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