import os import sys 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 auc from sklearn.metrics import roc_curve with open(sys.argv[1], "r") as f: files = f.readlines() files = [x.strip() for x in files] image_path = "/train_tmp/IJB_release/IJBC" 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) 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(method) 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)