fixed the lists for models and makeup using only bonafide files that also had makeup

bob/paper/makeup_aim/config/aim_vulun.py → bob/paper/makeup_aim/config/aim_vuln.py

@@ -5,7 +5,7 @@ AIM dataset for vulnurabitlity analysis agains makeup. Default configuration for
 
 """
 
-from bob.paper.makeup_aim.database import AIMVulunDataset
+from bob.paper.makeup_aim.database.aim_vuln import AIMVulnDataset
 
 
 ORIGINAL_DIRECTORY = "[AIM_DIRECTORY]"
@@ -13,7 +13,7 @@ ORIGINAL_EXTENSION = ".h5"
 ANNOTATION_DIRECTORY = "[AIM_ANNOTATION_DIRECTORY]"
 PROTOCOL = "grandtest"
 
-database = AIMVulunDataset(
+database = AIMVulnDataset(
     protocol=PROTOCOL,
     original_directory=ORIGINAL_DIRECTORY,
     original_extension=ORIGINAL_EXTENSION,

bob/paper/makeup_aim/config/exp_aim_vulun.py → bob/paper/makeup_aim/config/exp_aim_vuln.py

@@ -6,7 +6,7 @@ This file contains configuration to run vulunaribility analysis experiments for
 #--------------------------------------------------------------------
 
 # sub_directory where the results will be placed
-sub_directory = 'aim_vulun'
+sub_directory = 'aim_vuln'
 
 #--------------------------------------------------------------------
 

bob/paper/makeup_aim/database/aim_vulun.py → bob/paper/makeup_aim/database/aim_vuln.py

@@ -41,7 +41,7 @@ class File(VideoBioFile):
         return frame_selector(data)
 
 
-class AIMVulunDataset(FileListBioDatabase):
+class AIMVulnDataset(FileListBioDatabase):
     """
     A high level implementation of a Database class for AIM dataset used for vulnurabitliy analysis.
     """
@@ -49,7 +49,7 @@ class AIMVulunDataset(FileListBioDatabase):
 
     def __init__(
             self,
-            name="AIM_vulun",
+            name="AIM_vuln",
             original_directory=None,
             original_extension='.h5',
             protocol="grandtest",
@@ -82,11 +82,11 @@ class AIMVulunDataset(FileListBioDatabase):
 
         """
 
-        filelists_directory = pkg_resources.resource_filename( __name__, "/lists/aim_vulun/")
+        filelists_directory = pkg_resources.resource_filename( __name__, "/lists/aim_vuln/")
         self.filelists_directory = filelists_directory
 
         # init the parent class using super.
-        super(AIMVulunDataset, self).__init__(
+        super(AIMVulnDataset, self).__init__(
             filelists_directory=filelists_directory,
             name=name,
             protocol=protocol,
@@ -113,7 +113,7 @@ class AIMVulunDataset(FileListBioDatabase):
 
         """
         Computes annotations for a given file object ``f``, which
-        is an instance of the ``BatlPadFile`` class.
+        is an instance of the ``File`` class.
 
         NOTE: you can pre-compute annotation in your first experiment
         and then reuse them in other experiments setting

bob/paper/makeup_aim/database/lists/aim_vuln/grandtest/dev/for_models.lst

+face-station/01.02.18/002_01_000_0_00 002 002
+face-station/17.01.18/003_01_000_0_00 003 003
+face-station/17.01.18/004_01_000_0_00 004 004
+face-station/18.01.18/005_01_000_0_00 005 005
+face-station/22.01.18/006_01_000_0_00 006 006
+face-station/22.01.18/007_01_000_0_00 007 007
+face-station/26.01.18/008_01_000_0_00 008 008
+face-station/25.01.18/012_01_000_0_00 012 012
+face-station/18.01.18/018_01_000_0_00 018 018
+face-station/31.01.18/019_01_000_0_00 019 019
+face-station/01.02.18/038_01_000_0_00 038 038
+face-station/30.04.18/064_05_000_0_00 064 064
+face-station/01.05.18/065_07_000_0_00 065 065
+face-station/23.04.18/066_05_000_0_00 066 066
+face-station/23.04.18/067_05_000_0_00 067 067
+face-station/22.05.18/068_05_000_0_00 068 068
+face-station/15.05.18/069_05_000_0_00 069 069
+face-station/04.05.18/070_05_000_0_00 070 070
+face-station/04.05.18/071_05_000_0_00 071 071
+face-station/23.05.18/072_07_000_0_00 072 072

bob/paper/makeup_aim/database/lists/aim_vulun/grandtest/dev/for_models.lst

-face-station/24.01.18/001_01_000_0_00 001 001
-face-station/01.02.18/002_01_000_0_00 002 002
-face-station/17.01.18/003_01_000_0_00 003 003
-face-station/17.01.18/004_01_000_0_00 004 004
-face-station/18.01.18/005_01_000_0_00 005 005
-face-station/22.01.18/006_01_000_0_00 006 006
-face-station/22.01.18/007_01_000_0_00 007 007
-face-station/26.01.18/008_01_000_0_00 008 008
-face-station/23.01.18/009_01_000_0_00 009 009
-face-station/24.01.18/010_01_000_0_00 010 010
-face-station/24.01.18/011_01_000_0_00 011 011
-face-station/25.01.18/012_01_000_0_00 012 012
-face-station/24.01.18/013_01_000_0_00 013 013
-face-station/24.01.18/014_01_000_0_00 014 014
-face-station/25.01.18/015_01_000_0_00 015 015
-face-station/26.01.18/017_01_000_0_00 017 017
-face-station/18.01.18/018_01_000_0_00 018 018
-face-station/31.01.18/019_01_000_0_00 019 019
-face-station/29.01.18/020_01_000_0_00 020 020
-face-station/29.01.18/021_01_000_0_00 021 021
-face-station/29.01.18/022_01_000_0_00 022 022
-face-station/29.01.18/023_01_000_0_00 023 023
-face-station/29.01.18/024_01_000_0_00 024 024
-face-station/29.01.18/025_01_000_0_00 025 025
-face-station/30.01.18/026_01_000_0_00 026 026
-face-station/30.01.18/027_01_000_0_00 027 027
-face-station/30.01.18/028_01_000_0_00 028 028
-face-station/30.01.18/029_01_000_0_00 029 029
-face-station/30.01.18/030_01_000_0_00 030 030
-face-station/30.01.18/031_01_000_0_00 031 031
-face-station/31.01.18/032_01_000_0_00 032 032
-face-station/31.01.18/033_01_000_0_00 033 033
-face-station/31.01.18/034_01_000_0_00 034 034
-face-station/31.01.18/035_01_000_0_00 035 035
-face-station/31.01.18/036_01_000_0_00 036 036
-face-station/07.02.18/037_02_000_0_00 037 037
-face-station/01.02.18/038_01_000_0_00 038 038
-face-station/01.02.18/039_01_000_0_00 039 039
-face-station/01.02.18/040_01_000_0_00 040 040
-face-station/01.02.18/041_01_000_0_00 041 041
-face-station/07.02.18/042_02_000_0_00 042 042
-face-station/01.02.18/043_01_000_0_00 043 043
-face-station/07.02.18/044_02_000_0_00 044 044
-face-station/07.02.18/045_02_000_0_00 045 045
-face-station/01.02.18/046_01_000_0_00 046 046
-face-station/02.02.18/047_01_000_0_00 047 047
-face-station/02.02.18/048_01_000_0_00 048 048
-face-station/02.02.18/049_01_000_0_00 049 049
-face-station/02.02.18/050_01_000_0_00 050 050
-face-station/02.02.18/051_01_000_0_00 051 051
-face-station/02.02.18/052_01_000_0_00 052 052
-face-station/02.02.18/053_01_000_0_00 053 053
-face-station/02.02.18/054_01_000_0_00 054 054
-face-station/02.02.18/055_01_000_0_00 055 055
-face-station/07.02.18/056_02_000_0_00 056 056
-face-station/02.02.18/057_01_000_0_00 057 057
-face-station/02.02.18/058_01_000_0_00 058 058
-face-station/07.02.18/059_02_000_0_00 059 059
-face-station/02.02.18/060_01_000_0_00 060 060
-face-station/02.02.18/061_01_000_0_00 061 061
-face-station/06.02.18/062_02_000_0_00 062 062
-face-station/07.02.18/063_02_000_0_00 063 063
-face-station/30.04.18/064_05_000_0_00 064 064
-face-station/01.05.18/065_07_000_0_00 065 065
-face-station/23.04.18/066_05_000_0_00 066 066
-face-station/23.04.18/067_05_000_0_00 067 067
-face-station/22.05.18/068_05_000_0_00 068 068
-face-station/15.05.18/069_05_000_0_00 069 069
-face-station/04.05.18/070_05_000_0_00 070 070
-face-station/04.05.18/071_05_000_0_00 071 071
-face-station/23.05.18/072_07_000_0_00 072 072
-face-station/23.05.18/073_05_000_0_00 073 073

bob/paper/makeup_aim/script/plot_vuln.py

+import os
+import bob.measure
+import matplotlib.pyplot as plt
+import numpy
+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
+
+
+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']
+
+    return np.ascontiguousarray(gen), np.ascontiguousarray(makeup)
+
+
+def main():
+
+	# import ipdb; ipdb.set_trace()
+
+    directory = '/idiap/user/zmostaani/experiments/batl/aim_result/'
+    method = 'aim_vuln/grandtest/nonorm/'
+    filename = 'scores-dev'
+
+    scores = load_score(os.path.join(directory, method, filename))
+
+
+
+    gen, makeup = return_scores(scores)
+    print("deviding scores")
+
+    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)
+
+    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'],
+                    patch_artist=False, autorange=True, flierprops=flier_props,
+                    boxprops=bbox_props,
+                    whiskerprops=whisker_props,
+                    medianprops=median_props,
+                    whis=whis_props,
+                    widths=0.25,
+                    )
+
+
+    top = 1
+    bottom = 0
+    ax.set_ylim(bottom, top)
+    ax.set_aspect(1.5)
+
+    for line in bp['medians']:
+        # get position data for median line
+        x, y = line.get_xydata()[1]  # top of median line
+        # 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'))
+
+    print("plotting boxplot")
+    # plt.xlabel("Normalized count")
+
+    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)
+
+    ax = plt.gcf().add_subplot(gs[1:9])
+
+    title = "LightCNN FR"
+    spoof_label = "Makeup"
+
+    # fig, ax = plt.subplots()
+    # ax = plt.gcf().add_subplot()
+
+    th = bob.measure.frr_threshold([], gen_sim, 0.1)
+
+    color_scheme = {'genuine': '#2ca02c', 'impostors': '#1f77b4',
+                    'line': '#d4257b', 'makeup': '#ff7f0e'}
+
+    alpha_scheme = {'genuine': 0.9, 'impostors': 0.8, 'spoofs': 0.6}
+    hatch_scheme = {'genuine': '//', 'impostors': None, 'spoofs': None}
+
+    lines = []
+
+    line = plt.hist(gen_sim, bins=10, color=color_scheme['genuine'],
+                    alpha=alpha_scheme['genuine'],
+                    hatch=hatch_scheme['genuine'],
+                    label="Genuine", 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")
+    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)
+
+    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.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'))
+
+    print("ploting histogram")
+
+if __name__ == "__main__":
+
+    main()