_values()
x = range(len(vals1))
