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191 lines
7.9 KiB
Python
191 lines
7.9 KiB
Python
"""Helper for evaluation on the Labeled Faces in the Wild dataset
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"""
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# MIT License
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#
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# Copyright (c) 2016 David Sandberg
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import numpy as np
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from scipy import misc
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from sklearn.model_selection import KFold
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from scipy import interpolate
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import sklearn
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from sklearn.decomposition import PCA
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#import facenet
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def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca = 0):
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assert(embeddings1.shape[0] == embeddings2.shape[0])
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assert(embeddings1.shape[1] == embeddings2.shape[1])
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nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
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nrof_thresholds = len(thresholds)
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k_fold = KFold(n_splits=nrof_folds, shuffle=False)
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tprs = np.zeros((nrof_folds,nrof_thresholds))
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fprs = np.zeros((nrof_folds,nrof_thresholds))
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accuracy = np.zeros((nrof_folds))
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indices = np.arange(nrof_pairs)
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#print('pca', pca)
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if pca==0:
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diff = np.subtract(embeddings1, embeddings2)
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dist = np.sum(np.square(diff),1)
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for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
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#print('train_set', train_set)
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#print('test_set', test_set)
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if pca>0:
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print('doing pca on', fold_idx)
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embed1_train = embeddings1[train_set]
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embed2_train = embeddings2[train_set]
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_embed_train = np.concatenate( (embed1_train, embed2_train), axis=0 )
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#print(_embed_train.shape)
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pca_model = PCA(n_components=pca)
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pca_model.fit(_embed_train)
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embed1 = pca_model.transform(embeddings1)
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embed2 = pca_model.transform(embeddings2)
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embed1 = sklearn.preprocessing.normalize(embed1)
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embed2 = sklearn.preprocessing.normalize(embed2)
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#print(embed1.shape, embed2.shape)
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diff = np.subtract(embed1, embed2)
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dist = np.sum(np.square(diff),1)
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# Find the best threshold for the fold
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acc_train = np.zeros((nrof_thresholds))
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for threshold_idx, threshold in enumerate(thresholds):
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_, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set])
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best_threshold_index = np.argmax(acc_train)
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for threshold_idx, threshold in enumerate(thresholds):
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tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _ = calculate_accuracy(threshold, dist[test_set], actual_issame[test_set])
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_, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], actual_issame[test_set])
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tpr = np.mean(tprs,0)
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fpr = np.mean(fprs,0)
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return tpr, fpr, accuracy
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def calculate_accuracy(threshold, dist, actual_issame):
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predict_issame = np.less(dist, threshold)
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tp = np.sum(np.logical_and(predict_issame, actual_issame))
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fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
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tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))
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fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
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tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn)
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fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn)
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acc = float(tp+tn)/dist.size
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return tpr, fpr, acc
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def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):
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assert(embeddings1.shape[0] == embeddings2.shape[0])
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assert(embeddings1.shape[1] == embeddings2.shape[1])
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nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
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nrof_thresholds = len(thresholds)
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k_fold = KFold(n_splits=nrof_folds, shuffle=False)
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val = np.zeros(nrof_folds)
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far = np.zeros(nrof_folds)
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diff = np.subtract(embeddings1, embeddings2)
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dist = np.sum(np.square(diff),1)
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indices = np.arange(nrof_pairs)
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for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
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# Find the threshold that gives FAR = far_target
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far_train = np.zeros(nrof_thresholds)
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for threshold_idx, threshold in enumerate(thresholds):
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_, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])
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if np.max(far_train)>=far_target:
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f = interpolate.interp1d(far_train, thresholds, kind='slinear')
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threshold = f(far_target)
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else:
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threshold = 0.0
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val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])
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val_mean = np.mean(val)
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far_mean = np.mean(far)
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val_std = np.std(val)
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return val_mean, val_std, far_mean
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def calculate_val_far(threshold, dist, actual_issame):
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predict_issame = np.less(dist, threshold)
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true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
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false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
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n_same = np.sum(actual_issame)
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n_diff = np.sum(np.logical_not(actual_issame))
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val = float(true_accept) / float(n_same)
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far = float(false_accept) / float(n_diff)
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return val, far
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def evaluate(embeddings, actual_issame, nrof_folds=10, pca = 0):
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# Calculate evaluation metrics
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thresholds = np.arange(0, 4, 0.01)
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embeddings1 = embeddings[0::2]
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embeddings2 = embeddings[1::2]
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tpr, fpr, accuracy = calculate_roc(thresholds, embeddings1, embeddings2,
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np.asarray(actual_issame), nrof_folds=nrof_folds, pca = pca)
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thresholds = np.arange(0, 4, 0.001)
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val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2,
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np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)
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return tpr, fpr, accuracy, val, val_std, far
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def get_paths(lfw_dir, pairs, file_ext):
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nrof_skipped_pairs = 0
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path_list = []
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issame_list = []
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for pair in pairs:
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if len(pair) == 3:
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path0 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])+'.'+file_ext)
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path1 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[2])+'.'+file_ext)
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issame = True
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elif len(pair) == 4:
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path0 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])+'.'+file_ext)
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path1 = os.path.join(lfw_dir, pair[2], pair[2] + '_' + '%04d' % int(pair[3])+'.'+file_ext)
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issame = False
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if os.path.exists(path0) and os.path.exists(path1): # Only add the pair if both paths exist
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path_list += (path0,path1)
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issame_list.append(issame)
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else:
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nrof_skipped_pairs += 1
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if nrof_skipped_pairs>0:
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print('Skipped %d image pairs' % nrof_skipped_pairs)
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return path_list, issame_list
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def read_pairs(pairs_filename):
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pairs = []
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with open(pairs_filename, 'r') as f:
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for line in f.readlines()[1:]:
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pair = line.strip().split()
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pairs.append(pair)
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return np.array(pairs)
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