File size: 9,457 Bytes
f2e3176 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
from sklearn.model_selection import KFold
from scipy import interpolate
import sklearn
from sklearn.decomposition import PCA
import cv2 as cv
from tqdm import tqdm
def calculate_roc(thresholds,
embeddings1,
embeddings2,
actual_issame,
nrof_folds=10,
pca=0):
assert (embeddings1.shape[0] == embeddings2.shape[0])
assert (embeddings1.shape[1] == embeddings2.shape[1])
nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
nrof_thresholds = len(thresholds)
k_fold = KFold(n_splits=nrof_folds, shuffle=False)
tprs = np.zeros((nrof_folds, nrof_thresholds))
fprs = np.zeros((nrof_folds, nrof_thresholds))
accuracy = np.zeros((nrof_folds))
indices = np.arange(nrof_pairs)
# print('pca', pca)
if pca == 0:
diff = np.subtract(embeddings1, embeddings2)
dist = np.sum(np.square(diff), 1)
for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
# print('train_set', train_set)
# print('test_set', test_set)
if pca > 0:
print('doing pca on', fold_idx)
embed1_train = embeddings1[train_set]
embed2_train = embeddings2[train_set]
_embed_train = np.concatenate((embed1_train, embed2_train), axis=0)
# print(_embed_train.shape)
pca_model = PCA(n_components=pca)
pca_model.fit(_embed_train)
embed1 = pca_model.transform(embeddings1)
embed2 = pca_model.transform(embeddings2)
embed1 = sklearn.preprocessing.normalize(embed1)
embed2 = sklearn.preprocessing.normalize(embed2)
# print(embed1.shape, embed2.shape)
diff = np.subtract(embed1, embed2)
dist = np.sum(np.square(diff), 1)
# Find the best threshold for the fold
acc_train = np.zeros((nrof_thresholds))
for threshold_idx, threshold in enumerate(thresholds):
_, _, acc_train[threshold_idx] = calculate_accuracy(
threshold, dist[train_set], actual_issame[train_set])
best_threshold_index = np.argmax(acc_train)
for threshold_idx, threshold in enumerate(thresholds):
tprs[fold_idx,
threshold_idx], fprs[fold_idx,
threshold_idx], _ = calculate_accuracy(
threshold, dist[test_set],
actual_issame[test_set])
_, _, accuracy[fold_idx] = calculate_accuracy(
thresholds[best_threshold_index], dist[test_set],
actual_issame[test_set])
tpr = np.mean(tprs, 0)
fpr = np.mean(fprs, 0)
return tpr, fpr, accuracy
def calculate_accuracy(threshold, dist, actual_issame):
predict_issame = np.less(dist, threshold)
tp = np.sum(np.logical_and(predict_issame, actual_issame))
fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
tn = np.sum(
np.logical_and(np.logical_not(predict_issame),
np.logical_not(actual_issame)))
fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn)
fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn)
acc = float(tp + tn) / dist.size
return tpr, fpr, acc
def calculate_val(thresholds,
embeddings1,
embeddings2,
actual_issame,
far_target,
nrof_folds=10):
assert (embeddings1.shape[0] == embeddings2.shape[0])
assert (embeddings1.shape[1] == embeddings2.shape[1])
nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
nrof_thresholds = len(thresholds)
k_fold = KFold(n_splits=nrof_folds, shuffle=False)
val = np.zeros(nrof_folds)
far = np.zeros(nrof_folds)
diff = np.subtract(embeddings1, embeddings2)
dist = np.sum(np.square(diff), 1)
indices = np.arange(nrof_pairs)
for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
# Find the threshold that gives FAR = far_target
far_train = np.zeros(nrof_thresholds)
for threshold_idx, threshold in enumerate(thresholds):
_, far_train[threshold_idx] = calculate_val_far(
threshold, dist[train_set], actual_issame[train_set])
if np.max(far_train) >= far_target:
f = interpolate.interp1d(far_train, thresholds, kind='slinear')
threshold = f(far_target)
else:
threshold = 0.0
val[fold_idx], far[fold_idx] = calculate_val_far(
threshold, dist[test_set], actual_issame[test_set])
val_mean = np.mean(val)
far_mean = np.mean(far)
val_std = np.std(val)
return val_mean, val_std, far_mean
def calculate_val_far(threshold, dist, actual_issame):
predict_issame = np.less(dist, threshold)
true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
false_accept = np.sum(
np.logical_and(predict_issame, np.logical_not(actual_issame)))
n_same = np.sum(actual_issame)
n_diff = np.sum(np.logical_not(actual_issame))
val = float(true_accept) / float(n_same)
far = float(false_accept) / float(n_diff)
return val, far
def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0):
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
embeddings1 = embeddings[0::2]
embeddings2 = embeddings[1::2]
tpr, fpr, accuracy = calculate_roc(thresholds,
embeddings1,
embeddings2,
np.asarray(actual_issame),
nrof_folds=nrof_folds,
pca=pca)
thresholds = np.arange(0, 4, 0.001)
val, val_std, far = calculate_val(thresholds,
embeddings1,
embeddings2,
np.asarray(actual_issame),
1e-3,
nrof_folds=nrof_folds)
return tpr, fpr, accuracy, val, val_std, far
class LFW:
def __init__(self, root, target_size=250):
self.LFW_IMAGE_SIZE = 250
self.lfw_root = root
self.target_size = target_size
self.lfw_pairs_path = os.path.join(self.lfw_root, 'view2/pairs.txt')
self.image_path_pattern = os.path.join(self.lfw_root, 'lfw', '{person_name}', '{image_name}')
self.lfw_image_paths, self.id_list = self.load_pairs()
@property
def name(self):
return 'LFW'
def __len__(self):
return len(self.lfw_image_paths)
@property
def ids(self):
return self.id_list
def load_pairs(self):
image_paths = []
id_list = []
with open(self.lfw_pairs_path, 'r') as f:
for line in f.readlines()[1:]:
line = line.strip().split()
if len(line) == 3:
person_name = line[0]
image1_name = '{}_{:04d}.jpg'.format(person_name, int(line[1]))
image2_name = '{}_{:04d}.jpg'.format(person_name, int(line[2]))
image_paths += [
self.image_path_pattern.format(person_name=person_name, image_name=image1_name),
self.image_path_pattern.format(person_name=person_name, image_name=image2_name)
]
id_list.append(True)
elif len(line) == 4:
person1_name = line[0]
image1_name = '{}_{:04d}.jpg'.format(person1_name, int(line[1]))
person2_name = line[2]
image2_name = '{}_{:04d}.jpg'.format(person2_name, int(line[3]))
image_paths += [
self.image_path_pattern.format(person_name=person1_name, image_name=image1_name),
self.image_path_pattern.format(person_name=person2_name, image_name=image2_name)
]
id_list.append(False)
return image_paths, id_list
def __getitem__(self, key):
img = cv.imread(self.lfw_image_paths[key])
if self.target_size != self.LFW_IMAGE_SIZE:
img = cv.resize(img, (self.target_size, self.target_size))
return img
def eval(self, model):
ids = self.ids
embeddings = np.zeros(shape=(len(self), 128))
face_bboxes = np.load("./datasets/lfw_face_bboxes.npy")
for idx, img in tqdm(enumerate(self), desc="Evaluating {} with {} val set".format(model.name, self.name)):
embedding = model.infer(img, face_bboxes[idx])
embeddings[idx] = embedding
embeddings = sklearn.preprocessing.normalize(embeddings)
self.tpr, self.fpr, self.acc, self.val, self.std, self.far = evaluate(embeddings, ids, nrof_folds=10)
self.acc, self.std = np.mean(self.acc), np.std(self.acc)
def print_result(self):
print("==================== Results ====================")
print("Average Accuracy: {:.4f}".format(self.acc))
print("=================================================")
|