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Add script to evaluate face recognition by LFW #72
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7925d74
Add script to evaluate face recognition by LFW
WanliZhong 04a35c3
update the result of evaluation
WanliZhong e198dd2
add the result of SFace quantized model
WanliZhong e52bf90
Add face detection results to improve accuracy
WanliZhong 3033952
Merge branch 'master' into flw_eval
WanliZhong adabe26
Add explanation for lfw_face_bboxes.npy in README
WanliZhong 538736f
change the name of face_features to face_bboxes
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Original file line number | Diff line number | Diff line change |
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from __future__ import absolute_import | ||
from __future__ import division | ||
from __future__ import print_function | ||
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import os | ||
import numpy as np | ||
from scipy import misc | ||
from sklearn.model_selection import KFold | ||
from scipy import interpolate | ||
import sklearn | ||
from sklearn.decomposition import PCA | ||
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import cv2 as cv | ||
from tqdm import tqdm | ||
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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) | ||
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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) | ||
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if pca == 0: | ||
diff = np.subtract(embeddings1, embeddings2) | ||
dist = np.sum(np.square(diff), 1) | ||
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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) | ||
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# 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]) | ||
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tpr = np.mean(tprs, 0) | ||
fpr = np.mean(fprs, 0) | ||
return tpr, fpr, accuracy | ||
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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)) | ||
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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 | ||
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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) | ||
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val = np.zeros(nrof_folds) | ||
far = np.zeros(nrof_folds) | ||
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diff = np.subtract(embeddings1, embeddings2) | ||
dist = np.sum(np.square(diff), 1) | ||
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 | ||
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 | ||
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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) | ||
far_mean = np.mean(far) | ||
val_std = np.std(val) | ||
return val_mean, val_std, far_mean | ||
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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 | ||
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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 | ||
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class LFW: | ||
def __init__(self, root, target_size=250): | ||
self.LFW_IMAGE_SIZE = 250 | ||
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self.lfw_root = root | ||
self.target_size = target_size | ||
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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}') | ||
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self.lfw_image_paths, self.id_list = self.load_pairs() | ||
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@property | ||
def name(self): | ||
return 'LFW' | ||
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def __len__(self): | ||
return len(self.lfw_image_paths) | ||
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@property | ||
def ids(self): | ||
return self.id_list | ||
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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 | ||
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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 | ||
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def eval(self, model): | ||
ids = self.ids | ||
embeddings = np.zeros(shape=(len(self), 128)) | ||
for idx, img in tqdm(enumerate(self), desc="Evaluating {} with {} val set".format(model.name, self.name)): | ||
embedding = model.infer(img) | ||
embeddings[idx] = embedding | ||
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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) | ||
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def print_result(self): | ||
print("==================== Results ====================") | ||
print("Average Accuracy: {:.4f}".format(self.acc)) | ||
print("=================================================") |
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The image should be aligned before being fed into the model. This can be the problem of low accuracy.
To get aligned image, you need to get the bounding box of the face using YuNet. For simplicity, you can start with treating the bounding box of highest score as the only face in the image. So I suggest you:
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Thanks! After using YuNet to get bbox, the accuracy is 97.92%. this is still not 99.6% as described in the documentation. I will try to solve this problem.
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The difference can be YuNet failing to detect a face in some images. You can have a check on the images with low score faces or no face.
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It may not be the problem. Each image is detected with a face and the selected bbox scores are all above 0.8.
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Sometimes it may fail even with score 0.8. Please, take a look at those faces with score lower than 0.9.