Commit 851b1c18 authored by Zohreh MOSTAANI's avatar Zohreh MOSTAANI

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

parent aab3cf8f
......@@ -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,
......
......@@ -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'
#--------------------------------------------------------------------
......
......@@ -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
......
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This source diff could not be displayed because it is too large. You can view the blob instead.
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()
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