import os
import pandas as pd
import bob.measure
import numpy as np
import matplotlib as mpl
mpl.rcParams.update({'font.size': 14})
import matplotlib.pyplot as plt
%matplotlib inline

Select baselines

baselines = {
    'Gabor graph': 'gabor_graph',
    'LGBPHS': 'lgbphs',
    'FaceNet (D. Sandberg)': 'facenet-sanderberg',
    'Casia-Webface - Inception Resnet v1': 'inception-resnetv1-casiawebface',
    'Casia-Webface - Inception Resnet v2': 'inception-resnetv2-casiawebface',
    'MSCeleb - Inception Resnet v1': 'inception-resnetv1-msceleb',
    'MSCeleb - Inception Resnet v2': 'inception-resnetv2-msceleb'
}

Define some utilitary functions

results_dir = './results/'

# Function to load the scores in CSV format
def load_scores(baseline, protocol, group):
    scores = pd.read_csv(os.path.join(results_dir, 
                                      'multipie_{}'.format(protocol), 
                                      baseline, 
                                      'scores-{}.csv'.format(group)))
    return scores

# Function to separate genuines from impostors
def split(df):
    impostors = df[df['probe_reference_id'] != df['bio_ref_reference_id']]
    genuines = df[df['probe_reference_id'] == df['bio_ref_reference_id']]
    return impostors, genuines

Establish Camera to Angle conversion

cameras = ['11_0', '12_0','09_0','08_0','13_0','14_0','05_1','05_0', '04_1','19_0','20_0','01_0','24_0']
angles = np.linspace(-90, 90, len(cameras))
camera_to_angle = dict(zip(cameras, angles))

Run pose analysis and plot results

# Figure for Dev plot
fig1 = plt.figure(figsize=(8, 6))
# Figure for Eval plot
fig2 = plt.figure(figsize=(8, 6))
for name, baseline in baselines.items():
    
    # Load the score files and fill in the angle associated to each camera
    dev_scores = load_scores(baseline, 'P', 'dev')
    eval_scores = load_scores(baseline, 'P', 'eval')
    dev_scores['angle'] = dev_scores['probe_camera'].map(camera_to_angle)
    eval_scores['angle'] = eval_scores['probe_camera'].map(camera_to_angle)
    
    angles = []
    dev_hters = []
    eval_hters = []
    # Run the analysis per view angle
    for (angle, dev_df), (_, eval_df) in zip(dev_scores.groupby('angle'), eval_scores.groupby('angle')):
        # Separate impostors from genuines
        dev_impostors, dev_genuines = split(dev_df)
        eval_impostors, eval_genuines = split(eval_df)
        
        # Compute the min. HTER threshold on the Dev set
        threshold = bob.measure.min_hter_threshold(dev_impostors['score'], dev_genuines['score'])
        
        # Compute the HTER for the Dev and Eval set at this particular threshold
        dev_far, dev_frr = bob.measure.farfrr(dev_impostors['score'], dev_genuines['score'], threshold)
        eval_far, eval_frr = bob.measure.farfrr(eval_impostors['score'], eval_genuines['score'], threshold)
        angles.append(angle)
        dev_hters.append(1/2 * (dev_far + dev_frr))
        eval_hters.append(1/2 * (eval_far + eval_frr))
    
    # Update plots
    plt.figure(1)   
    plt.plot(angles, dev_hters, label=name, marker='x')
    
    plt.figure(2)
    plt.plot(angles, eval_hters, label=name, marker='x')

# Plot finalization
plt.figure(1)
plt.title('Dev. min. HTER')
plt.xlabel('Angle')
plt.ylabel('Min HTER')
plt.legend()
plt.grid()

plt.figure(2)
plt.title('Eval. HTER @Dev min. HTER')
plt.xlabel('Angle')
plt.ylabel('HTER')
plt.legend()
plt.grid()