[vuln] added script to prepare box-plot and histogram for vulnaribility analysis

bob/paper/makeup_aim/script/plot_vuln.py

+#!/usr/bin/env python
+"""
+Plots box-plot and histogram for similarity scores for face recognition system.
+
+Usage:
+  plot_vuln.py [options] <scores>
+  plot_vuln.py --help
+
+Options:
+  -h --help     Show this screen.
+  -o <path>, --output=<path>  The path to which the plots will be saved [default: ]
+"""
+
+
 import os
+from docopt import docopt
 import bob.measure
 import matplotlib.pyplot as plt
-import numpy
+import numpy as np
 from bob.bio.base.score.load import load_score
-from bob.pad.base.script.vuln_figure import _iapmr_plot
 from matplotlib import gridspec
 
-np = numpy
 
+#--------------------------------------------------------------------
+# Here are the global names used for the plots
 
-def return_scores(scores):
-
-    print("in function return_scores")
-    gen = scores[scores['real_id'] == scores['claimed_id']]['score']
-
-    makeup = scores[scores['real_id'] == 'makeup']['score']
+plot_title = "LightCNN FR"
+genuine_label = 'Genuine'
+makeup_label = 'Makeup'
 
-    return np.ascontiguousarray(gen), np.ascontiguousarray(makeup)
+# box-plot
+box_plot_filename = 'box-plot'
+box_plot_ylabel = 'Similarity Scores'
 
+# histogram plot
+historgram_filename = 'hist-plot'
+line_label = 'FNMR threshold'
+hist_plot_xlabel = 'Similarity Scores'
+hist_plot_ylabel = 'Normalized Count'
 
-def main():
+#--------------------------------------------------------------------
+# separating genuine and makeup scores
 
-	# import ipdb; ipdb.set_trace()
+def return_similarity_scores(scores):
+    '''
+    Separates the necessary scores from the score list for genuine and makeup.
+    '''
 
-    directory = '/idiap/user/zmostaani/experiments/batl/aim_result/'
-    method = 'aim_vuln/grandtest/nonorm/'
-    filename = 'scores-dev'
+    gen = scores[scores['real_id'] == scores['claimed_id']]['score']
+    makeup = scores[scores['real_id'] == 'makeup']['score']
 
-    scores = load_score(os.path.join(directory, method, filename))
+    gen_sim = 1 + np.ascontiguousarray(gen)
+    makeup_sim = 1 + np.ascontiguousarray(makeup)
 
+    return gen_sim, makeup_sim
 
+#--------------------------------------------------------------------
+# plotting the box-plot
+def plot_box_plot(gen_scores, makeup_scores, output):
+    '''
+        The box plots to show the effect of makeup on FR system
 
-    gen, makeup = return_scores(scores)
-    print("deviding scores")
+        - gen_scores: the scores for genuine faces without makeup
+        - makeup_scores: the scores for faces with makeup
+        - output: The file to which the plot will be saved.
+    '''
 
     plt.style.use('default')
 
@@ -41,21 +72,15 @@ def main():
     fig = plt.figure()
 
     gs = gridspec.GridSpec(9, 1, figure=fig)
+    ax = plt.gcf().add_subplot(gs[1:9])
 
-    ax = plt.gcf().add_subplot(gs[1:8])
-
-
-    gen_sim = 1 + gen
-    makeup_sim = 1 + makeup
-
-    line_props = dict(color="r", alpha=0.3)
     bbox_props = dict(color="b", alpha=0.9)
     flier_props = dict(marker="+", markersize=4, markeredgecolor="g")
     whis_props = [5, 95]
     whisker_props = dict(linestyle='--', dashes=(5, 5))
     median_props = dict(color="r")
 
-    bp = ax.boxplot([gen_sim, makeup_sim], labels=['Genuine', 'Makeup'],
+    bp = ax.boxplot([gen_scores, makeup_scores], labels=[genuine_label, makeup_label],
                     patch_artist=False, autorange=True, flierprops=flier_props,
                     boxprops=bbox_props,
                     whiskerprops=whisker_props,
@@ -64,7 +89,6 @@ def main():
                     widths=0.25,
                     )
 
-
     top = 1
     bottom = 0
     ax.set_ylim(bottom, top)
@@ -76,78 +100,95 @@ def main():
         # overlay median value
         ax.annotate(f"{y:.2f}", (x, y))
 
-    plt.ylabel("Similarity Scores")
-    # plt.xlabel("")
-    plt.title("LightCNN FR")
-    plt.savefig(os.path.join(directory, 'boxplot-bob-new.png'))
+    plt.ylabel(box_plot_ylabel)
 
-    print("plotting boxplot")
-    # plt.xlabel("Normalized count")
+    plt.title(plot_title)
+    plt.savefig(output)
+    plt.close()
 
-    plt.style.use('default')
+#--------------------------------------------------------------------
+# plotting the histogram
+def plot_histogram(gen_scores, makeup_scores, output):
+    '''
+        The histogram to show the effect of makeup on FR system
+
+        - gen_scores: the scores for genuine faces without makeup
+        - makeup_scores: the scores for faces with makeup
+        - output: The file to which the plot will be saved.
+    '''
 
+    plt.style.use('default')
     plt.rcParams['figure.figsize'] = (4, 3)
     plt.rcParams['figure.constrained_layout.use'] = True
 
     fig = plt.figure()
-    gs = gridspec.GridSpec(9, 1,            figure=fig)
+    gs = gridspec.GridSpec(9, 1, figure=fig)
 
     ax = plt.gcf().add_subplot(gs[1:9])
 
-    title = "LightCNN FR"
-    spoof_label = "Makeup"
+    th = bob.measure.frr_threshold([], gen_scores, 0.1)
 
-    # fig, ax = plt.subplots()
-    # ax = plt.gcf().add_subplot()
-
-    th = bob.measure.frr_threshold([], gen_sim, 0.1)
-
-    color_scheme = {'genuine': '#2ca02c', 'impostors': '#1f77b4',
+    color_scheme = {'genuine': '#2ca02c',
                     'line': '#d4257b', 'makeup': '#ff7f0e'}
-
-    alpha_scheme = {'genuine': 0.9, 'impostors': 0.8, 'spoofs': 0.6}
-    hatch_scheme = {'genuine': '//', 'impostors': None, 'spoofs': None}
+    alpha_scheme = {'genuine': 0.9, 'makeup': 0.6}
+    hatch_scheme = {'genuine': '//', 'makeup': None}
 
     lines = []
 
-    line = plt.hist(gen_sim, bins=10, color=color_scheme['genuine'],
+    line = plt.hist(gen_scores, bins=10, color=color_scheme['genuine'],
                     alpha=alpha_scheme['genuine'],
                     hatch=hatch_scheme['genuine'],
-                    label="Genuine", density=True)
+                    label=genuine_label, density=True)
     lines.append(line[-1][0])
 
     line = plt.axvline(x=th, ymin=0, ymax=1, linewidth=2,
                        color=color_scheme['line'], linestyle='--',
-                       label="FNMR threshold")
+                       label=line_label)
     lines.append(line)
 
-    line = plt.hist(makeup_sim, bins=10, color=color_scheme['makeup'],
-                    alpha=alpha_scheme['spoofs'],
-                    hatch=hatch_scheme['spoofs'],
-                    density=True, label=spoof_label)
+    line = plt.hist(makeup_scores, bins=10, color=color_scheme['makeup'],
+                    alpha=alpha_scheme['makeup'],
+                    hatch=hatch_scheme['makeup'],
+                    density=True, label=makeup_label)
 
     lines.append(line[-1][0])
 
-    #     ax.grid(True)
     hs, ls = plt.gca().get_legend_handles_labels()
-    # plt.sca(ax)
 
     ax.grid(True)
-    # ax.legend(handletextpad=0.9)
 
-    plt.xlabel("Similarity Scores")
-    plt.ylabel("Normalized Count")
-    plt.title(title)
+    plt.xlabel(hist_plot_xlabel)
+    plt.ylabel(hist_plot_ylabel)
+    plt.title(plot_title)
 
-    # plt.tight_layout()
-    # plt.subplots_adjust(top=0.80)
     by_label = dict(zip(ls, hs))
     fig.legend(by_label.values(), by_label.keys(),
                loc='upper center', ncol=3, framealpha=0.5)
 
-    plt.savefig(os.path.join(directory, 'makeup-FRR-bob-new.png'))
+    plt.savefig(output)
+    plt.close()
+
+
+def main():
+
+    args = docopt(__doc__)
+
+    score_directory = args['<scores>']
+    plots_directory = args['--output']
+
+    scores = load_score(score_directory)
+    gen_sim, makeup_sim = return_similarity_scores(scores)
+
+    output1=os.path.join(plots_directory, box_plot_filename)
+    plot_box_plot(gen_scores=gen_sim,
+                  makeup_scores=makeup_sim, output=output1)
+
+    output2=os.path.join(plots_directory, historgram_filename)
+    plot_histogram(gen_scores=gen_sim,
+                   makeup_scores=makeup_sim, output=output2)
+
+    print("The plots are saved in {} and {}".format(output1,output2))
 
-    print("ploting histogram")
 
 if __name__ == "__main__":
 

setup.py

@@ -89,6 +89,7 @@ setup(
       # scripts should be declared using this entry:
       'console_scripts' : [
         'annotate_db.py = bob.paper.makeup_aim.script.annotate_db:main',
+        'plot_vuln.py = bob.paper.makeup_aim.script.plot_vuln:main'
       ],
       'bob.bio.config': [
             'ymu = bob.paper.makeup_aim.config.ymu',