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| | import os
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| | from pathlib import Path
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| |
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| | import matplotlib.pyplot as plt
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| | import numpy as np
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| | import pandas as pd
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| | from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
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| | from prettytable import PrettyTable
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| | from sklearn.metrics import roc_curve, auc
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| |
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| | image_path = "/data/anxiang/IJB_release/IJBC"
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| | files = [
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| | "./ms1mv3_arcface_r100/ms1mv3_arcface_r100/ijbc.npy"
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| | ]
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| |
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| |
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| | def read_template_pair_list(path):
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| | pairs = pd.read_csv(path, sep=' ', header=None).values
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| | t1 = pairs[:, 0].astype(np.int)
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| | t2 = pairs[:, 1].astype(np.int)
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| | label = pairs[:, 2].astype(np.int)
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| | return t1, t2, label
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| |
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| |
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| | p1, p2, label = read_template_pair_list(
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| | os.path.join('%s/meta' % image_path,
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| | '%s_template_pair_label.txt' % 'ijbc'))
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| |
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| | methods = []
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| | scores = []
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| | for file in files:
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| | methods.append(file.split('/')[-2])
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| | scores.append(np.load(file))
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| |
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| | methods = np.array(methods)
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| | scores = dict(zip(methods, scores))
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| | colours = dict(
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| | zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))
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| | x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]
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| | tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])
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| | fig = plt.figure()
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| | for method in methods:
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| | fpr, tpr, _ = roc_curve(label, scores[method])
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| | roc_auc = auc(fpr, tpr)
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| | fpr = np.flipud(fpr)
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| | tpr = np.flipud(tpr)
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| | plt.plot(fpr,
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| | tpr,
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| | color=colours[method],
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| | lw=1,
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| | label=('[%s (AUC = %0.4f %%)]' %
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| | (method.split('-')[-1], roc_auc * 100)))
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| | tpr_fpr_row = []
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| | tpr_fpr_row.append("%s-%s" % (method, "IJBC"))
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| | for fpr_iter in np.arange(len(x_labels)):
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| | _, min_index = min(
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| | list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr)))))
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| | tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100))
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| | tpr_fpr_table.add_row(tpr_fpr_row)
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| | plt.xlim([10 ** -6, 0.1])
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| | plt.ylim([0.3, 1.0])
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| | plt.grid(linestyle='--', linewidth=1)
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| | plt.xticks(x_labels)
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| | plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True))
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| | plt.xscale('log')
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| | plt.xlabel('False Positive Rate')
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| | plt.ylabel('True Positive Rate')
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| | plt.title('ROC on IJB')
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| | plt.legend(loc="lower right")
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| | print(tpr_fpr_table)
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| |
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